Russell Poldrack

All Publications

• The response time paradox in functional magnetic resonance imaging analyses. Nature human behaviour Mumford, J. A., Bissett, P. G., Jones, H. M., Shim, S., Rios, J. A., Poldrack, R. A. 2023

  Abstract

  Response times (RTs) are often the main signal of interest in cognitive psychology but are often ignored in functional MRI (fMRI) analyses. In fMRI analysis the intensity of the signal serves as a proxy for the intensity of local neuronal activity, but changes in either the intensity or the duration of neuronal activity can yield identical fMRI signals. Therefore, if RTs are ignored and pair with neuronal durations, fMRI results claiming intensity differences may be confounded by RTs. We show how ignoring RTs goes beyond this confound, where longer RTs are paired with larger activation estimates, to lesser-known issues where RTs become confounds in group-level analyses and, surprisingly, how the RT confound can induce other artificial group-level associations with variables that are not related to the condition contrast or RTs. We propose a new time-series model to address these issues and encourage increasing focus on what the widespread RT-based signal represents.

  View details for DOI 10.1038/s41562-023-01760-0

  View details for PubMedID 37996498

  View details for PubMedCentralID 2622763

• Modelling human behaviour in cognitive tasks with latent dynamical systems. Nature human behaviour Jaffe, P. I., Poldrack, R. A., Schafer, R. J., Bissett, P. G. 2023

  Abstract

  Response time data collected from cognitive tasks are a cornerstone of psychology and neuroscience research, yet existing models of these data either make strong assumptions about the data-generating process or are limited to modelling single trials. We introduce task-DyVA, a deep learning framework in which expressive dynamical systems are trained to reproduce sequences of response times observed in data from individual human subjects. Models fitted to a large task-switching dataset captured subject-specific behavioural differences with high temporal precision, including task-switching costs. Through perturbation experiments and analyses of the models' latent dynamics, we find support for a rational account of switch costs in terms of a stability-flexibility trade-off. Thus, our framework can be used to discover interpretable cognitive theories that explain how the brain dynamically gives rise to behaviour.

  View details for DOI 10.1038/s41562-022-01510-8

  View details for PubMedID 36658212

• Interpreting mental state decoding with deep learning models. Trends in cognitive sciences Thomas, A. W., Ré, C., Poldrack, R. A. 2022; 26 (11): 972-986

  Abstract

  In mental state decoding, researchers aim to identify the set of mental states (e.g., experiencing happiness or fear) that can be reliably identified from the activity patterns of a brain region (or network). Deep learning (DL) models are highly promising for mental state decoding because of their unmatched ability to learn versatile representations of complex data. However, their widespread application in mental state decoding is hindered by their lack of interpretability, difficulties in applying them to small datasets, and in ensuring their reproducibility and robustness. We recommend approaching these challenges by leveraging recent advances in explainable artificial intelligence (XAI) and transfer learning, and also provide recommendations on how to improve the reproducibility and robustness of DL models in mental state decoding.

  View details for DOI 10.1016/j.tics.2022.07.003

  View details for PubMedID 36223760

• The OpenNeuro resource for sharing of neuroscience data. eLife Markiewicz, C. J., Gorgolewski, K. J., Feingold, F., Blair, R., Halchenko, Y. O., Miller, E., Hardcastle, N., Wexler, J., Esteban, O., Goncavles, M., Jwa, A., Poldrack, R. 2021; 10

  Abstract

  The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure (BIDS) standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.

  View details for DOI 10.7554/eLife.71774

  View details for PubMedID 34658334

• Severe violations of independence in response inhibition tasks. Science advances Bissett, P. G., Jones, H. M., Poldrack, R. A., Logan, G. D. 2021; 7 (12)

  Abstract

  The stop-signal paradigm, a primary experimental paradigm for understanding cognitive control and response inhibition, rests upon the theoretical foundation of race models, which assume that a go process races independently against a stop process that occurs after a stop-signal delay (SSD). We show that severe violations of this independence assumption at short SSDs occur systematically across a wide range of conditions, including fast and slow reaction times, auditory and visual stop signals, manual and saccadic responses, and especially in selective stopping. We also reanalyze existing data and show that conclusions can change when short SSDs are excluded. Last, we suggest experimental and analysis techniques to address this violation, and propose adjustments to extant models to accommodate this finding.

  View details for DOI 10.1126/sciadv.abf4355

  View details for PubMedID 33731357

• The physics of representation SYNTHESE Poldrack, R. A. 2020

  View details for DOI 10.1007/s11229-020-02793-y

  View details for Web of Science ID 000557199900001

• Variability in the analysis of a single neuroimaging dataset by many teams. Nature Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, R. A., Avesani, P., Baczkowski, B. M., Bajracharya, A., Bakst, L., Ball, S., Barilari, M., Bault, N., Beaton, D., Beitner, J., Benoit, R. G., Berkers, R. M., Bhanji, J. P., Biswal, B. B., Bobadilla-Suarez, S., Bortolini, T., Bottenhorn, K. L., Bowring, A., Braem, S., Brooks, H. R., Brudner, E. G., Calderon, C. B., Camilleri, J. A., Castrellon, J. J., Cecchetti, L., Cieslik, E. C., Cole, Z. J., Collignon, O., Cox, R. W., Cunningham, W. A., Czoschke, S., Dadi, K., Davis, C. P., Luca, A. D., Delgado, M. R., Demetriou, L., Dennison, J. B., Di, X., Dickie, E. W., Dobryakova, E., Donnat, C. L., Dukart, J., Duncan, N. W., Durnez, J., Eed, A., Eickhoff, S. B., Erhart, A., Fontanesi, L., Fricke, G. M., Fu, S., Galván, A., Gau, R., Genon, S., Glatard, T., Glerean, E., Goeman, J. J., Golowin, S. A., González-García, C., Gorgolewski, K. J., Grady, C. L., Green, M. A., Guassi Moreira, J. F., Guest, O., Hakimi, S., Hamilton, J. P., Hancock, R., Handjaras, G., Harry, B. B., Hawco, C., Herholz, P., Herman, G., Heunis, S., Hoffstaedter, F., Hogeveen, J., Holmes, S., Hu, C. P., Huettel, S. A., Hughes, M. E., Iacovella, V., Iordan, A. D., Isager, P. M., Isik, A. I., Jahn, A., Johnson, M. R., Johnstone, T., Joseph, M. J., Juliano, A. C., Kable, J. W., Kassinopoulos, M., Koba, C., Kong, X. Z., Koscik, T. R., Kucukboyaci, N. E., Kuhl, B. A., Kupek, S., Laird, A. R., Lamm, C., Langner, R., Lauharatanahirun, N., Lee, H., Lee, S., Leemans, A., Leo, A., Lesage, E., Li, F., Li, M. Y., Lim, P. C., Lintz, E. N., Liphardt, S. W., Losecaat Vermeer, A. B., Love, B. C., Mack, M. L., Malpica, N., Marins, T., Maumet, C., McDonald, K., McGuire, J. T., Melero, H., Méndez Leal, A. S., Meyer, B., Meyer, K. N., Mihai, G., Mitsis, G. D., Moll, J., Nielson, D. M., Nilsonne, G., Notter, M. P., Olivetti, E., Onicas, A. I., Papale, P., Patil, K. R., Peelle, J. E., Pérez, A., Pischedda, D., Poline, J. B., Prystauka, Y., Ray, S., Reuter-Lorenz, P. A., Reynolds, R. C., Ricciardi, E., Rieck, J. R., Rodriguez-Thompson, A. M., Romyn, A., Salo, T., Samanez-Larkin, G. R., Sanz-Morales, E., Schlichting, M. L., Schultz, D. H., Shen, Q., Sheridan, M. A., Silvers, J. A., Skagerlund, K., Smith, A., Smith, D. V., Sokol-Hessner, P., Steinkamp, S. R., Tashjian, S. M., Thirion, B., Thorp, J. N., Tinghög, G., Tisdall, L., Tompson, S. H., Toro-Serey, C., Torre Tresols, J. J., Tozzi, L., Truong, V., Turella, L., van 't Veer, A. E., Verguts, T., Vettel, J. M., Vijayarajah, S., Vo, K., Wall, M. B., Weeda, W. D., Weis, S., White, D. J., Wisniewski, D., Xifra-Porxas, A., Yearling, E. A., Yoon, S., Yuan, R., Yuen, K. S., Zhang, L., Zhang, X., Zosky, J. E., Nichols, T. E., Poldrack, R. A., Schonberg, T. 2020; 582 (7810): 84-88

  Abstract

  Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

  View details for DOI 10.1038/s41586-020-2314-9

  View details for PubMedID 32483374

• fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature methods Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., Gorgolewski, K. J. 2018

  Abstract

  Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.

  View details for PubMedID 30532080

• Quality assessment and control of unprocessed anatomical, functional, and diffusion MRI of the human brain using MRIQC. bioRxiv : the preprint server for biology Hagen, M. P., Provins, C., MacNicol, E., Li, J. K., Gomez, T., Garcia, M., Seeley, S. H., Legarreta, J. H., Norgaard, M., Bissett, P. G., Poldrack, R. A., Rokem, A., Esteban, O. 2024

  Abstract

  Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by MRIQC. The protocol describes how to execute the software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the "rating widget" ─ a utility that enables rapid annotation and minimizes bookkeeping errors. Integrating proper quality control checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged.

  View details for DOI 10.1101/2024.10.21.619532

  View details for PubMedID 39484445

  View details for PubMedCentralID PMC11526949

• Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol. Developmental cognitive neuroscience Dean, D. C., Tisdall, M. D., Wisnowski, J. L., Feczko, E., Gagoski, B., Alexander, A. L., Edden, R. A., Gao, W., Hendrickson, T. J., Howell, B. R., Huang, H., Humphreys, K. L., Riggins, T., Sylvester, C. M., Weldon, K. B., Yacoub, E., Ahtam, B., Beck, N., Banerjee, S., Boroday, S., Caprihan, A., Caron, B., Carpenter, S., Chang, Y., Chung, A. W., Cieslak, M., Clarke, W. T., Dale, A., Das, S., Davies-Jenkins, C. W., Dufford, A. J., Evans, A. C., Fesselier, L., Ganji, S. K., Gilbert, G., Graham, A. M., Gudmundson, A. T., Macgregor-Hannah, M., Harms, M. P., Hilbert, T., Hui, S. C., Irfanoglu, M. O., Kecskemeti, S., Kober, T., Kuperman, J. M., Lamichhane, B., Landman, B. A., Lecour-Bourcher, X., Lee, E. G., Li, X., MacIntyre, L., Madjar, C., Manhard, M. K., Mayer, A. R., Mehta, K., Moore, L. A., Murali-Manohar, S., Navarro, C., Nebel, M. B., Newman, S. D., Newton, A. T., Noeske, R., Norton, E. S., Oeltzschner, G., Ongaro-Carcy, R., Ou, X., Ouyang, M., Parrish, T. B., Pekar, J. J., Pengo, T., Pierpaoli, C., Poldrack, R. A., Rajagopalan, V., Rettmann, D. W., Rioux, P., Rosenberg, J. T., Salo, T., Satterthwaite, T. D., Scott, L. S., Shin, E., Simegn, G., Simmons, W. K., Song, Y., Tikalsky, B. J., Tkach, J., van Zijl, P. C., Vannest, J., Versluis, M., Zhao, Y., Zöllner, H. J., Fair, D. A., Smyser, C. D., Elison, J. T. 2024; 70: 101452

  Abstract

  The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

  View details for DOI 10.1016/j.dcn.2024.101452

  View details for PubMedID 39341120

• Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning. bioRxiv : the preprint server for biology Madsen, S. J., Uddin, L. Q., Mumford, J. A., Barch, D. M., Fair, D. A., Gotlib, I. H., Poldrack, R. A., Kuceyeski, A., Saggar, M. 2024

  Abstract

  Recent work has shown that deep learning is a powerful tool for predicting brain activation patterns evoked through various tasks using resting state features. We replicate and improve upon this recent work to introduce two models, BrainSERF and BrainSurfGCN, that perform at least as well as the state-of-the-art while greatly reducing memory and computational footprints. Our performance analysis observed that low predictability was associated with a possible lack of task engagement derived from behavioral performance. Furthermore, a deficiency in model performance was also observed for closely matched task contrasts, likely due to high individual variability confirmed by low test-retest reliability. Overall, we successfully replicate recently developed deep learning architecture and provide scalable models for further research.

  View details for DOI 10.1101/2024.09.10.612309

  View details for PubMedID 39314460

  View details for PubMedCentralID PMC11419026

• Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology Ekhtiari, H., Zare-Bidoky, M., Sangchooli, A., Valyan, A., Abi-Dargham, A., Cannon, D. M., Carter, C. S., Garavan, H., George, T. P., Ghobadi-Azbari, P., Juchem, C., Krystal, J. H., Nichols, T. E., Öngür, D., Pernet, C. R., Poldrack, R. A., Thompson, P. M., Paulus, M. P. 2024

  Abstract

  Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

  View details for DOI 10.1038/s41386-024-01973-5

  View details for PubMedID 39242922

  View details for PubMedCentralID 4936733

• Moving beyond processing- and analysis-related variation in resting-state functional brain imaging. Nature human behaviour Li, X., Bianchini Esper, N., Ai, L., Giavasis, S., Jin, H., Feczko, E., Xu, T., Clucas, J., Franco, A., Sólon Heinsfeld, A., Adebimpe, A., Vogelstein, J. T., Yan, C. G., Esteban, O., Poldrack, R. A., Craddock, C., Fair, D., Satterthwaite, T., Kiar, G., Milham, M. P. 2024

  Abstract

  When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation.

  View details for DOI 10.1038/s41562-024-01942-4

  View details for PubMedID 39103610

  View details for PubMedCentralID 3896030

• Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation. Scientific data Bissett, P. G., Eisenberg, I. W., Shim, S., Rios, J. A., Jones, H. M., Hagen, M. P., Enkavi, A. Z., Li, J. K., Mumford, J. A., MacKinnon, D. P., Marsch, L. A., Poldrack, R. A. 2024; 11 (1): 809

  Abstract

  We describe the following shared data from N = 103 healthy adults who completed a broad set of cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting state fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90-minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard.

  View details for DOI 10.1038/s41597-024-03636-y

  View details for PubMedID 39033226

  View details for PubMedCentralID 3041102

• REFORMS: Consensus-based Recommendations for Machine-learning-based Science. Science advances Kapoor, S., Cantrell, E. M., Peng, K., Pham, T. H., Bail, C. A., Gundersen, O. E., Hofman, J. M., Hullman, J., Lones, M. A., Malik, M. M., Nanayakkara, P., Poldrack, R. A., Raji, I. D., Roberts, M., Salganik, M. J., Serra-Garcia, M., Stewart, B. M., Vandewiele, G., Narayanan, A. 2024; 10 (18): eadk3452

  Abstract

  Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear recommendations for conducting and reporting ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist (recommendations for machine-learning-based science). It consists of 32 questions and a paired set of guidelines. REFORMS was developed on the basis of a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility.

  View details for DOI 10.1126/sciadv.adk3452

  View details for PubMedID 38691601

• hyve, a compositional visualisation engine for brain imaging data. bioRxiv : the preprint server for biology Ciric, R., Xu, A., Poldrack, R. A. 2024

  Abstract

  Neuroimaging visualisations facilitate the interpretation of geometrically structured data and results. However, heterogeneous geometries--such as volumes, surfaces, and networks--have traditionally mandated different software approaches. We introduce hyve, a Python library that uses a compositional functional framework to enable parametric implementation of custom visualisations for different brain geometries. Under this framework, users compose a reusable visualisation protocol from geometric primitives for representing data geometries, input primitives for common data formats and research objectives, and output primitives for producing interactive displays or configurable snapshots. hyve also writes documentation for user-constructed protocols, automates serial production of multiple visualisations, and includes an API for semantically organising an editable multi-panel figure. Through the seamless composition of input, output, and geometric primitives, hyve supports creating visualisations for a range of neuroimaging research objectives.

  View details for DOI 10.1101/2024.04.18.590179

  View details for PubMedID 38659772

• brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nature methods Hayashi, S., Caron, B. A., Heinsfeld, A. S., Vinci-Booher, S., McPherson, B., Bullock, D. N., Bertò, G., Niso, G., Hanekamp, S., Levitas, D., Ray, K., MacKenzie, A., Avesani, P., Kitchell, L., Leong, J. K., Nascimento-Silva, F., Koudoro, S., Willis, H., Jolly, J. K., Pisner, D., Zuidema, T. R., Kurzawski, J. W., Mikellidou, K., Bussalb, A., Chaumon, M., George, N., Rorden, C., Victory, C., Bhatia, D., Aydogan, D. B., Yeh, F. F., Delogu, F., Guaje, J., Veraart, J., Fischer, J., Faskowitz, J., Fabrega, R., Hunt, D., McKee, S., Brown, S. T., Heyman, S., Iacovella, V., Mejia, A. F., Marinazzo, D., Craddock, R. C., Olivetti, E., Hanson, J. L., Garyfallidis, E., Stanzione, D., Carson, J., Henschel, R., Hancock, D. Y., Stewart, C. A., Schnyer, D., Eke, D. O., Poldrack, R. A., Bollman, S., Stewart, A., Bridge, H., Sani, I., Freiwald, W. A., Puce, A., Port, N. L., Pestilli, F. 2024

  Abstract

  Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

  View details for DOI 10.1038/s41592-024-02237-2

  View details for PubMedID 38605111

  View details for PubMedCentralID 6910649

• Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task. bioRxiv : the preprint server for biology Demidenko, M. I., Mumford, J. A., Poldrack, R. A. 2024

  Abstract

  Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 120) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .44 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization, however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.

  View details for DOI 10.1101/2024.03.19.585755

  View details for PubMedID 38562804

  View details for PubMedCentralID PMC10983911

• A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework. bioRxiv : the preprint server for biology Quah, S. K., Jo, B., Geniesse, C., Uddin, L. Q., Mumford, J. A., Barch, D. M., Fair, D. A., Gotlib, I. H., Poldrack, R. A., Saggar, M. 2024

  Abstract

  Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns of the RDoC framework, our study employed a latent variable approach, specifically utilizing bifactor analysis. We examined a total of 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with a total of 6,192 participants. Within this set of 84 maps, a curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set of our analysis, and the remaining held-out maps formed the internal validation set. External validation was performed with 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Our results indicate that a bifactor model with a task-general domain and splitting the cognitive systems domain into sub-domains better fits the current corpus of tfMRI data than the current RDoC framework. Our data-driven validation supports revising the RDoC framework to accurately reflect underlying brain circuitry.

  View details for DOI 10.1101/2024.01.31.577486

  View details for PubMedID 38559071

• Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis. Schizophrenia bulletin Wannan, C. M., Nelson, B., Addington, J., Allott, K., Anticevic, A., Arango, C., Baker, J. T., Bearden, C. E., Billah, T., Bouix, S., Broome, M. R., Buccilli, K., Cadenhead, K. S., Calkins, M. E., Cannon, T. D., Cecci, G., Chen, E. Y., Cho, K. I., Choi, J., Clark, S. R., Coleman, M. J., Conus, P., Corcoran, C. M., Cornblatt, B. A., Diaz-Caneja, C. M., Dwyer, D., Ebdrup, B. H., Ellman, L. M., Fusar-Poli, P., Galindo, L., Gaspar, P. A., Gerber, C., Glenthøj, L. B., Glynn, R., Harms, M. P., Horton, L. E., Kahn, R. S., Kambeitz, J., Kambeitz-Ilankovic, L., Kane, J. M., Kapur, T., Keshavan, M. S., Kim, S. W., Koutsouleris, N., Kubicki, M., Kwon, J. S., Langbein, K., Lewandowski, K. E., Light, G. A., Mamah, D., Marcy, P. J., Mathalon, D. H., McGorry, P. D., Mittal, V. A., Nordentoft, M., Nunez, A., Pasternak, O., Pearlson, G. D., Perez, J., Perkins, D. O., Powers, A. R., Roalf, D. R., Sabb, F. W., Schiffman, J., Shah, J. L., Smesny, S., Spark, J., Stone, W. S., Strauss, G. P., Tamayo, Z., Torous, J., Upthegrove, R., Vangel, M., Verma, S., Wang, J., Rossum, I. W., Wolf, D. H., Wolff, P., Wood, S. J., Yung, A. R., Agurto, C., Alvarez-Jimenez, M., Amminger, P., Armando, M., Asgari-Targhi, A., Cahill, J., Carrión, R. E., Castro, E., Cetin-Karayumak, S., Mallar Chakravarty, M., Cho, Y. T., Cotter, D., D'Alfonso, S., Ennis, M., Fadnavis, S., Fonteneau, C., Gao, C., Gupta, T., Gur, R. E., Gur, R. C., Hamilton, H. K., Hoftman, G. D., Jacobs, G. R., Jarcho, J., Ji, J. L., Kohler, C. G., Lalousis, P. A., Lavoie, S., Lepage, M., Liebenthal, E., Mervis, J., Murty, V., Nicholas, S. C., Ning, L., Penzel, N., Poldrack, R., Polosecki, P., Pratt, D. N., Rabin, R., Rahimi Eichi, H., Rathi, Y., Reichenberg, A., Reinen, J., Rogers, J., Ruiz-Yu, B., Scott, I., Seitz-Holland, J., Srihari, V. H., Srivastava, A., Thompson, A., Turetsky, B. I., Walsh, B. C., Whitford, T., Wigman, J. T., Yao, B., Yuen, H. P., Ahmed, U., Byun, A. J., Chung, Y., Do, K., Hendricks, L., Huynh, K., Jeffries, C., Lane, E., Langholm, C., Lin, E., Mantua, V., Santorelli, G., Ruparel, K., Zoupou, E., Adasme, T., Addamo, L., Adery, L., Ali, M., Auther, A., Aversa, S., Baek, S. H., Bates, K., Bathery, A., Bayer, J. M., Beedham, R., Bilgrami, Z., Birch, S., Bonoldi, I., Borders, O., Borgatti, R., Brown, L., Bruna, A., Carrington, H., Castillo-Passi, R. I., Chen, J., Cheng, N., Ching, A. E., Clifford, C., Colton, B. L., Contreras, P., Corral, S., Damiani, S., Done, M., Estradé, A., Etuka, B. A., Formica, M., Furlan, R., Geljic, M., Germano, C., Getachew, R., Goncalves, M., Haidar, A., Hartmann, J., Jo, A., John, O., Kerins, S., Kerr, M., Kesselring, I., Kim, H., Kim, N., Kinney, K., Krcmar, M., Kotler, E., Lafanechere, M., Lee, C., Llerena, J., Markiewicz, C., Matnejl, P., Maturana, A., Mavambu, A., Mayol-Troncoso, R., McDonnell, A., McGowan, A., McLaughlin, D., McIlhenny, R., McQueen, B., Mebrahtu, Y., Mensi, M., Hui, C. L., Suen, Y. N., Wong, S. M., Morrell, N., Omar, M., Partridge, A., Phassouliotis, C., Pichiecchio, A., Politi, P., Porter, C., Provenzani, U., Prunier, N., Raj, J., Ray, S., Rayner, V., Reyes, M., Reynolds, K., Rush, S., Salinas, C., Shetty, J., Snowball, C., Tod, S., Turra-Fariña, G., Valle, D., Veale, S., Whitson, S., Wickham, A., Youn, S., Zamorano, F., Zavaglia, E., Zinberg, J., Woods, S. W., Shenton, M. E. 2024

  Abstract

  This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.

  View details for DOI 10.1093/schbul/sbae011

  View details for PubMedID 38451304

• The past, present, and future of the brain imaging data structure (BIDS). Imaging neuroscience (Cambridge, Mass.) Poldrack, R. A., Markiewicz, C. J., Appelhoff, S., Ashar, Y. K., Auer, T., Baillet, S., Bansal, S., Beltrachini, L., Benar, C. G., Bertazzoli, G., Bhogawar, S., Blair, R. W., Bortoletto, M., Boudreau, M., Brooks, T. L., Calhoun, V. D., Castelli, F. M., Clement, P., Cohen, A. L., Cohen-Adad, J., D'Ambrosio, S., de Hollander, G., de la Iglesia-Vayá, M., de la Vega, A., Delorme, A., Devinsky, O., Draschkow, D., Duff, E. P., DuPre, E., Earl, E., Esteban, O., Feingold, F. W., Flandin, G., Galassi, A., Gallitto, G., Ganz, M., Gau, R., Gholam, J., Ghosh, S. S., Giacomel, A., Gillman, A. G., Gleeson, P., Gramfort, A., Guay, S., Guidali, G., Halchenko, Y. O., Handwerker, D. A., Hardcastle, N., Herholz, P., Hermes, D., Honey, C. J., Innis, R. B., Ioanas, H. I., Jahn, A., Karakuzu, A., Keator, D. B., Kiar, G., Kincses, B., Laird, A. R., Lau, J. C., Lazari, A., Legarreta, J. H., Li, A., Li, X., Love, B. C., Lu, H., Marcantoni, E., Maumet, C., Mazzamuto, G., Meisler, S. L., Mikkelsen, M., Mutsaerts, H., Nichols, T. E., Nikolaidis, A., Nilsonne, G., Niso, G., Norgaard, M., Okell, T. W., Oostenveld, R., Ort, E., Park, P. J., Pawlik, M., Pernet, C. R., Pestilli, F., Petr, J., Phillips, C., Poline, J. B., Pollonini, L., Raamana, P. R., Ritter, P., Rizzo, G., Robbins, K. A., Rockhill, A. P., Rogers, C., Rokem, A., Rorden, C., Routier, A., Saborit-Torres, J. M., Salo, T., Schirner, M., Smith, R. E., Spisak, T., Sprenger, J., Swann, N. C., Szinte, M., Takerkart, S., Thirion, B., Thomas, A. G., Torabian, S., Varoquaux, G., Voytek, B., Welzel, J., Wilson, M., Yarkoni, T., Gorgolewski, K. J. 2024; 2: 1-19

  Abstract

  The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

  View details for DOI 10.1162/imag_a_00103

  View details for PubMedID 39308505

  View details for PubMedCentralID PMC11415029

• A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents. Developmental cognitive neuroscience Demidenko, M. I., Mumford, J. A., Ram, N., Poldrack, R. A. 2023; 65: 101337

  Abstract

  Interpreting the neural response elicited during task functional magnetic resonance imaging (fMRI) remains a challenge in neurodevelopmental research. The monetary incentive delay (MID) task is an fMRI reward processing task that is extensively used in the literature. However, modern psychometric tools have not been used to evaluate measurement properties of the MID task fMRI data. The current study uses data for a similar task design across three adolescent samples (N=346 [Agemean 12.0; 44 % Female]; N=97 [19.3; 58 %]; N=112 [20.2; 38 %]) to evaluate multiple measurement properties of fMRI responses on the MID task. Confirmatory factor analysis (CFA) is used to evaluate an a priori theoretical model for the task and its measurement invariance across three samples. Exploratory factor analysis (EFA) is used to identify the data-driven measurement structure across the samples. CFA results suggest that the a priori model is a poor representation of these MID task fMRI data. Across the samples, the data-driven EFA models consistently identify a six-to-seven factor structure with run and bilateral brain region factors. This factor structure is moderately-to-highly congruent across the samples. Altogether, these findings demonstrate a need to evaluate theoretical frameworks for popular fMRI task designs to improve our understanding and interpretation of brain-behavior associations.

  View details for DOI 10.1016/j.dcn.2023.101337

  View details for PubMedID 38160517

• Functional neuroimaging as a catalyst for integrated neuroscience. Nature Finn, E. S., Poldrack, R. A., Shine, J. M. 2023; 623 (7986): 263-273

  Abstract

  Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

  View details for DOI 10.1038/s41586-023-06670-9

  View details for PubMedID 37938706

  View details for PubMedCentralID 4811769

• Data sharing in neuroimaging: experiences from the BIDS project. Nature reviews. Neuroscience Ganz, M., Poldrack, R. A. 2023

  View details for DOI 10.1038/s41583-023-00762-1

  View details for PubMedID 37875580

  View details for PubMedCentralID 9057092

• A comparison of neuroelectrophysiology databases. Scientific data Subash, P., Gray, A., Boswell, M., Cohen, S. L., Garner, R., Salehi, S., Fisher, C., Hobel, S., Ghosh, S., Halchenko, Y., Dichter, B., Poldrack, R. A., Markiewicz, C., Hermes, D., Delorme, A., Makeig, S., Behan, B., Sparks, A., Arnott, S. R., Wang, Z., Magnotti, J., Beauchamp, M. S., Pouratian, N., Toga, A. W., Duncan, D. 2023; 10 (1): 719

  Abstract

  As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

  View details for DOI 10.1038/s41597-023-02614-0

  View details for PubMedID 37857685

  View details for PubMedCentralID PMC10587056

• Controversies and progress on standardization of large-scale brain network nomenclature NETWORK NEUROSCIENCE Uddin, L. Q., Betzel, R. F., Cohen, J. R., Damoiseaux, J. S., De Brigard, F., Eickhoff, S. B., Fornito, A., Gratton, C., Gordon, E. M., Laird, A. R., Larson-Prior, L., McIntosh, A., Nickerson, L. D., Pessoa, L., Pinho, A., Poldrack, R. A., Razi, A., Sadaghiani, S., Shine, J. M., Yendiki, A., Yeo, B., Spreng, R. 2023; 7 (3): 864-905

  View details for DOI 10.1162/netn_a_00323

  View details for Web of Science ID 001050899300001

• Controversies and progress on standardization of large-scale brain network nomenclature. Network neuroscience (Cambridge, Mass.) Uddin, L. Q., Betzel, R. F., Cohen, J. R., Damoiseaux, J. S., De Brigard, F., Eickhoff, S. B., Fornito, A., Gratton, C., Gordon, E. M., Laird, A. R., Larson-Prior, L., McIntosh, A. R., Nickerson, L. D., Pessoa, L., Pinho, A. L., Poldrack, R. A., Razi, A., Sadaghiani, S., Shine, J. M., Yendiki, A., Yeo, B. T., Spreng, R. N. 2023; 7 (3): 864-905

  Abstract

  Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

  View details for DOI 10.1162/netn_a_00323

  View details for PubMedID 37781138

  View details for PubMedCentralID PMC10473266

• Improving the Rigor of Mechanistic Behavioral Science: The Introduction of the Checklist for Investigating Mechanisms in Behavior-Change Research (CLIMBR). Behavior therapy Birk, J. L., Otto, M. W., Cornelius, T., Poldrack, R. A., Edmondson, D. 2023; 54 (4): 708-713

  Abstract

  Diverse fields rely on the development of effective interventions to change human behaviors, such as following prescribed medical regimens, engaging in recommended levels of physical activity, getting vaccinations that promote individual and public health, and getting a healthy amount of sleep. Despite recent advancements in behavioral intervention development and behavior-change science, systematic progress is stalled by the lack of a systematic approach to identifying and targeting mechanisms of action that underlie successful behavior change. Further progress in behavioral intervention science requires that mechanisms be universally prespecified, measurable, and malleable. We developed the CheckList for Investigating Mechanisms in Behavior-change Research (CLIMBR) to guide basic and applied researchers in the planning and reporting of manipulations and interventions relevant to understanding the underlying active ingredients that do-or do not-drive successful change in behavioral outcomes. We report the rationale for creating CLIMBR and detail the processes of its development and refinement based on feedback from behavior-change experts and NIH officials. The final version of CLIMBR is included in full.

  View details for DOI 10.1016/j.beth.2022.12.008

  View details for PubMedID 37330259

• Align with the NMIND consortium for better neuroimaging. Nature human behaviour Kiar, G., Clucas, J., Feczko, E., Goncalves, M., Jarecka, D., Markiewicz, C. J., Halchenko, Y. O., Hermosillo, R., Li, X., Miranda-Dominguez, O., Ghosh, S., Poldrack, R. A., Satterthwaite, T. D., Milham, M. P., Fair, D. 2023

  View details for DOI 10.1038/s41562-023-01647-0

  View details for PubMedID 37386112

  View details for PubMedCentralID 7771346

• Benchmarking explanation methods for mental state decoding with deep learning models. NeuroImage Thomas, A. W., Ré, C., Poldrack, R. A. 2023: 120109

  Abstract

  Deep learning (DL) models find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (e.g., experiencing anger or joy) and brain activity by identifying those spatial and temporal features of brain activity that allow to accurately identify (i.e., decode) these states. Once a DL model has been trained to accurately decode a set of mental states, neuroimaging researchers often make use of methods from explainable artificial intelligence research to understand the model's learned mappings between mental states and brain activity. Here, we benchmark prominent explanation methods in a mental state decoding analysis of multiple functional Magnetic Resonance Imaging (fMRI) datasets. Our findings demonstrate a gradient between two key characteristics of an explanation in mental state decoding, namely, its faithfulness and its alignment with other empirical evidence on the mapping between brain activity and decoded mental state: explanation methods with high explanation faithfulness, which capture the model's decision process well, generally provide explanations that align less well with other empirical evidence than the explanations of methods with less faithfulness. Based on our findings, we provide guidance for neuroimaging researchers on how to choose an explanation method to gain insight into the mental state decoding decisions of DL models.

  View details for DOI 10.1016/j.neuroimage.2023.120109

  View details for PubMedID 37059157

• On the Long-term Archiving of Research Data. Neuroinformatics Pernet, C., Svarer, C., Blair, R., Van Horn, J. D., Poldrack, R. A. 2023

  Abstract

  Accessing research data at any time is what FAIR (Findable Accessible Interoperable Reusable) data sharing aims to achieve at scale. Yet, we argue that it is not sustainable to keep accumulating and maintaining all datasets for rapid access, considering the monetary and ecological cost of maintaining repositories. Here, we address the issue of cold data storage: when to dispose of data for offline storage, how can this be done while maintaining FAIR principles and who should be responsible for cold archiving and long-term preservation.

  View details for DOI 10.1007/s12021-023-09621-x

  View details for PubMedID 36725822

• A dual-task approach to inform the taxonomy of inhibition-related processes. Journal of experimental psychology. Human perception and performance Bissett, P. G., Jones, H. M., Hagen, M. P., Bui, T. T., Li, J. K., Rios, J. A., Mumford, J. A., Shine, J. M., Poldrack, R. A. 2022

  Abstract

  Response inhibition is key to controlled behavior and is commonly investigated with the stop-signal paradigm. The authors investigated how response inhibition is situated within a taxonomy of control processes by combining multiple forms of control within dual tasks. Response inhibition, as measured by stop-signal reaction time (SSRT), was impaired when combined with shape matching, but not the flanker task, and when combined with cued task switching, but not predictable task switching, suggesting that response inhibition may be weakly or variably impaired when combined with selective attention and set shifting demands, respectively. Response inhibition was also consistently impaired when combined with the N-back or directed forgetting tasks, putative measures of working memory. Impairments of response inhibition by other control demands appeared to be primarily driven by task context, as SSRT slowing was similar for trials where control demands were either high (e.g., task switch) or low (e.g., task stay). These results demonstrate that response inhibition processes are often impaired in the context of other control demands, even on trials where direct engagement of those other control processes is not required. This suggests a taxonomy of control in which response inhibition overlaps with related control processes, especially working memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

  View details for DOI 10.1037/xhp0001073

  View details for PubMedID 36548061

• Precision Functional Mapping in Obsessive-Compulsive Disorder Using Dense Sampling Scanning Vaghi, M., Shim, S., Rios, J., Bissett, P., Mukunda, P., Rodriguez, C., Poldrack, R. SPRINGERNATURE. 2022: 303-304

  View details for Web of Science ID 000897934700576

• Harnessing the multiverse of neuroimaging standard references NATURE METHODS Esteban, O., Poldrack, R. A. 2022; 19 (12): 1526-1527

  View details for DOI 10.1038/s41592-022-01682-1

  View details for Web of Science ID 000928418500008

  View details for PubMedID 36456787

  View details for PubMedCentralID 4792175

• Precision Functional Mapping in Obsessive-Compulsive Disorder Using Dense Sampling Scanning Vaghi, M., Shim, S., Rios, J., Bissett, P., Mukunda, P., Rodriguez, C., Poldrack, R. SPRINGERNATURE. 2022: 303-304

  View details for Web of Science ID 000929613800576

• TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models. Nature methods Ciric, R., Thompson, W. H., Lorenz, R., Goncalves, M., MacNicol, E. E., Markiewicz, C. J., Halchenko, Y. O., Ghosh, S. S., Gorgolewski, K. J., Poldrack, R. A., Esteban, O. 2022; 19 (12): 1568-1571

  Abstract

  Reference anatomies of the brain ('templates') and corresponding atlases are the foundation for reporting standardized neuroimaging results. Currently, there is no registry of templates and atlases; therefore, the redistribution of these resources occurs either bundled within existing software or in ad hoc ways such as downloads from institutional sites and general-purpose data repositories. We introduce TemplateFlow as a publicly available framework for human and non-human brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to share their resources under FAIR-findable, accessible, interoperable, and reusable-principles. TemplateFlow enables multifaceted insights into brains across species, and supports multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species.

  View details for DOI 10.1038/s41592-022-01681-2

  View details for PubMedID 36456786

• Estimating the Time to Do Nothing: Toward Next-Generation Models of Response Inhibition CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE Bissett, P. G., Poldrack, R. A. 2022

  View details for DOI 10.1177/09637214221121753

  View details for Web of Science ID 000885686000001

• NEMAR: an open access data, tools and compute resource operating on neuroelectromagnetic data. Database : the journal of biological databases and curation Delorme, A., Truong, D., Youn, C., Sivagnanam, S., Stirm, C., Yoshimoto, K., Poldrack, R. A., Majumdar, A., Makeig, S. 2022; 2022

  Abstract

  To preserve scientific data created by publicly and/or philanthropically funded research projects and to make it ready for exploitation using recent and ongoing advances in advanced and large-scale computational modeling methods, publicly available data must use in common, now-evolving standards for formatting, identifying and annotating should share data. The OpenNeuro.org archive, built first as a repository for magnetic resonance imaging data based on the Brain Imaging Data Structure formatting standards, aims to house and share all types of human neuroimaging data. Here, we present NEMAR.org, a web gateway to OpenNeuro data for human neuroelectromagnetic data. NEMAR allows users to search through, visually explore and assess the quality of shared electroencephalography (EEG), magnetoencephalography and intracranial EEG data and then to directly process selected data using high-performance computing resources of the San Diego Supercomputer Center via the Neuroscience Gateway (nsgportal.org, NSG), a freely available web portal to high-performance computing serving a variety of neuroscientific analysis environments and tools. Combined, OpenNeuro, NEMAR and NSG form an efficient, integrated data, tools and compute resource for human neuroimaging data analysis and meta-analysis. Database URL: https://nemar.org.

  View details for DOI 10.1093/database/baac096

  View details for PubMedID 36367313

• Can we have a second helping? A preregistered direct replication study on the neurobiological mechanisms underlying self-control. Human brain mapping Scholz, C., Chan, H., Poldrack, R. A., de Ridder, D. T., Smidts, A., van der Laan, L. N. 2022

  Abstract

  Self-control is of vital importance for human wellbeing. Hare et al. (2009) were among the first to provide empirical evidence on the neural correlates of self-control. This seminal study profoundly impacted theory and empirical work across multiple fields. To solidify the empirical evidence supporting self-control theory, we conducted a preregistered replication of this work. Further, we tested the robustness of the findings across analytic strategies. Participants underwent functional magnetic resonance imaging while rating 50 food items on healthiness and tastiness and making choices about food consumption. We closely replicated the original analysis pipeline and supplemented it with additional exploratory analyses to follow-up on unexpected findings and to test the sensitivity of results to key analytical choices. Our replication data provide support for the notion that decisions are associated with a value signal in ventromedial prefrontal cortex (vmPFC), which integrates relevant choice attributes to inform a final decision. We found that vmPFC activity was correlated with goal values regardless of the amount of self-control and it correlated with both taste and health in self-controllers but only taste in non-self-controllers. We did not find strong support for the hypothesized role of left dorsolateral prefrontal cortex (dlPFC) in self-control. The absence of statistically significant group differences in dlPFC activity during successful self-control in our sample contrasts with the notion that dlPFC involvement is required in order to effectively integrate longer-term goals into subjective value judgments. Exploratory analyses highlight the sensitivity of results (in terms of effect size) to the analytical strategy, for instance, concerning the approach to region-of-interest analysis.

  View details for DOI 10.1002/hbm.26065

  View details for PubMedID 36082693

• Predicting brain activation maps for arbitrary tasks with cognitive encoding models. NeuroImage Walters, J., King, M., Bissett, P. G., Ivry, R. B., Diedrichsen, J., Poldrack, R. A. 2022: 119610

  Abstract

  A deep understanding of the neural architecture of mental function should enable the accurate prediction of a specific pattern of brain activity for any psychological task, based only on the cognitive functions known to be engaged by that task. Encoding models (EMs), which predict neural responses from known features (e.g., stimulus properties), have succeeded in circumscribed domains (e.g., visual neuroscience), but implementing domain-general EMs that predict brain-wide activity for arbitrary tasks has been limited mainly by availability of datasets that 1) sufficiently span a large space of psychological functions, and 2) are sufficiently annotated with such functions to allow robust EM specification. We examine the use of EMs based on a formal specification of psychological function, to predict cortical activation patterns across a broad range of tasks. We utilized the Multi-Domain Task Battery, a dataset in which 24 subjects completed 32 ten-minute fMRI scans, switching tasks every 35 seconds and engaging in 44 total conditions of diverse psychological manipulations. Conditions were annotated by a group of experts using the Cognitive Atlas ontology to identify putatively engaged functions, and region-wise cognitive EMs (CEMs) were fit, for individual subjects, on neocortical responses. We found that CEMs predicted cortical activation maps of held-out tasks with high accuracy, outperforming a permutation-based null model while approaching the noise ceiling of the data, without being driven solely by either cognitive or perceptual-motor features. Hierarchical clustering on the similarity structure of CEM generalization errors revealed relationships amongst psychological functions. Spatial distributions of feature importances systematically overlapped with large-scale resting-state functional networks (RSNs), supporting the hypothesis of functional specialization within RSNs while grounding their function in an interpretable data-driven manner. Our implementation and validation of CEMs provides a proof of principle for the utility of formal ontologies in cognitive neuroscience and motivates the use of CEMs in the further testing of cognitive theories.

  View details for DOI 10.1016/j.neuroimage.2022.119610

  View details for PubMedID 36064138

• Neuroscout, a unified platform for generalizable andreproducible fMRI research. eLife de la Vega, A., Rocca, R., Blair, R. W., Markiewicz, C. J., Mentch, J., Kent, J. D., Herholz, P., Ghosh, S. S., Poldrack, R. A., Yarkoni, T. 2022; 11

  Abstract

  Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli-such as movies and narratives-allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.

  View details for DOI 10.7554/eLife.79277

  View details for PubMedID 36040302

• Relating psychiatric symptoms and self-regulation during the COVID-19 crisis. Translational psychiatry Vaghi, M. M., Hagen, M. P., Jones, H. M., Mumford, J. A., Bissett, P. G., Poldrack, R. A. 2022; 12 (1): 271

  Abstract

  Disruptions of self-regulation are a hallmark of numerous psychiatric disorders. Here, we examine the relationship between transdiagnostic dimensions of psychopathology and changes in self-regulation in the early phase of the COVID-19 pandemic. We used a data-driven approach on a large number of cognitive tasks and self-reported surveys in training datasets. Then, we derived measures of self-regulation and psychiatric functioning in an independent population sample (N=102) tested both before and after the onset of the COVID-19 pandemic, when the restrictions in place represented a threat to mental health and forced people to flexibly adjust to modifications of daily routines. We found independent relationships between transdiagnostic dimensions of psychopathology and longitudinal alterations in specific domains of self-regulation defined using a diffusion decision model. Compared to the period preceding the onset of the pandemic, a symptom dimension related to anxiety and depression was characterized by a more cautious behavior, indexed by the need to accumulate more evidence before making a decision. Instead, social withdrawal related to faster non-decision processes. Self-reported measures of self-regulation predicted variance in psychiatric symptoms both concurrently and prospectively, revealing the psychological dimensions relevant for separate transdiagnostic dimensions of psychiatry, but tasks did not. Taken together, our results are suggestive of potential cognitive vulnerabilities in the domain of self-regulation in people with underlying psychiatric difficulties in face of real-life stressors. More generally, they also suggest that the study of cognition needs to take into account the dynamic nature of real-world events as well as within-subject variability over time.

  View details for DOI 10.1038/s41398-022-02030-9

  View details for PubMedID 35820995

• Addressing privacy risk in neuroscience data: from data protection to harm prevention. Journal of law and the biosciences Jwa, A. S., Poldrack, R. A. 2022; 9 (2): lsac025

  Abstract

  A recent increase in the amount and availability of neuroscience data within and outside of research and clinical contexts will enhance reproducibility of neuroscience research leading to new discoveries on the mechanisms of brain function in healthy and disease states. However, the uniquely sensitive nature of neuroscience data raises critical concerns regarding data privacy. In response to these concerns, various policy and regulatory approaches have been proposed to control access to and disclosure of neuroscience data, but excessive restriction may hamper open science practice in the field. This article argues that it may now be time to expand the scope of regulatory discourse beyond protection of neuroscience data and to begin contemplating how to prevent potential harm. Legal prohibition of harmful use of neuroscience data could provide an ultimate safeguard against privacy risks and would help us chart a path toward protecting data subjects without unduly limiting the benefits of open science practice. Here we take the Genetic Information Non-Discrimination Act (GINA) as a reference for this new legislation and search for answers to the core regulatory questions based on what we have learned from the enactment of the GINA and the merits and weaknesses of the protection it provides.

  View details for DOI 10.1093/jlb/lsac025

  View details for PubMedID 36072418

• ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion. Nature methods Adebimpe, A., Bertolero, M., Dolui, S., Cieslak, M., Murtha, K., Baller, E. B., Boeve, B., Boxer, A., Butler, E. R., Cook, P., Colcombe, S., Covitz, S., Davatzikos, C., Davila, D. G., Elliott, M. A., Flounders, M. W., Franco, A. R., Gur, R. E., Gur, R. C., Jaber, B., McMillian, C., ALLFTD Consortium, Milham, M., Mutsaerts, H. J., Oathes, D. J., Olm, C. A., Phillips, J. S., Tackett, W., Roalf, D. R., Rosen, H., Tapera, T. M., Tisdall, M. D., Zhou, D., Esteban, O., Poldrack, R. A., Detre, J. A., Satterthwaite, T. D., Apostolova, L., Appleby, B., Barmada, S., Bordelon, Y., Botha, H., Boxer, A. L., Bozoki, A., Brushaber, D., Clark, D., Coppola, G., Darby, R., Dickson, D., Domoto-Reilly, K., Faber, K., Fagan, A., Fields, J. A., Foroud, T., Forsberg, L. K., Geschwind, D., Goldman, J., Galasko, D. R., Gavrilova, R., Gendron, T., Graff-Radford, J., Graff-Radford, N., Grant, I. M., Grossman, M., Hall, M., Huang, E., Heuer, H. W., Hsiung, G. R., Huey, E. D., Irwin, D., Jones, D. T., Kantarci, K., Kaufer, D., Kerwin, D., Knopman, D., Kornak, J., Kramer, J., Kremers, W., Lapid, M., Lago, A. L., Leger, G., Ljubenkov, P., Litvan, I., Lucente, D., Mackenzie, I. R., Masdeu, J. C., McGinnis, S., Mendez, M., Mester, C., Miller, B. L., Onyike, C., Pascual, M. B., Petrucelli, L., Pressman, P., Rademakers, R., Ramanan, V., Ramos, E. M., Rao, M., Rascovsky, K., Rankin, K. P., Ritter, A., Roberson, E. D., Rojas-Martinez, J., Rosen, H. J., Savica, R., Seeley, W., Syrjanen, J., Staffaroni, A. M., Tartaglia, M. C., Taylor, J., VandeVrede, L., Weintraub, S., Wong, B., Wszolek, Z. 2022; 19 (6): 683-686

  Abstract

  Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

  View details for DOI 10.1038/s41592-022-01458-7

  View details for PubMedID 35689029

• Survey on Open Science Practices in Functional Neuroimaging. NeuroImage Paret, C., Unverhau, N., Feingold, F., Poldrack, R. A., Stirner, M., Schmahl, C., Sicorello, M. 2022: 119306

  Abstract

  Replicability and reproducibility of scientific findings is paramount for sustainable progress in neuroscience. Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. Email invitations to participate in the survey were sent to addresses received through a PubMed search of human functional magnetic resonance imaging studies that were published between 2010 and 2020. 283 persons completed the questionnaire. Although half of the participants were experienced with preregistration, the willingness to preregister studies in the future was modest. The majority of participants had experience with the sharing of primary neuroimaging data. Most of the participants were interested in implementing a standardized data structure such as BIDS in their labs. Based on demographic variables, we compared participants on seven subscales, which had been generated through factor analysis. Exploratory analyses found that experienced researchers at lower career level had higher fear of being transparent and researchers with residence in the EU had a higher need for data governance. Additionally, researchers at medical faculties as compared to other university faculties reported a more unsupportive supervisor with regards to open science practices and a higher need for data governance. The results suggest growing adoption of open science practices but also highlight a number of important impediments.

  View details for DOI 10.1016/j.neuroimage.2022.119306

  View details for PubMedID 35595201

• The spectrum of data sharing policies in neuroimaging data repositories. Human brain mapping Jwa, A. S., Poldrack, R. A. 2022

  Abstract

  Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promoted in diverse fields and neuroscience has been no exception to this movement. For all its promise, however, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Recently, the heightened risks to data privacy posed by the rapid advances in artificial intelligence and machine learning techniques have made data sharing more challenging; the regulatory landscape around data sharing has also been evolving rapidly. Here we present an in-depth ethical and regulatory analysis that examines how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies in the United States and how advanced software tools and algorithms might undermine subjects' privacy in neuroimaging data sharing. The implications of these novel technological threats to privacy in neuroimaging data sharing practices and policies will also be discussed. We then conclude with a proposal for a legal prohibition against malicious use of neuroscience data as a regulatory mechanism to address privacy risks associated with the data while maximizing the benefits of data sharing and open science practice in the field of neuroscience.

  View details for DOI 10.1002/hbm.25803

  View details for PubMedID 35142409

• Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data. Neuroinformatics Poline, J., Kennedy, D. N., Sommer, F. T., Ascoli, G. A., Van Essen, D. C., Ferguson, A. R., Grethe, J. S., Hawrylycz, M. J., Thompson, P. M., Poldrack, R. A., Ghosh, S. S., Keator, D. B., Athey, T. L., Vogelstein, J. T., Mayberg, H. S., Martone, M. E. 1800

  Abstract

  In this perspective article, we consider the critical issue of data and other research object standardisation and, specifically, how international collaboration, and organizations such as the International Neuroinformatics Coordinating Facility (INCF) can encourage that emerging neuroscience data be Findable, Accessible, Interoperable, and Reusable (FAIR). As neuroscientists engaged in the sharing and integration of multi-modal and multiscale data, we see the current insufficiency of standards as a major impediment in the Interoperability and Reusability of research results. We call for increased international collaborative standardisation of neuroscience data to foster integration and efficient reuse of research objects.

  View details for DOI 10.1007/s12021-021-09557-0

  View details for PubMedID 35061216

• Momentary Influences on Self-Regulation in Two Populations With Health Risk Behaviors: Adults Who Smoke and Adults Who Are Overweight and Have Binge-Eating Disorder. Frontiers in digital health Scherer, E. A., Metcalf, S. A., Whicker, C. L., Bartels, S. M., Grabinski, M., Kim, S. J., Sweeney, M. A., Lemley, S. M., Lavoie, H., Xie, H., Bissett, P. G., Dallery, J., Kiernan, M., Lowe, M. R., Onken, L., Prochaska, J. J., Stoeckel, L. E., Poldrack, R. A., MacKinnon, D. P., Marsch, L. A. 2022; 4: 798895

  Abstract

  Introduction: Self-regulation has been implicated in health risk behaviors and is a target of many health behavior interventions. Despite most prior research focusing on self-regulation as an individual-level trait, we hypothesize that self-regulation is a time-varying mechanism of health and risk behavior that may be influenced by momentary contexts to a substantial degree. Because most health behaviors (e.g., eating, drinking, smoking) occur in the context of everyday activities, digital technologies may help us better understand and influence these behaviors in real time. Using a momentary self-regulation measure, the current study (which was part of a larger multi-year research project on the science of behavior change) used ecological momentary assessment (EMA) to assess if self-regulation can be engaged and manipulated on a momentary basis in naturalistic, non-laboratory settings.Methods: This one-arm, open-label exploratory study prospectively collected momentary data for 14 days from 104 participants who smoked regularly and 81 participants who were overweight and had binge-eating disorder. Four times per day, participants were queried about momentary self-regulation, emotional state, and social and environmental context; recent smoking and exposure to smoking cues (smoking sample only); and recent eating, binge eating, and exposure to binge-eating cues (binge-eating sample only). This study used a novel, momentary self-regulation measure comprised of four subscales: momentary perseverance, momentary sensation seeking, momentary self-judgment, and momentary mindfulness. Participants were also instructed to engage with Laddr, a mobile application that provides evidence-based health behavior change tools via an integrated platform. The association between momentary context and momentary self-regulation was explored via mixed-effects models. Exploratory assessments of whether recent Laddr use (defined as use within 12 h of momentary responses) modified the association between momentary context and momentary self-regulation were performed via mixed-effects models.Results: Participants (mean age 35.2; 78% female) in the smoking and binge-eating samples contributed a total of 3,233 and 3,481 momentary questionnaires, respectively. Momentary self-regulation subscales were associated with several momentary contexts, in the combined as well as smoking and binge-eating samples. For example, in the combined sample momentary perseverance was associated with location, positively associated with positive affect, and negatively associated with negative affect, stress, and tiredness. In the smoking sample, momentary perseverance was positively associated with momentary difficulty in accessing cigarettes, caffeine intake, and momentary restraint in smoking, and negatively associated with temptation and urge to smoke. In the binge-eating sample, momentary perseverance was positively associated with difficulty in accessing food and restraint in eating, and negatively associated with urge to binge eat. While recent Laddr use was not associated directly with momentary self-regulation subscales, it did modify several of the contextual associations, including challenging contexts.Conclusions: Overall, this study provides preliminary evidence that momentary self-regulation may vary in response to differing momentary contexts in samples from two exemplar populations with risk behaviors. In addition, the Laddr application may modify some of these relationships. These findings demonstrate the possibility of measuring momentary self-regulation in a trans-diagnostic way and assessing the effects of momentary, mobile interventions in context. Health behavior change interventions may consider measuring and targeting momentary self-regulation in addition to trait-level self-regulation to better understand and improve health risk behaviors. This work will be used to inform a later stage of research focused on assessing the transdiagnostic mediating effect of momentary self-regulation on medical regimen adherence and health outcomes.Clinical Trial Registration: ClinicalTrials.gov, Identifier: NCT03352713.

  View details for DOI 10.3389/fdgth.2022.798895

  View details for PubMedID 35373179

• Consensus-based guidance for conducting and reporting multi-analyst studies. eLife Aczel, B., Szaszi, B., Nilsonne, G., van den Akker, O. R., Albers, C. J., van Assen, M. A., Bastiaansen, J. A., Benjamin, D., Boehm, U., Botvinik-Nezer, R., Bringmann, L. F., Busch, N. A., Caruyer, E., Cataldo, A. M., Cowan, N., Delios, A., van Dongen, N. N., Donkin, C., van Doorn, J. B., Dreber, A., Dutilh, G., Egan, G. F., Gernsbacher, M. A., Hoekstra, R., Hoffmann, S., Holzmeister, F., Huber, J., Johannesson, M., Jonas, K. J., Kindel, A. T., Kirchler, M., Kunkels, Y. K., Lindsay, D. S., Mangin, J., Matzke, D., Munafo, M. R., Newell, B. R., Nosek, B. A., Poldrack, R. A., van Ravenzwaaij, D., Rieskamp, J., Salganik, M. J., Sarafoglou, A., Schonberg, T., Schweinsberg, M., Shanks, D., Silberzahn, R., Simons, D. J., Spellman, B. A., St-Jean, S., Starns, J. J., Uhlmann, E. L., Wicherts, J., Wagenmakers, E. 2021; 10

  Abstract

  Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.

  View details for DOI 10.7554/eLife.72185

  View details for PubMedID 34751133

• The OpenNeuro resource for sharing of neuroscience data ELIFE Markiewicz, C. J., Gorgolewski, K. J., Feingold, F., Blair, R., Halchenko, Y. O., Miller, E., Hardcastle, N., Wexler, J., Esteban, O., Goncavles, M., Jwa, A., Poldrack, R. 2021; 10

  View details for DOI 10.7554/eLife.71774.sa2

  View details for Web of Science ID 000712035100001

• Reward learning and working memory: Effects of massed versus spaced training and post-learning delay period. Memory & cognition Wimmer, G. E., Poldrack, R. A. 2021

  Abstract

  Neuroscience research has illuminated the mechanisms supporting learning from reward feedback, demonstrating a critical role for the striatum and midbrain dopamine system. However, in humans, short-term working memory that is dependent on frontal and parietal cortices can also play an important role, particularly in commonly used paradigms in which learning is relatively condensed in time. Given the growing use of reward-based learning tasks in translational studies in computational psychiatry, it is important to understand the extent of the influence ofworking memory and alsohow coregradual learning mechanisms can be better isolated. In our experiments, we manipulated the spacing between repetitions along with a post-learning delay preceding a test phase. We found that learning was slower for stimuli repeated after a long delay (spaced-trained) compared to those repeated immediately (massed-trained), likely reflecting the remaining contribution of feedback learning mechanisms when working memory is not available. For massed learning,brief interruptions led to drops in subsequent performance,and individual differences in working memory capacity positively correlated with overallperformance. Interestingly, when testedafter a delay period but not immediately, relative preferences decayed in the massed condition and increased in the spaced condition. Our results provide additional support for a large role of working memory in reward-based learning in temporally condensed designs. We suggest that spacing training within or between sessions is a promising approach to better isolate and understand mechanisms supporting gradual reward-based learning, with particular importance for understanding potential learning dysfunctions in addiction and psychiatric disorders.

  View details for DOI 10.3758/s13421-021-01233-7

  View details for PubMedID 34519968

• Diving into the deep end: a personal reflection on the MyConnectome study CURRENT OPINION IN BEHAVIORAL SCIENCES Poldrack, R. A. 2021; 40: 1-4

  View details for DOI 10.1016/j.cobeha.2020.10.008

  View details for Web of Science ID 000709388900002

• Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology Lam, M., Chen, C., Ge, T., Xia, Y., Hill, D. W., Trampush, J. W., Yu, J., Knowles, E., Davies, G., Stahl, E. A., Huckins, L., Liewald, D. C., Djurovic, S., Melle, I., Christoforou, A., Reinvang, I., DeRosse, P., Lundervold, A. J., Steen, V. M., Espeseth, T., Raikkonen, K., Widen, E., Palotie, A., Eriksson, J. G., Giegling, I., Konte, B., Hartmann, A. M., Roussos, P., Giakoumaki, S., Burdick, K. E., Payton, A., Ollier, W., Chiba-Falek, O., Koltai, D. C., Need, A. C., Cirulli, E. T., Voineskos, A. N., Stefanis, N. C., Avramopoulos, D., Hatzimanolis, A., Smyrnis, N., Bilder, R. M., Freimer, N. B., Cannon, T. D., London, E., Poldrack, R. A., Sabb, F. W., Congdon, E., Conley, E. D., Scult, M. A., Dickinson, D., Straub, R. E., Donohoe, G., Morris, D., Corvin, A., Gill, M., Hariri, A. R., Weinberger, D. R., Pendleton, N., Bitsios, P., Rujescu, D., Lahti, J., Le Hellard, S., Keller, M. C., Andreassen, O. A., Deary, I. J., Glahn, D. C., Huang, H., Liu, C., Malhotra, A. K., Lencz, T. 2021

  Abstract

  Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N=373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

  View details for DOI 10.1038/s41386-021-01023-4

  View details for PubMedID 34035472

• Measurement in Intensive Longitudinal Data. Structural equation modeling : a multidisciplinary journal McNeish, D., Mackinnon, D. P., Marsch, L. A., Poldrack, R. A. 2021; 28 (5): 807-822

  Abstract

  Technological advances have increased the prevalence of intensive longitudinal data as well as statistical techniques appropriate for these data, such as dynamic structural equation modeling (DSEM). Intensive longitudinal designs often investigate constructs related to affect or mood and do so with multiple item scales. However, applications of intensive longitudinal methods often rely on simple sums or averages of the administered items rather than considering a proper measurement model. This paper demonstrates how to incorporate measurement models into DSEM to (1) provide more rigorous measurement of constructs used in intensive longitudinal studies and (2) assess whether scales are invariant across time and across people, which is not possible when item responses are summed or averaged. We provide an example from an ecological momentary assessment study on self-regulation in adults with binge eating disorder and walkthrough how to fit the model in Mplus and how to interpret the results.

  View details for DOI 10.1080/10705511.2021.1915788

  View details for PubMedID 34737528

  View details for PubMedCentralID PMC8562472

• Measurement in Intensive Longitudinal Data STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL McNeish, D., Mackinnon, D. P., Marsch, L. A., Poldrack, R. A. 2021

  View details for DOI 10.1080/10705511.2021.1915788

  View details for Web of Science ID 000653545600001

• The cognitive and perceptual correlates of ideological attitudes: a data-driven approach. Philosophical transactions of the Royal Society of London. Series B, Biological sciences Zmigrod, L., Eisenberg, I. W., Bissett, P. G., Robbins, T. W., Poldrack, R. A. 2021; 376 (1822): 20200424

  Abstract

  Although human existence is enveloped by ideologies, remarkably little is understood about the relationships between ideological attitudes and psychological traits. Even less is known about how cognitive dispositions-individual differences in how information is perceived and processed- sculpt individuals' ideological worldviews, proclivities for extremist beliefs and resistance (or receptivity) to evidence. Using an unprecedented number of cognitive tasks (n = 37) and personality surveys (n = 22), along with data-driven analyses including drift-diffusion and Bayesian modelling, we uncovered the specific psychological signatures of political, nationalistic, religious and dogmatic beliefs. Cognitive and personality assessments consistently outperformed demographic predictors in accounting for individual differences in ideological preferences by 4 to 15-fold. Furthermore, data-driven analyses revealed that individuals' ideological attitudes mirrored their cognitive decision-making strategies. Conservatism and nationalism were related to greater caution in perceptual decision-making tasks and to reduced strategic information processing, while dogmatism was associated with slower evidence accumulation and impulsive tendencies. Religiosity was implicated in heightened agreeableness and risk perception. Extreme pro-group attitudes, including violence endorsement against outgroups, were linked to poorer working memory, slower perceptual strategies, and tendencies towards impulsivity and sensation-seeking-reflecting overlaps with the psychological profiles of conservatism and dogmatism. Cognitive and personality signatures were also generated for ideologies such as authoritarianism, system justification, social dominance orientation, patriotism and receptivity to evidence or alternative viewpoints; elucidating their underpinnings and highlighting avenues for future research. Together these findings suggest that ideological worldviews may be reflective of low-level perceptual and cognitive functions. This article is part of the theme issue 'The political brain: neurocognitive and computational mechanisms'.

  View details for DOI 10.1098/rstb.2020.0424

  View details for PubMedID 33611995

• Design issues and solutions for stop-signal data from the Adolescent Brain Cognitive Development [ABCD] study. eLife Bissett, P. G., Hagen, M. P., Jones, H. M., Poldrack, R. A. 2021; 10

  Abstract

  The Adolescent Brain Cognitive Development (ABCD) study is an unprecedented longitudinal neuroimaging sample that tracks the brain development of over 10,000 9-10 year olds through adolescence. At the core of this study are the three tasks that are completed repeatedly within the MRI scanner, one of which is the stop-signal task. In analyzing the available stopping experimental code and data, we identified a set of design issues that we believe significantly compromise its value. These issues include but are not limited to: variable stimulus durations that violate basic assumptions of dominant stopping models, trials in which stimuli are incorrectly not presented, and faulty stop-signal delays. We present eight issues, show their effect on the existing ABCD data, suggest prospective solutions including task changes for future data collection and preliminary computational models, and suggest retrospective solutions for data users who wish to make the most of the existing data.

  View details for DOI 10.7554/eLife.60185

  View details for PubMedID 33661097

• A data-driven framework for mapping domains of human neurobiology Nature Neuroscience Beam, E., Potts, C., Poldrack, R. A., Etkin, A. 2021

  View details for DOI 10.1038/s41593-021-00948-9

• Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective. GigaScience Levitis, E., van Praag, C. D., Gau, R., Heunis, S., DuPre, E., Kiar, G., Bottenhorn, K. L., Glatard, T., Nikolaidis, A., Whitaker, K. J., Mancini, M., Niso, G., Afyouni, S., Alonso-Ortiz, E., Appelhoff, S., Arnatkeviciute, A., Atay, S. M., Auer, T., Baracchini, G., Bayer, J. M., Beauvais, M. J., Bijsterbosch, J. D., Bilgin, I. P., Bollmann, S., Bollmann, S., Botvinik-Nezer, R., Bright, M. G., Calhoun, V. D., Chen, X., Chopra, S., Chuan-Peng, H., Close, T. G., Cookson, S. L., Craddock, R. C., De La Vega, A., De Leener, B., Demeter, D. V., Di Maio, P., Dickie, E. W., Eickhoff, S. B., Esteban, O., Finc, K., Frigo, M., Ganesan, S., Ganz, M., Garner, K. G., Garza-Villarreal, E. A., Gonzalez-Escamilla, G., Goswami, R., Griffiths, J. D., Grootswagers, T., Guay, S., Guest, O., Handwerker, D. A., Herholz, P., Heuer, K., Huijser, D. C., Iacovella, V., Joseph, M. J., Karakuzu, A., Keator, D. B., Kobeleva, X., Kumar, M., Laird, A. R., Larson-Prior, L. J., Lautarescu, A., Lazari, A., Legarreta, J. H., Li, X. Y., Lv, J., Mansour L, S., Meunier, D., Moraczewski, D., Nandi, T., Nastase, S. A., Nau, M., Noble, S., Norgaard, M., Obungoloch, J., Oostenveld, R., Orchard, E. R., Pinho, A. L., Poldrack, R. A., Qiu, A., Raamana, P. R., Rokem, A., Rutherford, S., Sharan, M., Shaw, T. B., Syeda, W. T., Testerman, M. M., Toro, R., Valk, S. L., Van Den Bossche, S., Varoquaux, G., Váša, F., Veldsman, M., Vohryzek, J., Wagner, A. S., Walsh, R. J., White, T., Wong, F. T., Xie, X., Yan, C. G., Yang, Y. F., Yee, Y., Zanitti, G. E., Van Gulick, A. E., Duff, E., Maumet, C. 2021; 10 (8)

  Abstract

  As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

  View details for DOI 10.1093/gigascience/giab051

  View details for PubMedID 34414422

• Searching for Imaging Biomarkers of Psychotic Dysconnectivity. Biological psychiatry. Cognitive neuroscience and neuroimaging Rodrigue, A. L., Mastrovito, D., Esteban, O., Durnez, J., Koenis, M. M., Janssen, R., Alexander-Bloch, A., Knowles, E. M., Mathias, S. R., Mollon, J., Pearlson, G. D., Frangou, S., Blangero, J., Poldrack, R. A., Glahn, D. C. 2020

  Abstract

  BACKGROUND: Progress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging.METHODS: We leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics.RESULTS: Although we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range= 0.30) and across datasets (average support vector machine AUC range= 0.50; average ridge regression AUC range= 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N= 888; diffusion tensor imaging, N= 860) did not improve model performance (maximal AUC= 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results.CONCLUSIONS: Connectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.

  View details for DOI 10.1016/j.bpsc.2020.12.002

  View details for PubMedID 33622655

• Sharing voxelwise neuroimaging results from rhesus monkeys and other species with Neurovault. NeuroImage Fox, A. S., Holley, D., Klink, P. C., Arbuckle, S. A., Barnes, C. A., Diedrichsen, J., Kwok, S. C., Kyle, C., Pruszynski, J. A., Seidlitz, J., Zhou, X., Poldrack, R. A., Gorgolewski, K. J. 2020; 225: 117518

  Abstract

  Animal neuroimaging studies can provide unique insights into brain structure and function, and can be leveraged to bridge the gap between animal and human neuroscience. In part, this power comes from the ability to combine mechanistic interventions with brain-wide neuroimaging. Due to their phylogenetic proximity to humans, nonhuman primate neuroimaging holds particular promise. Because nonhuman primate neuroimaging studies are often underpowered, there is a great need to share data amongst translational researchers. Data sharing efforts have been limited, however, by the lack of standardized tools and repositories through which nonhuman neuroimaging data can easily be archived and accessed. Here, we provide an extension of the Neurovault framework to enable sharing of statistical maps and related voxelwise neuroimaging data from other species and template-spaces. Neurovault, which was previously limited to human neuroimaging data, now allows researchers to easily upload and share nonhuman primate neuroimaging results. This promises to facilitate open, integrative, cross-species science while affording researchers the increased statistical power provided by data aggregation. In addition, the Neurovault code-base now enables the addition of other species and template-spaces. Together, these advances promise to bring neuroimaging data sharing to research in other species, for supplemental data, location-based atlases, and data that would otherwise be relegated to a "file-drawer". As increasing numbers of researchers share their nonhuman neuroimaging data on Neurovault, this resource will enable novel, large-scale, cross-species comparisons that were previously impossible.

  View details for DOI 10.1016/j.neuroimage.2020.117518

  View details for PubMedID 33137472

• Reflections on the past two decades of neuroscience. Nature reviews. Neuroscience Bassett, D. S., Cullen, K. E., Eickhoff, S. B., Farah, M. J., Goda, Y., Haggard, P., Hu, H., Hurd, Y. L., Josselyn, S. A., Khakh, B. S., Knoblich, J. A., Poirazi, P., Poldrack, R. A., Prinz, M., Roelfsema, P. R., Spires-Jones, T. L., Sur, M., Ueda, H. R. 2020

  Abstract

  The first issue of Nature Reviews Neuroscience was published 20 years ago, in 2000. To mark this anniversary, in this Viewpoint article we asked a selection of researchers from across the field who have authored pieces published in the journal in recent years for their thoughts on notable and interesting developments in neuroscience, and particularly in their areas of the field, over the past two decades. They also provide some thoughts on current lines of research and questions that excite them.

  View details for DOI 10.1038/s41583-020-0363-6

  View details for PubMedID 32879507

• Implications of the Lacking Relationship Between Cognitive Task and Self-report Measures for Psychiatry. Biological psychiatry. Cognitive neuroscience and neuroimaging Enkavi, A. Z., Poldrack, R. A. 2020

  View details for DOI 10.1016/j.bpsc.2020.06.010

  View details for PubMedID 32712212

• Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nature protocols Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E., Ye, Z., Salo, T., Valabregue, R., Amlien, I. K., Liem, F., Jacoby, N., Stojic, H., Cieslak, M., Urchs, S., Halchenko, Y. O., Ghosh, S. S., De La Vega, A., Yarkoni, T., Wright, J., Thompson, W. H., Poldrack, R. A., Gorgolewski, K. J. 2020

  Abstract

  Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

  View details for DOI 10.1038/s41596-020-0327-3

  View details for PubMedID 32514178

• Dataset decay and the problem of sequential analyses on open datasets. eLife Thompson, W. H., Wright, J., Bissett, P. G., Poldrack, R. A. 2020; 9

  Abstract

  Open data allows researchers to explore pre-existing datasets in new ways. However, if many researchers reuse the same dataset, multiple statistical testing may increase false positives. Here we demonstrate that sequential hypothesis testing on the same dataset by multiple researchers can inflate error rates. We go on to discuss a number of correction procedures that can reduce the number of false positives, and the challenges associated with these correction procedures.

  View details for DOI 10.7554/eLife.53498

  View details for PubMedID 32425159

• Trauma in Affective and Nonaffective Psychosis: Associations and Dissociations With Cognitive Functioning in Childhood and Adulthood Mollon, J., Mathias, S., Knowles, E., Rodrigue, A., Pearlson, G., Barrett, J., Poldrack, R., Blangero, J., Glahn, D. ELSEVIER SCIENCE INC. 2020: S458

  View details for Web of Science ID 000535308201364

• Disambiguating the role of blood flow and global signal with partial information decomposition. NeuroImage Colenbier, N., Van de Steen, F., Uddin, L. Q., Poldrack, R. A., Calhoun, V. D., Marinazzo, D. 2020: 116699

  Abstract

  Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas.

  View details for DOI 10.1016/j.neuroimage.2020.116699

  View details for PubMedID 32179104

• Correlation Database of 60 Cross-Disciplinary Surveys and Cognitive Tasks Assessing Self-Regulation. Journal of personality assessment Mazza, G. L., Smyth, H. L., Bissett, P. G., Canning, J. R., Eisenberg, I. W., Enkavi, A. Z., Gonzalez, O., Kim, S. J., Metcalf, S. A., Muniz, F., Pelham, W. E., Scherer, E. A., Valente, M. J., Xie, H., Poldrack, R. A., Marsch, L. A., MacKinnon, D. P. 2020: 1–8

  Abstract

  Self-regulation is studied across various disciplines, including personality, social, cognitive, health, developmental, and clinical psychology; psychiatry; neuroscience; medicine; pharmacology; and economics. Widespread interest in self-regulation has led to confusion regarding both the constructs within the nomological network of self-regulation and the measures used to assess these constructs. To facilitate the integration of cross-disciplinary measures of self-regulation, we estimated product-moment and distance correlations among 60 cross-disciplinary measures of self-regulation (23 self-report surveys, 37 cognitive tasks) and measures of health and substance use based on 522 participants. The correlations showed substantial variability, though the surveys demonstrated greater convergent validity than did the cognitive tasks. Variables derived from the surveys only weakly correlated with variables derived from the cognitive tasks (M = .049, range = .000 to .271 for the absolute value of the product-moment correlation; M = .085, range = .028 to .241 for the distance correlation), thus challenging the notion that these surveys and cognitive tasks measure the same construct. We conclude by outlining several potential uses for this publicly available database of correlations.

  View details for DOI 10.1080/00223891.2020.1732994

  View details for PubMedID 32148088

• The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression. NeuroImage Tozzi, L., Staveland, B., Holt-Gosselin, B., Chesnut, M., Chang, S. E., Choi, D., Shiner, M. L., Wu, H., Lerma-Usabiaga, G., Sporns, O., Barch, D., Gotlib, I. H., Hastie, T. J., Kerr, A. B., Poldrack, R. A., Wandell, B. A., Wintermark, M., Williams, L. M. 2020: 116715

  Abstract

  Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest ("Emotion", "Gambling" and "Continuous Performance" tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.

  View details for DOI 10.1016/j.neuroimage.2020.116715

  View details for PubMedID 32147367

• Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation. Human brain mapping Thompson, W. H., Kastrati, G. n., Finc, K. n., Wright, J. n., Shine, J. M., Poldrack, R. A. 2020

  Abstract

  In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behavior. Here, we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient [PC]) in time-varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occurring. Further, we present a temporal extension to the PC measure (temporal PC) that circumnavigates this problem by jointly considering all community partitions assigned to a node through time. The proposed method allows us to track a node's integration through time while adjusting for the possible changes in the community structure of the overall network.

  View details for DOI 10.1002/hbm.24950

  View details for PubMedID 32058633

• Effective Self-Management for Early Career Researchers in the Natural and Life Sciences. Neuron Bielczyk, N. Z., Ando, A. n., Badhwar, A. n., Caldinelli, C. n., Gao, M. n., Haugg, A. n., Hernandez, L. M., Ito, K. L., Kessler, D. n., Lurie, D. n., Makary, M. M., Nikolaidis, A. n., Veldsman, M. n., Allen, C. n., Bankston, A. n., Bottenhorn, K. L., Braukmann, R. n., Calhoun, V. n., Cheplygina, V. n., Boffino, C. C., Ercan, E. n., Finc, K. n., Foo, H. n., Khatibi, A. n., La, C. n., Mehler, D. M., Narayanan, S. n., Poldrack, R. A., Raamana, P. R., Salo, T. n., Godard-Sebillotte, C. n., Uddin, L. Q., Valeriani, D. n., Valk, S. L., Walton, C. C., Ward, P. G., Yanes, J. A., Zhou, X. n. 2020; 106 (2): 212–17

  Abstract

  Early career researchers (ECRs) are faced with a range of competing pressures in academia, making self-management key to building a successful career. The Organization for Human Brain Mapping undertook a group effort to gather helpful advice for ECRs in self-management.

  View details for DOI 10.1016/j.neuron.2020.03.015

  View details for PubMedID 32325057

• Cognitive impairment from early to middle adulthood in patients with affective and nonaffective psychotic disorders PSYCHOLOGICAL MEDICINE Mollon, J., Mathias, S. R., Knowles, E. M., Rodrigue, A., Koenis, M. G., Pearlson, G. D., Reichenberg, A., Barrett, J., Denbow, D., Aberizk, K., Zatony, M., Poldrack, R. A., Blangero, J., Glahn, D. C. 2020; 50 (1): 48–57

  View details for DOI 10.1017/S0033291718003938

  View details for Web of Science ID 000510452400006

• Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Network neuroscience (Cambridge, Mass.) Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liegeois, R., Lindquist, M. A., McIntosh, A. R., Poldrack, R. A., Shine, J. M., Thompson, W. H., Bielczyk, N. Z., Douw, L., Kraft, D., Miller, R. L., Muthuraman, M., Pasquini, L., Razi, A., Vidaurre, D., Xie, H., Calhoun, V. D. 2020; 4 (1): 30–69

  Abstract

  The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

  View details for DOI 10.1162/netn_a_00116

  View details for PubMedID 32043043

• How Can Neuroscientists Respond to the Climate Emergency? Neuron Aron, A. R., Ivry, R. B., Jeffery, K. J., Poldrack, R. A., Schmidt, R. n., Summerfield, C. n., Urai, A. E. 2020; 106 (1): 17–20

  Abstract

  The world faces a climate emergency. Here, we consider the actions that can be taken by neuroscientists to tackle climate change. We encourage neuroscientists to put emissions reductions at the center of their everyday professional activities.

  View details for DOI 10.1016/j.neuron.2020.02.019

  View details for PubMedID 32272064

• Associations of cannabis use disorder with cognition, brain structure, and brain function in African Americans. Human brain mapping Koenis, M. M., Durnez, J. n., Rodrigue, A. L., Mathias, S. R., Alexander-Bloch, A. F., Barrett, J. A., Doucet, G. E., Frangou, S. n., Knowles, E. E., Mollon, J. n., Denbow, D. n., Aberizk, K. n., Zatony, M. n., Janssen, R. J., Curran, J. E., Blangero, J. n., Poldrack, R. A., Pearlson, G. D., Glahn, D. C. 2020

  Abstract

  Although previous studies have highlighted associations of cannabis use with cognition and brain morphometry, critical questions remain with regard to the association between cannabis use and brain structural and functional connectivity. In a cross-sectional community sample of 205 African Americans (age 18-70) we tested for associations of cannabis use disorder (CUD, n = 57) with multi-domain cognitive measures and structural, diffusion, and resting state brain-imaging phenotypes. Post hoc model evidence was computed with Bayes factors (BF) and posterior probabilities of association (PPA) to account for multiple testing. General cognitive functioning, verbal intelligence, verbal memory, working memory, and motor speed were lower in the CUD group compared with non-users (p < .011; 1.9 < BF < 3,217). CUD was associated with altered functional connectivity in a network comprising the motor-hand region in the superior parietal gyri and the anterior insula (p < .04). These differences were not explained by alcohol, other drug use, or education. No associations with CUD were observed in cortical thickness, cortical surface area, subcortical or cerebellar volumes (0.12 < BF < 1.5), or graph-theoretical metrics of resting state connectivity (PPA < 0.01). In a large sample collected irrespective of cannabis used to minimize recruitment bias, we confirm the literature on poorer cognitive functioning in CUD, and an absence of volumetric brain differences between CUD and non-CUD. We did not find evidence for or against a disruption of structural connectivity, whereas we did find localized resting state functional dysconnectivity in CUD. There was sufficient proof, however, that organization of functional connectivity as determined via graph metrics does not differ between CUD and non-user group.

  View details for DOI 10.1002/hbm.25324

  View details for PubMedID 33340172

• NeuroQuery, comprehensive meta-analysis of human brain mapping. eLife Dockès, J. n., Poldrack, R. A., Primet, R. n., Gözükan, H. n., Yarkoni, T. n., Suchanek, F. n., Thirion, B. n., Varoquaux, G. n. 2020; 9

  Abstract

  Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms. The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Existing meta-analysis methods perform statistical tests on sets of publications associated with a particular concept. Thus, large-scale meta-analyses only tackle single terms that occur frequently. We propose a new paradigm, focusing on prediction rather than inference. Our multivariate model predicts the spatial distribution of neurological observations, given text describing an experiment, cognitive process, or disease. This approach handles text of arbitrary length and terms that are too rare for standard meta-analysis. We capture the relationships and neural correlates of 7547 neuroscience terms across 13459 neuroimaging publications. The resulting meta-analytic tool, neuroquery.org, can ground hypothesis generation and data-analysis priors on a comprehensive view of published findings on the brain.

  View details for DOI 10.7554/eLife.53385

  View details for PubMedID 32129761

• A Psychometric Analysis of the Brief Self-Control Scale. Assessment Manapat, P. D., Edwards, M. C., MacKinnon, D. P., Poldrack, R. A., Marsch, L. A. 2019: 1073191119890021

  Abstract

  The Brief Self-Control Scale (BSCS) is a widely used measure of self-control, a construct associated with beneficial psychological outcomes. Several studies have investigated the psychometric properties of the BSCS but have failed to reach consensus. This has resulted in an unstable and ambiguous understanding of the scale and its psychometric properties. The current study sought resolution by implementing scale evaluation approaches guided by modern psychometric literature. Additionally, our goal was to provide a more comprehensive item analysis via the item response theory (IRT) framework. Results from the current study support both unidimensional and multidimensional factor structures for the 13-item version of the BSCS. The addition of an IRT analysis provided a new perspective on item- and test-level functioning. The goal of a more defensible psychometric grounding for the BSCS is to promote greater consistency, stability, and trust in future results.

  View details for DOI 10.1177/1073191119890021

  View details for PubMedID 31786956

• Establishment of Best Practices for Evidence for Prediction: A Review. JAMA psychiatry Poldrack, R. A., Huckins, G., Varoquaux, G. 2019

  Abstract

  Importance: Great interest exists in identifying methods to predict neuropsychiatric disease states and treatment outcomes from high-dimensional data, including neuroimaging and genomics data. The goal of this review is to highlight several potential problems that can arise in studies that aim to establish prediction.Observations: A number of neuroimaging studies have claimed to establish prediction while establishing only correlation, which is an inappropriate use of the statistical meaning of prediction. Statistical associations do not necessarily imply the ability to make predictions in a generalized manner; establishing evidence for prediction thus requires testing of the model on data separate from those used to estimate the model's parameters. This article discusses various measures of predictive performance and the limitations of some commonly used measures, with a focus on the importance of using multiple measures when assessing performance. For classification, the area under the receiver operating characteristic curve is an appropriate measure; for regression analysis, correlation should be avoided, and median absolute error is preferred.Conclusions and Relevance: To ensure accurate estimates of predictive validity, the recommended best practices for predictive modeling include the following: (1) in-sample model fit indices should not be reported as evidence for predictive accuracy, (2) the cross-validation procedure should encompass all operations applied to the data, (3) prediction analyses should not be performed with samples smaller than several hundred observations, (4) multiple measures of prediction accuracy should be examined and reported, (5) the coefficient of determination should be computed using the sums of squares formulation and not the correlation coefficient, and (6) k-fold cross-validation rather than leave-one-out cross-validation should be used.

  View details for DOI 10.1001/jamapsychiatry.2019.3671

  View details for PubMedID 31774490

• Reply to Friedman and Banich: Right measures for the research question. Proceedings of the National Academy of Sciences of the United States of America Enkavi, A. Z., Eisenberg, I. W., Bissett, P. G., Mazza, G. L., MacKinnon, D. P., Marsch, L. A., Poldrack, R. A. 2019

  View details for DOI 10.1073/pnas.1917123116

  View details for PubMedID 31719202

• Advancing functional connectivity research from association to causation. Nature neuroscience Reid, A. T., Headley, D. B., Mill, R. D., Sanchez-Romero, R., Uddin, L. Q., Marinazzo, D., Lurie, D. J., Valdes-Sosa, P. A., Hanson, S. J., Biswal, B. B., Calhoun, V., Poldrack, R. A., Cole, M. W. 2019

  Abstract

  Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.

  View details for DOI 10.1038/s41593-019-0510-4

  View details for PubMedID 31611705

• The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei. Neuron Shine, J. M., Hearne, L. J., Breakspear, M., Hwang, K., Muller, E. J., Sporns, O., Poldrack, R. A., Mattingley, J. B., Cocchi, L. 2019

  Abstract

  Cognitive activity emerges from large-scale neuronal dynamics that are constrained to a low-dimensional manifold. How this low-dimensional manifold scales with cognitive complexity, and which brain regions regulate this process, are not well understood. We addressed this issue by analyzing sub-second high-field fMRI data acquired during performance of a task that systematically varied the complexity of cognitive reasoning. We show that task performance reconfigures the low-dimensional manifold and that deviations from these patterns relate to performance errors. We further demonstrate that individual differences in thalamic activity relate to reconfigurations of the low-dimensional architecture during task engagement.

  View details for DOI 10.1016/j.neuron.2019.09.002

  View details for PubMedID 31653463

• Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nature neuroscience King, M., Hernandez-Castillo, C. R., Poldrack, R. A., Ivry, R. B., Diedrichsen, J. 2019

  Abstract

  There is compelling evidence that the human cerebellum is engaged in a wide array of motor and cognitive tasks. A fundamental question centers on whether the cerebellum is organized into distinct functional subregions. To address this question, we employed a rich task battery designed to tap into a broad range of cognitive processes. During four functional MRI sessions, participants performed a battery of 26 diverse tasks comprising 47 unique conditions. Using the data from this multi-domain task battery, we derived a comprehensive functional parcellation of the cerebellar cortex and evaluated it by predicting functional boundaries in a novel set of tasks. The new parcellation successfully identified distinct functional subregions, providing significant improvements over existing parcellations derived from task-free data. Lobular boundaries, commonly used to summarize functional data, did not coincide with functional subdivisions. The new parcellation provides a functional atlas to guide future neuroimaging studies.

  View details for DOI 10.1038/s41593-019-0436-x

  View details for PubMedID 31285616

• fMRI data of mixed gambles from the Neuroimaging Analysis Replication and Prediction Study. Scientific data Botvinik-Nezer, R., Iwanir, R., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Dreber, A., Camerer, C. F., Poldrack, R. A., Schonberg, T. 2019; 6 (1): 106

  Abstract

  There is an ongoing debate about the replicability of neuroimaging research. It was suggested that one of the main reasons for the high rate of false positive results is the many degrees of freedom researchers have during data analysis. In the Neuroimaging Analysis Replication and Prediction Study (NARPS), we aim to provide the first scientific evidence on the variability of results across analysis teams in neuroscience. We collected fMRI data from 108 participants during two versions of the mixed gambles task, which is often used to study decision-making under risk. For each participant, the dataset includes an anatomical (T1 weighted) scan and fMRI as well as behavioral data from four runs of the task. The dataset is shared through OpenNeuro and is formatted according to the Brain Imaging Data Structure (BIDS) standard. Data pre-processed with fMRIprep and quality control reports are also publicly shared. This dataset can be used to study decision-making under risk and to test replicability and interpretability of previous results in the field.

  View details for DOI 10.1038/s41597-019-0113-7

  View details for PubMedID 31263104

• Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biological psychiatry Kebets, V., Holmes, A. J., Orban, C., Tang, S., Li, J., Sun, N., Kong, R., Poldrack, R. A., Yeo, B. T. 2019

  Abstract

  BACKGROUND: There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes, and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thereby neglecting the potential influence of huge swaths of the brain.METHODS: A multivariate data-driven approach (partial least squares) was used to identify latent components linking a large set of clinical, cognitive, and personality measures to whole-brain resting-state functional connectivity patterns across 224 participants. The participants were either healthy (n= 110) or diagnosed with bipolar disorder (n= 40), attention-deficit/hyperactivity disorder (n= 37), schizophrenia (n= 29), or schizoaffective disorder (n= 8). In contrast to traditional case-control analyses, the diagnostic categories were not used in the partial least squares analysis but were helpful for interpreting the components.RESULTS: Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction, and impulsivity. Each component was associated with a unique whole-brain resting-state functional connectivity signature and was shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network and its connectivity with subcortical structures and cortical executive networks.CONCLUSIONS: We identified three distinct dimensions with dissociable (but overlapping) whole-brain resting-state functional connectivity signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span across diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

  View details for DOI 10.1016/j.biopsych.2019.06.013

  View details for PubMedID 31515054

• Human cognition involves the dynamic integration of neural activity and neuromodulatory systems (vol 22, pg 289, 2019) NATURE NEUROSCIENCE Shine, J. M., Breakspear, M., Bell, P. T., Martens, K., Shine, R., Koyejo, O., Sporns, O., Poldrack, R. A. 2019; 22 (6): 1036

  View details for DOI 10.1038/s41593-019-0347-x

  View details for Web of Science ID 000468883100023

• Uncovering the structure of self-regulation through data-driven ontology discovery. Nature communications Eisenberg, I. W., Bissett, P. G., Zeynep Enkavi, A., Li, J., MacKinnon, D. P., Marsch, L. A., Poldrack, R. A. 2019; 10 (1): 2319

  Abstract

  Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issues that are particularly damaging for the study of multifaceted constructs like self-regulation. Here, we derive a psychological ontology from a study of individual differences across a broad range of behavioral tasks, self-report surveys, and self-reported real-world outcomes associated with self-regulation. Though both tasks and surveys putatively measure self-regulation, they show little empirical relationship. Within tasks and surveys, however, the ontology identifies reliable individual traits and reveals opportunities for theoretic synthesis. We then evaluate predictive power of the psychological measurements and find that while surveys modestly and heterogeneously predict real-world outcomes, tasks largely do not. We conclude that self-regulation lacks coherence as a construct, and that data-driven ontologies lay the groundwork for a cumulative psychological science.

  View details for DOI 10.1038/s41467-019-10301-1

  View details for PubMedID 31127115

• The Utility of Connectivity Phenotypes as Successful Biomarkers for Psychosis Diagnoses Rodrigue, A., Mastrovito, D., Durnez, J., Koenis, M., Alexander-Bloch, A., Pearlson, G., Frangou, S., Blangero, J., Poldrack, R., Glahn, D. ELSEVIER SCIENCE INC. 2019: S198–S199

  View details for DOI 10.1016/j.biopsych.2019.03.501

  View details for Web of Science ID 000472661000482

• Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function (vol 9, 2098, 2018) NATURE COMMUNICATIONS Davies, G., Lam, M., Harris, S. E., Trampush, J. W., Luciano, M., Hill, W., Hagenaars, S. P., Ritchie, S. J., Marioni, R. E., Fawns-Ritchie, C., Liewald, D. M., Okely, J. A., Ahola-Olli, A. V., Barnes, C. K., Bertram, L., Bis, J. C., Burdick, K. E., Christoforou, A., DeRosse, P., Djurovic, S., Espeseth, T., Giakoumaki, S., Giddaluru, S., Gustavson, D. E., Hayward, C., Hofer, E., Ikram, M., Karlsson, R., Knowles, E., Lahti, J., Leber, M., Li, S., Mather, K. A., Melle, I., Morris, D., Oldmeadow, C., Palviainen, T., Payton, A., Pazoki, R., Petrovic, K., Reynolds, C. A., Sargurupremraj, M., Scholz, M., Smith, J. A., Smith, A. V., Terzikhan, N., Thalamuthu, A., Trompet, S., van der Lee, S. J., Ware, E. B., Windham, B., Wright, M. J., Yang, J., Yu, J., Ames, D., Amin, N., Amouyel, P., Andreassen, O. A., Armstrong, N. J., Assareh, A. A., Attia, J. R., Attix, D., Avramopoulos, D., Bennett, D. A., Boehmer, A. C., Boyle, P. A., Brodaty, H., Campbell, H., Cannon, T. D., Cirulli, E. T., Congdon, E., Conley, E., Corley, J., Cox, S. R., Dale, A. M., Dehghan, A., Dick, D., Dickinson, D., Eriksson, J. G., Evangelou, E., Faul, J. D., Ford, I., Freimer, N. A., Gao, H., Giegling, I., Gillespie, N. A., Gordon, S. D., Gottesman, R. F., Griswold, M. E., Gudnason, V., Harris, T. B., Hartmann, A. M., Hatzimanolis, A., Heiss, G., Holliday, E. G., Joshi, P. K., Kaehoenen, M., Kardia, S. R., Karlsson, I., Kleineidam, L., Knopman, D. S., Kochan, N. A., Konte, B., Kwok, J. B., Le Hellard, S., Lee, T., Lehtimaeki, T., Li, S., Lill, C. M., Liu, T., Koini, M., London, E., Longstreth, W. T., Lopez, O. L., Loukola, A., Luck, T., Lundervold, A. J., Lundquist, A., Lyytikaeinen, L., Martin, N. G., Montgomery, G. W., Murray, A. D., Need, A. C., Noordam, R., Nyberg, L., Ollier, W., Papenberg, G., Pattie, A., Polasek, O., Poldrack, R. A., Psaty, B. M., Reppermund, S., Riedel-Heller, S. G., Rose, R. J., Rotter, J. I., Roussos, P., Rovio, S. P., Saba, Y., Sabb, F. W., Sachdev, P. S., Satizabal, C. L., Schmid, M., Scott, R. J., Scult, M. A., Simino, J., Slagboom, P., Smyrnis, N., Soumare, A., Stefanis, N. C., Stott, D. J., Straub, R. E., Sundet, K., Taylor, A. M., Taylor, K. D., Tzoulaki, I., Tzourio, C., Uitterlinden, A., Vitart, V., Voineskos, A. N., Kaprio, J., Wagner, M., Wagner, H., Weinhold, L., Wen, K., Widen, E., Yang, Q., Zhao, W., Adams, H. H., Arking, D. E., Bilder, R. M., Bitsios, P., Boerwinkle, E., Chiba-Falek, O., Corvin, A., De Jager, P. L., Debette, S., Donohoe, G., Elliott, P., Fitzpatrick, A. L., Gill, M., Glahn, D. C., Hagg, S., Hansell, N. K., Hariri, A. R., Ikram, M., Jukema, J., Vuoksimaa, E., Keller, M. C., Kremen, W. S., Launer, L., Lindenberger, U., Palotie, A., Pedersen, N. L., Pendleton, N., Porteous, D. J., Raeikkoenen, K., Raitakari, O. T., Ramirez, A., Reinvang, I., Rudan, I., Rujescu, D., Schmidt, R., Schmidt, H., Schofield, P. W., Schofield, P. R., Starr, J. M., Steen, V. M., Trollor, J. N., Turner, S. T., Van Duijn, C. M., Villringer, A., Weinberger, D. R., Weir, D. R., Wilson, J. F., Malhotra, A., McIntosh, A. M., Gale, C. R., Seshadri, S., Mosley, T. H., Bressler, J., Lencz, T., Deary, I. J. 2019; 10

  View details for DOI 10.1038/s41467-019-10160-w

  View details for Web of Science ID 000466339700001

• A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task ELIFE Verbruggen, F., Aron, A. R., Band, G. H., Beste, C., Bissett, P. G., Brockett, A. T., Brown, J. W., Chamberlain, S. R., Chambers, C. D., Colonius, H., Colzato, L. S., Corneil, B. D., Coxon, J. P., Dupuis, A., Eagle, D. M., Garavan, H., Greenhouse, I., Heathcote, A., Huster, R. J., Jahfari, S., Kenemans, J., Leunissen, I., Li, C. R., Logan, G. D., Matzke, D., Morein-Zamir, S., Murthy, A., Pare, M., Poldrack, R. A., Ridderinkhof, K., Robbins, T. W., Roesch, M. R., Rubia, K., Schachar, R. J., Schall, J. D., Stock, A., Swann, N. C., Thakkar, K. N., van der Molen, M. W., Vermeylen, L., Vink, M., Wessel, J. R., Whelan, R., Zandbelt, B. B., Boehler, C. 2019; 8

  View details for DOI 10.7554/eLife.46323

  View details for Web of Science ID 000468863600001

• Editorial: Reliability and Reproducibility in Functional Connectomics (vol 13, 117, 2019) FRONTIERS IN NEUROSCIENCE Zuo, X., Biswal, B. B., Poldrack, R. A. 2019; 13

  View details for DOI 10.3389/fnins.2019.00374

  View details for Web of Science ID 000465074900001

• Corrigendum: Editorial: Reliability and Reproducibility in Functional Connectomics. Frontiers in neuroscience Zuo, X. N., Biswal, B. B., Poldrack, R. A. 2019; 13: 374

  Abstract

  [This corrects the article DOI: 10.3389/fnins.2019.00117.].

  View details for DOI 10.3389/fnins.2019.00374

  View details for PubMedID 31057360

  View details for PubMedCentralID PMC6477511

• Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines SCIENTIFIC DATA Esteban, O., Blair, R. W., Nielson, D. M., Varada, J. C., Marrett, S., Thomas, A. G., Poldrack, R. A., Gorgolewski, K. J. 2019; 6: 30

  Abstract

  The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.

  View details for PubMedID 30975998

• Dopamine depletion alters macroscopic network dynamics in Parkinson's disease BRAIN Shine, J. M., Bell, P. T., Matar, E., Poldrack, R. A., Lewis, S. G., Halliday, G. M., O'Callaghan, C. 2019; 142: 1024–34

  View details for DOI 10.1093/brain/awz034

  View details for Web of Science ID 000472805000020

• Predictive models avoid excessive reductionism in cognitive neuroimaging CURRENT OPINION IN NEUROBIOLOGY Varoquaux, G., Poldrack, R. A. 2019; 55: 1–6

  View details for DOI 10.1016/j.conb.2018.11.002

  View details for Web of Science ID 000472127600002

• Dopamine depletion alters macroscopic network dynamics in Parkinson's disease. Brain : a journal of neurology Shine, J. M., Bell, P. T., Matar, E., Poldrack, R. A., Lewis, S. J., Halliday, G. M., O'Callaghan, C. 2019

  Abstract

  Parkinson's disease is primarily characterized by diminished dopaminergic function; however, the impact of these impairments on large-scale brain dynamics remains unclear. It has been difficult to disentangle the direct effects of Parkinson's disease from compensatory changes that reconfigure the functional signature of the whole brain network. To examine the causal role of dopamine depletion in network-level topology, we investigated time-varying network structure in 37 individuals with idiopathic Parkinson's disease, both ON and OFF dopamine replacement therapy, along with 50 age-matched, healthy control subjects using resting state functional MRI. By tracking dynamic network-level topology, we found that the Parkinson's disease OFF state was associated with greater network-level integration than in the ON state. The extent of integration in the OFF state inversely correlated with motor symptom severity, suggesting that a shift toward a more integrated network topology may be a compensatory mechanism associated with preserved motor function in the dopamine depleted OFF state. Furthermore, we were able to demonstrate that measures of both cognitive and brain reserve (i.e. premorbid intelligence and whole brain grey matter volume) had a positive relationship with the relative increase in network integration observed in the dopaminergic OFF state. This suggests that each of these factors plays an important role in promoting network integration in the dopaminergic OFF state. Our findings provide a mechanistic basis for understanding the Parkinson's disease OFF state and provide a further conceptual link with network-level reconfiguration. Together, our results highlight the mechanisms responsible for pathological and compensatory change in Parkinson's disease.

  View details for PubMedID 30887035

• Large-scale analysis of test-retest reliabilities of self-regulation measures. Proceedings of the National Academy of Sciences of the United States of America Enkavi, A. Z., Eisenberg, I. W., Bissett, P. G., Mazza, G. L., MacKinnon, D. P., Marsch, L. A., Poldrack, R. A. 2019

  Abstract

  The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including self-report surveys and behavioral tasks. Accurate characterization of individual differences requires measurement reliability, a property frequently characterized in self-report surveys, but rarely assessed in behavioral tasks. We remedy this gap by (i) providing a comprehensive literature review on an extensive set of self-regulation measures and (ii) empirically evaluating test-retest reliability of this battery in a new sample. We find that dependent variables (DVs) from self-report surveys of self-regulation have high test-retest reliability, while DVs derived from behavioral tasks do not. This holds both in the literature and in our sample, although the test-retest reliability estimates in the literature are highly variable. We confirm that this is due to differences in between-subject variability. We also compare different types of task DVs (e.g., model parameters vs. raw response times) in their suitability as individual difference DVs, finding that certain model parameters are as stable as raw DVs. Our results provide greater psychometric footing for the study of self-regulation and provide guidance for future studies of individual differences in this domain.

  View details for PubMedID 30842284

• Good practice in food-related neuroimaging AMERICAN JOURNAL OF CLINICAL NUTRITION Smeets, P. M., Dagher, A., Hare, T. A., Kullmann, S., van der Laan, L. N., Poldrack, R. A., Preissl, H., Small, D., Stice, E., Veldhuizen, M. G. 2019; 109 (3): 491–503

  View details for DOI 10.1093/ajcn/nqy344

  View details for Web of Science ID 000460616400002

• Editorial: Reliability and Reproducibility in Functional Connectomics. Frontiers in neuroscience Zuo, X. N., Biswal, B. B., Poldrack, R. A. 2019; 13: 117

  View details for DOI 10.3389/fnins.2019.00117

  View details for PubMedID 30842722

  View details for PubMedCentralID PMC6391345

• Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature neuroscience Shine, J. M., Breakspear, M., Bell, P. T., Ehgoetz Martens, K., Shine, R., Koyejo, O., Sporns, O., Poldrack, R. A. 2019

  Abstract

  The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here we investigated the spatial, dynamic, and molecular signatures of system-wide neural activity across a range of cognitive tasks. We found that neuronal activity converged onto a low-dimensional manifold that facilitates the execution of diverse task states. Flow within this attractor space was associated with dissociable cognitive functions, unique patterns of network-level topology, and individual differences in fluid intelligence. The axes of the low-dimensional neurocognitive architecture aligned with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between neural activity, neuromodulatory systems, and cognitive function.

  View details for PubMedID 30664771

• Cognitive impairment from early to middle adulthood in patients with affective and nonaffective psychotic disorders. Psychological medicine Mollon, J., Mathias, S. R., Knowles, E. E., Rodrigue, A., Koenis, M. M., Pearlson, G. D., Reichenberg, A., Barrett, J., Denbow, D., Aberizk, K., Zatony, M., Poldrack, R. A., Blangero, J., Glahn, D. C. 2019: 1–10

  Abstract

  BACKGROUND: Cognitive impairment is a core feature of psychotic disorders, but the profile of impairment across adulthood, particularly in African-American populations, remains unclear.METHODS: Using cross-sectional data from a case-control study of African-American adults with affective (n = 59) and nonaffective (n = 68) psychotic disorders, we examined cognitive functioning between early and middle adulthood (ages 20-60) on measures of general cognitive ability, language, abstract reasoning, processing speed, executive function, verbal memory, and working memory.RESULTS: Both affective and nonaffective psychosis patients showed substantial and widespread cognitive impairments. However, comparison of cognitive functioning between controls and psychosis groups throughout early (ages 20-40) and middle (ages 40-60) adulthood also revealed age-associated group differences. During early adulthood, the nonaffective psychosis group showed increasing impairments with age on measures of general cognitive ability and executive function, while the affective psychosis group showed increasing impairment on a measure of language ability. Impairments on other cognitive measures remained mostly stable, although decreasing impairments on measures of processing speed, memory and working memory were also observed.CONCLUSIONS: These findings suggest similarities, but also differences in the profile of cognitive dysfunction in adults with affective and nonaffective psychotic disorders. Both affective and nonaffective patients showed substantial and relatively stable impairments across adulthood. The nonaffective group also showed increasing impairments with age in general and executive functions, and the affective group showed an increasing impairment in verbal functions, possibly suggesting different underlying etiopathogenic mechanisms.

  View details for PubMedID 30606277

• The Costs of Reproducibility. Neuron Poldrack, R. A. 2019; 101 (1): 11–14

  Abstract

  Improving the reproducibility of neuroscience research is of great concern, especially to early-career researchers (ECRs). Here I outline the potential costs for ECRs in adopting practices to improve reproducibility. I highlight the ways in which ECRs can achieve their career goals while doing better science and the need for established researchers to support them in these efforts.

  View details for PubMedID 30605654

• Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways. American journal of human genetics Lam, M. n., Hill, W. D., Trampush, J. W., Yu, J. n., Knowles, E. n., Davies, G. n., Stahl, E. n., Huckins, L. n., Liewald, D. C., Djurovic, S. n., Melle, I. n., Sundet, K. n., Christoforou, A. n., Reinvang, I. n., DeRosse, P. n., Lundervold, A. J., Steen, V. M., Espeseth, T. n., Räikkönen, K. n., Widen, E. n., Palotie, A. n., Eriksson, J. G., Giegling, I. n., Konte, B. n., Hartmann, A. M., Roussos, P. n., Giakoumaki, S. n., Burdick, K. E., Payton, A. n., Ollier, W. n., Chiba-Falek, O. n., Attix, D. K., Need, A. C., Cirulli, E. T., Voineskos, A. N., Stefanis, N. C., Avramopoulos, D. n., Hatzimanolis, A. n., Arking, D. E., Smyrnis, N. n., Bilder, R. M., Freimer, N. A., Cannon, T. D., London, E. n., Poldrack, R. A., Sabb, F. W., Congdon, E. n., Conley, E. D., Scult, M. A., Dickinson, D. n., Straub, R. E., Donohoe, G. n., Morris, D. n., Corvin, A. n., Gill, M. n., Hariri, A. R., Weinberger, D. R., Pendleton, N. n., Bitsios, P. n., Rujescu, D. n., Lahti, J. n., Le Hellard, S. n., Keller, M. C., Andreassen, O. A., Deary, I. J., Glahn, D. C., Malhotra, A. K., Lencz, T. n. 2019; 105 (2): 334–50

  Abstract

  Susceptibility to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and the genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published genome-wide association studies (GWASs) in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected ("concordant") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants that demonstrated the counterintuitive ("discordant") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education, and/or schizophrenia at p < 5 × 10-8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs. Many of these have been validated by larger, more recent single-phenotype GWASs. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms-early neurodevelopmental pathways that characterize concordant allelic variation and adulthood synaptic pruning pathways-that were linked to the paradoxical positive genetic association between education and schizophrenia. Furthermore, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia but also to the broader biological dimensions implicated in both general health outcomes and psychiatric illness.

  View details for DOI 10.1016/j.ajhg.2019.06.012

  View details for PubMedID 31374203

• The importance of standards for sharing of computational models and data. Computational brain & behavior Poldrack, R. A., Feingold, F. n., Frank, M. J., Gleeson, P. n., de Hollander, G. n., Huys, Q. J., Love, B. C., Markiewicz, C. J., Moran, R. n., Ritter, P. n., Rogers, T. T., Turner, B. M., Yarkoni, T. n., Zhan, M. n., Cohen, J. D. 2019; 2 (3-4): 229–32

  Abstract

  The Target Article by Lee et al. (2019) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.

  View details for DOI 10.1007/s42113-019-00062-x

  View details for PubMedID 32440654

  View details for PubMedCentralID PMC7241435

• Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE, VOL 2, 2019 Poldrack, R. A., Gorgolewski, K. J., Varoquaux, G., Altman, R. B., Levitt, M. 2019; 2: 119–38

  View details for DOI 10.1146/annurev-biodatasci-072018-021237

  View details for Web of Science ID 000484798500006

• PyBIDS: Python tools for BIDS datasets. Journal of open source software Yarkoni, T., Markiewicz, C. J., de la Vega, A., Gorgolewski, K. J., Salo, T., Halchenko, Y. O., McNamara, Q., DeStasio, K., Poline, J., Petrov, D., Hayot-Sasson, V., Nielson, D. M., Carlin, J., Kiar, G., Whitaker, K., DuPre, E., Wagner, A., Tirrell, L. S., Jas, M., Hanke, M., Poldrack, R. A., Esteban, O., Appelhoff, S., Holdgraf, C., Staden, I., Thirion, B., Kleinschmidt, D. F., Lee, J. A., Visconti di Oleggio Castello, M., Notter, M. P., Blair, R. 2019; 4 (40)

  View details for DOI 10.21105/joss.01294

  View details for PubMedID 32775955

• fMRIPrep: a robust preprocessing pipeline for functional MRI NATURE METHODS Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., Gorgolewski, K. J. 2019; 16 (1): 111-+

  View details for DOI 10.1038/s41592-018-0235-4

  View details for Web of Science ID 000454162400038

• Introduction to the special issue on Reproducibility in Neuroimaging. NeuroImage Poldrack, R. A., Whitaker, K. n., Kennedy, D. n. 2019: 116357

  View details for DOI 10.1016/j.neuroimage.2019.116357

  View details for PubMedID 31733374

• A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLife Verbruggen, F. n., Aron, A. R., Band, G. P., Beste, C. n., Bissett, P. G., Brockett, A. T., Brown, J. W., Chamberlain, S. R., Chambers, C. D., Colonius, H. n., Colzato, L. S., Corneil, B. D., Coxon, J. P., Dupuis, A. n., Eagle, D. M., Garavan, H. n., Greenhouse, I. n., Heathcote, A. n., Huster, R. J., Jahfari, S. n., Kenemans, J. L., Leunissen, I. n., Logan, G. D., Matzke, D. n., Morein-Zamir, S. n., Murthy, A. n., Li, C. R., Paré, M. n., Poldrack, R. A., Ridderinkhof, K. R., Robbins, T. W., Roesch, M. n., Rubia, K. n., Schachar, R. J., Schall, J. D., Stock, A. K., Swann, N. C., Thakkar, K. N., van der Molen, M. W., Vermeylen, L. n., Vink, M. n., Wessel, J. R., Whelan, R. n., Zandbelt, B. B., Boehler, C. N. 2019; 8

  Abstract

  Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide twelve easy-to-implement consensus recommendations and point out the problems that can arise when these are not followed. Furthermore we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

  View details for PubMedID 31033438

• Transitions in information processing dynamics at the whole-brain network level are driven by alterations in neural gain. PLoS computational biology Li, M. n., Han, Y. n., Aburn, M. J., Breakspear, M. n., Poldrack, R. A., Shine, J. M., Lizier, J. T. 2019; 15 (10): e1006957

  Abstract

  A key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system, via changes in neural gain (in terms of the amplification and non-linearity in stimulus-response transfer function of brain regions). In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain parameters led to a 'critical' transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain parameters would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain.

  View details for DOI 10.1371/journal.pcbi.1006957

  View details for PubMedID 31613882

• Predictive models avoid excessive reductionism in cognitive neuroimaging. Current opinion in neurobiology Varoquaux, G., Poldrack, R. A. 2018; 55: 1–6

  Abstract

  Understanding the organization of complex behavior as it relates to the brain requires modeling the behavior, the relevant mental processes, and the corresponding neural activity. Experiments in cognitive neuroscience typically study a psychological process via controlled manipulations, reducing behavior to one of its components. Such reductionism can easily lead to paradigm-bound theories. Predictive models can generalize brain-mind associations to arbitrary new tasks and stimuli. We argue that they are needed to broaden theories beyond specific paradigms. Predicting behavior from neural activity can support robust reverse inference, isolating brain structures that support particular mental processes. The converse prediction enables modeling brain responses as a function of a complete description of the task, rather than building on oppositions.

  View details for PubMedID 30513462

• Atlases of cognition with large-scale human brain mapping. PLoS computational biology Varoquaux, G., Schwartz, Y., Poldrack, R. A., Gauthier, B., Bzdok, D., Poline, J., Thirion, B. 2018; 14 (11): e1006565

  Abstract

  To map the neural substrate of mental function, cognitive neuroimaging relies on controlled psychological manipulations that engage brain systems associated with specific cognitive processes. In order to build comprehensive atlases of cognitive function in the brain, it must assemble maps for many different cognitive processes, which often evoke overlapping patterns of activation. Such data aggregation faces contrasting goals: on the one hand finding correspondences across vastly different cognitive experiments, while on the other hand precisely describing the function of any given brain region. Here we introduce a new analysis framework that tackles these difficulties and thereby enables the generation of brain atlases for cognitive function. The approach leverages ontologies of cognitive concepts and multi-label brain decoding to map the neural substrate of these concepts. We demonstrate the approach by building an atlas of functional brain organization based on 30 diverse functional neuroimaging studies, totaling 196 different experimental conditions. Unlike conventional brain mapping, this functional atlas supports robust reverse inference: predicting the mental processes from brain activity in the regions delineated by the atlas. To establish that this reverse inference is indeed governed by the corresponding concepts, and not idiosyncrasies of experimental designs, we show that it can accurately decode the cognitive concepts recruited in new tasks. These results demonstrate that aggregating independent task-fMRI studies can provide a more precise global atlas of selective associations between brain and cognition.

  View details for PubMedID 30496171

• Atlases of cognition with large-scale human brain mapping PLOS COMPUTATIONAL BIOLOGY Varoquaux, G., Schwartz, Y., Poldrack, R. A., Gauthier, B., Bzdok, D., Poline, J., Thirion, B. 2018; 14 (11)

  View details for DOI 10.1371/journal.pcbi.1006565

  View details for Web of Science ID 000451835900030

• Neural correlates of effort-based valuation with prospective choices. NeuroImage Aridan, N., Malecek, N. J., Poldrack, R. A., Schonberg, T. 2018

  Abstract

  How is effort integrated in value-based decision-making? Animal models and human neuroimaging studies primarily linked the anterior cingulate cortex (ACC) and ventral striatum (VS) to the integration of effort in valuation. Other studies demonstrated the role of these regions in invigoration to effort demands, thus it is hard to separate the neural activity linked to anticipation and subjective valuation from actual performance. Here, we studied the neural basis of effort valuation separated from performance. We scanned forty participants with fMRI, while they were asked to accept or reject monetary gambles that could be resolved with future performance of a familiar grip force effort challenge or a fixed risk prospect. Participants' willingness to accept prospective gambles reflected discounting of values by physical effort and risk. Choice-locked neural activation in contralateral primary sensory cortex and ventromedial prefrontal cortex (vmPFC) tracked the magnitude of prospective effort the participants faced, independent of choice time and monetary stakes. Estimates of subjective value discounted by effort were found to be tracked by the activation of a network of regions common to valuation under risk and delay, including vmPFC, VS and sensorimotor cortex. Together, our findings show separate neural mechanisms underlying prospective effort and actual effort performance.

  View details for PubMedID 30347281

• Principles of dynamic network reconfiguration across diverse brain states. NeuroImage Shine, J. M., Poldrack, R. A. 2018; 180 (Pt B): 396–405

  Abstract

  Recent methodological advances have enabled researchers to track the network structure of the human brain over time. Together, these studies provide novel insights into effective brain function, highlighting the importance of the systems-level perspective in understanding the manner in which the human brain organizes its activity to facilitate behavior. Here, we review a range of recent fMRI and electrophysiological studies that have mapped the relationship between inter-regional communication and network structure across a diverse range of brain states. In doing so, we identify both behavioral and biological axes that may underlie the tendency for network reconfiguration. We conclude our review by providing suggestions for future research endeavors that may help to refine our understanding of the functioning of the human brain.

  View details for PubMedID 28782684

• Multi-Trait Analysis of GWAS and Biological Insights Into Cognition: A Response to Hill (2018) TWIN RESEARCH AND HUMAN GENETICS Lam, M., Trampush, J. W., Yu, J., Knowles, E., Djurovic, S., Melle, I., Sundet, K., Christoforou, A., Reinvang, I., DeRosse, P., Lundervold, A. J., Steen, V. M., Espeseth, T., Raikkonen, K., Widen, E., Palotie, A., Eriksson, J. G., Giegling, I., Konte, B., Roussos, P., Giakoumaki, S., Burdick, K. E., Payton, A., Ollier, W., Chiba-Falek, O., Attix, D. K., Need, A. C., Cirulli, E. T., Voineskos, A. N., Stefanis, N. C., Avramopoulos, D., Hatzimanolis, A., Arking, D. E., Smyrnis, N., Bilder, R. M., Freimer, N. A., Cannon, T. D., London, E., Poldrack, R. A., Sabb, F. W., Congdon, E., Conley, E., Scult, M. A., Dickinson, D., Straub, R. E., Donohoe, G., Morris, D., Corvin, A., Gill, M., Hariri, A. R., Weinberger, D. R., Pendleton, N., Bitsios, P., Rujescu, D., Lahti, J., Le Hellard, S., Keller, M. C., Andreassen, O. A., Glahn, D. C., Malhotra, A. K., Lencz, T. 2018; 21 (5): 394–97

  Abstract

  Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84-88) presented a critique of our recently published paper in Cell Reports entitled 'Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets' (Lam et al., Cell Reports, Vol. 21, 2017, 2597-2613). Specifically, Hill offered several interrelated comments suggesting potential problems with our use of a new analytic method called Multi-Trait Analysis of GWAS (MTAG) (Turley et al., Nature Genetics, Vol. 50, 2018, 229-237). In this brief article, we respond to each of these concerns. Using empirical data, we conclude that our MTAG results do not suffer from 'inflation in the FDR [false discovery rate]', as suggested by Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84-88), and are not 'more relevant to the genetic contributions to education than they are to the genetic contributions to intelligence'.

  View details for PubMedID 30001766

• Reward Learning over Weeks Versus Minutes Increases the Neural Representation of Value in the Human Brain JOURNAL OF NEUROSCIENCE Wimmer, G., Li, J. K., Gorgolewski, K. J., Poldrack, R. A. 2018; 38 (35): 7649–66

  Abstract

  Over the past few decades, neuroscience research has illuminated the neural mechanisms supporting learning from reward feedback. Learning paradigms are increasingly being extended to study mood and psychiatric disorders as well as addiction. However, one potentially critical characteristic that this research ignores is the effect of time on learning: human feedback learning paradigms are usually conducted in a single rapidly paced session, whereas learning experiences in ecologically relevant circumstances and in animal research are almost always separated by longer periods of time. In our experiments, we examined reward learning in short condensed sessions distributed across weeks versus learning completed in a single "massed" session in male and female participants. As expected, we found that after equal amounts of training, accuracy was matched between the spaced and massed conditions. However, in a 3-week follow-up, we found that participants exhibited significantly greater memory for the value of spaced-trained stimuli. Supporting a role for short-term memory in massed learning, we found a significant positive correlation between initial learning and working memory capacity. Neurally, we found that patterns of activity in the medial temporal lobe and prefrontal cortex showed stronger discrimination of spaced- versus massed-trained reward values. Further, patterns in the striatum discriminated between spaced- and massed-trained stimuli overall. Our results indicate that single-session learning tasks engage partially distinct learning mechanisms from distributed training. Our studies begin to address a large gap in our knowledge of human learning from reinforcement, with potential implications for our understanding of mood disorders and addiction.SIGNIFICANCE STATEMENT Humans and animals learn to associate predictive value with stimuli and actions, and these values then guide future behavior. Such reinforcement-based learning often happens over long time periods, in contrast to most studies of reward-based learning in humans. In experiments that tested the effect of spacing on learning, we found that associations learned in a single massed session were correlated with short-term memory and significantly decayed over time, whereas associations learned in short massed sessions over weeks were well maintained. Additionally, patterns of activity in the medial temporal lobe and prefrontal cortex discriminated the values of stimuli learned over weeks but not minutes. These results highlight the importance of studying learning over time, with potential applications to drug addiction and psychiatry.

  View details for PubMedID 30061189

• Spacing of cue-approach training leads to better maintenance of behavioral change PLOS ONE Bakkour, A., Botvinik-Nezer, R., Cohen, N., Hover, A. M., Poldrack, R. A., Schonberg, T. 2018; 13 (7): e0201580

  Abstract

  The maintenance of behavioral change over the long term is essential to achieve public health goals such as combatting obesity and drug use. Previous work by our group has demonstrated a reliable shift in preferences for appetitive foods following a novel non-reinforced training paradigm. In the current studies, we tested whether distributing training trials over two consecutive days would affect preferences immediately after training as well as over time at a one-month follow-up. In four studies, three different designs and an additional pre-registered replication of one sample, we found that spacing of cue-approach training induced a shift in food choice preferences over one month. The spacing and massing schedule employed governed the long-term changes in choice behavior. Applying spacing strategies to training paradigms that target automatic processes could prove a useful tool for the long-term maintenance of health improvement goals with the development of real-world behavioral change paradigms that incorporate distributed practice principles.

  View details for PubMedID 30059542

• Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence NATURE GENETICS Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. I., Grasby, K. L., Hammerschlag, A. R., Kaminski, J. A., Karlsson, R., Krapohl, E., Lam, M., Nygaard, M., Reynolds, C. A., Trampush, J. W., Young, H., Zabaneh, D., Hagg, S., Hansell, N. K., Karlsson, I. K., Linnarsson, S., Montgomery, G. W., Munoz-Manchado, A. B., Quinlan, E. B., Schumann, G., Skene, N. G., Webb, B. T., White, T., Arking, D. E., Avramopoulos, D., Bilder, R. M., Bitsios, P., Burdick, K. E., Cannon, T. D., Chiba-Falek, O., Christoforou, A., Cirulli, E. T., Congdon, E., Corvin, A., Davies, G., Deary, I. J., DeRosse, P., Dickinson, D., Djurovic, S., Donohoe, G., Conley, E., Eriksson, J. G., Espeseth, T., Freimer, N. A., Giakoumaki, S., Giegling, I., Gill, M., Glahn, D. C., Hariri, A. R., Hatzimanolis, A., Keller, M. C., Knowles, E., Koltai, D., Konte, B., Lahti, J., Le Hellard, S., Lencz, T., Liewald, D. C., London, E., Lundervold, A. J., Malhotra, A. K., Melle, I., Morris, D., Need, A. C., Ollier, W., Palotie, A., Payton, A., Pendleton, N., Poldrack, R. A., Raikkonen, K., Reinvang, I., Roussos, P., Rujescu, D., Sabb, F. W., Scult, M. A., Smeland, O. B., Smyrnis, N., Starr, J. M., Steen, V. M., Stefanis, N. C., Straub, R. E., Sundet, K., Tiemeier, H., Voineskos, A. N., Weinberger, D. R., Widen, E., Yu, J., Abecasis, G., Andreassen, O. A., Breen, G., Christiansen, L., Debrabant, B., Dick, D. M., Heinz, A., Hjerling-Leffler, J., Ikram, M., Kendler, K. S., Martin, N. G., Medland, S. E., Pedersen, N. L., Plomin, R., Polderman, T. C., Ripke, S., van der Sluis, S., Sullivan, P. F., Vrieze, S. I., Wright, M. J., Posthuma, D. 2018; 50 (7): 912-+

  Abstract

  Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

  View details for PubMedID 29942086

• Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function NATURE COMMUNICATIONS Davies, G., Lam, M., Harris, S. E., Trampush, J. W., Luciano, M., Hill, W., Hagenaars, S. P., Ritchie, S. J., Marioni, R. E., Fawns-Ritchie, C., Liewald, D. M., Okely, J. A., Ahola-Olli, A. V., Barnes, C. K., Bertram, L., Bis, J. C., Burdick, K. E., Christoforou, A., DeRosse, P., Djurovic, S., Espeseth, T., Giakoumaki, S., Giddaluru, S., Gustavson, D. E., Hayward, C., Hofer, E., Ikram, M., Karlsson, R., Knowles, E., Lahti, J., Leber, M., Li, S., Mather, K. A., Melle, I., Morris, D., Oldmeadow, C., Palviainen, T., Payton, A., Pazoki, R., Petrovic, K., Reynolds, C. A., Sargurupremraj, M., Scholz, M., Smith, J. A., Smith, A. V., Terzikhan, N., Thalamuthu, A., Trompet, S., van der Lee, S. J., Ware, E. B., Windham, B., Wright, M. J., Yang, J., Yu, J., Ames, D., Amin, N., Amouyel, P., Andreassen, O. A., Armstrong, N. J., Assareh, A. A., Attia, J. R., Attix, D., Avramopoulos, D., Bennett, D. A., Boehmer, A. C., Boyle, P. A., Brodaty, H., Campbell, H., Cannon, T. D., Cirulli, E. T., Congdon, E., Conley, E., Corley, J., Cox, S. R., Dale, A. M., Dehghan, A., Dick, D., Dickinson, D., Eriksson, J. G., Evangelou, E., Faul, J. D., Ford, I., Freimer, N. A., Gao, H., Giegling, I., Gillespie, N. A., Gordon, S. D., Gottesman, R. F., Griswold, M. E., Gudnason, V., Harris, T. B., Hartmann, A. M., Hatzimanolis, A., Heiss, G., Holliday, E. G., Joshi, P. K., Kahonen, M., Kardia, S. R., Karlsson, I., Kleineidam, L., Knopman, D. S., Kochan, N. A., Konte, B., Kwok, J. B., Le Hellard, S., Lee, T., Lehtimaki, T., Li, S., Liu, T., Koini, M., London, E., Longstreth, W. T., Lopez, O. L., Loukola, A., Luck, T., Lundervold, A. J., Lundquist, A., Lyytikainen, L., Martin, N. G., Montgomery, G. W., Murray, A. D., Need, A. C., Noordam, R., Nyberg, L., Ollier, W., Papenberg, G., Pattie, A., Polasek, O., Poldrack, R. A., Psaty, B. M., Reppermund, S., Riedel-Heller, S. G., Rose, R. J., Rotter, J. I., Roussos, P., Rovio, S. P., Saba, Y., Sabb, F. W., Sachdev, P. S., Satizabal, C. L., Schmid, M., Scott, R. J., Scult, M. A., Simino, J., Slagboom, P., Smyrnis, N., Soumare, A., Stefanis, N. C., Stott, D. J., Straub, R. E., Sundet, K., Taylor, A. M., Taylor, K. D., Tzoulaki, I., Tzourio, C., Uitterlinden, A., Vitart, V., Voineskos, A. N., Kaprio, J., Wagner, M., Wagner, H., Weinhold, L., Wen, K., Widen, E., Yang, Q., Zhao, W., Adams, H. H., Arking, D. E., Bilder, R. M., Bitsios, P., Boerwinkle, E., Chiba-Falek, O., Corvin, A., De Jager, P. L., Debette, S., Donohoe, G., Elliott, P., Fitzpatrick, A. L., Gill, M., Glahn, D. C., Hagg, S., Hansell, N. K., Hariri, A. R., Ikram, M., Jukema, J., Vuoksimaa, E., Keller, M. C., Kremen, W. S., Launer, L., Lindenberger, U., Palotie, A., Pedersen, N. L., Pendleton, N., Porteous, D. J., Raikkonen, K., Raitakari, O. T., Ramirez, A., Reinvang, I., Rudan, I., Rujescu, D., Schmidt, R., Schmidt, H., Schofield, P. W., Schofield, P. R., Starr, J. M., Steen, V. M., Trollor, J. N., Turner, S. T., Van Duijn, C. M., Villringer, A., Weinberger, D. R., Weir, D. R., Wilson, J. F., Malhotra, A., McIntosh, A. M., Gale, C. R., Seshadri, S., Mosley, T. H., Bressler, J., Lencz, T., Deary, I. J. 2018; 9: 2098

  Abstract

  General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10-8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.

  View details for PubMedID 29844566

• Cognitive Computational Neuroscience: A New Conference for an Emerging Discipline TRENDS IN COGNITIVE SCIENCES Naselaris, T., Bassett, D. S., Fletcher, A. K., Kording, K., Kriegeskorte, N., Nienborg, H., Poldrack, R. A., Shohamy, D., Kay, K. 2018; 22 (5): 365–67

  Abstract

  Understanding the computational principles that underlie complex behavior is a central goal in cognitive science, artificial intelligence, and neuroscience. In an attempt to unify these disconnected communities, we created a new conference called Cognitive Computational Neuroscience (CCN). The inaugural meeting revealed considerable enthusiasm but significant obstacles remain.

  View details for PubMedID 29500078

  View details for PubMedCentralID PMC5911192

• GWAS meta-analysis (N=279,930) identifies new genes and functional links to general cognitive ability Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. I., Grasby, K. L., Hammerschlag, A. R., Kaminski, J., Karlsson, R., Krapohl, E., Lam, M., Nygaard, M., Reynolds, C. A., Trampush, J. W., Young, H., Zabaneh, D., Hagg, S., Hansell, N. K., Karlsson, I. K., Linnarsson, S., Montgomery, G. W., Munoz-Manchado, A. B., Quinlan, E. B., Schumann, G., Skene, N., Webb, B. T., White, T., Arking, D. E., Attix, D. K., Avramopoulos, D., Bilder, R. M., Bitsios, P., Burdick, K. E., Cannon, T. D., Chiba-Falek, O., Christoforou, A., Cirulli, E. T., Congdon, E., Corvin, A., Davies, G., Deary, I. J., DeRosse, P., Dickinson, D., Djurovic, S., Donohoe, G., Conley, E., Eriksson, J. G., Espeseth, T., Freimer, N. A., Giakoumaki, S., Giegling, I., Gill, M., Glahn, D. C., Hariri, A. R., Hatzimanolis, A., Keller, M. C., Knowles, E., Konte, B., Lahti, J., Le Hellard, S., Lencz, T., Liewald, D. C., London, E., Lundervold, A. J., Malhotra, A. K., Melle, I., Morris, D., Need, A. C., Ollier, W., Palotie, A., Payton, A., Pendleton, N., Poldrack, R. A., Raikkonen, K., Reinvang, I., Roussos, P., Rujescu, D., Sabb, F. W., Scult, M. A., Smeland, O. B., Smyrnis, N., Starr, J. M., Steen, V. M., Stefanis, N. C., Straub, R. E., Sundet, K., Voineskos, A. N., Weinberger, D. R., Widen, E., Yu, J., Abecasis, G., Andreassen, O. A., Breen, G., Christiansen, L., Debrabant, B., Dick, D. M., Heinz, A., Hjerling-Leffler, J., Ikram, M., Kendler, K. S., Martin, N. G., Medland, S. E., Pedersen, N. L., Plomin, R., Polderman, T. C., Ripke, S., van der Sluis, S., Sullivan, P. F., Tiemeier, H., Vrieze, S. I., Wright, M. J., Posthuma, D. BIOMED CENTRAL LTD. 2018

  View details for Web of Science ID 000427728400089

• Deficits in visual working-memory capacity and general cognition in African Americans with psychosis SCHIZOPHRENIA RESEARCH Mathias, S. R., Knowles, E. M., Barrett, J., Beetham, T., Leach, O., Buccheri, S., Aberizk, K., Blangero, J., Poldrack, R. A., Glahn, D. C. 2018; 193: 100–106

  Abstract

  On average, patients with psychosis perform worse than controls on visual change-detection tasks, implying that psychosis is associated with reduced capacity of visual working memory (WM). In the present study, 79 patients diagnosed with various psychotic disorders and 166 controls, all African Americans, completed a change-detection task and several other neurocognitive measures. The aims of the study were to (1) determine whether we could observe a between-group difference in performance on the change-detection task in this sample; (2) establish whether such a difference could be specifically attributed to reduced WM capacity (k); and (3) estimate k in the context of the general cognitive deficit in psychosis. Consistent with previous studies, patients performed worse than controls on the change-detection task, on average. Bayesian hierarchical cognitive modeling of the data suggested that this between-group difference was driven by reduced k in patients, rather than differences in other psychologically meaningful model parameters (guessing behavior and lapse rate). Using the same modeling framework, we estimated the effect of psychosis on k while controlling for general intellectual ability (g, obtained from the other neurocognitive measures). The results suggested that reduced k in patients was stronger than predicted by the between-group difference in g. Moreover, a mediation analysis suggested that the relationship between psychosis and g (i.e., the general cognitive deficit) was mediated by k. The results were consistent with the idea that reduced k is a specific deficit in psychosis, which contributes to the general cognitive deficit.

  View details for PubMedID 28843437

  View details for PubMedCentralID PMC5825248

• Predicting Violent Behavior: What Can Neuroscience Add? TRENDS IN COGNITIVE SCIENCES Poldrack, R. A., Monahan, J., Imrey, P. B., Reyna, V., Raichle, M. E., Faigman, D., Buckholtz, J. W. 2018; 22 (2): 111–23

  Abstract

  The ability to accurately predict violence and other forms of serious antisocial behavior would provide important societal benefits, and there is substantial enthusiasm for the potential predictive accuracy of neuroimaging techniques. Here, we review the current status of violence prediction using actuarial and clinical methods, and assess the current state of neuroprediction. We then outline several questions that need to be addressed by future studies of neuroprediction if neuroimaging and other neuroscientific markers are to be successfully translated into public policy.

  View details for PubMedID 29183655

  View details for PubMedCentralID PMC5794654

• Applying novel technologies and methods to inform the ontology of self-regulation. Behaviour research and therapy Eisenberg, I. W., Bissett, P. G., Canning, J. R., Dallery, J., Enkavi, A. Z., Whitfield-Gabrieli, S., Gonzalez, O., Green, A. I., Greene, M. A., Kiernan, M., Kim, S. J., Li, J., Lowe, M. R., Mazza, G. L., Metcalf, S. A., Onken, L., Parikh, S. S., Peters, E., Prochaska, J. J., Scherer, E. A., Stoeckel, L. E., Valente, M. J., Wu, J., Xie, H., MacKinnon, D. P., Marsch, L. A., Poldrack, R. A. 2018; 101: 46-57

  Abstract

  Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psychological and neural components of self-regulation remain poorly understood, which contributes to theoretical inconsistencies and hinders maximally effective intervention development. We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers. After identifying ontological targets that precipitate maladaptive behavior, we will craft interventions that engage these targets. If successful, this work will provide a structured, holistic account of self-regulation in the form of an explicit ontology, which will better clarify the pattern of deficits related to maladaptive health behavior, and provide direction for more effective behavior change interventions.

  View details for DOI 10.1016/j.brat.2017.09.014

  View details for PubMedID 29066077

  View details for PubMedCentralID PMC5801197

• The modulation of neural gain facilitates a transition between functional segregation and integration in the brain. eLife Shine, J. M., Aburn, M. J., Breakspear, M., Poldrack, R. A. 2018; 7

  Abstract

  Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico, we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain directed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal 'rich club' regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain.

  View details for PubMedID 29376825

• False Discovery Rate Smoothing JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Tansey, W., Koyejo, O., Poldrack, R. A., Scott, J. G. 2018; 113 (523): 1156–71

  View details for DOI 10.1080/01621459.2017.1319838

  View details for Web of Science ID 000446710500017

• Text to Brain: Predicting the Spatial Distribution of Neuroimaging Observations from Text Reports Dockes, J., Wassermann, D., Poldrack, R., Suchanek, F., Thirion, B., Varoquaux, G., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 584–92

  View details for DOI 10.1007/978-3-030-00931-1_67

  View details for Web of Science ID 000477769700067

• Making replication prestigious BEHAVIORAL AND BRAIN SCIENCES Gorgolewski, K. J., Nichols, T., Kennedy, D. N., Poline, J., Poldrack, R. A. 2018; 41

  View details for DOI 10.1017/S0140525X18000663

  View details for Web of Science ID 000458790700046

• Making replication prestigious. The Behavioral and brain sciences Gorgolewski, K. J., Nichols, T., Kennedy, D. N., Poline, J. B., Poldrack, R. A. 2018; 41: e131

  Abstract

  Making replication studies widely conducted and published requires new incentives. Academic awards can provide such incentives by highlighting the best and most important replications. The Organization for Human Brain Mapping (OHBM) has led such efforts by recently introducing the OHBM Replication Award. Other communities can adopt this approach to promote replications and reduce career cost for researchers performing them.

  View details for DOI 10.1017/S0140525X18000663

  View details for PubMedID 31064546

• Catecholaminergic manipulation alters dynamic network topology across cognitive states. Network neuroscience (Cambridge, Mass.) Shine, J. M., van den Brink, R. L., Hernaus, D., Nieuwenhuis, S., Poldrack, R. A. 2018; 2 (3): 381–96

  Abstract

  The human brain is able to flexibly adapt its information processing capacity to meet a variety of cognitive challenges. Recent evidence suggests that this flexibility is reflected in the dynamic reorganization of the functional connectome. The ascending catecholaminergic arousal systems of the brain are a plausible candidate mechanism for driving alterations in network architecture, enabling efficient deployment of cognitive resources when the environment demands them. We tested this hypothesis by analyzing both resting-state and task-based fMRI data following the administration of atomoxetine, a noradrenaline reuptake inhibitor, compared with placebo, in two separate human fMRI studies. Our results demonstrate that the manipulation of central catecholamine levels leads to a reorganization of the functional connectome in a manner that is sensitive to ongoing cognitive demands.

  View details for PubMedID 30294705

• The New Mind Readers What Neuroimaging Can and Cannot Reveal about Our Thoughts Poldrack, R. A. Princeton University Press. 2018
• Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets CELL REPORTS Lam, M., Trampush, J. W., Yu, J., Knowles, E., Davies, G., Liewald, D. C., Starr, J. M., Djurovic, S., Melle, I., Sundet, K., Christoforou, A., Reinvang, I., DeRosse, P., Lundervold, A. J., Steen, V. M., Espeseth, T., Raikkonen, K., Widen, E., Palotie, A., Eriksson, J. G., Giegling, I., Konte, B., Roussos, P., Giakoumaki, S., Burdick, K. E., Payton, A., Ollier, W., Chiba-Falek, O., Attix, D. K., Need, A. C., Cirulli, E. T., Voineskos, A. N., Stefanis, N. C., Avramopoulos, D., Hatzimanolis, A., Arking, D. E., Smyrnis, N., Bilder, R. M., Freimer, N. A., Cannon, T. D., London, E., Poldrack, R. A., Sabb, F. W., Congdon, E., Conley, E., Scult, M. A., Dickinson, D., Straub, R. E., Donohoe, G., Morris, D., Corvin, A., Gill, M., Hariri, A. R., Weinberger, D. R., Pendleton, N., Bitsios, P., Rujescu, D., Lahti, J., Le Hellard, S., Keller, M. C., Andreassen, O. A., Deary, I. J., Glahn, D. C., Malhotra, A. K., Lencz, T. 2017; 21 (9): 2597–2613

  Abstract

  Here, we present a large (n = 107,207) genome-wide association study (GWAS) of general cognitive ability ("g"), further enhanced by combining results with a large-scale GWAS of educational attainment. We identified 70 independent genomic loci associated with general cognitive ability. Results showed significant enrichment for genes causing Mendelian disorders with an intellectual disability phenotype. Competitive pathway analysis implicated the biological processes of neurogenesis and synaptic regulation, as well as the gene targets of two pharmacologic agents: cinnarizine, a T-type calcium channel blocker, and LY97241, a potassium channel inhibitor. Transcriptome-wide and epigenome-wide analysis revealed that the implicated loci were enriched for genes expressed across all brain regions (most strongly in the cerebellum). Enrichment was exclusive to genes expressed in neurons but not oligodendrocytes or astrocytes. Finally, we report genetic correlations between cognitive ability and disparate phenotypes including psychiatric disorders, several autoimmune disorders, longevity, and maternal age at first birth.

  View details for PubMedID 29186694

• Distinct Patterns of Temporal and Directional Connectivity among Intrinsic Networks in the Human Brain JOURNAL OF NEUROSCIENCE Shine, J. M., Kucyi, A., Foster, B. L., Bickel, S., Wang, D., Liu, H., Poldrack, R. A., Hsieh, L., Hsiang, J., Parvizi, J. 2017; 37 (40): 9667–74

  Abstract

  To determine the spatiotemporal relationships among intrinsic networks of the human brain, we recruited seven neurosurgical patients (four males and three females) who were implanted with intracranial depth electrodes. We first identified canonical resting-state networks at the individual subject level using an iterative matching procedure on each subject's resting-state fMRI data. We then introduced single electrical pulses to fMRI pre-identified nodes of the default network (DN), frontoparietal network (FPN), and salience network (SN) while recording evoked responses in other recording sites within the same networks. We found bidirectional signal flow across the three networks, albeit with distinct patterns of evoked responses within different time windows. We used a data-driven clustering approach to show that stimulation of the FPN and SN evoked a rapid (<70 ms) response that was predominantly higher within the SN sites, whereas stimulation of the DN led to sustained responses in later time windows (85-200 ms). Stimulations in the medial temporal lobe components of the DN evoked relatively late effects (>130 ms) in other nodes of the DN, as well as FPN and SN. Our results provide temporal information about the patterns of signal flow between intrinsic networks that provide insights into the spatiotemporal dynamics that are likely to constrain the architecture of the brain networks supporting human cognition and behavior.SIGNIFICANCE STATEMENT Despite great progress in the functional neuroimaging of the human brain, we still do not know the precise set of rules that define the patterns of temporal organization between large-scale networks of the brain. In this study, we stimulated and then recorded electrical evoked potentials within and between three large-scale networks of the brain, the default network (DN), frontoparietal network (FPN), and salience network (SN), in seven subjects undergoing invasive neurosurgery. Using a data-driven clustering approach, we observed distinct temporal and directional patterns between the three networks, with FPN and SN activity predominant in early windows and DN stimulation affecting the network in later windows. These results provide important temporal information about the interactions between brain networks supporting human cognition and behavior.

  View details for PubMedID 28893929

• Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition PLOS COMPUTATIONAL BIOLOGY Rubin, T. N., Koyejo, O., Gorgolewski, K. J., Jones, M. N., Poldrack, R. A., Yarkoni, T. 2017; 13 (10): e1005649

  Abstract

  A central goal of cognitive neuroscience is to decode human brain activity-that is, to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive-that is, capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a probabilistic decoding framework based on a novel topic model-Generalized Correspondence Latent Dirichlet Allocation-that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to "seed" decoder priors with arbitrary images and text-enabling researchers, for the first time, to generate quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

  View details for PubMedID 29059185

  View details for PubMedCentralID PMC5683652

• MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites PLOS ONE Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., Gorgolewski, K. J. 2017; 12 (9): e0184661

  Abstract

  Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N = 1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%±13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N = 265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.

  View details for PubMedID 28945803

• Precision Neuroscience: Dense Sampling of Individual Brains. Neuron Poldrack, R. A. 2017; 95 (4): 727-729

  Abstract

  In this issue, Gordon et al. (2017) use dense sampling of resting and task fMRI within individuals to demonstrate that patterns of correlation in resting fMRI are closely aligned with functional architecture as identified using task fMRI.

  View details for DOI 10.1016/j.neuron.2017.08.002

  View details for PubMedID 28817793

• Neural mechanisms of cue-approach training NEUROIMAGE Bakkour, A., Lewis-Peacock, J. A., Poldrack, R. A., Schonberg, T. 2017; 151: 92-104

  Abstract

  Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training.

  View details for DOI 10.1016/j.neuroimage.2016.09.059

  View details for Web of Science ID 000401071100011

• Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval JOURNAL OF NEUROSCIENCE Xiao, X., Dong, Q., Gao, J., Men, W., Poldrack, R. A., Xue, G. 2017; 37 (11): 2986-2998

  Abstract

  Contemporary models of episodic memory posit that remembering involves the reenactment of encoding processes. Although encoding-retrieval similarity has been consistently reported and linked to memory success, the nature of neural pattern reinstatement is poorly understood. Using high-resolution fMRI on human subjects, our results obtained clear evidence for item-specific pattern reinstatement in the frontoparietal cortex, even when the encoding-retrieval pairs shared no perceptual similarity. No item-specific pattern reinstatement was found in the ventral visual cortex. Importantly, the brain regions and voxels carrying item-specific representation differed significantly between encoding and retrieval, and the item specificity for encoding-retrieval similarity was smaller than that for encoding or retrieval, suggesting different nature of representations between encoding and retrieval. Moreover, cross-region representational similarity analysis suggests that the encoded representation in the ventral visual cortex was reinstated in the frontoparietal cortex during retrieval. Together, these results suggest that, in addition to reinstatement of the originally encoded pattern in the brain regions that perform encoding processes, retrieval may also involve the reinstatement of a transformed representation of the encoded information. These results emphasize the constructive nature of memory retrieval that helps to serve important adaptive functions.SIGNIFICANCE STATEMENT Episodic memory enables humans to vividly reexperience past events, yet how this is achieved at the neural level is barely understood. A long-standing hypothesis posits that memory retrieval involves the faithful reinstatement of encoding-related activity. We tested this hypothesis by comparing the neural representations during encoding and retrieval. We found strong pattern reinstatement in the frontoparietal cortex, but not in the ventral visual cortex, that represents visual details. Critically, even within the same brain regions, the nature of representation during retrieval was qualitatively different from that during encoding. These results suggest that memory retrieval is not a faithful replay of past event but rather involves additional constructive processes to serve adaptive functions.

  View details for DOI 10.1523/JNEUROSCI.2324-16.2017

  View details for Web of Science ID 000397808000017

  View details for PubMedID 28202612

• GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium MOLECULAR PSYCHIATRY Trampush, J. W., Yang, M. L., Yu, J., Knowles, E., Davies, G., Liewald, D. C., Starr, J. M., Djurovic, S., Melle, I., Sundet, K., Christoforou, A., Reinvang, I., DeRosse, P., Lundervold, A. J., Steen, V. M., Espeseth, T., RAIKKONEN, K., Widen, E., PALOTIE, A., Eriksson, J. G., Giegling, I., Konte, B., Roussos, P., Giakoumaki, S., Burdick, K. E., Payton, A., Ollier, W., Horan, M., Chiba-Falek, O., Attix, D. K., Need, A. C., Cirulli, E. T., Voineskos, A. N., Stefanis, N. C., Avramopoulos, D., Hatzimanolis, A., Arking, D. E., Smyrnis, N., Bilder, R. M., Freimer, N. A., Cannon, T. D., London, E., Poldrack, R. A., Sabb, F. W., Congdon, E., Conley, E. D., Scult, M. A., Dickinson, D., Straub, R. E., Donohoe, G., Morris, D., Corvin, A., Gill, M., Hariri, A. R., Weinberger, D. R., Pendleton, N., Bitsios, P., Rujescu, D., Lahti, J., Le Hellard, S., Keller, M. C., Andreassen, O. A., Deary, I. J., Glahn, D. C., Malhotra, A. K., Lencz, T. 2017; 22 (3): 336-345

  Abstract

  The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide significance level (P<5 × 10(-8)). Gene-based analysis identified an additional three Bonferroni-corrected significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated SNP heritability of 21.5% (s.e.=0.01%) for general cognitive function. Integration with prior GWAS of cognitive performance and educational attainment yielded several additional significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/weight and smoking behavior, as well as a novel genetic association to the personality trait of openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.

  View details for DOI 10.1038/mp.2016.244

  View details for PubMedID 28093568

• BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods PLOS COMPUTATIONAL BIOLOGY Gorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capota, M., Chakravarty, M. M., Churchill, N. W., Cohen, A. L., Craddock, R. C., Devenyi, G. A., Eklund, A., Esteban, O., Flandin, G., Ghosh, S. S., Guntupalli, J. S., Jenkinson, M., Keshavan, A., Kiar, G., Liem, F., Raamana, P. R., Raffelt, D., Steele, C. J., Quirion, P., Smith, R. E., Strother, S. C., Varoquaux, G., Wang, Y., Yarkoni, T., Poldrack, R. A. 2017; 13 (3)

  Abstract

  The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

  View details for DOI 10.1371/journal.pcbi.1005209

  View details for PubMedID 28278228

• Best practices in data analysis and sharing in neuroimaging using MRI. Nature neuroscience Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., Kriegeskorte, N., Milham, M. P., Poldrack, R. A., Poline, J., Proal, E., Thirion, B., Van Essen, D. C., White, T., Yeo, B. T. 2017; 20 (3): 299-303

  Abstract

  Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

  View details for DOI 10.1038/nn.4500

  View details for PubMedID 28230846

• What's in a pattern? Examining the type of signal multivariate analysis uncovers at the group level. NeuroImage Gilron, R., Rosenblatt, J., Koyejo, O., Poldrack, R. A., Mukamel, R. 2017; 146: 113-120

  Abstract

  Multivoxel pattern analysis (MVPA) has gained enormous popularity in the neuroimaging community over the past few years. At the group level, most MVPA studies adopt an "information based" approach in which the sign of the effect of individual subjects is discarded and a non-directional summary statistic is carried over to the second level. This is in contrast to a directional "activation based" approach typical in univariate group level analysis, in which both signal magnitude and sign are taken into account. The transition from examining effects in one voxel at a time vs. several voxels (univariate vs. multivariate) has thus tacitly entailed a transition from directional to non-directional signal definition at the group level. While a directional group-level MVPA approach implies that individuals have similar multivariate spatial patterns of activity, in a non-directional approach each individual may have a distinct spatial pattern. Using an experimental dataset, we show that directional and non-directional group-level MVPA approaches uncover distinct brain regions with only partial overlap. We propose a method to quantify the degree of spatial similarity in activation patterns over subjects. Applied to an auditory task, we find higher values in auditory regions compared to control regions.

  View details for DOI 10.1016/j.neuroimage.2016.11.019

  View details for PubMedID 27851996

• A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks FRONTIERS IN NEUROSCIENCE Acikalin, M. Y., Gorgolewski, K. J., Poldrack, R. A. 2017; 11

  Abstract

  Previous research has provided qualitative evidence for overlap in a number of brain regions across the subjective value network (SVN) and the default mode network (DMN). In order to quantitatively assess this overlap, we conducted a series of coordinate-based meta-analyses (CBMA) of results from 466 functional magnetic resonance imaging experiments on task-negative or subjective value-related activations in the human brain. In these analyses, we first identified significant overlaps and dissociations across activation foci related to SVN and DMN. Second, we investigated whether these overlapping subregions also showed similar patterns of functional connectivity, suggesting a shared functional subnetwork. We find considerable overlap between SVN and DMN in subregions of central ventromedial prefrontal cortex (cVMPFC) and dorsal posterior cingulate cortex (dPCC). Further, our findings show that similar patterns of bidirectional functional connectivity between cVMPFC and dPCC are present in both networks. We discuss ways in which our understanding of how subjective value (SV) is computed and represented in the brain can be synthesized with what we know about the DMN, mind-wandering, and self-referential processing in light of our findings.

  View details for DOI 10.3389/fnins.2017.00001

  View details for Web of Science ID 000392159000001

  View details for PubMedID 28154520

  View details for PubMedCentralID PMC5243799

• The Processing-Speed Impairment in Psychosis Is More Than Just Accelerated Aging. Schizophrenia bulletin Mathias, S. R., Knowles, E. E., Barrett, J., Leach, O., Buccheri, S., Beetham, T., Blangero, J., Poldrack, R. A., Glahn, D. C. 2017

  Abstract

  Processing speed is impaired in patients with psychosis, and deteriorates as a function of normal aging. These observations, in combination with other lines of research, suggest that psychosis may be a syndrome of accelerated aging. But do patients with psychosis perform poorly on tasks of processing speed for the same reasons as older adults? Fifty-one patients with psychotic illnesses and 90 controls with similar mean IQ (aged 19-69 years, all African American) completed a computerized processing-speed task, reminiscent of the classic digit-symbol coding task. The data were analyzed using the drift-diffusion model (DDM), and Bayesian inference was used to determine whether psychosis and aging had similar or divergent effects on the DDM parameters. Psychosis and aging were both associated with poor performance, but had divergent effects on the DDM parameters. Patients had lower information-processing efficiency ("drift rate") and longer nondecision time than controls, and psychosis per se did not influence response caution. By contrast, the primary effect of aging was to increase response caution, and had inconsistent effects on drift rate and nondecision time across patients and controls. The results reveal that psychosis and aging influenced performance in different ways, suggesting that the processing-speed impairment in psychosis is more than just accelerated aging. This study also demonstrates the potential utility of computational models and Bayesian inference for finely mapping the contributions of cognitive functions on simple neurocognitive tests.

  View details for DOI 10.1093/schbul/sbw168

  View details for PubMedID 28062652

• OpenfMRI: Open sharing of task fMRI data NEUROIMAGE Poldrack, R. A., Gorgolewski, K. J. 2017; 144: 259-261

  Abstract

  OpenfMRI is a repository for the open sharing of task-based fMRI data. Here we outline its goals, architecture, and current status of the repository, as well as outlining future plans for the project.

  View details for DOI 10.1016/j.neuroimage.2015.05.073

  View details for Web of Science ID 000390987000002

  View details for PubMedCentralID PMC4669234

• Applying novel technologies and methods to inform the ontology of self-regulation Behaviour Research and Therapy Eisenberg, I. W., Bissett, P. G., Enkavi, A. Z., Poldrack, R. A. 2017: 46–57

  Abstract

  Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psychological and neural components of self-regulation remain poorly understood, which contributes to theoretical inconsistencies and hinders maximally effective intervention development. We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers. After identifying ontological targets that precipitate maladaptive behavior, we will craft interventions that engage these targets. If successful, this work will provide a structured, holistic account of self-regulation in the form of an explicit ontology, which will better clarify the pattern of deficits related to maladaptive health behavior, and provide direction for more effective behavior change interventions.

  View details for DOI 10.1016/j.brat.2017.09.014

  View details for PubMedCentralID PMC5801197

• Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Research Gorgolewski, K. J., Durnez, J., Poldrack, R. A. 2017; 6: 1262

  Abstract

  Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.

  View details for PubMedID 29152222

• Scanning the horizon: towards transparent and reproducible neuroimaging research. Nature reviews. Neuroscience Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafò, M. R., Nichols, T. E., Poline, J., Vul, E., Yarkoni, T. 2017; 18 (2): 115-126

  Abstract

  Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

  View details for DOI 10.1038/nrn.2016.167

  View details for PubMedID 28053326

• Enabling an Open Data Ecosystem for the Neurosciences. Neuron Wiener, M., Sommer, F. T., Ives, Z. G., Poldrack, R. A., Litt, B. 2016; 92 (3): 617-621

  Abstract

  As the pace and complexity of neuroscience data grow, an open data ecosystem must develop and grow with it to allow neuroscientists the ability to reach for new heights of discovery. However, the problems and complexities of neuroscience data sharing must first be addressed. Among the challenges facing data sharing in neuroscience, the problem of incentives, discoverability, and sustainability may be the most pressing. We here describe these problems and provide potential future solutions to help cultivate an ecosystem for data sharing.

  View details for DOI 10.1016/j.neuron.2016.10.037

  View details for PubMedID 27810004

• Putting the brakes on the brakes: negative emotion disrupts cognitive control network functioning and alters subsequent stopping ability. Experimental brain research Patterson, T. K., Lenartowicz, A., Berkman, E. T., Ji, D., Poldrack, R. A., Knowlton, B. J. 2016; 234 (11): 3107-3118

  Abstract

  The ability to inhibit unwanted responses is critical for effective control of behavior, and inhibition failures can have disastrous consequences in real-world situations. Here, we examined how prior exposure to negative emotional stimuli affects the response-stopping network. Participants performed the stop-signal task, which relies on inhibitory control processes, after they viewed blocks of either negatively emotional or neutral images. In Experiment 1, we found that neural activity was reduced following negative image viewing. When participants were required to inhibit responding after neutral image viewing, we observed activation consistent with previous studies using the stop-signal task. However, when participants were required to inhibit responding after negative image viewing, we observed reductions in the activation of ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, medial frontal cortex, and parietal cortex. Furthermore, analysis of neural connectivity during stop-signal task blocks indicated that across participants, emotion-induced changes in behavioral performance were associated with changes in functional connectivity, such that greater behavioral impairment after negative image viewing was associated with greater weakening of connectivity. In Experiment 2, we collected behavioral data from a larger sample of participants and found that stopping performance was impaired after negative image viewing, as seen in longer stop-signal reaction times. The present results demonstrate that negative emotional events can prospectively disrupt the neural network supporting response inhibition.

  View details for PubMedID 27349996

• The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., Poldrack, R. A. 2016; 92 (2): 544-554

  Abstract

  Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions; however, it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of fMRI data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.

  View details for DOI 10.1016/j.neuron.2016.09.018

  View details for PubMedID 27693256

  View details for PubMedCentralID PMC5073034

• Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index. Cerebral cortex Hodgson, K., Poldrack, R. A., Curran, J. E., Knowles, E. E., Mathias, S., Göring, H. H., Yao, N., Olvera, R. L., Fox, P. T., Almasy, L., Duggirala, R., Barch, D. M., Blangero, J., Glahn, D. C. 2016: -?

  Abstract

  Head movements are typically viewed as a nuisance to functional magnetic resonance imaging (fMRI) analysis, and are particularly problematic for resting state fMRI. However, there is growing evidence that head motion is a behavioral trait with neural and genetic underpinnings. Using data from a large randomly ascertained extended pedigree sample of Mexican Americans (n = 689), we modeled the genetic structure of head motion during resting state fMRI and its relation to 48 other demographic and behavioral phenotypes. A replication analysis was performed using data from the Human Connectome Project, which uses an extended twin design (n = 864). In both samples, head motion was significantly heritable (h(2) = 0.313 and 0.427, respectively), and phenotypically correlated with numerous traits. The most strongly replicated relationship was between head motion and body mass index, which showed evidence of shared genetic influences in both data sets. These results highlight the need to view head motion in fMRI as a complex neurobehavioral trait correlated with a number of other demographic and behavioral phenotypes. Given this, when examining individual differences in functional connectivity, the confounding of head motion with other traits of interest needs to be taken into consideration alongside the critical important of addressing head motion artifacts.

  View details for PubMedID 27744290

• Neural mechanisms of cue-approach training. NeuroImage Bakkour, A., Lewis-Peacock, J. A., Poldrack, R. A., Schonberg, T. 2016

  Abstract

  Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training.

  View details for DOI 10.1016/j.neuroimage.2016.09.059

  View details for PubMedID 27677231

• From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure. Annual review of psychology Poldrack, R. A., Yarkoni, T. 2016; 67: 587-612

  Abstract

  A major goal of cognitive neuroscience is to delineate how brain systems give rise to mental function. Here we review the increasingly large role informatics-driven approaches are playing in such efforts. We begin by reviewing a number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings--for example, the difficulty in establishing the specificity of postulated associations. Next, we demonstrate how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis--including meta-analytic methods that synthesize hundreds or thousands of studies at a time; latent-variable approaches that seek to extract structure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively inferring mental states from patterns of brain activity. We highlight the underappreciated but critical role for formal cognitive ontologies in helping to clarify, refine, and test theories of brain and cognitive function. Finally, we conclude with a speculative discussion of what future informatics developments may hold for cognitive neuroscience.

  View details for DOI 10.1146/annurev-psych-122414-033729

  View details for PubMedID 26393866

  View details for PubMedCentralID PMC4701616

• Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Shine, J. M., Koyejo, O., Poldrack, R. A. 2016; 113 (35): 9888-9891

  Abstract

  Little is currently known about the coordination of neural activity over longitudinal timescales and how these changes relate to behavior. To investigate this issue, we used resting-state fMRI data from a single individual to identify the presence of two distinct temporal states that fluctuated over the course of 18 mo. These temporal states were associated with distinct patterns of time-resolved blood oxygen level dependent (BOLD) connectivity within individual scanning sessions and also related to significant alterations in global efficiency of brain connectivity as well as differences in self-reported attention. These patterns were replicated in a separate longitudinal dataset, providing additional supportive evidence for the presence of fluctuations in functional network topology over time. Together, our results underscore the importance of longitudinal phenotyping in cognitive neuroscience.

  View details for DOI 10.1073/pnas.1604898113

  View details for Web of Science ID 000383090700065

  View details for PubMedID 27528672

  View details for PubMedCentralID PMC5024627

• A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research. PLoS biology Gorgolewski, K. J., Poldrack, R. A. 2016; 14 (7)

  Abstract

  Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.

  View details for DOI 10.1371/journal.pbio.1002506

  View details for PubMedID 27389358

  View details for PubMedCentralID PMC4936733

• Pain in the ACC? Proceedings of the National Academy of Sciences of the United States of America Wager, T. D., Atlas, L. Y., Botvinick, M. M., Chang, L. J., Coghill, R. C., Davis, K. D., Iannetti, G. D., Poldrack, R. A., Shackman, A. J., Yarkoni, T. 2016; 113 (18): E2474-5

  View details for DOI 10.1073/pnas.1600282113

  View details for PubMedID 27095849

  View details for PubMedCentralID PMC4983860

• The Experiment Factory: Standardizing Behavioral Experiments FRONTIERS IN PSYCHOLOGY Sochat, V. V., Eisenberg, I. W., Enkavi, A. Z., Li, J., Bissett, P. G., Poldrack, R. A. 2016; 7

  Abstract

  The administration of behavioral and experimental paradigms for psychology research is hindered by lack of a coordinated effort to develop and deploy standardized paradigms. While several frameworks (Mason and Suri, 2011; McDonnell et al., 2012; de Leeuw, 2015; Lange et al., 2015) have provided infrastructure and methods for individual research groups to develop paradigms, missing is a coordinated effort to develop paradigms linked with a system to easily deploy them. This disorganization leads to redundancy in development, divergent implementations of conceptually identical tasks, disorganized and error-prone code lacking documentation, and difficulty in replication. The ongoing reproducibility crisis in psychology and neuroscience research (Baker, 2015; Open Science Collaboration, 2015) highlights the urgency of this challenge: reproducible research in behavioral psychology is conditional on deployment of equivalent experiments. A large, accessible repository of experiments for researchers to develop collaboratively is most efficiently accomplished through an open source framework. Here we present the Experiment Factory, an open source framework for the development and deployment of web-based experiments. The modular infrastructure includes experiments, virtual machines for local or cloud deployment, and an application to drive these components and provide developers with functions and tools for further extension. We release this infrastructure with a deployment (http://www.expfactory.org) that researchers are currently using to run a set of over 80 standardized web-based experiments on Amazon Mechanical Turk. By providing open source tools for both deployment and development, this novel infrastructure holds promise to bring reproducibility to the administration of experiments, and accelerate scientific progress by providing a shared community resource of psychological paradigms.

  View details for DOI 10.3389/fpsyg.2016.00610

  View details for Web of Science ID 000374735800001

  View details for PubMedID 27199843

  View details for PubMedCentralID PMC4844768

• Neural correlates of state-based decision-making in younger and older adults NEUROIMAGE Worthy, D. A., Davis, T., Gorlick, M. A., Cooper, J. A., Bakkour, A., Mumford, J. A., Poldrack, R. A., Maddox, W. T. 2016; 130: 13-23

  Abstract

  Older and younger adults performed a state-based decision-making task while undergoing functional MRI (fMRI). We proposed that younger adults would be more prone to base their decisions on expected value comparisons, but that older adults would be more reactive decision-makers who would act in response to recent changes in rewards or states, rather than on a comparison of expected values. To test this we regressed BOLD activation on two measures from a sophisticated reinforcement learning (RL) model. A value-based regressor was computed by subtracting the immediate value of the selected alternative from its long-term value. The other regressor was a state-change uncertainty signal that served as a proxy for whether the participant's state improved or declined, relative to the previous trial. Younger adults' activation was modulated by the value-based regressor in ventral striatal and medial PFC regions implicated in reinforcement learning. Older adults' activation was modulated by state-change uncertainty signals in right dorsolateral PFC, and activation in this region was associated with improved performance in the task. This suggests that older adults may depart from standard expected-value based strategies and recruit lateral PFC regions to engage in reactive decision-making strategies.

  View details for DOI 10.1016/j.neuroimage.2015.12.004

  View details for Web of Science ID 000372745600002

  View details for PubMedCentralID PMC4808466

• Neural correlates of state-based decision-making in younger and older adults. NeuroImage Worthy, D. A., Davis, T., Gorlick, M. A., Cooper, J. A., Bakkour, A., Mumford, J. A., Poldrack, R. A., Todd Maddox, W. 2016; 130: 13-23

  Abstract

  Older and younger adults performed a state-based decision-making task while undergoing functional MRI (fMRI). We proposed that younger adults would be more prone to base their decisions on expected value comparisons, but that older adults would be more reactive decision-makers who would act in response to recent changes in rewards or states, rather than on a comparison of expected values. To test this we regressed BOLD activation on two measures from a sophisticated reinforcement learning (RL) model. A value-based regressor was computed by subtracting the immediate value of the selected alternative from its long-term value. The other regressor was a state-change uncertainty signal that served as a proxy for whether the participant's state improved or declined, relative to the previous trial. Younger adults' activation was modulated by the value-based regressor in ventral striatal and medial PFC regions implicated in reinforcement learning. Older adults' activation was modulated by state-change uncertainty signals in right dorsolateral PFC, and activation in this region was associated with improved performance in the task. This suggests that older adults may depart from standard expected-value based strategies and recruit lateral PFC regions to engage in reactive decision-making strategies.

  View details for DOI 10.1016/j.neuroimage.2015.12.004

  View details for PubMedID 26690805

  View details for PubMedCentralID PMC4808466

• Mechanisms of Choice Behavior Shift Using Cue-approach Training FRONTIERS IN PSYCHOLOGY Bakkour, A., Leuker, C., Hover, A. M., Giles, N., Poldrack, R. A., Schonberg, T. 2016; 7

  View details for DOI 10.3389/fpsyg.2016.00421

  View details for Web of Science ID 000372568500001

• NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain. NeuroImage Gorgolewski, K. J., Varoquaux, G., Rivera, G., Schwartz, Y., Sochat, V. V., Ghosh, S. S., Maumet, C., Nichols, T. E., Poline, J., Yarkoni, T., Margulies, D. S., Poldrack, R. A. 2016; 124: 1242-1244

  Abstract

  NeuroVault.org is dedicated to storing outputs of analyses in the form of statistical maps, parcellations and atlases, a unique strategy that contrasts with most neuroimaging repositories that store raw acquisition data or stereotaxic coordinates. Such maps are indispensable for performing meta-analyses, validating novel methodology, and deciding on precise outlines for regions of interest (ROIs). NeuroVault is open to maps derived from both healthy and clinical populations, as well as from various imaging modalities (sMRI, fMRI, EEG, MEG, PET, etc.). The repository uses modern web technologies such as interactive web-based visualization, cognitive decoding, and comparison with other maps to provide researchers with efficient, intuitive tools to improve the understanding of their results. Each dataset and map is assigned a permanent Universal Resource Locator (URL), and all of the data is accessible through a REST Application Programming Interface (API). Additionally, the repository supports the NIDM-Results standard and has the ability to parse outputs from popular FSL and SPM software packages to automatically extract relevant metadata. This ease of use, modern web-integration, and pioneering functionality holds promise to improve the workflow for making inferences about and sharing whole-brain statistical maps.

  View details for DOI 10.1016/j.neuroimage.2015.04.016

  View details for PubMedID 25869863

• Computational specificity in the human brain. Behavioral and brain sciences Shine, J. M., Eisenberg, I., Poldrack, R. A. 2016; 39

  Abstract

  Although meta-analytic neuroimaging studies demonstrate a relative lack of specificity in the brain, this evidence may be the result of limits inherent to these types of studies. From this perspective, we review recent findings that suggest that brain function is most appropriately categorized according to the computational capacity of each brain system, rather than the specific task states that elicit its activity.

  View details for DOI 10.1017/S0140525X1500165X

  View details for PubMedID 27562637

• From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure ANNUAL REVIEW OF PSYCHOLOGY, VOL 67 Poldrack, R. A., Yarkoni, T. 2016; 67: 587-612

  View details for DOI 10.1146/annurev-psych-122414-033729

  View details for Web of Science ID 000368344500025

• The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., Poldrack, R. A. 2016; 3: 160044-?

  Abstract

  The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

  View details for DOI 10.1038/sdata.2016.44

  View details for PubMedID 27326542

• Mechanisms of Choice Behavior Shift Using Cue-approach Training. Frontiers in psychology Bakkour, A., Leuker, C., Hover, A. M., Giles, N., Poldrack, R. A., Schonberg, T. 2016; 7: 421-?

  Abstract

  Cue-approach training has been shown to effectively shift choices for snack food items by associating a cued button-press motor response to particular food items. Furthermore, attention was biased toward previously cued items, even when the cued item is not chosen for real consumption during a choice phase. However, the exact mechanism by which preferences shift during cue-approach training is not entirely clear. In three experiments, we shed light on the possible underlying mechanisms at play during this novel paradigm: (1) Uncued, wholly predictable motor responses paired with particular food items were not sufficient to elicit a preference shift; (2) Cueing motor responses early - concurrently with food item onset - and thus eliminating the need for heightened top-down attention to the food stimulus in preparation for a motor response also eliminated the shift in food preferences. This finding reinforces our hypothesis that heightened attention at behaviorally relevant points in time is key to changing choice behavior in the cue-approach task; (3) Crucially, indicating choice using eye movements rather than manual button presses preserves the effect, thus demonstrating that the shift in preferences is not governed by a learned motor response but more likely via modulation of subjective value in higher associative regions, consistent with previous neuroimaging results. Cue-approach training drives attention at behaviorally relevant points in time to modulate the subjective value of individual items, providing a mechanism for behavior change that does not rely on external reinforcement and that holds great promise for developing real world behavioral interventions.

  View details for DOI 10.3389/fpsyg.2016.00421

  View details for PubMedID 27047435

• Long-term neural and physiological phenotyping of a single human. Nature communications Poldrack, R. A., Laumann, T. O., Koyejo, O., Gregory, B., Hover, A., Chen, M. Y., Gorgolewski, K. J., Luci, J., Joo, S. J., Boyd, R. L., Hunicke-Smith, S., Simpson, Z. B., Caven, T., Sochat, V., Shine, J. M., Gordon, E., Snyder, A. Z., Adeyemo, B., Petersen, S. E., Glahn, D. C., Reese Mckay, D., Curran, J. E., Göring, H. H., Carless, M. A., Blangero, J., Dougherty, R., Leemans, A., Handwerker, D. A., Frick, L., Marcotte, E. M., Mumford, J. A. 2015; 6: 8885

  Abstract

  Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.

  View details for DOI 10.1038/ncomms9885

  View details for PubMedID 26648521

  View details for PubMedCentralID PMC4682164

• Long-term neural and physiological phenotyping of a single human NATURE COMMUNICATIONS Poldrack, R. A., Laumann, T. O., Koyejo, O., Gregory, B., Hover, A., Chen, M., Gorgolewski, K. J., Luci, J., Joo, S. J., Boyd, R. L., Hunicke-Smith, S., Simpson, Z. B., Caven, T., Sochat, V., Shine, J. M., Gordon, E., Snyder, A. Z., Adeyemo, B., Petersen, S. E., Glahn, D. C., McKay, D. R., Curran, J. E., Goering, H. H., Carless, M. A., Blangero, J., Dougherty, R., Leemans, A., Handwerker, D. A., Frick, L., Marcotte, E. M., Mumford, J. A. 2015; 6

  Abstract

  Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.

  View details for DOI 10.1038/ncomms9885

  View details for Web of Science ID 000367577400002

  View details for PubMedCentralID PMC4682164

• Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage Shine, J. M., Koyejo, O., Bell, P. T., Gorgolewski, K. J., Gilat, M., Poldrack, R. A. 2015; 122: 399-407

  Abstract

  Functional connectivity provides an informative and powerful framework for exploring brain organization. Despite this, few statistical methods are available for the accurate estimation of dynamic changes in functional network architecture. To date, the majority of existing statistical techniques have assumed that connectivity structure is stationary, which is in direct contrast to emerging data that suggests that the strength of connectivity between regions is variable over time. Therefore, the development of statistical methods that enable exploration of dynamic changes in functional connectivity is currently of great importance to the neuroscience community. In this paper, we introduce the 'Multiplication of Temporal Derivatives' (MTD) and then demonstrate the utility of this metric to: (i) detect dynamic changes in connectivity using data from a novel state-switching simulation; (ii) accurately estimate graph structure in a previously-described 'ground-truth' simulated dataset; and (iii) identify task-driven alterations in functional connectivity. We show that the MTD is more sensitive than existing sliding-window methods in detecting dynamic alterations in connectivity structure across a range of correlation strengths and window lengths in simulated data. In addition to the temporal precision offered by MTD, we demonstrate that the metric is also able to accurately estimate stationary network structure in both simulated and real task-based data, suggesting that the method may be used to identify dynamic changes in network structure as they evolve through time.

  View details for DOI 10.1016/j.neuroimage.2015.07.064

  View details for PubMedID 26231247

• Effects of thresholding on correlation-based image similarity metrics FRONTIERS IN NEUROSCIENCE Sochat, V. V., Gorgolewski, K. J., Koyejo, O., Durnez, J., Poldrack, R. A. 2015; 9

  Abstract

  The computation of image similarity is important for a wide range of analyses in neuroimaging, from decoding to meta-analysis. In many cases the images being compared have empty voxels, but the effects of such empty voxels on image similarity metrics are poorly understood. We present a detailed investigation of the influence of different degrees of image thresholding on the outcome of pairwise image comparison. Given a pair of brain maps for which one of the maps is thresholded, we show that an analysis using the intersection of non-zero voxels across images at a threshold of Z = ±1.0 maximizes accuracy for retrieval of a list of maps of the same contrast, and thresholding up to Z = ±2.0 can increase accuracy as compared to comparison using unthresholded maps. Finally, maps can be thresholded up to to Z = ±3.0 (corresponding to 25% of voxels non-empty within a standard brain mask) and still maintain a lower bound of 90% accuracy. Our results suggest that a small degree of thresholding may improve the accuracy of image similarity computations, and that robust meta-analytic image similarity comparisons can be obtained using thresholded images.

  View details for DOI 10.3389/fnins.2015.00418

  View details for Web of Science ID 000366713100001

  View details for PubMedID 26578875

  View details for PubMedCentralID PMC4625081

• Progress and challenges in probing the human brain NATURE Poldrack, R. A., Farah, M. J. 2015; 526 (7573): 371-379

  View details for DOI 10.1038/nature15692

  View details for Web of Science ID 000362730200040

  View details for PubMedID 26469048

• Functional System and Areal Organization of a Highly Sampled Individual Human Brain NEURON Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M., Gilmore, A. W., McDermott, K. B., Nelson, S. M., Dosenbach, N. U., Schlaggar, B. L., Mumford, J. A., Poldrack, R. A., Petersen, S. E. 2015; 87 (3): 657-670

  Abstract

  Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT.

  View details for DOI 10.1016/j.neuron.2015.06.037

  View details for Web of Science ID 000361145000016

  View details for PubMedID 26212711

• OpenfMRI: Open sharing of task fMRI data. NeuroImage Poldrack, R. A., Gorgolewski, K. J. 2015

  Abstract

  OpenfMRI is a repository for the open sharing of task-based fMRI data. Here we outline its goals, architecture, and current status of the repository, as well as outlining future plans for the project.

  View details for DOI 10.1016/j.neuroimage.2015.05.073

  View details for PubMedID 26048618

  View details for PubMedCentralID PMC4669234

• Orthogonalization of Regressors in fMRI Models PLOS ONE Mumford, J. A., Poline, J., Poldrack, R. A. 2015; 10 (4)

  Abstract

  The occurrence of collinearity in fMRI-based GLMs (general linear models) may reduce power or produce unreliable parameter estimates. It is commonly believed that orthogonalizing collinear regressors in the model will solve this problem, and some software packages apply automatic orthogonalization. However, the effects of orthogonalization on the interpretation of the resulting parameter estimates is widely unappreciated or misunderstood. Here we discuss the nature and causes of collinearity in fMRI models, with a focus on the appropriate uses of orthogonalization. Special attention is given to how the two popular fMRI data analysis software packages, SPM and FSL, handle orthogonalization, and pitfalls that may be encountered in their usage. Strategies are discussed for reducing collinearity in fMRI designs and addressing their effects when they occur.

  View details for DOI 10.1371/journal.pone.0126255

  View details for Web of Science ID 000353659400093

  View details for PubMedID 25919488

  View details for PubMedCentralID PMC4412813

• Multiple brain networks contribute to the acquisition of bias in perceptual decision-making FRONTIERS IN NEUROSCIENCE Chen, M., Jimura, K., White, C. N., Maddox, W. T., Poldrack, R. A. 2015; 9

  Abstract

  Bias occurs in perceptual decisions when the reward associated with a particular response dominates the sensory evidence in support of a choice. However, it remains unclear how this bias is acquired and once acquired, how it influences perceptual decision processes in the brain. We addressed these questions using model-based neuroimaging in a motion discrimination paradigm where contextual cues suggested which one of two options would receive higher rewards on each trial. We found that participants gradually learned to choose the higher-rewarded option in each context when making a perceptual decision. The amount of bias on each trial was fit well by a reinforcement-learning model that estimated the subjective value of each option within the current context. The brain mechanisms underlying this bias acquisition process were similar to those observed in reward-based decision tasks: prediction errors correlated with the fMRI signals in ventral striatum, dlPFC, and parietal cortex, whereas the amount of acquired bias correlated with activity in ventromedial prefrontal (vmPFC), dorsolateral frontal (dlPFC), and parietal cortices. Moreover, psychophysiological interaction analysis revealed that as bias increased, functional connectivity increased within multiple brain networks (dlPFC-vmPFC-visual, vmPFC-motor, and parietal-anterior-cingulate), suggesting that multiple mechanisms contribute to bias in perceptual decisions through integration of value processing with action, sensory, and control systems. These provide a novel link between the neural mechanisms underlying perceptual and economic decision-making.

  View details for DOI 10.3389/fnins.2015.00063

  View details for Web of Science ID 000352965800001

  View details for PubMedID 25798082

  View details for PubMedCentralID PMC4350407

• If all your friends jumped off a bridge: The effect of others' actions on engagement in and recommendation of risky behaviors. Journal of experimental psychology. General Helfinstein, S. M., Mumford, J. A., Poldrack, R. A. 2015; 144 (1): 12-17

  Abstract

  There is a large gap between the types of risky behavior we recommend to others and those we engage in ourselves. In this study, we hypothesized that a source of this gap is greater reliance on information about others' behavior when deciding whether to take a risk oneself than when deciding whether to recommend it to others. To test this hypothesis, we asked participants either to report their willingness to engage in a series of risky behaviors themselves; their willingness to recommend those behaviors to a loved one; or, how good of an idea it would be for either them or a loved one to engage in the behaviors. We then asked them to evaluate those behaviors on criteria related to the expected utility of the risk (benefits, costs, and likelihood of costs), and on engagement in the activity by people they knew. We found that, after accounting for effects of perceived benefit, cost, and likelihood of cost, perceptions of others' behavior had a dramatically larger impact on participants' willingness to engage in a risk than on their willingness to recommend the risk or their prescriptive evaluation of the risk. These findings indicate that the influence of others' choices on risk-taking behavior is large, direct, cannot be explained by an economic utility model of risky decision-making, and goes against one's own better judgment. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

  View details for DOI 10.1037/xge0000043

  View details for PubMedID 25485604

• Is "efficiency" a useful concept in cognitive neuroscience? DEVELOPMENTAL COGNITIVE NEUROSCIENCE Poldrack, R. A. 2015; 11: 12-17

  Abstract

  It is common in the cognitive neuroscience literature to explain differences in activation in terms of differences in the "efficiency" of neural function. I argue here that this usage of the concept of efficiency is empty and simply redescribes activation differences rather than providing a useful explanation of them. I examine a number of possible explanations for differential activation in terms of task performance, neuronal computation, neuronal energetics, and network organization. While the concept of "efficiency" is vacuous as it is commonly employed in the neuroimaging literature, an examination of brain development in the context of neural coding, neuroenergetics, and network structure provides a roadmap for future investigation, which is fundamental to an improved understanding of developmental effects and group differences in neuroimaging signals.

  View details for DOI 10.1016/j.dcn.2014.06.001

  View details for Web of Science ID 000349403000003

  View details for PubMedID 24981045

• The publication and reproducibility challenges of shared data. Trends in cognitive sciences Poldrack, R. A., Poline, J. 2015; 19 (2): 59-61

  Abstract

  The amount of shared data available for re-analysis has greatly increased in the last few years. Here we discuss some of the challenges raised by the analysis of these shared datasets and propose some strategies to address these issues.

  View details for DOI 10.1016/j.tics.2014.11.008

  View details for PubMedID 25532702

• NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Frontiers in neuroinformatics Gorgolewski, K. J., Varoquaux, G., Rivera, G., Schwarz, Y., Ghosh, S. S., Maumet, C., Sochat, V. V., Nichols, T. E., Poldrack, R. A., Poline, J., Yarkoni, T., Margulies, D. S. 2015; 9: 8-?

  Abstract

  Here we present NeuroVault-a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta- and coactivation analyses.

  View details for DOI 10.3389/fninf.2015.00008

  View details for PubMedID 25914639

  View details for PubMedCentralID PMC4392315

• The impact of study design on pattern estimation for single-trial multivariate pattern analysis NEUROIMAGE Mumford, J. A., Davis, T., Poldrack, R. A. 2014; 103: 130-138

  Abstract

  A prerequisite for a pattern analysis using functional magnetic resonance imaging (fMRI) data is estimating the patterns from time series data, which then are input into the pattern analysis. Here we focus on how the combination of study design (order and spacing of trials) with pattern estimator impacts the Type I error rate of the subsequent pattern analysis. When Type I errors are inflated, the results are no longer valid, so this work serves as a guide for designing and analyzing MVPA studies with controlled false positive rates. The MVPA strategies examined are pattern classification and similarity, utilizing single trial activation patterns from the same functional run. Primarily focusing on the Least Squares Single and Least Square All pattern estimators, we show that collinearities in the models, along with temporal autocorrelation, can cause false positive correlations between activation pattern estimates that adversely impact the false positive rates of pattern similarity and classification analyses. It may seem intuitive that increasing the interstimulus interval (ISI) would alleviate this issue, but remaining weak correlations between activation patterns persist and have a strong influence in pattern similarity analyses. Pattern similarity analyses using only activation patterns estimated from the same functional run of data are susceptible to inflated false positives unless trials are randomly ordered, with a different randomization for each subject. In other cases, where there is any structure to trial order, valid pattern similarity analysis results can only be obtained if similarity computations are restricted to pairs of activation patterns from independent runs. Likewise, for pattern classification, false positives are minimized when the testing and training sets in cross validation do not contain patterns estimated from the same run.

  View details for DOI 10.1016/j.neuroimage.2014.09.026

  View details for Web of Science ID 000345393100013

  View details for PubMedID 25241907

• Right inferior frontal cortex: addressing the rebuttals. Frontiers in human neuroscience Aron, A. R., Robbins, T. W., Poldrack, R. A. 2014; 8: 905

  View details for DOI 10.3389/fnhum.2014.00905

  View details for PubMedID 25426053

  View details for PubMedCentralID PMC4227507

• Making big data open: data sharing in neuroimaging NATURE NEUROSCIENCE Poldrack, R. A., Gorgolewski, K. J. 2014; 17 (11): 1510-1517

  Abstract

  In the last decade, major advances have been made in the availability of shared neuroimaging data, such that there are more than 8,000 shared MRI (magnetic resonance imaging) data sets available online. Here we outline the state of data sharing for task-based functional MRI (fMRI) data, with a focus on various forms of data and their relative utility for subsequent analyses. We also discuss challenges to the future success of data sharing and highlight the ethical argument that data sharing may be necessary to maximize the contribution of human subjects.

  View details for DOI 10.1038/nn.3818

  View details for Web of Science ID 000343969300015

• Evidence for Corticostriatal Dysfunction During Cognitive Skill Learning in Adolescent Siblings of Patients With Childhood-Onset Schizophrenia SCHIZOPHRENIA BULLETIN Wagshal, D., Knowlton, B. J., Suthana, N. A., Cohen, J. R., Poldrack, R. A., Bookheimer, S. Y., Bilder, R. M., Asarnow, R. F. 2014; 40 (5): 1030-1039

  Abstract

  Patients with schizophrenia perform poorly on cognitive skill learning tasks. This study is the first to investigate the neural basis of impairment in cognitive skill learning in first-degree adolescent relatives of patients with schizophrenia. We used functional magnetic resonance imaging to compare activation in 16 adolescent siblings of patients with childhood-onset schizophrenia (COS) and 45 adolescent controls to determine whether impaired cognitive skill learning in individuals with genetic risk for schizophrenia was associated with specific patterns of neural activation. The siblings of patients with COS were severely impaired on the Weather Prediction Task (WPT) and showed a relative deactivation in frontal regions and in the striatum after extensive training on the WPT compared with controls. These differences were not accounted for by performance differences in the 2 groups. The results suggest that corticostriatal dysfunction may be part of the liability for schizophrenia.

  View details for DOI 10.1093/schbul/sbt147

  View details for Web of Science ID 000344610800013

  View details for PubMedID 24162516

• What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis NEUROIMAGE Davis, T., LaRocque, K. F., Mumford, J. A., Norman, K. A., Wagner, A. D., Poldrack, R. A. 2014; 97: 271-283

  Abstract

  Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results.

  View details for DOI 10.1016/j.neuroimage.2014.04.037

  View details for Web of Science ID 000337988700028

  View details for PubMedCentralID PMC4115449

• What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis. NeuroImage Davis, T., LaRocque, K. F., Mumford, J. A., Norman, K. A., Wagner, A. D., Poldrack, R. A. 2014; 97: 271-283

  Abstract

  Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results.

  View details for DOI 10.1016/j.neuroimage.2014.04.037

  View details for PubMedID 24768930

• Decomposing Decision Components in the Stop-signal Task: A Model-based Approach to Individual Differences in Inhibitory Control JOURNAL OF COGNITIVE NEUROSCIENCE White, C. N., Congdon, E., Mumford, J. A., Karlsgodt, K. H., Sabb, F. W., Freimer, N. B., London, E. D., Cannon, T. D., Bilder, R. M., Poldrack, R. A. 2014; 26 (8): 1601-1614

  Abstract

  The stop-signal task, in which participants must inhibit prepotent responses, has been used to identify neural systems that vary with individual differences in inhibitory control. To explore how these differences relate to other aspects of decision making, a drift-diffusion model of simple decisions was fitted to stop-signal task data from go trials to extract measures of caution, motor execution time, and stimulus processing speed for each of 123 participants. These values were used to probe fMRI data to explore individual differences in neural activation. Faster processing of the go stimulus correlated with greater activation in the right frontal pole for both go and stop trials. On stop trials, stimulus processing speed also correlated with regions implicated in inhibitory control, including the right inferior frontal gyrus, medial frontal gyrus, and BG. Individual differences in motor execution time correlated with activation of the right parietal cortex. These findings suggest a robust relationship between the speed of stimulus processing and inhibitory processing at the neural level. This model-based approach provides novel insight into the interrelationships among decision components involved in inhibitory control and raises interesting questions about strategic adjustments in performance and inhibitory deficits associated with psychopathology.

  View details for DOI 10.1162/jocn_a_00567

  View details for Web of Science ID 000338194800001

  View details for PubMedID 24405185

• Quantifying the Internal Structure of Categories Using a Neural Typicality Measure CEREBRAL CORTEX Davis, T., Poldrack, R. A. 2014; 24 (7): 1720-1737

  Abstract

  How categories are represented continues to be hotly debated across neuroscience and psychology. One topic that is central to cognitive research on category representation but underexplored in neurobiological research concerns the internal structure of categories. Internal structure refers to how the natural variability between-category members is coded so that we are able to determine which members are more typical or better examples of their category. Psychological categorization models offer tools for predicting internal structure and suggest that perceptions of typicality arise from similarities between the representations of category members in a psychological space. Inspired by these models, we develop a neural typicality measure that allows us to measure which category members elicit patterns of activation that are similar to other members of their category and are thus more central in a neural space. Using an artificial categorization task, we test how psychological and physical typicality contribute to neural typicality, and find that neural typicality in occipital and temporal regions is significantly correlated with subjects' perceptions of typicality. The results reveal a convergence between psychological and neural category representations and suggest that our neural typicality measure is a useful tool for connecting psychological and neural measures of internal category structure.

  View details for DOI 10.1093/cercor/bht014

  View details for Web of Science ID 000338110900004

  View details for PubMedID 23442348

• Interdisciplinary perspectives on the development, integration, and application of cognitive ontologies FRONTIERS IN NEUROINFORMATICS Hastings, J., Frishkoff, G. A., Smith, B., Jensen, M., Poldrack, R. A., Lomax, J., Bandrowski, A., Imam, F., Turner, J. A., Martone, M. E. 2014; 8

  Abstract

  We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data.

  View details for DOI 10.3389/fninf.2014.00062

  View details for Web of Science ID 000348112900001

  View details for PubMedID 24999329

• Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory JOURNAL OF NEUROSCIENCE Davis, T., Xue, G., Love, B. C., Preston, A. R., Poldrack, R. A. 2014; 34 (22): 7472-7484

  Abstract

  Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels.

  View details for DOI 10.1523/JNEUROSCI.3376-13.2014

  View details for Web of Science ID 000337131800008

  View details for PubMedID 24872552

• The neural basis of task switching changes with skill acquisition FRONTIERS IN HUMAN NEUROSCIENCE Jimura, K., Cazalis, F., Stover, E. R., Poldrack, R. A. 2014; 8

  Abstract

  Learning novel skills involves reorganization and optimization of cognitive processing involving a broad network of brain regions. Previous work has shown asymmetric costs of switching to a well-trained task vs. a poorly-trained task, but the neural basis of these differential switch costs is unclear. The current study examined the neural signature of task switching in the context of acquisition of new skill. Human participants alternated randomly between a novel visual task (mirror-reversed word reading) and a highly practiced one (plain word reading), allowing the isolation of task switching and skill set maintenance. Two scan sessions were separated by 2 weeks, with behavioral training on the mirror reading task in between the two sessions. Broad cortical regions, including bilateral prefrontal, parietal, and extrastriate cortices, showed decreased activity associated with learning of the mirror reading skill. In contrast, learning to switch to the novel skill was associated with decreased activity in a focal subcortical region in the dorsal striatum. Switching to the highly practiced task was associated with a non-overlapping set of regions, suggesting substantial differences in the neural substrates of switching as a function of task skill. Searchlight multivariate pattern analysis also revealed that learning was associated with decreased pattern information for mirror vs. plain reading tasks in fronto-parietal regions. Inferior frontal junction and posterior parietal cortex showed a joint effect of univariate activation and pattern information. These results suggest distinct learning mechanisms task performance and executive control as a function of learning.

  View details for DOI 10.3389/fnhum.2014.00339

  View details for Web of Science ID 000339497400001

  View details for PubMedID 24904378

• Women are more sensitive than men to prior trial events on the Stop-signal task BRITISH JOURNAL OF PSYCHOLOGY Thakkar, K. N., Congdon, E., Poldrack, R. A., Sabb, F. W., London, E. D., Cannon, T. D., Bilder, R. M. 2014; 105 (2): 254-272

  Abstract

  Sexual dimorphism in the brain and cognition is a topic of widespread interest. Many studies of sex differences have focused on visuospatial and verbal abilities, but few studies have investigated sex differences in executive functions. We examined two key components of executive function - response inhibition and response monitoring - in healthy men (n = 285) and women (n = 346) performing the Stop-signal task. In this task, participants are required to make a key press to a stimulus, unless a tone is presented at some delay following the initial stimulus presentation; on these infrequent trials, participants are instructed to inhibit their planned response. Response inhibition was assessed with an estimate of the latency needed to inhibit a response (stop-signal reaction time), and response monitoring was measured by calculating the degree to which participants adjusted their reaction times based on the immediately preceding trial (e.g., speeding following correct trials and slowing following errors). There were no sex differences in overall accuracy or response inhibition, but women showed greater sensitivity to trial history. Women sped up more than men following correct 'Go' trials, and slowed down more than men following errors. These small but statistically significant effects (Cohen's d = 0.25-0.3) suggest more flexible adjustments in speed-accuracy trade-offs in women and greater cognitive flexibility associated with the responsive control of action.

  View details for DOI 10.1111/bjop.12034

  View details for Web of Science ID 000334798600009

  View details for PubMedID 24754812

• Neural activation during response inhibition in adult attention-deficit/hyperactivity disorder: Preliminary findings on the effects of medication and symptom severity PSYCHIATRY RESEARCH-NEUROIMAGING Congdon, E., Altshuler, L. L., Mumford, J. A., Karlsgodt, K. H., Sabb, F. W., Ventura, J., McGough, J. J., London, E. D., Cannon, T. D., Bilder, R. M., Poldrack, R. A. 2014; 222 (1-2): 17-28

  Abstract

  Studies of adults with attention-deficit/hyperactivity disorder (ADHD) have suggested that they have deficient response inhibition, but findings concerning the neural correlates of inhibition in this patient population are inconsistent. We used the Stop-Signal task and functional magnetic resonance imaging (fMRI) to compare neural activation associated with response inhibition between adults with ADHD (N=35) and healthy comparison subjects (N=62), and in follow-up tests to examine the effect of current medication use and symptom severity. There were no differences in Stop-Signal task performance or neural activation between ADHD and control participants. Among the ADHD participants, however, significant differences were associated with current medication, with individuals taking psychostimulants (N=25) showing less stopping-related activation than those not currently receiving psychostimulant medication (N=10). Follow-up analyses suggested that this difference in activation was independent of symptom severity. These results provide evidence that deficits in inhibition-related neural activation persist in a subset of adult ADHD individuals, namely those individuals currently taking psychostimulants. These findings help to explain some of the disparities in the literature, and advance our understanding of why deficits in response inhibition are more variable in adult, as compared with child and adolescent, ADHD patients.

  View details for DOI 10.1016/j.pscychresns.2014.02.002

  View details for Web of Science ID 000334739500003

  View details for PubMedID 24581734

• Changing value through cued approach: an automatic mechanism of behavior change NATURE NEUROSCIENCE Schonberg, T., Bakkour, A., Hover, A. M., Mumford, J. A., Nagar, L., Perez, J., Poldrack, R. A. 2014; 17 (4): 625-U195

  Abstract

  It is believed that choice behavior reveals the underlying value of goods. The subjective values of stimuli can be changed through reward-based learning mechanisms as well as by modifying the description of the decision problem, but it has yet to be shown that preferences can be manipulated by perturbing intrinsic values of individual items. Here we show that the value of food items can be modulated by the concurrent presentation of an irrelevant auditory cue to which subjects must make a simple motor response (i.e., cue-approach training). Follow-up tests showed that the effects of this pairing on choice lasted at least 2 months after prolonged training. Eye-tracking during choice confirmed that cue-approach training increased attention to the cued items. Neuroimaging revealed the neural signature of a value change in the form of amplified preference-related activity in ventromedial prefrontal cortex.

  View details for DOI 10.1038/nn.3673

  View details for Web of Science ID 000333405300024

  View details for PubMedID 24609465

  View details for PubMedCentralID PMC4041518

• inhibitrion and the right inferior frontal cortex: one decade on TRENDS IN COGNITIVE SCIENCES Aron, A. R., Robbins, T. W., Poldrack, R. A. 2014; 18 (4): 177-185

  Abstract

  In our TICS Review in 2004, we proposed that a sector of the right inferior frontal cortex (rIFC) in humans is critical for inhibiting response tendencies. Here we survey new evidence, discuss ongoing controversies, and provide an updated theory. We propose that the rIFC (along with one or more fronto-basal-ganglia networks) is best characterized as a brake. This brake can be turned on in different modes (totally, to outright suppress a response; or partially, to pause), and in different contexts (externally, by stop or salient signals; or internally, by goals). We affirm inhibition as a central component of executive control that relies upon the rIFC and associated networks, and explain why rIFC disruption could generally underpin response control disorders.

  View details for DOI 10.1016/j.tics.2013.12.003

  View details for Web of Science ID 000334132200006

  View details for PubMedID 24440116

• Decomposing Bias in Different Types of Simple Decisions JOURNAL OF EXPERIMENTAL PSYCHOLOGY-LEARNING MEMORY AND COGNITION White, C. N., Poldrack, R. A. 2014; 40 (2): 385-398

  Abstract

  The ability to adjust bias, or preference for an option, allows for great behavioral flexibility. Decision bias is also important for understanding cognition as it can provide useful information about underlying cognitive processes. Previous work suggests that bias can be adjusted in 2 primary ways: by adjusting how the stimulus under consideration is processed, or by adjusting how the response is prepared. The present study explored the experimental, behavioral, and theoretical distinctions between these biases. Different bias manipulations were employed in parallel across perceptual and memory-based decisions to assess the generality of the 2 biases. This is the 1st study to directly test whether conceptually similar bias instructions can induce dissociable bias effects across different decision tasks. The results show that stimulus and response biases can be separately induced in both tasks, suggesting that the biases generalize across different types of decisions. When analyzing behavioral data, the 2 biases can be differentiated by focusing on the time course of bias effects and/or by fitting choice reaction time models to the data. These findings have strong theoretical implications about how observed bias relates to underlying cognitive processes and how it should be used when testing cognitive theories. Guidelines are presented to help researchers identify how to induce the biases experimentally, how to dissociate them in the behavioral data, and how to quantify them using drift diffusion models. Because decision bias is pervasive across many domains of cognitive science, these guidelines can be useful for future work exploring decision bias and choice preferences.

  View details for DOI 10.1037/a0034851

  View details for Web of Science ID 000331869900006

  View details for PubMedID 24245536

• Impaired automatization of a cognitive skill in first-degree relatives of patients with schizophrenia PSYCHIATRY RESEARCH Wagshal, D., Knowlton, B. J., Cohen, J. R., Poldrack, R. A., Bookheimer, S. Y., Bilder, R. M., Asarnow, R. F. 2014; 215 (2): 294-299

  Abstract

  We studied healthy, first-degree relatives of patients with schizophrenia to test the hypothesis that deficits in cognitive skill learning are associated with genetic liability to schizophrenia. Using the Weather Prediction Task (WPT), 23 healthy controls and 10 adult first-degree Relatives Of Schizophrenia (ROS) patients were examined to determine the extent to which cognitive skill learning was automated using a dual-task paradigm to detect subtle impairments in skill learning. Automatization of a skill is the ability to execute a task without the demand for executive control and effortful behavior and is a skill in which schizophrenia patients possess a deficit. ROS patients did not differ from healthy controls in accuracy or reaction time on the WPT either during early or late training on the single-task trials. In contrast, the healthy control and ROS groups were differentially affected during the dual-task trials. Our results demonstrate that the ROS group did not automate the task as well as controls and continued to rely on controlled processing even after extensive practice. This suggests that adult ROS patients may engage in compensatory strategies to achieve normal levels of performance and support the hypothesis that impaired cognitive skill learning is associated with genetic risk for schizophrenia.

  View details for DOI 10.1016/j.psychres.2013.11.024

  View details for Web of Science ID 000332355800006

  View details for PubMedID 24359887

• Predicting risky choices from brain activity patterns PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Helfinstein, S. M., Schonberg, T., Congdon, E., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Cannon, T. D., London, E. D., Bilder, R. M., Poldrack, R. A. 2014; 111 (7): 2470-2475

  Abstract

  Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights.

  View details for DOI 10.1073/pnas.1321728111

  View details for Web of Science ID 000331396500024

  View details for PubMedID 24550270

  View details for PubMedCentralID PMC3932884

• Influencing Food Choices by Training: Evidence for Modulation of Frontoparietal Control Signals JOURNAL OF COGNITIVE NEUROSCIENCE Schonberg, T., Bakkour, A., Hover, A. M., Mumford, J. A., Poldrack, R. A. 2014; 26 (2): 247-268

  Abstract

  To overcome unhealthy behaviors, one must be able to make better choices. Changing food preferences is an important strategy in addressing the obesity epidemic and its accompanying public health risks. However, little is known about how food preferences can be effectively affected and what neural systems support such changes. In this study, we investigated a novel extensive training paradigm where participants chose from specific pairs of palatable junk food items and were rewarded for choosing the items with lower subjective value over higher value ones. In a later probe phase, when choices were made for real consumption, participants chose the lower-valued item more often in the trained pairs compared with untrained pairs. We replicated the behavioral results in an independent sample of participants while they were scanned with fMRI. We found that, as training progressed, there was decreased recruitment of regions that have been previously associated with cognitive control, specifically the left dorsolateral pFC and bilateral parietal cortices. Furthermore, we found that connectivity of the left dorsolateral pFC was greater with primary motor regions by the end of training for choices of lower-valued items that required exertion of self-control, suggesting a formation of a stronger stimulus-response association. These findings demonstrate that it is possible to influence food choices through training and that this training is associated with a decreasing need for top-down frontoparietal control. The results suggest that training paradigms may be promising as the basis for interventions to influence real-world food preferences.

  View details for DOI 10.1162/jocn_a_00495

  View details for Web of Science ID 000329162600004

  View details for PubMedID 24116842

• The ethics of secondary data analysis: Considering the application of Belmont principles to the sharing of neuroimaging data NEUROIMAGE Brakewood, B., Poldrack, R. A. 2013; 82: 671-676

  Abstract

  The sharing of data is essential to increasing the speed of scientific discovery and maximizing the value of public investment in scientific research. However, the sharing of human neuroimaging data poses unique ethical concerns. We outline how data sharing relates to the Belmont principles of respect-for-persons, justice, and beneficence. Whereas regulators of human subjects research often view data sharing solely in terms of potential risks to subjects, we argue that the principles of human subject research require an analysis of both risks and benefits, and that such an analysis suggests that researchers may have a positive duty to share data in order to maximize the contribution that individual participants have made.

  View details for DOI 10.1016/j.neuroimage.2013.02.040

  View details for Web of Science ID 000324568400063

  View details for PubMedID 23466937

• Function in the human connectome: Task-fMRI and individual differences in behavior NEUROIMAGE Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., Nolan, D., Bryant, E., Hartley, T., Footer, O., Bjork, J. M., Poldrack, R., Smith, S., Johansen-Berg, H., Snyder, A. Z., Van Essen, D. C. 2013; 80: 169-189

  Abstract

  The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels.

  View details for DOI 10.1016/j.neuroimage.2013.05.033

  View details for Web of Science ID 000322416000014

  View details for PubMedID 23684877

• Greater risk sensitivity of dorsolateral prefrontal cortex in young smokers than in nonsmokers PSYCHOPHARMACOLOGY Galvan, A., Schonberg, T., Mumford, J., Kohno, M., Poldrack, R. A., London, E. D. 2013; 229 (2): 345-355

  Abstract

  Despite a national reduction in the prevalence of cigarette smoking, ~19% of the adult US population persists in this behavior, with the highest prevalence among 18-25-year-olds. Given that the choice to smoke imposes a known health risk, clarification of brain function related to decision-making, particularly involving risk-taking, in smokers may inform prevention and smoking cessation strategies.This study aimed to compare brain function related to decision-making in young smokers and nonsmokers.The Balloon Analogue Risk Task (BART) is a computerized risky decision-making task in which participants pump virtual balloons, each pump associated with an incremental increase in potential payoff on a given trial but also with greater risk of balloon explosion and loss of payoff. We used this task to compare brain activation associated with risky decision-making in smokers (n = 18) and nonsmokers (n = 25), while they performed the BART during functional magnetic resonance imaging (fMRI). The participants were young men and women, 17-21 years of age.Risk level (number of pumps) modulated brain activation in the right dorsolateral and ventrolateral prefrontal cortices more in smokers than in nonsmokers, and smoking severity (Heaviness of Smoking Index) was positively related to this modulation in an adjacent frontal region.Given evidence for involvement of the right dorsolateral and ventrolateral prefrontal cortices in inhibitory control, these findings suggest that young smokers have a different contribution of prefrontal cortical substrates to risky decision-making than nonsmokers. Future studies are warranted to determine whether the observed neurobiological differences precede or result from smoking.

  View details for DOI 10.1007/s00213-013-3113-x

  View details for Web of Science ID 000324143700013

  View details for PubMedID 23644912

• Complementary Role of Frontoparietal Activity and Cortical Pattern Similarity in Successful Episodic Memory Encoding CEREBRAL CORTEX Xue, G., Dong, Q., Chen, C., Lu, Z., Mumford, J. A., Poldrack, R. A. 2013; 23 (7): 1562-1571

  Abstract

  One central goal in cognitive neuroscience of learning and memory is to characterize the neural processes that lead to long-lasting episodic memory. In addition to the stronger frontoparietal activity, greater category- or item-specific cortical representation during encoding, as measured by pattern similarity (PS), is also associated with better subsequent episodic memory. Nevertheless, it is unknown whether frontoparietal activity and cortical PS reflect distinct mechanisms. To address this issue, we reanalyzed previous data (Xue G, Dong Q, Chen C, Lu ZL, Mumford JA, Poldrack RA. 2010. Greater neural pattern similarity across repetitions is associated with better memory. Science. 330:97, Experiment 3) using a novel approach based on combined activation-based and information-based analyses. The results showed that across items, stronger frontoparietal activity was associated with greater PS in distributed brain regions, including those where the PS was predictive of better subsequent memory. Nevertheless, the item-specific PS was still associated with later episodic memory after controlling the effect of frontoparietal activity. Our results suggest that one possible mechanism of frontoparietal activity on episodic memory encoding is via enhancing PS, resulting in more unique and consistent input to the medial temporal lobe. In addition, they suggest that PS might index additional processes, such as pattern reinstatement as a result of study-phase retrieval, that contribute to episodic memory encoding.

  View details for DOI 10.1093/cercor/bhs143

  View details for Web of Science ID 000321163700007

  View details for PubMedID 22645250

• Toward open sharing of task-based fMRI data: the OpenfMRI project. Frontiers in neuroinformatics Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T. D., Wagner, A. D., Devlin, J. T., Cumba, C., Koyejo, O., Milham, M. P. 2013; 7: 12-?

  Abstract

  The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function.

  View details for DOI 10.3389/fninf.2013.00012

  View details for PubMedID 23847528

• Using fMRI to Constrain Theories of Cognition PERSPECTIVES ON PSYCHOLOGICAL SCIENCE White, C. N., Poldrack, R. A. 2013; 8 (1): 79-83

  Abstract

  Research on cognition often leads to debates that are centered on how many processes exist and how they interact to guide behavior. These debates occur across a range of domains and are often difficult to resolve with behavioral data because similar behavioral predictions can be made by models with different core assumptions. Such model mimicry limits researchers' ability to find differential support for one type of model over the other using behavioral data alone. We argue that functional neuroimaging can help overcome this problem by providing additional dependent measures to constrain model testing. Recent advances in analysis, like multivariate approaches, expand the amount and type of data available for model testing. We illustrate the benefits of this approach by highlighting imaging results that directly speak to the debate over the nature of recollection processes in memory. These results show how functional neuroimaging can advance studies of cognition by providing richer data sets for contrasting cognitive models.

  View details for DOI 10.1177/1745691612469029

  View details for Web of Science ID 000313817400010

  View details for PubMedID 26172254

• Differences in neural activation as a function of risk-taking task parameters. Frontiers in neuroscience Congdon, E., Bato, A. A., Schonberg, T., Mumford, J. A., Karlsgodt, K. H., Sabb, F. W., London, E. D., Cannon, T. D., Bilder, R. M., Poldrack, R. A. 2013; 7: 173-?

  Abstract

  Despite evidence supporting a relationship between impulsivity and naturalistic risk-taking, the relationship of impulsivity with laboratory-based measures of risky decision-making remains unclear. One factor contributing to this gap in our understanding is the degree to which different risky decision-making tasks vary in their details. We conducted an fMRI investigation of the Angling Risk Task (ART), which is an improved behavioral measure of risky decision-making. In order to examine whether the observed pattern of neural activation was specific to the ART or generalizable, we also examined correlates of the Balloon Analog Risk Taking (BART) task in the same sample of 23 healthy adults. Exploratory analyses were conducted to examine the relationship between neural activation, performance, impulsivity and self-reported risk-taking. While activation in a valuation network was associated with reward tracking during the ART but not the BART, increased fronto-cingulate activation was seen during risky choice trials in the BART as compared to the ART. Thus, neural activation during risky decision-making trials differed between the two tasks, and this observation was likely driven by differences in task parameters, namely the absence vs. presence of ambiguity and/or stationary vs. increasing probability of loss on the ART and BART, respectively. Exploratory association analyses suggest that sensitivity of neural response to the magnitude of potential reward during the ART was associated with a suboptimal performance strategy, higher scores on a scale of dysfunctional impulsivity (DI) and a greater likelihood of engaging in risky behaviors, while this pattern was not seen for the BART. Our results suggest that the ART is decomposable and associated with distinct patterns of neural activation; this represents a preliminary step toward characterizing a behavioral measure of risky decision-making that may support a better understanding of naturalistic risk-taking.

  View details for DOI 10.3389/fnins.2013.00173

  View details for PubMedID 24137106

• Learning Predictive Cognitive Structure from fMRI using Supervised Topic Models 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI) Koyejo, O., Patel, P., Ghosh, J., Poldrack, R. A. IEEE. 2013: 9–12

  View details for DOI 10.1109/PRNI.2013.12

  View details for Web of Science ID 000333958600003

• Differences in neural activation as a function of risk-taking task parameters FRONTIERS IN NEUROSCIENCE Congdon, E., Bato, A. A., Schonberg, T., Mumford, J. A., Karlsgodt, K. H., Sabb, F. W., London, E. D., Cannon, T. D., Bilder, R. M., Poldrack, R. A. 2013; 7

  View details for DOI 10.3389/fnins.2013.00173

  View details for Web of Science ID 000346567300171

• Toward open sharing of task-based fMRI data: the OpenfMRI project FRONTIERS IN NEUROINFORMATICS Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T. D., Wagner, A. D., Devlin, J. T., Cumba, C., Koyejo, O., Milham, M. P. 2013; 7

  Abstract

  The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org), which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function.

  View details for DOI 10.3389/fninf.2013.00012

  View details for Web of Science ID 000209207300011

  View details for PubMedCentralID PMC3703526

• Measuring neural representations with fMRI: practices and pitfalls YEAR IN COGNITIVE NEUROSCIENCE Davis, T., Poldrack, R. A. 2013; 1296: 108-134

  Abstract

  Recently, there has been a dramatic increase in the number of functional magnetic resonance imaging studies seeking to answer questions about how the brain represents information. Representational questions are of particular importance in connecting neuroscientific and cognitive levels of analysis because it is at the representational level that many formal models of cognition make distinct predictions. This review discusses techniques for univariate, adaptation, and multivoxel analysis, and how they have been used to answer questions about content specificity in different regions of the brain, how this content is organized, and how representations are shaped by and contribute to cognitive processes. Each of the analysis techniques makes different assumptions about the underlying neural code and thus differ in how they can be applied to specific questions. We also discuss the many pitfalls of representational analysis, from the flexibility in data analysis pipelines to emergent nonrepresentational relationships that can arise between stimuli in a task.

  View details for DOI 10.1111/nyas.12156

  View details for Web of Science ID 000324835300008

  View details for PubMedID 23738883

• Deficits in probabilistic classification learning and liability for schizophrenia PSYCHIATRY RESEARCH Wagshal, D., Knowlton, B. J., Cohen, J. R., Poldrack, R. A., Bookheimer, S. Y., Bilder, R. M., Fernandez, V. G., Asarnow, R. F. 2012; 200 (2-3): 167-172

  Abstract

  Patients with schizophrenia show deficits in skill learning. We tested the hypothesis that impaired skill learning is associated with liability for schizophrenia by determining if it is present in non-affected siblings of patients. This study examined cognitive skill learning in adolescent siblings of patients with childhood onset schizophrenia (COS), who are at high genetic risk for the disorder, and age-matched controls. A probabilistic classification task was used to assess cognitive skill learning, which has been shown to be impaired in patients with striatal dysfunction or schizophrenia. Differences between the groups emerged within the first 50 trials of training: the controls showed significant learning while the COS siblings did not. Furthermore, after extended training over 800 additional trials the siblings of COS probands reached a lower level of asymptotic performance than controls. These results suggest that a behavioral impairment in probabilistic classification learning in healthy, unaffected siblings mirrors the deficits seen in patients and thus may reflect genetic liability for the disease.

  View details for DOI 10.1016/j.psychres.2012.06.009

  View details for Web of Science ID 000313764700017

  View details for PubMedID 22763090

• Perceptual Criteria in the Human Brain JOURNAL OF NEUROSCIENCE White, C. N., Mumford, J. A., Poldrack, R. A. 2012; 32 (47): 16716-16724

  Abstract

  A critical component of decision making is the ability to adjust criteria for classifying stimuli. fMRI and drift diffusion models were used to explore the neural representations of perceptual criteria in decision making. The specific focus was on the relative engagement of perceptual- and decision-related neural systems in response to adjustments in perceptual criteria. Human participants classified visual stimuli as big or small based on criteria of different sizes, which effectively biased their choices toward one response over the other. A drift diffusion model was fit to the behavioral data to extract estimates of stimulus size, criterion size, and difficulty for each participant and condition. These parameter values were used as modulated regressors to create a highly constrained model for the fMRI analysis that accounted for several components of the decision process. The results show that perceptual criteria values were reflected by activity in left inferior temporal cortex, a region known to represent objects and their physical properties, whereas stimulus size was reflected by activation in occipital cortex. A frontoparietal network of regions, including dorsolateral prefrontal cortex and superior parietal lobule, corresponded to the decision variables resulting from the downstream stimulus-criterion comparison, independent of stimulus type. The results provide novel evidence that perceptual criteria are represented in stimulus space and serve as inputs to be compared with the presented stimulus, recruiting a common network of decision regions shown to be active in other simple decisions. This work advances our understanding of the neural correlates of decision flexibility and adjustments of behavioral bias.

  View details for DOI 10.1523/JNEUROSCI.1744-12.2012

  View details for Web of Science ID 000311420800017

  View details for PubMedID 23175825

• Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping PLOS COMPUTATIONAL BIOLOGY Poldrack, R. A., Mumford, J. A., Schonberg, T., Kalar, D., Barman, B., Yarkoni, T. 2012; 8 (10)

  Abstract

  Neuroimaging research has largely focused on the identification of associations between brain activation and specific mental functions. Here we show that data mining techniques applied to a large database of neuroimaging results can be used to identify the conceptual structure of mental functions and their mapping to brain systems. This analysis confirms many current ideas regarding the neural organization of cognition, but also provides some new insights into the roles of particular brain systems in mental function. We further show that the same methods can be used to identify the relations between mental disorders. Finally, we show that these two approaches can be combined to empirically identify novel relations between mental disorders and mental functions via their common involvement of particular brain networks. This approach has the potential to discover novel endophenotypes for neuropsychiatric disorders and to better characterize the structure of these disorders and the relations between them.

  View details for DOI 10.1371/journal.pcbi.1002707

  View details for Web of Science ID 000310568800008

  View details for PubMedID 23071428

• Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs NEUROIMAGE Turner, B. O., Mumford, J. A., Poldrack, R. A., Ashby, F. G. 2012; 62 (3): 1429-1438

  Abstract

  Despite growing interest in multi-voxel pattern analysis (MVPA) methods for fMRI, a major problem remains--that of generating estimates in rapid event-related (ER) designs, where the BOLD responses of temporally adjacent events will overlap. While this problem has been investigated for methods that reduce each event to a single parameter per voxel (Mumford et al., 2012), most of these methods make strong parametric assumptions about the shape of the hemodynamic response, and require exact knowledge of the temporal profile of the underlying neural activity. A second class of methods uses multiple parameters per event (per voxel) to capture temporal information more faithfully. In addition to enabling a more accurate estimate of ER responses, this allows for the extension of the standard classification paradigm into the temporal domain (e.g., Mourão-Miranda et al., 2007). However, existing methods in this class were developed for use with block and slow ER data, and there has not yet been an exploration of how to adapt such methods to data collected using rapid ER designs. Here, we demonstrate that the use of multiple parameters preserves or improves classification accuracy, while additionally providing information on the evolution of class discrimination. Additionally, we explore an alternative to the method of Mourão-Miranda et al. tailored to use in rapid ER designs that yields equivalent classification accuracies, but is better at unmixing responses to temporally adjacent events. The current work paves the way for wider adoption of spatiotemporal classification analyses, and greater use of MVPA with rapid ER designs.

  View details for DOI 10.1016/j.neuroimage.2012.05.057

  View details for Web of Science ID 000307369000011

  View details for PubMedID 22659443

• The future of fMRI in cognitive neuroscience NEUROIMAGE Poldrack, R. A. 2012; 62 (2): 1216-1220

  Abstract

  Over the last 20 years, fMRI has revolutionized cognitive neuroscience. Here I outline a vision for what the next 20 years of fMRI in cognitive neuroscience might look like. Some developments that I hope for include increased methodological rigor, an increasing focus on connectivity and pattern analysis as opposed to "blobology", a greater focus on selective inference powered by open databases, and increased use of ontologies and computational models to describe underlying processes.

  View details for DOI 10.1016/j.neuroimage.2011.08.007

  View details for Web of Science ID 000306390600089

  View details for PubMedID 21856431

• The Relationship Between Measures of Impulsivity and Alcohol Misuse: An Integrative Structural Equation Modeling Approach ALCOHOLISM-CLINICAL AND EXPERIMENTAL RESEARCH Courtney, K. E., Arellano, R., Barkley-Levenson, E., Galvan, A., Poldrack, R. A., MacKillop, J., Jentsch, J. D., Ray, L. A. 2012; 36 (6): 923-931

  Abstract

  Higher levels of impulsivity have been implicated in the development of alcohol use disorders. Recent findings suggest that impulsivity is not a unitary construct, highlighted by the diverse ways in which the various measures of impulsivity relate to alcohol use outcomes. This study simultaneously tested the following dimensions of impulsivity as determinants of alcohol use and alcohol problems: risky decision making, self-reported risk-attitudes, response inhibition, and impulsive decision making.Participants were a community sample of nontreatment seeking problem drinkers (n = 158). Structural equation modeling (SEM) analyses employed behavioral measures of impulsive decision making (delay discounting task [DDT]), response inhibition (stop signal task [SST]), and risky decision making (Balloon Analogue Risk Task [BART]), and a self-report measure of risk-attitudes (domain-specific risk-attitude scale [DOSPERT]), as predictors of alcohol use and of alcohol-related problems in this sample.The model fits well, accounting for 38% of the variance in alcohol problems, and identified 2 impulsivity dimensions that significantly loaded onto alcohol outcomes: (i) impulsive decision making, indexed by the DDT; and (ii) risky decision making, measured by the BART.The impulsive decision-making dimension of impulsivity, indexed by the DDT, was the strongest predictor of alcohol use and alcohol pathology in this sample of problem drinkers. Unexpectedly, a negative relationship was found between risky decision making and alcohol problems. The results highlight the importance of considering the distinct facets of impulsivity to elucidate their individual and combined effects on alcohol use initiation, escalation, and dependence.

  View details for DOI 10.1111/j.1530-0277.2011.01635.x

  View details for Web of Science ID 000304712800018

  View details for PubMedID 22091877

• Striatal Dopamine D-2/ D-3 Receptors Mediate Response Inhibition and Related Activity in Frontostriatal Neural Circuitry in Humans JOURNAL OF NEUROSCIENCE Ghahremani, D. G., Lee, B., Robertson, C. L., Tabibnia, G., Morgan, A. T., De Shetler, N., Brown, A. K., Monterosso, J. R., Aron, A. R., Mandelkern, M. A., Poldrack, R. A., London, E. D. 2012; 32 (21): 7316-7324

  Abstract

  Impulsive behavior is thought to reflect a traitlike characteristic that can have broad consequences for an individual's success and well-being, but its neurobiological basis remains elusive. Although striatal dopamine D₂-like receptors have been linked with impulsive behavior and behavioral inhibition in rodents, a role for D₂-like receptor function in frontostriatal circuits mediating inhibitory control in humans has not been shown. We investigated this role in a study of healthy research participants who underwent positron emission tomography with the D₂/D₃ dopamine receptor ligand [¹⁸F]fallypride and BOLD fMRI while they performed the Stop-signal Task, a test of response inhibition. Striatal dopamine D₂/D₃ receptor availability was negatively correlated with speed of response inhibition (stop-signal reaction time) and positively correlated with inhibition-related fMRI activation in frontostriatal neural circuitry. Correlations involving D₂/D₃ receptor availability were strongest in the dorsal regions (caudate and putamen) of the striatum, consistent with findings of animal studies relating dopamine receptors and response inhibition. The results suggest that striatal D₂-like receptor function in humans plays a major role in the neural circuitry that mediates behavioral control, an ability that is essential for adaptive responding and is compromised in a variety of common neuropsychiatric disorders.

  View details for DOI 10.1523/JNEUROSCI.4284-11.2012

  View details for Web of Science ID 000304421000024

  View details for PubMedID 22623677

• Analyses of regional-average activation and multivoxel pattern information tell complementary stories NEUROPSYCHOLOGIA Jimura, K., Poldrack, R. A. 2012; 50 (4): 544-552

  Abstract

  Multivariate pattern analysis (MVPA) has recently received increasing attention in functional neuroimaging due to its ability to decode mental states from fMRI signals. However, questions remain regarding both the empirical and conceptual relationships between results from MVPA and standard univariate analyses. In the current study, whole-brain univariate and searchlight MVPAs of parametric manipulations of monetary gain and loss in a decision making task (Tom et al., 2007) were compared to identify the differences in the results across these methods and the implications for understanding the underlying mental processes. The MVPA and univariate results did identify some overlapping regions in whole brain analyses. However, an analysis of consistency revealed that in many regions the effect size estimates obtained from MVPA and univariate analysis were uncorrelated. Moreover, comparison of sensitivity showed a general trend towards greater sensitivity to task manipulations by MVPA compared to univariate analysis. These results demonstrate that MVPA methods may provide a different view of the functional organization of mental processing compared to univariate analysis, wherein MVPA is more sensitive to distributed coding of information whereas univariate analysis is more sensitive to global engagement in ongoing tasks. The results also highlight the need for better ways to integrate these methods.

  View details for DOI 10.1016/j.neuropsychologia.2011.11.007

  View details for Web of Science ID 000301898200011

  View details for PubMedID 22100534

• Human Anterior and Posterior Hippocampus Respond Distinctly to State and Trait Anxiety EMOTION Satpute, A. B., Mumford, J. A., Naliboff, B. D., Poldrack, R. A. 2012; 12 (1): 58-68

  Abstract

  We examined whether anterior and posterior hippocampal subregions in humans show distinct relationships to state and trait anxiety. In rodents, the ventral (but not dorsal) hippocampus is critically involved in contextual anxiety, whereas dorsal hippocampus is affected by chronic stress and genetically bred trait anxiety. These studies suggest that state forms of anxiety may be more associated with anterior (ventral in rodents) hippocampus, whereas trait forms of anxiety maybe more associated with posterior (dorsal in rodents) hippocampus. Participants were placed under alternating blocks of threat of shock and safety conditions while performing a secondary task, and state and trait anxiety measures were obtained. Using subject-specific anatomically defined masks, we found that state anxiety was related to activity in anterior but not posterior hippocampus, whereas trait anxiety showed the opposite pattern. Additionally, a psychophysiological connectivity analysis showed that activity in anterior hippocampus was more strongly related to activity in ventromedial prefrontal cortex under threat than under safety conditions, significantly more so than activity in posterior hippocampus was. Hence, anterior hippocampus shows a distinct moment-to-moment connectivity profile with other neural regions during threat relative to posterior hippocampus. The findings provide several lines of evidence for functional differentiation of anterior and posterior hippocampal involvement across state and trait components of anxiety in humans.

  View details for DOI 10.1037/a0026517

  View details for Web of Science ID 000299980100011

  View details for PubMedID 22309734

• Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses NEUROIMAGE Mumford, J. A., Turner, B. O., Ashby, F. G., Poldrack, R. A. 2012; 59 (3): 2636-2643

  Abstract

  Use of multivoxel pattern analysis (MVPA) to predict the cognitive state of a subject during task performance has become a popular focus of fMRI studies. The input to these analyses consists of activation patterns corresponding to different tasks or stimulus types. These activation patterns are fairly straightforward to calculate for blocked trials or slow event-related designs, but for rapid event-related designs the evoked BOLD signal for adjacent trials will overlap in time, complicating the identification of signal unique to specific trials. Rapid event-related designs are often preferred because they allow for more stimuli to be presented and subjects tend to be more focused on the task, and thus it would be beneficial to be able to use these types of designs in MVPA analyses. The present work compares 8 different models for estimating trial-by-trial activation patterns for a range of rapid event-related designs varying by interstimulus interval and signal-to-noise ratio. The most effective approach obtains each trial's estimate through a general linear model including a regressor for that trial as well as another regressor for all other trials. Through the analysis of both simulated and real data we have found that this model shows some improvement over the standard approaches for obtaining activation patterns. The resulting trial-by-trial estimates are more representative of the true activation magnitudes, leading to a boost in classification accuracy in fast event-related designs with higher signal-to-noise. This provides the potential for fMRI studies that allow simultaneous optimization of both univariate and MVPA approaches.

  View details for DOI 10.1016/j.neuroimage.2011.08.076

  View details for Web of Science ID 000299494000063

  View details for PubMedID 21924359

• Decreasing ventromedial prefrontal cortex activity during sequential risk-taking: an fMRI investigation of the balloon analog risk task FRONTIERS IN NEUROSCIENCE Schonberg, T., Fox, C. R., Mumford, J. A., Congdon, E., Trepel, C., Poldrack, R. A. 2012; 6

  View details for DOI 10.3389/fnins.2012.00080

  View details for Web of Science ID 000209165300089

• Frontiers in brain imaging methods grand challenge. Frontiers in neuroscience Poline, J., Poldrack, R. A. 2012; 6: 96-?

  View details for DOI 10.3389/fnins.2012.00096

  View details for PubMedID 22783159

• Data sharing in neuroimaging research. Frontiers in neuroinformatics Poline, J., Breeze, J. L., Ghosh, S., Gorgolewski, K., Halchenko, Y. O., Hanke, M., Haselgrove, C., Helmer, K. G., Keator, D. B., Marcus, D. S., Poldrack, R. A., Schwartz, Y., Ashburner, J., Kennedy, D. N. 2012; 6: 9-?

  Abstract

  Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

  View details for DOI 10.3389/fninf.2012.00009

  View details for PubMedID 22493576

• Measurement and reliability of response inhibition. Frontiers in psychology Congdon, E., Mumford, J. A., Cohen, J. R., Galvan, A., Canli, T., Poldrack, R. A. 2012; 3: 37-?

  Abstract

  Response inhibition plays a critical role in adaptive functioning and can be assessed with the Stop-signal task, which requires participants to suppress prepotent motor responses. Evidence suggests that this ability to inhibit a prepotent motor response (reflected as Stop-signal reaction time (SSRT)) is a quantitative and heritable measure of interindividual variation in brain function. Although attention has been given to the optimal method of SSRT estimation, and initial evidence exists in support of its reliability, there is still variability in how Stop-signal task data are treated across samples. In order to examine this issue, we pooled data across three separate studies and examined the influence of multiple SSRT calculation methods and outlier calling on reliability (using Intra-class correlation). Our results suggest that an approach which uses the average of all available sessions, all trials of each session, and excludes outliers based on predetermined lenient criteria yields reliable SSRT estimates, while not excluding too many participants. Our findings further support the reliability of SSRT, which is commonly used as an index of inhibitory control, and provide support for its continued use as a neurocognitive phenotype.

  View details for DOI 10.3389/fpsyg.2012.00037

  View details for PubMedID 22363308

• Decreasing ventromedial prefrontal cortex activity during sequential risk-taking: an FMRI investigation of the balloon analog risk task. Frontiers in neuroscience Schonberg, T., Fox, C. R., Mumford, J. A., Congdon, E., Trepel, C., Poldrack, R. A. 2012; 6: 80-?

  Abstract

  Functional imaging studies examining the neural correlates of risk have mainly relied on paradigms involving exposure to simple chance gambles and an economic definition of risk as variance in the probability distribution over possible outcomes. However, there is little evidence that choices made during gambling tasks predict naturalistic risk-taking behaviors such as drug use, extreme sports, or even equity investing. To better understand the neural basis of naturalistic risk-taking, we scanned participants using fMRI while they completed the Balloon Analog Risk Task, an experimental measure that includes an active decision/choice component and that has been found to correlate with a number of naturalistic risk-taking behaviors. In the task, as in many naturalistic settings, escalating risk-taking occurs under uncertainty and might be experienced either as the accumulation of greater potential rewards, or as exposure to increasing possible losses (and decreasing expected value). We found that areas previously linked to risk and risk-taking (bilateral anterior insula, anterior cingulate cortex, and right dorsolateral prefrontal cortex) were activated as participants continued to inflate balloons. Interestingly, we found that ventromedial prefrontal cortex (vmPFC) activity decreased as participants further expanded balloons. In light of previous findings implicating the vmPFC in value calculation, this result suggests that escalating risk-taking in the task might be perceived as exposure to increasing possible losses (and decreasing expected value) rather than the increasing potential total reward relative to the starting point of the trial. A better understanding of how neural activity changes with risk-taking behavior in the task offers insight into the potential neural mechanisms driving naturalistic risk-taking.

  View details for DOI 10.3389/fnins.2012.00080

  View details for PubMedID 22675289

• Measurement and reliability of response inhibition FRONTIERS IN PSYCHOLOGY Congdon, E., Mumford, J. A., Cohen, J. R., Galvan, A., Canli, T., Poldrack, R. A. 2012; 3

  View details for DOI 10.3389/fpsyg.2012.00037

  View details for Web of Science ID 000208863900051

• Data sharing in neuroimaging research FRONTIERS IN NEUROINFORMATICS Poline, J., Breeze, J. L., Ghosh, S., Gorgolewski, K., Halchenko, Y. O., Hanke, M., Haselgrove, C., Helmer, K. G., Keator, D. B., Marcus, D. S., Poldrack, R. A., Schwartz, Y., Ashburner, J., Kennedy, D. N. 2012; 6

  View details for DOI 10.3389/fninf.2012.00009

  View details for Web of Science ID 000209207100009

• Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding NEURON Poldrack, R. A. 2011; 72 (5): 692-697

  Abstract

  A common goal of neuroimaging research is to use imaging data to identify the mental processes that are engaged when a subject performs a mental task. The use of reasoning from activation to mental functions, known as "reverse inference," has been previously criticized on the basis that it does not take into account how selectively the area is activated by the mental process in question. In this Perspective, I outline the critique of informal reverse inference and describe a number of new developments that provide the ability to more formally test the predictive power of neuroimaging data.

  View details for DOI 10.1016/j.neuron.2011.11.001

  View details for Web of Science ID 000297971100005

  View details for PubMedID 22153367

  View details for PubMedCentralID PMC3240863

• Inhibition-related Activation in the Right Inferior Frontal Gyrus in the Absence of Inhibitory Cues JOURNAL OF COGNITIVE NEUROSCIENCE Lenartowicz, A., Verbruggen, F., Logan, G. D., Poldrack, R. A. 2011; 23 (11): 3388-3399

  Abstract

  The right inferior frontal gyrus (rIFG) has been hypothesized to mediate response inhibition. Typically response inhibition is signaled by an external stop cue, which provides a top-down signal to initiate the process. However, recent behavioral findings suggest that response inhibition can also be triggered automatically by bottom-up processes. In the present study, we evaluated whether rIFG activity would also be observed during automatic inhibition, in which no stop cue was presented and no motor inhibition was actually required. We measured rIFG activation in response to stimuli that were previously associated with stop signals but which required a response on the current trial (reversal trials). The results revealed an increase in rIFG (pars triangularis) activity, suggesting that it can be activated by associations between stimuli and stopping. Moreover, its role in inhibition tasks is not contingent on the presence of an external stop cue. We conclude that rIFG involvement in stopping is consistent with a role in reprogramming of action plans, which may comprise inhibition, and its activity can be triggered through automatic, bottom-up processing.

  View details for Web of Science ID 000295869500018

  View details for PubMedID 21452946

• Large-scale automated synthesis of human functional neuroimaging data NATURE METHODS Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., Wager, T. D. 2011; 8 (8): 665-U95

  Abstract

  The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.

  View details for DOI 10.1038/NMETH.1635

  View details for Web of Science ID 000293220600023

  View details for PubMedID 21706013

• Spaced Learning Enhances Subsequent Recognition Memory by Reducing Neural Repetition Suppression JOURNAL OF COGNITIVE NEUROSCIENCE Xue, G., Mei, L., Chen, C., Lu, Z., Poldrack, R., Dong, Q. 2011; 23 (7): 1624-1633

  Abstract

  Spaced learning usually leads to better recognition memory as compared with massed learning, yet the underlying neural mechanisms remain elusive. One open question is whether the spacing effect is achieved by reducing neural repetition suppression. In this fMRI study, participants were scanned while intentionally memorizing 120 novel faces, half under the massed learning condition (i.e., four consecutive repetitions with jittered interstimulus interval) and the other half under the spaced learning condition (i.e., the four repetitions were interleaved). Recognition memory tests afterward revealed a significant spacing effect: Participants recognized more items learned under the spaced learning condition than under the massed learning condition. Successful face memory encoding was associated with stronger activation in the bilateral fusiform gyrus, which showed a significant repetition suppression effect modulated by subsequent memory status and spaced learning. Specifically, remembered faces showed smaller repetition suppression than forgotten faces under both learning conditions, and spaced learning significantly reduced repetition suppression. These results suggest that spaced learning enhances recognition memory by reducing neural repetition suppression.

  View details for Web of Science ID 000290473000005

  View details for PubMedID 20617892

• Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington's disease using machine learning techniques NEUROIMAGE Rizk-Jackson, A., Stoffers, D., Sheldon, S., Kuperman, J., Dale, A., Goldstein, J., Corey-Bloom, J., Poldrack, R. A., Aron, A. R. 2011; 56 (2): 788-796

  Abstract

  The development of MRI measures as biomarkers for neurodegenerative disease could prove extremely valuable for the assessment of neuroprotective therapies. Much current research is aimed at developing such biomarkers for use in people who are gene-positive for Huntington's disease yet exhibit few or no clinical symptoms of the disease (pre-HD). We acquired structural (T1), diffusion weighted and functional MRI (fMRI) data from 39 pre-HD volunteers and 25 age-matched controls. To determine whether it was possible to decode information about disease state from neuroimaging data, we applied multivariate pattern analysis techniques to several derived voxel-based and segmented region-based datasets. We found that different measures of structural, diffusion weighted, and functional MRI could successfully classify pre-HD and controls using support vector machines (SVM) and linear discriminant analysis (LDA) with up to 76% accuracy. The model producing the highest classification accuracy used LDA with a set of six volume measures from the basal ganglia. Furthermore, using support vector regression (SVR) and linear regression models, we were able to generate quantitative measures of disease progression that were significantly correlated with established measures of disease progression (estimated years to clinical onset, derived from age and genetic information) from several different neuroimaging measures. The best performing regression models used SVR with neuroimaging data from regions within the grey matter (caudate), white matter (corticospinal tract), and fMRI (insular cortex). These results highlight the utility of machine learning analyses in addition to conventional ones. We have shown that several neuroimaging measures contain multivariate patterns of information that are useful for the development of disease-state biomarkers for HD.

  View details for DOI 10.1016/j.neuroimage.2010.04.273

  View details for Web of Science ID 000290081900037

  View details for PubMedID 20451620

• Effect of Modafinil on Learning and Task-Related Brain Activity in Methamphetamine-Dependent and Healthy Individuals NEUROPSYCHOPHARMACOLOGY Ghahremani, D. G., Tabibnia, G., Monterosso, J., Hellemann, G., Poldrack, R. A., London, E. D. 2011; 36 (5): 950-959

  Abstract

  Methamphetamine (MA)-dependent individuals exhibit deficits in cognition and prefrontal cortical function. Therefore, medications that improve cognition in these subjects may improve the success of therapy for their addiction, especially when cognitive behavioral therapies are used. Modafinil has been shown to improve cognitive performance in neuropsychiatric patients and healthy volunteers. We therefore conducted a randomized, double-blind, placebo-controlled, cross-over study, using functional magnetic resonance imaging, to examine the effects of modafinil on learning and neural activity related to cognitive function in abstinent, MA-dependent, and healthy control participants. Modafinil (200 mg) and placebo were administered orally (one single dose each), in counterbalanced fashion, 2 h before each of two testing sessions. Under placebo conditions, MA-dependent participants showed worse learning performance than control participants. Modafinil boosted learning in MA-dependent participants, bringing them to the same performance level as control subjects; the control group did not show changes in performance with modafinil. After controlling for performance differences, MA-dependent participants showed a greater effect of modafinil on brain activation in bilateral insula/ventrolateral prefrontal cortex and anterior cingulate cortices than control participants. The findings suggest that modafinil improves learning in MA-dependent participants, possibly by enhancing neural function in regions important for learning and cognitive control. These results suggest that modafinil may be a suitable pharmacological adjunct for enhancing the efficiency of cognitive-based therapies for MA dependence.

  View details for DOI 10.1038/npp.2010.233

  View details for Web of Science ID 000288493600004

  View details for PubMedID 21289606

• Neural Correlates of Response Inhibition and Cigarette Smoking in Late Adolescence NEUROPSYCHOPHARMACOLOGY Galvan, A., Poldrack, R. A., Baker, C. M., McGlennen, K. M., London, E. D. 2011; 36 (5): 970-978

  Abstract

  Smoking is usually initiated in adolescence, and is the leading preventable cause of death in the United States. Little is known, however, about the links between smoking and neurobiological function in adolescent smokers. This study aimed to probe prefrontal cortical function in late adolescent smokers, using a response inhibition task, and to assess possible relationships between inhibition-related brain activity, clinical features of smoking behavior, and exposure to cigarette smoking. Participants in this study were otherwise healthy late adolescent smokers (15-21 years of age; n=25), who reported daily smoking for at least the 6 months before testing, and age- and education-matched nonsmokers (16-21 years of age; n=25), who each reported smoking fewer than five cigarettes in their lifetimes. The subjects performed the Stop-signal Task, while undergoing functional magnetic resonance imaging. There were no significant group differences in prefrontal cortical activity during response inhibition, but the Heaviness of Smoking Index, a measure of smoking behavior and dependence, was negatively related to neural function in cortical regions of the smokers. These findings suggest that smoking can modulate prefrontal cortical function. Given the late development of the prefrontal cortex, which continues through adolescence, it is possible that smoking may influence the trajectory of brain development during this critical developmental period.

  View details for DOI 10.1038/npp.2010.235

  View details for Web of Science ID 000288493600006

  View details for PubMedID 21270772

• Different Forms of Self-Control Share a Neurocognitive Substrate JOURNAL OF NEUROSCIENCE Tabibnia, G., Monterosso, J. R., Baicy, K., Aron, A. R., Poldrack, R. A., Chakrapani, S., Lee, B., London, E. D. 2011; 31 (13): 4805-4810

  Abstract

  Psychological and neurocognitive studies have suggested that different kinds of self-control may share a common psychobiological component. If this is true, performance in affective and nonaffective inhibitory control tasks in the same individuals should be correlated and should rely upon integrity of this region. To test this hypothesis, we acquired high-resolution magnetic resonance images from 44 healthy and 43 methamphetamine-dependent subjects. Individuals with methamphetamine dependence were tested because of prior findings that they suffer inhibitory control deficits. Gray matter structure of the inferior frontal gyrus was assessed using voxel-based morphometry. Subjects participated in tests of motor and affective inhibitory control (stop-signal task and emotion reappraisal task, respectively); and methamphetamine-dependent subjects provided self-reports of their craving for methamphetamine. Performance levels on the two inhibitory control tasks were correlated with one another and with gray matter intensity in the right pars opercularis region of the inferior frontal gyrus in healthy subjects. Gray matter intensity of this region was also correlated with methamphetamine craving. Compared with healthy subjects, methamphetamine-dependent subjects exhibited lower gray matter intensity in this region, worse motor inhibitory control, and less success in affect regulation. These findings suggest that self-control in different psychological domains involves a common substrate in the right pars opercularis, and that successful self-control depends on integrity of this substrate.

  View details for DOI 10.1523/JNEUROSCI.2859-10.2011

  View details for Web of Science ID 000288938200006

  View details for PubMedID 21451018

• The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Frontiers in neuroinformatics Poldrack, R. A., Kittur, A., Kalar, D., Miller, E., Seppa, C., Gil, Y., Parker, D. S., Sabb, F. W., Bilder, R. M. 2011; 5: 17-?

  Abstract

  Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what "mental processes" exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain.

  View details for DOI 10.3389/fninf.2011.00017

  View details for PubMedID 21922006

• Decoding continuous variables from neuroimaging data: basic and clinical applications. Frontiers in neuroscience Cohen, J. R., Asarnow, R. F., Sabb, F. W., Bilder, R. M., Bookheimer, S. Y., Knowlton, B. J., Poldrack, R. A. 2011; 5: 75-?

  Abstract

  The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.

  View details for DOI 10.3389/fnins.2011.00075

  View details for PubMedID 21720520

• Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience TRENDS IN COGNITIVE SCIENCES Schonberg, T., Fox, C. R., Poldrack, R. A. 2011; 15 (1): 11-19

  Abstract

  Economists define risk in terms of the variability of possible outcomes, whereas clinicians and laypeople generally view risk as exposure to possible loss or harm. Neuroeconomic studies using relatively simple behavioral tasks have identified a network of brain regions that respond to economic risk, but these studies have had limited success predicting naturalistic risk-taking. By contrast, more complex behavioral tasks developed by clinicians (e.g. Balloon Analogue Risk Task and Iowa Gambling Task) correlate with naturalistic risk-taking but resist decomposition into distinct cognitive constructs. We propose here that to bridge this gap and better understand neural substrates of naturalistic risk-taking, new tasks are needed that: are decomposable into basic cognitive and/or economic constructs; predict naturalistic risk-taking; and engender dynamic, affective engagement.

  View details for DOI 10.1016/j.tics.2010.10.002

  View details for Web of Science ID 000286865500003

  View details for PubMedID 21130018

• Decoding continuous variables from neuroimaging data: basic and clinical applications FRONTIERS IN NEUROSCIENCE Cohen, J. R., Asarnow, R. F., Sabb, F. W., Bilder, R. M., Bookheimer, S. Y., Knowlton, B. J., Poldrack, R. A. 2011; 5

  View details for DOI 10.3389/fnins.2011.00075

  View details for Web of Science ID 000209200600071

• Altered Functional Connectivity in Frontal Lobe Circuits Is Associated with Variation in the Autism Risk Gene CNTNAP2 SCIENCE TRANSLATIONAL MEDICINE Scott-Van Zeeland, A. A., Abrahams, B. S., Alvarez-Retuerto, A. I., Sonnenblick, L. I., Rudie, J. D., Ghahremani, D., Mumford, J. A., Poldrack, R. A., Dapretto, M., Geschwind, D. H., Bookheimer, S. Y. 2010; 2 (56)

  Abstract

  Genetic studies are rapidly identifying variants that shape risk for disorders of human cognition, but the question of how such variants predispose to neuropsychiatric disease remains. Noninvasive human brain imaging allows assessment of the brain in vivo, and the combination of genetics and imaging phenotypes remains one of the only ways to explore functional genotype-phenotype associations in human brain. Common variants in contactin-associated protein-like 2 (CNTNAP2), a neurexin superfamily member, have been associated with several allied neurodevelopmental disorders, including autism and specific language impairment, and CNTNAP2 is highly expressed in frontal lobe circuits in the developing human brain. Using functional neuroimaging, we have demonstrated a relationship between frontal lobar connectivity and common genetic variants in CNTNAP2. These data provide a mechanistic link between specific genetic risk for neurodevelopmental disorders and empirical data implicating dysfunction of long-range connections within the frontal lobe in autism. The convergence between genetic findings and cognitive-behavioral models of autism provides evidence that genetic variation at CNTNAP2 predisposes to diseases such as autism in part through modulation of frontal lobe connectivity.

  View details for DOI 10.1126/scitranslmed.3001344

  View details for Web of Science ID 000288441100001

  View details for PubMedID 21048216

• Cognitive neuroscience 2.0: building a cumulative science of human brain function TRENDS IN COGNITIVE SCIENCES Yarkoni, T., Poldrack, R. A., Van Essen, D. C., Wager, T. D. 2010; 14 (11): 489-496

  Abstract

  Cognitive neuroscientists increasingly recognize that continued progress in understanding human brain function will require not only the acquisition of new data, but also the synthesis and integration of data across studies and laboratories. Here we review ongoing efforts to develop a more cumulative science of human brain function. We discuss the rationale for an increased focus on formal synthesis of the cognitive neuroscience literature, provide an overview of recently developed tools and platforms designed to facilitate the sharing and integration of neuroimaging data, and conclude with a discussion of several emerging developments that hold even greater promise in advancing the study of human brain function.

  View details for DOI 10.1016/j.tics.2010.08.004

  View details for Web of Science ID 000284499800006

  View details for PubMedID 20884276

• Engagement of large-scale networks is related to individual differences in inhibitory control NEUROIMAGE Congdon, E., Mumford, J. A., Cohen, J. R., Galvan, A., Aron, A. R., Xue, G., Miller, E., Poldrack, R. A. 2010; 53 (2): 653-663

  Abstract

  Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examining the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of voxelwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent component analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research.

  View details for DOI 10.1016/j.neuroimage.2010.06.062

  View details for Web of Science ID 000281688000030

  View details for PubMedID 20600962

• Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed? PERSPECTIVES ON PSYCHOLOGICAL SCIENCE Poldrack, R. A. 2010; 5 (6): 753-761

  Abstract

  The goal of cognitive neuroscience is to identify the mapping between brain function and mental processing. In this article, I examine the strategies that have been used to identify such mappings and argue that they may be fundamentally unable to identify selective structure-function mappings. To understand the functional anatomy of mental processes, it will be necessary for researchers to move from the brain-mapping strategies that the field has employed toward a search for selective associations. This will require a greater focus on the structure of cognitive processes, which can be achieved through the development of formal ontologies that describe the structure of mental processes. In this article, I outline the Cognitive Atlas Project, which is developing such ontologies, and show how this knowledge could be used in conjunction with data-mining approaches to more directly relate mental processes and brain function.

  View details for DOI 10.1177/1745691610388777

  View details for Web of Science ID 000286983600015

  View details for PubMedID 25076977

• Neurocognitive Phenotypes and Genetic Dissection of Disorders of Brain and Behavior NEURON Congdon, E., Poldrack, R. A., Freimer, N. B. 2010; 68 (2): 218-230

  Abstract

  Elucidating the molecular mechanisms underlying quantitative neurocognitive phenotypes will further our understanding of the brain's structural and functional architecture and advance the diagnosis and treatment of the psychiatric disorders that these traits underlie. Although many neurocognitive traits are highly heritable, little progress has been made in identifying genetic variants unequivocally associated with these phenotypes. A major obstacle to such progress is the difficulty in identifying heritable neurocognitive measures that are precisely defined and systematically assessed and represent unambiguous mental constructs, yet are also amenable to the high-throughput phenotyping necessary to obtain adequate power for genetic association studies. In this perspective we compare the current status of genetic investigations of neurocognitive phenotypes to that of other categories of biomedically relevant traits and suggest strategies for genetically dissecting traits that may underlie disorders of brain and behavior.

  View details for DOI 10.1016/j.neuron.2010.10.007

  View details for Web of Science ID 000284304300011

  View details for PubMedID 20955930

• Facilitating Memory for Novel Characters by Reducing Neural Repetition Suppression in the Left Fusiform Cortex PLOS ONE Xue, G., Mei, L., Chen, C., Lu, Z., Poldrack, R. A., Dong, Q. 2010; 5 (10)

  Abstract

  The left midfusiform and adjacent regions have been implicated in processing and memorizing familiar words, yet its role in memorizing novel characters has not been well understood.Using functional MRI, the present study examined the hypothesis that the left midfusiform is also involved in memorizing novel characters and spaced learning could enhance the memory by enhancing the left midfusiform activity during learning. Nineteen native Chinese readers were scanned while memorizing the visual form of 120 Korean characters that were novel to the subjects. Each character was repeated four times during learning. Repetition suppression was manipulated by using two different repetition schedules: massed learning and spaced learning, pseudo-randomly mixed within the same scanning session. Under the massed learning condition, the four repetitions were consecutive (with a jittered inter-repetition interval to improve the design efficiency). Under the spaced learning condition, the four repetitions were interleaved with a minimal inter-repetition lag of 6 stimuli. Spaced learning significantly improved participants' performance during the recognition memory test administered one hour after the scan. Stronger left midfusiform and inferior temporal gyrus activities during learning (summed across four repetitions) were associated with better memory of the characters, based on both within- and cross-subjects analyses. Compared to massed learning, spaced learning significantly reduced neural repetition suppression and increased the overall activities in these regions, which were associated with better memory for novel characters.These results demonstrated a strong link between cortical activity in the left midfusiform and memory for novel characters, and thus challenge the visual word form area (VWFA) hypothesis. Our results also shed light on the neural mechanisms of the spacing effect in memorizing novel characters.

  View details for DOI 10.1371/journal.pone.0013204

  View details for Web of Science ID 000282568400015

  View details for PubMedID 20949093

• Greater Neural Pattern Similarity Across Repetitions Is Associated with Better Memory SCIENCE Xue, G., Dong, Q., Chen, C., Lu, Z., Mumford, J. A., Poldrack, R. A. 2010; 330 (6000): 97-101

  Abstract

  Repeated study improves memory, but the underlying neural mechanisms of this improvement are not well understood. Using functional magnetic resonance imaging and representational similarity analysis of brain activity, we found that, compared with forgotten items, subsequently remembered faces and words showed greater similarity in neural activation across multiple study in many brain regions, including (but not limited to) the regions whose mean activities were correlated with subsequent memory. This result addresses a longstanding debate in the study of memory by showing that successful episodic memory encoding occurs when the same neural representations are more precisely reactivated across study episodes, rather than when patterns of activation are more variable across time.

  View details for DOI 10.1126/science.1193125

  View details for Web of Science ID 000282334500044

  View details for PubMedID 20829453

  View details for PubMedCentralID PMC2952039

• Towards an Ontology of Cognitive Control TOPICS IN COGNITIVE SCIENCE Lenartowicz, A., Kalar, D. J., Congdon, E., Poldrack, R. A. 2010; 2 (4): 678-692

  Abstract

  The goal of cognitive neuroscience is to map mental functions onto their neural substrates. We argue here that this goal requires a formal approach to the characterization of mental processes, and we present one such approach by using ontologies to describe cognitive processes and their relations. Using a classifier analysis of data from the BrainMap database, we examine the concept of "cognitive control" to determine whether the proposed component processes in this domain are mapped to independent neural systems. These results show that some subcomponents can be uniquely classified, whereas others cannot, suggesting that these different components may vary in their ontological reality. We relate these concepts to the broader emerging field of phenomics, which aims to characterize cognitive phenotypes on a global scale.

  View details for DOI 10.1111/j.1756-8765.2010.01100.x

  View details for Web of Science ID 000283870400006

  View details for PubMedID 25164049

• Detecting network modules in fMRI time series: A weighted network analysis approach NEUROIMAGE Mumford, J. A., Horvath, S., Oldham, M. C., Langfelder, P., Geschwind, D. H., Poldrack, R. A. 2010; 52 (4): 1465-1476

  Abstract

  Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA), is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel's membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results.

  View details for DOI 10.1016/j.neuroimage.2010.05.047

  View details for Web of Science ID 000280695200034

  View details for PubMedID 20553896

• Everything you never wanted to know about circular analysis, but were afraid to ask JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM Kriegeskorte, N., Lindquist, M. A., Nichols, T. E., Poldrack, R. A., Vul, E. 2010; 30 (9): 1551-1557

  Abstract

  Over the past year, a heated discussion about 'circular' or 'nonindependent' analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in

  View details for DOI 10.1038/jcbfm.2010.86

  View details for Web of Science ID 000281562300001

  View details for PubMedID 20571517

• Neural Components Underlying Behavioral Flexibility in Human Reversal Learning CEREBRAL CORTEX Ghahremani, D. G., Monterosso, J., Jentsch, J. D., Bilder, R. M., Poldrack, R. A. 2010; 20 (8): 1843-1852

  Abstract

  The ability to flexibly respond to changes in the environment is critical for adaptive behavior. Reversal learning (RL) procedures test adaptive response updating when contingencies are altered. We used functional magnetic resonance imaging to examine brain areas that support specific RL components. We compared neural responses to RL and initial learning (acquisition) to isolate reversal-related brain activation independent of cognitive control processes invoked during initial feedback-based learning. Lateral orbitofrontal cortex (OFC) was more activated during reversal than acquisition, suggesting its relevance for reformation of established stimulus-response associations. In addition, the dorsal anterior cingulate (dACC) and right inferior frontal gyrus (rIFG) correlated with change in postreversal accuracy. Because optimal RL likely requires suppression of a prior learned response, we hypothesized that similar regions serve both response inhibition (RI) and inhibition of learned associations during reversal. However, reversal-specific responding and stopping (requiring RI and assessed via the stop-signal task) revealed distinct frontal regions. Although RI-related regions do not appear to support inhibition of prepotent learned associations, a subset of these regions, dACC and rIFG, guide actions consistent with current reward contingencies. These regions and lateral OFC represent distinct neural components that support behavioral flexibility important for adaptive learning.

  View details for DOI 10.1093/cercor/bhp247

  View details for Web of Science ID 000280597100007

  View details for PubMedID 19915091

• Decoding developmental differences and individual variability in response inhibition through predictive analyses across individuals FRONTIERS IN HUMAN NEUROSCIENCE Cohen, J. R., Asarnow, R. F., Sabb, F. W., Bilder, R. M., Bookheimer, S. Y., Knowlton, B. J., Poldrack, R. A. 2010; 4

  Abstract

  Response inhibition is thought to improve throughout childhood and into adulthood. Despite the relationship between age and the ability to stop ongoing behavior, questions remain regarding whether these age-related changes reflect improvements in response inhibition or in other factors that contribute to response performance variability. Functional neuroimaging data shows age-related changes in neural activity during response inhibition. While traditional methods of exploring neuroimaging data are limited to determining correlational relationships, newer methods can determine predictability and can begin to answer these questions. Therefore, the goal of the current study was to determine which aspects of neural function predict individual differences in age, inhibitory function, response speed, and response time variability. We administered a stop-signal task requiring rapid inhibition of ongoing motor responses to healthy participants aged 9-30. We conducted a standard analysis using GLM and a predictive analysis using high-dimensional regression methods. During successful response inhibition we found regions typically involved in motor control, such as the ACC and striatum, that were correlated with either age, response inhibition (as indexed by stop-signal reaction time; SSRT), response speed, or response time variability. However, when examining which variables neural data could predict, we found that age and SSRT, but not speed or variability of response execution, were predicted by neural activity during successful response inhibition. This predictive relationship provides novel evidence that developmental differences and individual differences in response inhibition are related specifically to inhibitory processes. More generally, this study demonstrates a new approach to identifying the neurocognitive bases of individual differences.

  View details for DOI 10.3389/fnhum.2010.00047

  View details for Web of Science ID 000289303000001

  View details for PubMedID 20661296

• Inhibitory Motor Control in Response Stopping and Response Switching JOURNAL OF NEUROSCIENCE Kenner, N. M., Mumford, J. A., Hommer, R. E., Skup, M., Leibenluft, E., Poldrack, R. A. 2010; 30 (25): 8512-8518

  Abstract

  While much is known about the neural regions recruited in the human brain when a dominant motor response becomes inappropriate and must be stopped, less is known about the regions that support switching to a new, appropriate, response. Using functional magnetic resonance imaging with two variants of the stop-signal paradigm that require either stopping altogether or switching to a different response, we examined the brain systems involved in these two forms of executive control. Both stopping trials and switching trials showed common recruitment of the right inferior frontal gyrus, presupplementary motor area, and midbrain. Contrasting switching trials with stopping trials showed activation similar to that observed on response trials (where the initial response remains appropriate and no control is invoked), whereas there were no regions that showed significantly greater activity for stopping trials compared with switching trials. These results show that response switching can be supported by the same neural systems as response inhibition, and suggest that the same mechanism of rapid, nonselective response inhibition that is thought to support speeded response stopping can also support speeded response switching when paired with execution of the new, appropriate, response.

  View details for DOI 10.1523/JNEUROSCI.1096-10.2010

  View details for Web of Science ID 000279076900017

  View details for PubMedID 20573898

• A unique adolescent response to reward prediction errors NATURE NEUROSCIENCE Cohen, J. R., Asarnow, R. F., Sabb, F. W., Bilder, R. M., Bookheimer, S. Y., Knowlton, B. J., Poldrack, R. A. 2010; 13 (6): 669-671

  Abstract

  Previous work has shown that human adolescents may be hypersensitive to rewards, but it is not known which aspect of reward processing is responsible for this. We separated decision value and prediction error signals and found that neural prediction error signals in the striatum peaked in adolescence, whereas neural decision value signals varied depending on how value was modeled. This suggests that heightened dopaminergic prediction error responsivity contributes to adolescent reward seeking.

  View details for DOI 10.1038/nn.2558

  View details for Web of Science ID 000278003300010

  View details for PubMedID 20473290

  View details for PubMedCentralID PMC2876211

• Interpreting Developmental Changes in Neuroimaging Signals HUMAN BRAIN MAPPING Poldrack, R. A. 2010; 31 (6): 872-878

  Abstract

  The imaging of developmental changes in brain function is challenging, but great strides have been made in addressing many of the conceptual issues that this work raises. I highlight a set of issues that remain to be addressed in this literature. First, I argue that the appeal to developmental neurobiology is often misplaced, as it focuses on neurodevelopmental processes that are mostly completed by the age at which neuroimaging studies can be performed. Second, I argue that the concept of "normative" development needs to be reexamined, as it reflects fundamental value judgments about brain development that seem inappropriate for scientific investigation. Third, I examine the ways in which developmental changes are often interpreted, arguing that common interpretations, including the concepts of "efficiency" and "focalization" may be less useful than commonly supposed. To put developmental neuroimaging on stronger footing, we need to develop stronger connections between computational and neurobiological accounts of developmental changes.

  View details for DOI 10.1002/hbm.21039

  View details for Web of Science ID 000278341200005

  View details for PubMedID 20496378

• Reward Processing in Autism AUTISM RESEARCH Scott-Van Zeeland, A. A., Dapretto, M., Ghahremani, D. G., Poldrack, R. A., Bookheimer, S. Y. 2010; 3 (2): 53-67

  Abstract

  The social motivation hypothesis of autism posits that infants with autism do not experience social stimuli as rewarding, thereby leading to a cascade of potentially negative consequences for later development. While possible downstream effects of this hypothesis such as altered face and voice processing have been examined, there has not been a direct investigation of social reward processing in autism. Here we use functional magnetic resonance imaging to examine social and monetary rewarded implicit learning in children with and without autism spectrum disorders (ASD). Sixteen males with ASD and sixteen age- and IQ-matched typically developing (TD) males were scanned while performing two versions of a rewarded implicit learning task. In addition to examining responses to reward, we investigated the neural circuitry supporting rewarded learning and the relationship between these factors and social development. We found diminished neural responses to both social and monetary rewards in ASD, with a pronounced reduction in response to social rewards (SR). Children with ASD also demonstrated a further deficit in frontostriatal response during social, but not monetary, rewarded learning. Moreover, we show a relationship between ventral striatum activity and social reciprocity in TD children. Together, these data support the hypothesis that children with ASD have diminished neural responses to SR, and that this deficit relates to social learning impairments.

  View details for DOI 10.1002/aur.122

  View details for Web of Science ID 000277206100002

  View details for PubMedID 20437601

• Common and Dissociable Prefrontal Loci Associated with Component Mechanisms of Analogical Reasoning CEREBRAL CORTEX Cho, S., Moody, T. D., Fernandino, L., Mumford, J. A., Poldrack, R. A., Cannon, T. D., Knowlton, B. J., Holyoak, K. J. 2010; 20 (3): 524-533

  Abstract

  The ability to draw analogies requires 2 key cognitive processes, relational integration and resolution of interference. The present study aimed to identify the neural correlates of both component processes of analogical reasoning within a single, nonverbal analogy task using event-related functional magnetic resonance imaging. Participants verified whether a visual analogy was true by considering either 1 or 3 relational dimensions. On half of the trials, there was an additional need to resolve interference in order to make a correct judgment. Increase in the number of dimensions to integrate was associated with increased activation in the lateral prefrontal cortex as well as lateral frontal pole in both hemispheres. When there was a need to resolve interference during reasoning, activation increased in the lateral prefrontal cortex but not in the frontal pole. We identified regions in the middle and inferior frontal gyri which were exclusively sensitive to demands on each component process, in addition to a partial overlap between these neural correlates of each component process. These results indicate that analogical reasoning is mediated by the coordination of multiple regions of the prefrontal cortex, of which some are sensitive to demands on only one of these 2 component processes, whereas others are sensitive to both.

  View details for DOI 10.1093/cercor/bhp121

  View details for Web of Science ID 000274488600003

  View details for PubMedID 19549622

• Six problems for causal inference from fMRI NEUROIMAGE Ramsey, J. D., Hanson, S. J., Hanson, C., Halchenko, Y. O., Poldrack, R. A., Glymour, C. 2010; 49 (2): 1545-1558

  Abstract

  Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.

  View details for DOI 10.1016/j.neuroimage.2009.08.065

  View details for Web of Science ID 000272808400038

  View details for PubMedID 19747552

• Striatal Dopamine D-2/D-3 Receptor Availability Is Reduced in Methamphetamine Dependence and Is Linked to Impulsivity JOURNAL OF NEUROSCIENCE Lee, B., London, E. D., Poldrack, R. A., Farahi, J., Nacca, A., Monterosso, J. R., Mumford, J. A., Bokarius, A. V., Dahlbom, M., Mukherjee, J., Bilder, R. M., Brody, A. L., Mandelkern, M. A. 2009; 29 (47): 14734-14740

  Abstract

  While methamphetamine addiction has been associated with both impulsivity and striatal dopamine D(2)/D(3) receptor deficits, human studies have not directly linked the latter two entities. We therefore compared methamphetamine-dependent and healthy control subjects using the Barratt Impulsiveness Scale (version 11, BIS-11) and positron emission tomography with [(18)F]fallypride to measure striatal dopamine D(2)/D(3) receptor availability. The methamphetamine-dependent subjects reported recent use of the drug 3.3 g per week, and a history of using methamphetamine, on average, for 12.5 years. They had higher scores than healthy control subjects on all BIS-11 impulsiveness subscales (p < 0.001). Volume-of-interest analysis found lower striatal D(2)/D(3) receptor availability in methamphetamine-dependent than in healthy control subjects (p < 0.01) and a negative relationship between impulsiveness and striatal D(2)/D(3) receptor availability in the caudate nucleus and nucleus accumbens that reached statistical significance in methamphetamine-dependent subjects. Combining data from both groups, voxelwise analysis indicated that impulsiveness was related to D(2)/D(3) receptor availability in left caudate nucleus and right lateral putamen/claustrum (p < 0.05, determined by threshold-free cluster enhancement). In separate group analyses, correlations involving the head and body of the caudate and the putamen of methamphetamine-dependent subjects and the lateral putamen/claustrum of control subjects were observed at a weaker threshold (p < 0.12 corrected). The findings suggest that low striatal D(2)/D(3) receptor availability may mediate impulsive temperament and thereby influence addiction.

  View details for DOI 10.1523/JNEUROSCI.3765-09.2009

  View details for Web of Science ID 000272185100005

  View details for PubMedID 19940168

• PHENOMICS: THE SYSTEMATIC STUDY OF PHENOTYPES ON A GENOME-WIDE SCALE NEUROSCIENCE Bilder, R. M., Sabb, F. W., Cannon, T. D., London, E. D., Jentsch, J. D., Parker, D. S., Poldrack, R. A., Evans, C., Freimer, N. B. 2009; 164 (1): 30-42

  Abstract

  Phenomics is an emerging transdiscipline dedicated to the systematic study of phenotypes on a genome-wide scale. New methods for high-throughput genotyping have changed the priority for biomedical research to phenotyping, but the human phenome is vast and its dimensionality remains unknown. Phenomics research strategies capable of linking genetic variation to public health concerns need to prioritize development of mechanistic frameworks that relate neural systems functioning to human behavior. New approaches to phenotype definition will benefit from crossing neuropsychiatric syndromal boundaries, and defining phenotypic features across multiple levels of expression from proteome to syndrome. The demand for high throughput phenotyping may stimulate a migration from conventional laboratory to web-based assessment of behavior, and this offers the promise of dynamic phenotyping-the iterative refinement of phenotype assays based on prior genotype-phenotype associations. Phenotypes that can be studied across species may provide greatest traction, particularly given rapid development in transgenic modeling. Phenomics research demands vertically integrated research teams, novel analytic strategies and informatics infrastructure to help manage complexity. The Consortium for Neuropsychiatric Phenomics at UCLA has been supported by the National Institutes of Health Roadmap Initiative to illustrate these principles, and is developing applications that may help investigators assemble, visualize, and ultimately test multi-level phenomics hypotheses. As the transdiscipline of phenomics matures, and work is extended to large-scale international collaborations, there is promise that systematic new knowledge bases will help fulfill the promise of personalized medicine and the rational diagnosis and treatment of neuropsychiatric syndromes.

  View details for DOI 10.1016/j.neuroscience.2009.01.027

  View details for Web of Science ID 000271609000004

  View details for PubMedID 19344640

• CHALLENGES IN PHENOTYPE DEFINITION IN THE WHOLE-GENOME ERA: MULTIVARIATE MODELS OF MEMORY AND INTELLIGENCE NEUROSCIENCE Sabb, F. W., Burggren, A. C., Higier, R. G., Fox, J., He, J., Parker, D. S., Poldrack, R. A., Chu, W., Cannon, T. D., Freimer, N. B., Bilder, R. M. 2009; 164 (1): 88-107

  Abstract

  Refining phenotypes for the study of neuropsychiatric disorders is of paramount importance in neuroscience. Poor phenotype definition provides the greatest obstacle for making progress in disorders like schizophrenia, bipolar disorder, Attention Deficit/Hyperactivity Disorder (ADHD), and autism. Using freely available informatics tools developed by the Consortium for Neuropsychiatric Phenomics (CNP), we provide a framework for defining and refining latent constructs used in neuroscience research and then apply this strategy to review known genetic contributions to memory and intelligence in healthy individuals. This approach can help us begin to build multi-level phenotype models that express the interactions between constructs necessary to understand complex neuropsychiatric diseases. These results are available online through the http://www.phenowiki.org database. Further work needs to be done in order to provide consensus-building applications for the broadly defined constructs used in neuroscience research.

  View details for DOI 10.1016/j.neuroscience.2009.05.013

  View details for Web of Science ID 000271609000008

  View details for PubMedID 19450667

• Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals PSYCHOLOGICAL SCIENCE Anonymous 2009; 20 (11): 1364-1372

  Abstract

  Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes are organized in the brain. Using a variety of classifier techniques, we achieved cross-validated classification accuracy of 80% across individuals (chance = 13%). Using a neural network classifier, we recovered a low-dimensional representation common to all the cognitive-perceptual tasks in our data set, and we used an ontology of cognitive processes to determine the cognitive concepts most related to each dimension. These results revealed a small organized set of large-scale networks that map cognitive processes across a highly diverse set of mental tasks, suggesting a novel way to characterize the neural basis of cognition.

  View details for Web of Science ID 000271526700010

  View details for PubMedID 19883493

  View details for PubMedCentralID PMC2935493

• Functional MRI at the crossroads INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY Van Horn, J. D., Poldrack, R. A. 2009; 73 (1): 3-9

  Abstract

  Since the observation of the blood oxygenation level dependent (BOLD) effect on measured MR signal in the brain, functional magnetic resonance imaging (fMRI) has rapidly become the tool of choice for exploring brain function in cognitive neuroscience. Although fMRI is an exciting and powerful means to examining the brain in vivo, the field has sometimes permitted itself to believe that patterns of BOLD activity reveal more than it is possible to measure given the method's spatial and temporal sampling, while concurrently not fully exploring the amount of information it provides. In this article, we examine some of the constraints on the kinds of inferences that can be supported by fMRI. We critique the concept of reverse inference that is often employed to claim some cognitive function must be present given activity in a specific region. We review the consideration of functional and effective connectivity that remain infrequently applied in cognitive neuroimaging, highlighting recent thinking on the ways in which functional imaging can be used to characterize inter-regional communication. Recent advances in neuroimaging that make it possible to assess anatomical connectivity using diffusion tensor imaging (DTI) and we discuss how these may inform interpretation of fMRI results. Descriptions of fMRI studies in the media, in some instances, serve to misrepresent fMRI's capabilities. We comment on how researchers need to faithfully represent fMRI's promise and limitations in dealing with the media. Finally, as we stand at the crossroads of fMRI research, where one pathway leads toward a rigorous understanding of cognitive operations using fMRI and another leads us to a predictable collection of observations absent of clear insight, we offer our impressions of a fruitful path for future functional imaging research.

  View details for DOI 10.1016/j.ijpsycho.2008.11.003

  View details for Web of Science ID 000267456200002

  View details for PubMedID 19041348

• Independence in ROI analysis: where is the voodoo? SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE Poldrack, R. A., Mumford, J. A. 2009; 4 (2): 208-213

  Abstract

  We discuss the effects of non-independence on region of interest (ROI) analysis of functional magnetic resonance imaging data, which has recently been raised in a prominent article by Vul et al. We outline the problem of non-independence, and use a previously published dataset to examine the effects of non-independence. These analyses show that very strong correlations (exceeding 0.8) can occur even when the ROI is completely independent of the data being analyzed, suggesting that the claims of Vul et al. regarding the implausibility of these high correlations are incorrect. We conclude with some recommendations to help limit the potential problems caused by non-independence.

  View details for DOI 10.1093/scan/nsp011

  View details for Web of Science ID 000266499600011

  View details for PubMedID 19470529

• CNTRICS Final Task Selection: Executive Control SCHIZOPHRENIA BULLETIN Barch, D. M., Braver, T. S., Carter, C. S., Poldrack, R. A., Robbins, T. W. 2009; 35 (1): 115-135

  Abstract

  The third meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) was focused on selecting promising measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of executive control, the 2 constructs of interest were "rule generation and selection" and "dynamic adjustments in control." CNTRICS received 4 task nominations for each of these constructs, and the breakout group for executive control evaluated the degree to which each of these tasks met prespecified criteria. For rule generation and selection, the breakout group for executive control recommended the intradimensional/extradimensional shift task and the switching Stroop for translation for use in clinical trial contexts in schizophrenia research. For dynamic adjustments in control, the breakout group recommended conflict and error adaptation in the Stroop and the stop signal task for translation for use in clinical trials. This article describes the ways in which each of these tasks met the criteria used by the breakout group to recommend tasks for further development.

  View details for DOI 10.1093/schbul/sbn154

  View details for Web of Science ID 000261682700015

  View details for PubMedID 19011235

• Cognitive ontologies for neuropsychiatric phenomics research. Cognitive neuropsychiatry Bilder, R. M., Sabb, F. W., Parker, D. S., Kalar, D., Chu, W. W., Fox, J., Freimer, N. B., Poldrack, R. A. 2009; 14 (4-5): 419-450

  Abstract

  Now that genome-wide association studies (GWAS) are dominating the landscape of genetic research on neuropsychiatric syndromes, investigators are being faced with complexity on an unprecedented scale. It is now clear that phenomics, the systematic study of phenotypes on a genome-wide scale, comprises a rate-limiting step on the road to genomic discovery. To gain traction on the myriad paths leading from genomic variation to syndromal manifestations, informatics strategies must be deployed to navigate increasingly broad domains of knowledge and help researchers find the most important signals. The success of the Gene Ontology project suggests the potential benefits of developing schemata to represent higher levels of phenotypic expression. Challenges in cognitive ontology development include the lack of formal definitions of key concepts and relations among entities, the inconsistent use of terminology across investigators and time, and the fact that relations among cognitive concepts are not likely to be well represented by simple hierarchical "tree" structures. Because cognitive concept labels are labile, there is a need to represent empirical findings at the cognitive test indicator level. This level of description has greater consistency, and benefits from operational definitions of its concepts and relations to quantitative data. Considering cognitive test indicators as the foundation of cognitive ontologies carries several implications, including the likely utility of cognitive task taxonomies. The concept of cognitive "test speciation" is introduced to mark the evolution of paradigms sufficiently unique that their results cannot be "mated" productively with others in meta-analysis. Several projects have been initiated to develop cognitive ontologies at the Consortium for Neuropsychiatric Phenomics (www.phenomics.ucla.edu), in the hope that these ultimately will enable more effective collaboration, and facilitate connections of information about cognitive phenotypes to other levels of biological knowledge. Several free web applications are available already to support examination and visualisation of cognitive concepts in the literature (PubGraph, PubAtlas, PubBrain) and to aid collaborative development of cognitive ontologies (Phenowiki and the Cognitive Atlas). It is hoped that these tools will help formalise inference about cognitive concepts in behavioural and neuroimaging studies, and facilitate discovery of the genetic bases of both healthy cognition and cognitive disorders.

  View details for DOI 10.1080/13546800902787180

  View details for PubMedID 19634038

• Neural Substrates for Reversing Stimulus-Outcome and Stimulus-Response Associations JOURNAL OF NEUROSCIENCE Xue, G., Ghahremani, D. G., Poldrack, R. A. 2008; 28 (44): 11196-11204

  Abstract

  Adaptive goal-directed actions require the ability to quickly relearn behaviors in a changing environment, yet how the brain supports this ability is barely understood. Using functional magnetic resonance imaging and a novel reversal learning paradigm, the present study examined the neural mechanisms associated with reversal learning for outcomes versus motor responses. Participants were extensively trained to classify novel visual symbols (Japanese Hiraganas) into two arbitrary classes ("male" or "female"), in which subjects could acquire both stimulus-outcome associations and stimulus-response associations. They were then required to relearn either the outcome or the motor response associated with the symbols, or both. The results revealed that during reversal learning, a network including anterior cingulate, posterior inferior frontal, and parietal regions showed extended activation for all types of reversal trials, whereas their activation decreased quickly for trials not involving reversal, suggesting their role in domain-general interference resolution. The later increase of right ventral lateral prefrontal cortex and caudate for reversal of stimulus-outcome associations suggests their importance in outcome reversal learning in the face of interference.

  View details for DOI 10.1523/JNEUROSCI.4001-08.2008

  View details for Web of Science ID 000260502400011

  View details for PubMedID 18971462

• Common neural substrates for inhibition of spoken and manual responses CEREBRAL CORTEX Xue, G., Aron, A. R., Poldrack, R. A. 2008; 18 (8): 1923-1932

  Abstract

  The inhibition of speech acts is a critical aspect of human executive control over thought and action, but its neural underpinnings are poorly understood. Using functional magnetic resonance imaging and the stop-signal paradigm, we examined the neural correlates of speech control in comparison to manual motor control. Initiation of a verbal response activated left inferior frontal cortex (IFC: Broca's area). Successful inhibition of speech (naming of letters or pseudowords) engaged a region of right IFC (including pars opercularis and anterior insular cortex) as well as presupplementary motor area (pre-SMA); these regions were also activated by successful inhibition of a hand response (i.e., a button press). Moreover, the speed with which subjects inhibited their responses, stop-signal reaction time, was significantly correlated between speech and manual inhibition tasks. These findings suggest a functional dissociation of left and right IFC in initiating versus inhibiting vocal responses, and that manual responses and speech acts share a common inhibitory mechanism localized in the right IFC and pre-SMA.

  View details for DOI 10.1093/cercor/bhm220

  View details for Web of Science ID 000257787300018

  View details for PubMedID 18245044

• The role of fMRI in Cognitive Neuroscience: where do we stand? CURRENT OPINION IN NEUROBIOLOGY Poldrack, R. A. 2008; 18 (2): 223-226

  Abstract

  Functional magnetic resonance imaging (fMRI) has quickly become the most prominent tool in cognitive neuroscience. In this article, I outline some of the limits on the kinds of inferences that can be supported by fMRI, focusing particularly on reverse inference, in which the engagement of specific mental processes is inferred from patterns of brain activation. Although this form of inference is weak, newly developed methods from the field of machine learning offer the potential to formalize and strengthen reverse inferences. I conclude by discussing the increasing presence of fMRI results in the popular media and the ethical implications of the increasing predictive power of fMRI.

  View details for DOI 10.1016/j.conb.2008.07.006

  View details for Web of Science ID 000260279400017

  View details for PubMedID 18678252

• Diffusion tensor imaging of the superior longitudinal fasciculus and working memory in recent-onset schizophrenia BIOLOGICAL PSYCHIATRY Karlsgodt, K. H., van Erp, T. G., Poldrack, R. A., Bearden, C. E., Nuechterlein, K. H., Cannon, T. D. 2008; 63 (5): 512-518

  Abstract

  Structural and functional abnormalities in frontal-parietal circuitry are thought to be associated with working memory (WM) deficits in patients with schizophrenia. This study examines whether recent-onset schizophrenia is associated with anatomical changes in the superior longitudinal fasciculus (SLF), the main frontal-parietal white matter connection, and whether the integrity of the SLF is related to WM performance.We applied a novel registration approach (Tract-Based Spatial Statistics [TBSS]) to diffusion tensor imaging data to examine fractional anisotropy (FA) in the left and right SLF in 12 young adult patients with recent-onset schizophrenia and 17 matched control subjects.Schizophrenia patients showed lower FA values than control subjects across the entire SLF, with particular deficits on the left SLF. Fractional anisotropy values were correlated with performance on a verbal WM task in both patient and control groups in the left but not right SLF.Recent-onset schizophrenia patients show deficits in frontal-parietal connections, key components of WM circuitry. Moreover, the integrity of this physiological connection predicted performance on a verbal WM task, indicating that this structural change may have important functional implications. These findings support the view that schizophrenia is a disorder of brain connectivity and implicate white matter changes detectable in the early phases of the illness as one source of this dysfunction.

  View details for DOI 10.1016/j.biopsych.2007.06.017

  View details for Web of Science ID 000253256300011

  View details for PubMedID 17720147

• Automaticity in motor sequence learning does not impair response inhibition PSYCHONOMIC BULLETIN & REVIEW Cohen, J. R., Poldrack, R. A. 2008; 15 (1): 108-115

  Abstract

  We examined the relationship between automaticity and response inhibition in the serial reaction time (SRT) task to test the common assertion that automatic behavior is ballistic. Participants trained for 3 h on the SRT, using blocks of a second-order conditional sequence interleaved with random blocks. Automaticity was measured using a concurrent secondary letter-counting task. Response inhibition was measured using a stop-signal task. RTs decreased with training, with agreater decrease for sequenced versusrandom blocks. Training correlated with a decreased RT cost to performing the secondary task concurrently with the SRT, indicating the development of automaticity. Crucially, there was no change in the ability to inhibit responses at the end of training, even in individuals who showed no dual-task interference. These results demonstrate that the ability to inhibit a motor response does not decrease with automaticity, suggesting that some aspects of automatic behavior are not ballistic.

  View details for DOI 10.3758/PBR.15.1.108

  View details for Web of Science ID 000257217600016

  View details for PubMedID 18605489

• Automatic independent component labeling for artifact removal in fMRI NEUROIMAGE Tohka, J., Foerde, K., Aron, A. R., Tom, S. M., Toga, A. W., Poldrack, R. A. 2008; 39 (3): 1227-1245

  Abstract

  Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) are often small compared to the level of noise in the data. The sources of noise are numerous including different kinds of motion artifacts and physiological noise with complex patterns. This complicates the statistical analysis of the fMRI data. In this study, we propose an automatic method to reduce fMRI artifacts based on independent component analysis (ICA). We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise. After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related. The classifier was a novel global (fixed structure) decision tree trained in a Neyman-Pearson (NP) framework, which allowed the shape of the decision regions to be controlled effectively. Additionally, the conservativeness of the classifier could be tuned by modifying the NP threshold. The classifier was tested against the component classifications by an expert with the data from a category learning task. The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set. The misclassification rate was between 0.2 and 0.3 for both the event-related and blocked designs and it was consistent among variety of different NP thresholds. The effects of denoising on the group-level statistical analyses were as expected: The denoising generally decreased Z-scores in the white matter, where extreme Z-values can be expected to reflect artifacts. A similar but weaker decrease in Z-scores was observed in the gray matter on average. These two observations suggest that denoising was likely to reduce artifacts from gray matter and could be useful to improve the detection of activations. We conclude that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.

  View details for DOI 10.1016/j.neuroiniage.2007.10.013

  View details for Web of Science ID 000252691800029

  View details for PubMedID 18042495

• Construction of a 3D probabilistic atlas of human cortical structures NEUROIMAGE Shattuck, D. W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K. L., Poldrack, R. A., Bilder, R. M., Toga, A. W. 2008; 39 (3): 1064-1080

  Abstract

  We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998) paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al., 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines.

  View details for DOI 10.1016/j.neuroimage.2007.09.031

  View details for Web of Science ID 000252691800016

  View details for PubMedID 18037310

• Category learning and the memory systems debate NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS Poldrack, R. A., Foerde, K. 2008; 32 (2): 197-205

  Abstract

  A substantial and growing body of evidence from cognitive neuroscience supports the concept of multiple memory systems (MMS). However, the existence of multiple systems has been questioned by theorists who instead propose that dissociations can be accounted for within a single memory system. We present convergent evidence from neuroimaging and neuropsychological studies of category learning in favor of the existence of MMS for category learning and declarative knowledge. Whereas single-system theorists have argued that their approach is more parsimonious because it only postulates a single form of memory representation, we show that the MMS approach is superior in its ability to account for a broad range of data from psychology and neuroscience.

  View details for DOI 10.1016/j.neubiorev.2007.07.007

  View details for Web of Science ID 000253400500002

  View details for PubMedID 17869339

• Selective corticostriatal dysfunction in schizophrenia: Examination of motor and cognitive skill learning NEUROPSYCHOLOGY Foerde, K., Poldrack, R. A., Knowlton, B. J., Sabb, F. W., Bookheirner, S. Y., Bilder, R. M., Guthrie, D., Granholm, E., Nuechterlein, K. H., Marder, S. R., Asarnow, R. F. 2008; 22 (1): 100-109

  Abstract

  It has been suggested that patients with schizophrenia have corticostriatal circuit dysfunction (Carlsson & Carlsson, 1990). Skill learning is thought to rely on corticostriatal circuitry and different types of skill learning may be related to separable corticostriatal loops (Grafton, Hazeltine, & Ivry, 1995; Poldrack, Prabhakaran, Seger, & Gabrieli, 1999). The authors examined motor (Serial Reaction Time task, SRT) and cognitive (Probabilistic Classification task, PCT) skill learning in patients with schizophrenia and normal controls. Development of automaticity was examined, using a dual task paradigm, across three training sessions. Patients with schizophrenia were impaired at learning on the PCT compared to controls. Performance gains of controls occurred within the first session, whereas patients only improved gradually and never reached the performance level of controls. In contrast, patients were not impaired at learning on the SRT relative to controls, suggesting that patients with schizophrenia may have dysfunction in a specific corticostriatal subcircuit.

  View details for DOI 10.1037/0894-4105.22.1.100

  View details for Web of Science ID 000252555500011

  View details for PubMedID 18211159

• The neural substrates of visual perceptual learning of words: Implications for the visual word form area hypothesis JOURNAL OF COGNITIVE NEUROSCIENCE Xue, G., Poldrack, R. A. 2007; 19 (10): 1643-1655

  Abstract

  Abstract It remains under debate whether the fusiform visual word form area (VWFA) is specific to visual word form and whether visual expertise increases its sensitivity (Xue et al., 2006; Cohen et al., 2002). The present study examined three related issues: (1) whether the VWFA is also involved in processing foreign writing that significantly differs from the native one, (2) the effect of visual word form training on VWFA activation after controlling the task difficulty, and (3) the transfer of visual word form learning. Eleven native English speakers were trained, during five sessions, to judge whether two subsequently flashed (100-msec duration with 200-msec interval) foreign characters (i.e., Korean Hangul) were identical or not. Visual noise was added to the stimuli to manipulate task difficulty. In functional magnetic resonance imaging scans before and after training, subjects performed the task once with the same noise level (i.e., parameter-matched scan) and once with noise level changed to match performance from pretraining to posttraining (i.e., performance-matched scan). Results indicated that training increased the accuracy in parameter-matched condition but remained constant in performance-matched condition (because of increasing task difficulty). Pretraining scans revealed stronger activation for English words than for Korean characters in the left inferior temporal gyrus and the left inferior frontal cortex, but not in the VWFA. Visual word form training significantly decreased the activation in the bilateral middle and left posterior fusiform when either parameters or performance were matched and for both trained and new items. These results confirm our conjecture that the VWFA is not dedicated to words, and visual expertise acquired with training reduces rather than increases its activity.

  View details for Web of Science ID 000249763900007

  View details for PubMedID 18271738

• In praise of tedious anatomy NEUROIMAGE Devlin, J. T., Poldrack, R. A. 2007; 37 (4): 1033-1041

  Abstract

  Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localised to neuroanatomy and suggest that the gold standard should be localisation to the individual's or group's own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localisation may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localisation including a common "standard" space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann's areas.

  View details for DOI 10.1016/j.neuroimage.2006.09.055

  View details for Web of Science ID 000249773600001

  View details for PubMedID 17870621

• Modeling group fMRI data SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE Mumford, J. A., Poldrack, R. A. 2007; 2 (3): 251-257

  Abstract

  The analysis of group fMRI data requires a statistical model known as the mixed effects model. This article motivates the need for a mixed effects model and outlines the different stages of the mixed model used to analyze group fMRI data. Different modeling options and their impact on analysis results are also described.

  View details for DOI 10.1093/scan/nsm019

  View details for Web of Science ID 000253813600010

  View details for PubMedID 18985145

• Secondary-task effects on classification learning MEMORY & COGNITION Foerde, K., Poldrack, R. A., Knowlton, B. J. 2007; 35 (5): 864-874

  Abstract

  Probabilistic classification learning can be supported by implicit knowledge of cue-response associations. We investigated whether forming these associations depends on attention by assessing the effect of performing a secondary task on learning in the probabilistic classification task (PCT). Experiment I showed that concurrent task performance significantly interfered with performance of the PCT. Experiment 2 showed that this interference did not prevent learning from occurring. On the other hand, the secondary task did disrupt acquisition of explicit knowledge about cue-outcome associations. These results show that concurrent task performance can have different effects on implicit and explicit knowledge acquired within the same task and also underscore the importance of considering effects on learning and performance separately.

  View details for Web of Science ID 000249316100003

  View details for PubMedID 17910172

• Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional MRI JOURNAL OF NEUROSCIENCE Aron, A. R., Behrens, T. E., Smith, S., Frank, M. J., Poldrack, R. A. 2007; 27 (14): 3743-3752

  Abstract

  The ability to stop motor responses depends critically on the right inferior frontal cortex (IFC) and also engages a midbrain region consistent with the subthalamic nucleus (STN). Here we used diffusion-weighted imaging (DWI) tractography to show that the IFC and the STN region are connected via a white matter tract, which could underlie a "hyperdirect" pathway for basal ganglia control. Using a novel method of "triangulation" analysis of tractography data, we also found that both the IFC and the STN region are connected with the presupplementary motor area (preSMA). We hypothesized that the preSMA could play a conflict detection/resolution role within a network between the preSMA, the IFC, and the STN region. A second experiment tested this idea with functional magnetic resonance imaging (fMRI) using a conditional stop-signal paradigm, enabling examination of behavioral and neural signatures of conflict-induced slowing. The preSMA, IFC, and STN region were significantly activated the greater the conflict-induced slowing. Activation corresponded strongly with spatial foci predicted by the DWI tract analysis, as well as with foci activated by complete response inhibition. The results illustrate how tractography can reveal connections that are verifiable with fMRI. The results also demonstrate a three-way functional-anatomical network in the right hemisphere that could either brake or completely stop responses.

  View details for DOI 10.1523/JNEUROSCI.0519-07.2007

  View details for Web of Science ID 000245468300015

  View details for PubMedID 17409238

• Region of interest analysis for fMRI SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE Poldrack, R. A. 2007; 2 (1): 67-70

  Abstract

  A common approach to the analysis of fMRI data involves the extraction of signal from specified regions of interest (or ROI's). Three approaches to ROI analysis are described, and the strengths and assumptions of each method are outlined.

  View details for DOI 10.1093/scan/nsm006

  View details for Web of Science ID 000253813300010

  View details for PubMedID 18985121

• Elaborative verbal encoding and altered anterior parahippocampal activation in adolescents and young adults at genetic risk for schizophrenia using fMRI BIOLOGICAL PSYCHIATRY Thermenos, H. W., Seidman, L. J., Poldrack, R. A., Peace, N. K., Koch, J. K., Faraone, S. V., Tsuang, M. T. 2007; 61 (4): 564-574

  Abstract

  First-degree relatives of persons with schizophrenia are at elevated risk for the illness, demonstrate deficits in verbal memory, and exhibit structural abnormalities in the medial temporal lobe (MTL). We used functional magnetic resonance imaging (fMRI) to assess brain activity in the MTL during novel and repeated word-pair encoding.Participants were 21 non-psychotic, first-degree relatives of persons with schizophrenia and 26 matched healthy controls (ages 13-28). fMRI signal change was measured using a Siemens 1.5T MR scanner, and data were analyzed using SPM-2. Verbal memory was assessed using the Miller Selfridge (MS) Context Memory test prior to scanning.The groups were comparable on demographics, intelligence and post-scan word recognition. Relatives at genetic risk (GR) had significantly more psychopathology than controls and worse performance on the MS test (p < .05). GR participants exhibited greater repetition suppression of activation in the left and right anterior parahippocampus (PHA, in the region of the entorhinal cortex region), after controlling for possible confounders. Controls and GR participants with above-median MS performance showed significantly greater repetition suppression of activation in left inferior frontal gyrus than those scoring below the median.This is the first study to demonstrate an alteration of brain activity in the PHA in persons at GR for schizophrenia.

  View details for DOI 10.1016/j.biopsych.2006.04.044

  View details for Web of Science ID 000244344400019

  View details for PubMedID 17276751

• The neural basis of loss aversion in decision-making under risk SCIENCE Tom, S. M., Fox, C. R., Trepel, C., Poldrack, R. A. 2007; 315 (5811): 515-518

  Abstract

  People typically exhibit greater sensitivity to losses than to equivalent gains when making decisions. We investigated neural correlates of loss aversion while individuals decided whether to accept or reject gambles that offered a 50/50 chance of gaining or losing money. A broad set of areas (including midbrain dopaminergic regions and their targets) showed increasing activity as potential gains increased. Potential losses were represented by decreasing activity in several of these same gain-sensitive areas. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions, including the ventral striatum and prefrontal cortex.

  View details for DOI 10.1126/science.1134239

  View details for Web of Science ID 000243726600047

  View details for PubMedID 17255512

• RETRACTED: Elaborative Verbal Encoding and Altered Anterior Parahippocampal Activation in Adolescents and Young Adults at Genetic Risk for Schizophrenia Using fMRI. Biological psychiatry Thermenos, H. W., Seidman, L. J., Poldrack, R. A., Peace, N. K., Koch, J. K., Faraone, S. V., Tsuang, M. T. 2006: -?

  Abstract

  This article has been retracted, consistent with Elsevier Policy on Article Withdrawal. Please see . The Publisher apologises for any inconvenience this may cause.

  View details for PubMedID 16950224

• Modulation of competing memory systems by distraction PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Foerde, K., Knowlton, B. J., Poldrack, R. A. 2006; 103 (31): 11778-11783

  Abstract

  Different forms of learning and memory depend on functionally and anatomically separable neural circuits [Squire, L. R. (1992) Psychol. Rev. 99, 195-231]. Declarative memory relies on a medial temporal lobe system, whereas habit learning relies on the striatum [Cohen, N. J. & Eichenbaum, H. (1993) Memory, Amnesia, and the Hippocampal System (MIT Press, Cambridge, MA)]. How these systems are engaged to optimize learning and behavior is not clear. Here, we present results from functional neuroimaging showing that the presence of a demanding secondary task during learning modulates the degree to which subjects solve a problem using either declarative memory or habit learning. Dual-task conditions did not reduce accuracy but reduced the amount of declarative learning about the task. Medial temporal lobe activity was correlated with task performance and declarative knowledge after learning under single-task conditions, whereas performance was correlated with striatal activity after dual-task learning conditions. These results demonstrate a fundamental difference in these memory systems in their sensitivity to concurrent distraction. The results are consistent with the notion that declarative and habit learning compete to mediate task performance, and they suggest that the presence of distraction can bias this competition. These results have implications for learning in multitask situations, suggesting that, even if distraction does not decrease the overall level of learning, it can result in the acquisition of knowledge that can be applied less flexibly in new situations.

  View details for DOI 10.1073/pnas.0602659103

  View details for Web of Science ID 000239616400063

  View details for PubMedID 16868087

• Altered brain activation in dorsolateral prefrontal cortex in adolescents and young adults at genetic risk for schizophrenia: An fMRI study of working memory SCHIZOPHRENIA RESEARCH Seidman, L. J., Thermenos, H. W., Poldrack, R. A., Peace, N. K., Koch, J. K., Faraone, S. V., Tsuang, M. T. 2006; 85 (1-3): 58-72

  Abstract

  Adult first-degree relatives of persons with schizophrenia carry elevated genetic risk for the illness, demonstrate working memory (WM) impairments, and manifest alterations in dorsolateral prefrontal cortical (DLPFC) function during WM. Because substantially less is known about these phenotypes in adolescent subjects we sought to demonstrate that young relatives of persons with schizophrenia manifest impaired WM and altered prefrontal activation.Participants were 21 non-psychotic, unmedicated first-degree relatives of persons with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder, depressed type and 24 unmedicated controls, recruited from the community and hospitals in metropolitan Boston (ages 13-28). We compared groups on an auditory WM task with interference prior to scanning and used functional magnetic resonance imaging (fMRI) to compare groups while performing visual 2-back WM and control vigilance tasks. Blood oxygen level dependent signal change was measured using two whole-brain gradient echo EPI pulse acquisitions (21 contiguous, 5mm axial slices), acquired on a Siemens 1.5T MR scanner. Data were analyzed using Statistical Parametric Mapping-99.The high risk subjects were significantly impaired on the auditory WM task, had significantly greater Phobic Anxiety, and marginally greater Psychoticism than controls on the Symptom Checklist-90-Revised, and showed significantly greater task-elicited activation in the right DLPFC (BA 46). Psychopathology, IQ, and in-scanner WM performance did not account for group differences in brain activation.Data support a physiological difference (an exaggerated fMRI response) in DLPFC in adolescents at genetic risk for schizophrenia, independent of psychosis. Future work can study the relationship of these measures to possible onset of schizophrenia.

  View details for DOI 10.1016/j.schres.2006.03.019

  View details for Web of Science ID 000239131500007

  View details for PubMedID 16632333

• Ventral-striatal/nucleus-accumbens sensitivity to prediction errors during classification learning HUMAN BRAIN MAPPING Rodriguez, P. F., Aron, A. R., Poldrack, R. A. 2006; 27 (4): 306-313

  Abstract

  A prominent theory in neuroscience suggests reward learning is driven by the discrepancy between a subject's expectation of an outcome and the actual outcome itself. Furthermore, it is postulated that midbrain dopamine neurons relay this mismatch to target regions including the ventral striatum. Using functional MRI (fMRI), we tested striatal responses to prediction errors for probabilistic classification learning with purely cognitive feedback. We used a version of the Rescorla-Wagner model to generate prediction errors for each subject and then entered these in a parametric analysis of fMRI activity. Activation in ventral striatum/nucleus-accumbens (Nacc) increased parametrically with prediction error for negative feedback. This result extends recent neuroimaging findings in reward learning by showing that learning with cognitive feedback also depends on the same circuitry and dopaminergic signaling mechanisms.

  View details for DOI 10.1002/hbm.20186

  View details for Web of Science ID 000236093700004

  View details for PubMedID 16092133

• Cortical and subcortical contributions to stop signal response inhibition: Role of the subthalamic nucleus JOURNAL OF NEUROSCIENCE Aron, A. R., Poldrack, R. A. 2006; 26 (9): 2424-2433

  Abstract

  Suppressing an already initiated manual response depends critically on the right inferior frontal cortex (IFC), yet it is unclear how this inhibitory function is implemented in the motor system. It has been suggested that the subthalamic nucleus (STN), which is a part of the basal ganglia, may play a role because it is well placed to suppress the "direct" fronto-striatal pathway that is activated by response initiation. In two experiments, we investigated this hypothesis with functional magnetic resonance imaging and a Stop-signal task. Subjects responded to Go signals and attempted to inhibit the initiated response to occasional Stop signals. In experiment 1, Going significantly activated frontal, striatal, pallidal, and motor cortical regions, consistent with the direct pathway, whereas Stopping significantly activated right IFC and STN. In addition, Stopping-related activation was significantly greater for fast inhibitors than slow ones in both IFC and STN, and activity in these regions was correlated across subjects. In experiment 2, high-resolution functional and structural imaging confirmed the location of Stopping activation within the vicinity of the STN. We propose that the role of the STN is to suppress thalamocortical output, thereby blocking Go response execution. These results provide convergent data for a role for the STN in Stop-signal response inhibition. They also suggest that the speed of Go and Stop processes could relate to the relative activation of different neural pathways. Future research is required to establish whether Stop-signal inhibition could be implemented via a direct functional neuroanatomic projection between IFC and STN (a "hyperdirect" pathway).

  View details for DOI 10.1523/JNEUROSCI.4682-05.2006

  View details for Web of Science ID 000235720400007

  View details for PubMedID 16510720

• Long-term test-retest reliability of functional MRI in a classification learning task NEUROIMAGE Aron, A. R., Gluck, M. A., Poldrack, R. A. 2006; 29 (3): 1000-1006

  Abstract

  Functional MRI is widely used for imaging the neural correlates of psychological processes and how these brain processes change with learning, development and neuropsychiatric disorder. In order to interpret changes in imaging signals over time, for example, in patient studies, the long-term reliability of fMRI must first be established. Here, eight healthy adult subjects were scanned on two sessions, 1 year apart, while performing a classification learning task known to activate frontostriatal circuitry. We show that behavioral performance and frontostriatal activation were highly concordant at a group level at both time-points. Furthermore, intra-class correlation coefficients (ICCs), which index the degree of correlation between subjects at different time-points, were high for behavior and for functional activation. ICC was significantly higher within the network recruited by learning than outside that network. We conclude that fMRI can have high long-term test-retest reliability, making it suitable as a biomarker for brain development and neurodegeneration.

  View details for DOI 10.1016/j.neuroimage.2005.08.010

  View details for Web of Science ID 000235227400032

  View details for PubMedID 16139527

• Can cognitive processes be inferred from neuroimaging data? TRENDS IN COGNITIVE SCIENCES Poldrack, R. A. 2006; 10 (2): 59-63

  Abstract

  There is much interest currently in using functional neuroimaging techniques to understand better the nature of cognition. One particular practice that has become common is 'reverse inference', by which the engagement of a particular cognitive process is inferred from the activation of a particular brain region. Such inferences are not deductively valid, but can still provide some information. Using a Bayesian analysis of the BrainMap neuroimaging database, I characterize the amount of additional evidence in favor of the engagement of a cognitive process that can be offered by a reverse inference. Its usefulness is particularly limited by the selectivity of activation in the region of interest. I argue that cognitive neuroscientists should be circumspect in the use of reverse inference, particularly when selectivity of the region in question cannot be established or is known to be weak.

  View details for DOI 10.1016/j.tics.2005.12.004

  View details for Web of Science ID 000235728700007

  View details for PubMedID 16406760

• Hormonal cycle modulates arousal circuitry in women using functional magnetic resonance imaging JOURNAL OF NEUROSCIENCE Goldstein, J. M., Jerram, M., Poldrack, R., Ahern, T., Kennedy, D. N., Seidman, L. J., Makris, N. 2005; 25 (40): 9309-9316

  Abstract

  Sex-specific behaviors are in part based on hormonal regulation of brain physiology. This functional magnetic resonance imaging (fMRI) study demonstrated significant differences in activation of hypothalamic-pituitary-adrenal (HPA) circuitry in adult women with attenuation during ovulation and increased activation during early follicular phase. Twelve normal premenopausal women were scanned twice during the early follicular menstrual cycle phase compared with late follicular/midcycle, using negative valence/high arousal versus neutral visual stimuli, validated by concomitant electrodermal activity (EDA). Significantly greater magnitude of blood oxygenation level-dependent signal changes were found during early follicular compared with midcycle timing in central amygdala, paraventricular and ventromedial hypothalamic nuclei, hippocampus, orbitofrontal cortex (OFC), anterior cingulate gyrus (aCING), and peripeduncular nucleus of the brainstem, a network of regions implicated in the stress response. Arousal (EDA) correlated positively with brain activity in amygdala, OFC, and aCING during midcycle but not in early follicular, suggesting less cortical control of amygdala during early follicular, when arousal was increased. This is the first evidence suggesting that estrogen may likely attenuate arousal in women via cortical-subcortical control within HPA circuitry. Findings have important implications for normal sex-specific physiological functioning and may contribute to understanding higher rates of mood and anxiety disorders in women and differential sensitivity to trauma than men.

  View details for DOI 10.1523/JNEUROSCI.2239-05.2005

  View details for Web of Science ID 000232355200027

  View details for PubMedID 16207891

• Imaging phonology without print: Assessing the neural correlates of phonemic awareness using fMRI NEUROIMAGE Katzir, T., Misra, M., Poldrack, R. A. 2005; 27 (1): 106-115

  Abstract

  Acquisition of phonological processing skills, such as the ability to segment words into corresponding speech sounds, is critical to the development of efficient reading. Prior neuroimaging studies of phonological processing have often relied on auditory stimuli or print-mediated tasks that may be problematic for various theoretical and empirical reasons. For the current study, we developed a task to evaluate phonological processing that used visual stimuli but did not require interpretation of orthographic forms. This task requires the subject to retrieve the names of objects and to compare their first sounds; then, the subject must indicate if the initial sounds of the names of the pictures are the same. The phonological analysis task was compared to both a baseline matching task and a more complex control condition in which the participants evaluated two different pictures and indicated whether they represented the same object. The complex picture-matching condition controls for the visual complexity of the stimuli but does not require phonological analysis of the names of the objects. While both frontal and ventral posterior areas were activated in response to phonological analysis of the names of pictures, only inferior and superior frontal gyrus exhibited differential sensitivity to the phonological comparison task as compared to the complex picture-matching control task. These findings suggest that phonological processing that is not mediated by print relies primarily on frontal language processing areas among skilled readers.

  View details for DOI 10.1016/j.neuroimage.2005.04.013

  View details for Web of Science ID 000230701200010

  View details for PubMedID 15901490

• Sex differences in prefrontal cortical brain activity during fMRI of auditory verbal working memory NEUROPSYCHOLOGY Goldstein, J. M., Jerram, M., Poldrack, R., Anagnoson, R., Breiter, H. C., Makris, N., Goodman, J. M., Tsuang, M. T., Seidman, L. J. 2005; 19 (4): 509-519

  Abstract

  Functional imaging studies of sex effects in working memory (WMEM) are few, despite significant normal sex differences in brain regions implicated in WMEM. This functional MRI (fMRI) study tested for sex effects in an auditory verbal WMEM task in prefrontal, parietal, cingulate, and insula regions. Fourteen healthy, right-handed community subjects were comparable between the sexes, including on WMEM performance. Per statistical parametric mapping, women exhibited greater signal intensity changes in middle, inferior, and orbital prefrontal cortices than men (corrected for multiple comparisons). A test of mixed-sex groups, comparable on performance, showed no significant differences in the hypothesized regions, providing evidence for discriminant validity for significant sex differences. The findings suggest that combining men and women in fMRI studies of cognition may obscure or bias results.

  View details for DOI 10.1037/0894-4105.19.4.509

  View details for Web of Science ID 000230902800012

  View details for PubMedID 16060826

• The cognitive neuroscience of response inhibition: Relevance for genetic research in attention-deficit/hyperactivity disorder Conference on Advancing the Neuroscience of Attention-Deficit/Hyperactivity Disorder (ADHD) Aron, A. R., Poldrack, R. A. ELSEVIER SCIENCE INC. 2005: 1285–92

  Abstract

  Psychological functions that are behaviorally and neurally well specified may serve as endophenotypes for attention-deficit/hyperactivity disorder (ADHD) research. Such endophenotypes, which lie between genes and symptoms, may relate more directly to relevant genetic variability than does the clinical ADHD syndrome itself. Here we review evidence in favor of response inhibition as an endophenotype for ADHD research. We show that response inhibition--operationalized by Go/NoGo or Stop-signal tasks--requires the prefrontal cortex (PFC), in particular the right inferior frontal cortex (IFC); that patients with ADHD have significant response inhibition deficits and show altered functional activation and gray matter volumes in right IFC; and that a number of studies indicate that response inhibition performance is heritable. Additionally, we review evidence concerning the role of the basal ganglia in response inhibition, as well as the role of neuromodulatory systems. All things considered, a combined right IFC structure/function/response inhibition phenotype is a particularly good candidate for future heritability and association studies. Moreover, a dissection of response inhibition into more basic components such as rule maintenance, vigilance, and target detection may provide yet better targets for association with genes for neuromodulation and brain development.

  View details for DOI 10.1016/j.biopsych.2004.10.026

  View details for Web of Science ID 000229570500011

  View details for PubMedID 15950000

• The neural correlates of motor skill automaticity JOURNAL OF NEUROSCIENCE Poldrack, R. A., Sabb, F. W., Foerde, K., Tom, S. M., Asarnow, R. F., Bookheimer, S. Y., Knowlton, B. J. 2005; 25 (22): 5356-5364

  Abstract

  Acquisition of a new skill is generally associated with a decrease in the need for effortful control over performance, leading to the development of automaticity. Automaticity by definition has been achieved when performance of a primary task is minimally affected by other ongoing tasks. The neural basis of automaticity was examined by testing subjects in a serial reaction time (SRT) task under both single-task and dual-task conditions. The diminishing cost of dual-task performance was used as an index for automaticity. Subjects performed the SRT task during two functional magnetic imaging sessions separated by 3 h of behavioral training over multiple days. Behavioral data showed that, by the end of testing, subjects had automated performance of the SRT task. Before behavioral training, performance of the SRT task concurrently with the secondary task elicited activation in a wide network of frontal and striatal regions, as well as parietal lobe. After extensive behavioral training, dual-task performance showed comparatively less activity in bilateral ventral premotor regions, right middle frontal gyrus, and right caudate body; activity in other prefrontal and striatal regions decreased equally for single-task and dual-task conditions. These data suggest that lateral and dorsolateral prefrontal regions, and their corresponding striatal targets, subserve the executive processes involved in novice dual-task performance. The results also showed that supplementary motor area and putamen/globus pallidus regions showed training-related decreases for sequence conditions but not for random conditions, confirming the role of these regions in the representation of learned motor sequences.

  View details for DOI 10.1523/JNEUROSCI.3880-04.2005

  View details for Web of Science ID 000229495100013

  View details for PubMedID 15930384

• The effect of working memory performance on functional MRI in schizophrenia SCHIZOPHRENIA RESEARCH Thermenos, H. W., Goldstein, J. M., Buka, S. L., Poldrack, R. A., Koch, J. K., Tsuang, M. T., Seidman, L. J. 2005; 74 (2-3): 179-194

  Abstract

  Studies of prefrontal cortical (PFC) function in schizophrenia have been inconsistent, with studies showing both increased and decreased PFC activation compared to healthy controls. Discrepant findings may be due to task performance effects or demographic differences between samples. We report functional magnetic resonance imaging (fMRI) data comparing subjects with schizophrenia and healthy controls performing a 2-back working memory (WM) task, addressing the effects of task performance.Twenty-two controls and 14 patients with DSM-IV schizophrenia, scanned on a Siemens 1.5 T scanner, performed a visual letter 2-back task and control task (CPT-X) during fMRI. Data were analyzed using Statistical Parametric Mapping (SPM)-99.After statistical adjustment for performance differences, persons with schizophrenia showed significantly greater activation than controls in the right medial frontal gyrus and left inferior parietal lobule/medial temporal gyrus region (BA 39/40), and a trend toward greater activation in the left ventrolateral PFC. This pattern was also observed in demographically matched subgroups of participants.Data are consistent with findings reported in recent studies showing increased PFC and parietal activation in schizophrenia when the effects of reduced WM task performance in patients with schizophrenia are addressed. Further studies are needed to clarify the pathophysiological basis of WM load sensitivity in schizophrenia and its relationship to genes.

  View details for DOI 10.1016/j.schres.2004.07.021

  View details for Web of Science ID 000227481800006

  View details for PubMedID 15721998

• Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk COGNITIVE BRAIN RESEARCH Trepel, C., Fox, C. R., Poldrack, R. A. 2005; 23 (1): 34-50

  Abstract

  Most decisions must be made without advance knowledge of their consequences. Economists and psychologists have devoted much attention to modeling decisions made under conditions of risk in which options can be characterized by a known probability distribution over possible outcomes. The descriptive shortcomings of classical economic models motivated the development of prospect theory (D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk. Econometrica, 4 (1979) 263-291; A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5 (4) (1992) 297-323) the most successful behavioral model of decision under risk. In the prospect theory, subjective value is modeled by a value function that is concave for gains, convex for losses, and steeper for losses than for gains; the impact of probabilities are characterized by a weighting function that overweights low probabilities and underweights moderate to high probabilities. We outline the possible neural bases of the components of prospect theory, surveying evidence from human imaging, lesion, and neuropharmacology studies as well as animal neurophysiology studies. These results provide preliminary suggestions concerning the neural bases of prospect theory that include a broad set of brain regions and neuromodulatory systems. These data suggest that focused studies of decision making in the context of quantitative models may provide substantial leverage towards a fuller understanding of the cognitive neuroscience of decision making.

  View details for DOI 10.1016/j.cogbrainres.2005.01.016

  View details for Web of Science ID 000228655800005

  View details for PubMedID 15795132

• Functional neuroanatomy of working memory in adults with attention-deficit/hyperactivity disorder BIOLOGICAL PSYCHIATRY Valera, E. M., Faraone, S. V., Biederman, J., Poldrack, R. A., Seidman, L. J. 2005; 57 (5): 439-447

  Abstract

  Attention-deficit/hyperactivity disorder (ADHD) in adults is an increasingly recognized psychiatric disorder, linked with impairments in numerous life domains and with neurocognitive dysfunctions. However, the neural substrate of cognitive functioning in adults with this disorder has been relatively unexamined. The objective of this study was to examine neural functioning in ADHD adults during performance on a verbal working memory task.A sample of unmedicated adults with ADHD (n = 20) and control subjects (n = 20) performed a 2-back task of working memory, and the blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) response was used as a measure of neural activity during working memory performance.Though working memory performance did not differ significantly between ADHD adults and control subjects, ADHD adults showed significantly decreased activity in cerebellar and occipital regions and a trend toward decreased activation in an a priori predicted region of the prefrontal cortex.ADHD adults showed altered patterns of neural activity despite comparable performance on a verbal working memory task. These findings suggest that the cerebellum is involved in the pathophysiology of at least some cognitive deficits associated with ADHD and emphasize the need for additional research aimed at elucidating the role of the cerebellum in ADHD symptomatology.

  View details for DOI 10.1016/j.biopsych.2004.11.034

  View details for Web of Science ID 000227415400001

  View details for PubMedID 15737657

• Medial temporal and prefrontal lobe activation during verbal encoding following glucose ingestion in schizophrenia: A pilot fMRI study NEUROBIOLOGY OF LEARNING AND MEMORY Stone, W. S., Thermenos, H. W., Tarbox, S. I., Poldrack, R. A., Seidman, L. J. 2005; 83 (1): 54-64

  Abstract

  Verbal declarative memory is one of the most reliably impaired cognitive functions in schizophrenia. Important issues are whether the problem is reversible, and which brain regions underlie improvement. We showed previously that glucose administration improved declarative memory in patients with schizophrenia, and sought in this pilot study to identify whether glucose affects the location or degree of activation of brain regions involved in a verbal encoding task. Seven clinically stable and medicated patients with schizophrenia or schizoaffective disorder, who showed deficits on a clinical test of memory, participated in the study. Subjects served as their own controls in a double-blind, crossover protocol that consisted of two sessions about a week apart. In each session, subjects ingested a beverage flavored with lemonade that contained 50 g of glucose on one occasion, and saccharin on the other. Blood glucose was measured before and 15, 50, and 75 min after ingestion. After ingesting the beverage, they performed a verbal encoding task while undergoing brain functional magnetic resonance imaging. The results showed significantly greater activation of the left parahippocampus during novel sentence encoding in the glucose condition, compared to the saccharin condition, despite no change in memory performance. A trend towards greater activation of the left dorsolateral prefrontal cortex (p<.07) was also evident in the glucose condition. These pilot findings emphasize the sensitivity of both the medial temporal and prefrontal regions to effects of glucose administration during encoding, and are consistent with the hypothesis that these regions also participate in declarative memory improvements following glucose administration.

  View details for DOI 10.1016/j.nlm.2004.07.009

  View details for Web of Science ID 000226327300007

  View details for PubMedID 15607689

• How do memory systems interact? Evidence from human classification learning NEUROBIOLOGY OF LEARNING AND MEMORY Poldrack, R. A., Rodriguez, P. 2004; 82 (3): 324-332

  Abstract

  Studies of human classification learning using functional neuroimaging have suggested that basal ganglia and medial temporal lobe memory systems may interact during learning. We review these results and outline a set of possible mechanisms for such interactions. Effective connectivity analyses suggest that interaction between basal ganglia and medial temporal lobe are mediated by prefrontal cortex rather than by direct connectivity between regions. A review of possible neurobiological mechanisms suggests that interactions may be driven by neuromodulatory systems in addition to mediation by interaction of inputs to prefrontal cortical neurons. These results suggest that memory system interactions may reflect multiple mechanisms that combine to optimize behavior based on experience.

  View details for DOI 10.1016/j.nlm.2004.05.003

  View details for Web of Science ID 000224596300013

  View details for PubMedID 15464413

• Human midbrain sensitivity to cognitive feedback and uncertainty during classification learning JOURNAL OF NEUROPHYSIOLOGY Aron, A. R., Shohamy, D., Clark, J., Myers, C., Gluck, M. A., Poldrack, R. A. 2004; 92 (2): 1144-1152

  Abstract

  Mesencephalic dopaminergic system (MDS) neurons may participate in learning by providing a prediction error signal to their targets, which include ventral striatal, orbital, and medial frontal regions, as well as by showing sensitivity to the degree of uncertainty associated with individual stimuli. We investigated the mechanisms of probabilistic classification learning in humans using functional magnetic resonance imaging to examine the effects of feedback and uncertainty. The design was optimized for separating neural responses to stimulus, delay, and negative and positive feedback components. Compared with fixation, stimulus and feedback activated brain regions consistent with the MDS, whereas the delay period did not. Midbrain activity was significantly different for negative versus positive feedback (consistent with coding of the "prediction error") and was reliably correlated with the degree of uncertainty as well as with activity in MDS target regions. Purely cognitive feedback apparently engages the same regions as rewarding stimuli, consistent with a broader characterization of this network.

  View details for DOI 10.1152/jn.01209.2003

  View details for Web of Science ID 000222908200042

  View details for PubMedID 15014103

• Cortico-striatal contributions to feedback-based learning: converging data from neuroimaging and neuropsychology BRAIN Shohamy, D., Myers, C. E., Grossman, S., Sage, J., Gluck, M. A., Poldrack, R. A. 2004; 127: 851-859

  Abstract

  The striatum has been widely implicated in cognition, but a precise understanding of its role remains elusive. Here we present converging evidence for the role of the striatum in feedback-based learning. In a prior functional imaging study, healthy controls showed striatal activity during a feedback-based learning task, which was decreased when the same task was learned without feedback. In the present study, we show that individuals with striatal dysfunction due to Parkinson's disease are impaired on the feedback-based task, but not on a non-feedback version of the same task. Parkinson's patients and controls also used different learning strategies depending on feedback structure. This study provides direct behavioural evidence from humans that cortico-striatal systems are necessary for feedback-based learning on a cognitive task. These findings also link between learning impairments in Parkinson's disease and the physiological and computational evidence for the role of midbrain dopaminergic systems in feedback processing.

  View details for DOI 10.1093/brain/awh100

  View details for Web of Science ID 000220485500019

  View details for PubMedID 15013954

• Inhibition and the right inferior frontal cortex TRENDS IN COGNITIVE SCIENCES Aron, A. R., Robbins, T. W., Poldrack, R. A. 2004; 8 (4): 170-177

  Abstract

  It is controversial whether different cognitive functions can be mapped to discrete regions of the prefrontal cortex (PFC). The localisationist tradition has associated one cognitive function - inhibition - by turns with dorsolateral prefrontal cortex (DLPFC), inferior frontal cortex (IFC), or orbital frontal cortex (OFC). Inhibition is postulated to be a mechanism by which PFC exerts its effects on subcortical and posterior-cortical regions to implement executive control. We review evidence concerning inhibition of responses and task-sets. Whereas neuroimaging implicates diverse PFC foci, advances in human lesion-mapping support the functional localization of such inhibition to right IFC alone. Future research should investigate the generality of this proposed inhibitory function to other task domains, and its interaction within a wider network.

  View details for DOI 10.1016/j.tics.2004.02.010

  View details for Web of Science ID 000220895700008

  View details for PubMedID 15050513

• Functional magnetic resonance imaging during auditory verbal working memory in nonpsychotic relatives of persons with schizophrenia: A pilot study BIOLOGICAL PSYCHIATRY Thermenos, H. W., Seidman, L. J., Breiter, H., Goldstein, J. M., Goodman, J. M., Poldrack, R., Faraone, S. V., Tsuang, M. T. 2004; 55 (5): 490-500

  Abstract

  First-degree relatives of persons with schizophrenia carry elevated genetic risk for the illness and show deficits on high-load information processing tasks. We used functional magnetic resonance imaging (fMRI) to test whether nonpsychotic relatives show altered functional activation in the prefrontal cortex (PFC), thalamus, hippocampus, and anterior cingulate during a working memory task requiring interference resolution.Twelve nonpsychotic relatives of persons with schizophrenia and 12 healthy control subjects were administered an auditory, verbal working memory version of the Continuous Performance Test during fMRI. An asymmetric, spin-echo, T2*-weighted sequence (15 contiguous, 7-mm axial slices) was acquired on a full-body MR scanner. Data were analyzed by Statistical Parametric Mapping (SPM).Compared with control subjects, relatives showed greater task-elicited activation in the PFC and the anterior and dorsomedial thalamus. When task performance was controlled, relatives showed significantly greater activation in the anterior cingulate. When effects of other potentially confounding variables were controlled, relatives generally showed significantly greater activation in the dorsomedial thalamus and anterior cingulate.This pilot study suggests that relatives of persons with schizophrenia have subtle differences in brain function in the absence of psychosis. These differences add to the growing literature identifying neurobiological vulnerabilities to schizophrenia.

  View details for DOI 10.1016/j.biopsych.2003.11.014

  View details for Web of Science ID 000189290000009

  View details for PubMedID 15023577

• Neural systems for rapid automatized naming in skilled readers: Unraveling the RAN-reading relationship SCIENTIFIC STUDIES OF READING Misra, M., Katzir, T., Wolf, M., Poldrack, R. A. 2004; 8 (3): 241-256

  View details for Web of Science ID 000222377600004

• Pediatric functional magnetic resonance imaging: progress and challenges. Topics in magnetic resonance imaging Poldrack, R. A., Paré-Blagoev, E. J., Grant, P. E. 2002; 13 (1): 61-70

  Abstract

  Functional magnetic resonance imaging (fMRI) in the pediatric population promises to provide novel insights into the nature of both normal and abnormal functional brain development as well as changes in brain function due to various interventions. Although acquisition of fMRI data from children is associated with a number of methodological challenges, primarily compliance and head motion, good quality data can be obtained. For example, conditioning and personal interactions can improve compliance, and motion reduction techniques can successfully reduce artifacts due to head motion. Analysis of pediatric fMRI data also involves challenges regarding spatial normalization and characterization of the hemodynamic response across development. Substantial progress has been made in understanding cognitive function and developmental disorders in children, but attention to the methodological issues raised in this review and continued investigations in this area are expected to result in further progress.

  View details for PubMedID 11847501

• Interactive memory systems in the human brain NATURE Poldrack, R. A., Clark, J., Pare-Blagoev, E. J., Shohamy, D., Moyano, J. C., Myers, C., Gluck, M. A. 2001; 414 (6863): 546-550

  Abstract

  Learning and memory in humans rely upon several memory systems, which appear to have dissociable brain substrates. A fundamental question concerns whether, and how, these memory systems interact. Here we show using functional magnetic resonance imaging (FMRI) that these memory systems may compete with each other during classification learning in humans. The medial temporal lobe and basal ganglia were differently engaged across subjects during classification learning depending upon whether the task emphasized declarative or nondeclarative memory, even when the to-be-learned material and the level of performance did not differ. Consistent with competition between memory systems suggested by animal studies and neuroimaging, activity in these regions was negatively correlated across individuals. Further examination of classification learning using event-related FMRI showed rapid modulation of activity in these regions at the beginning of learning, suggesting that subjects relied upon the medial temporal lobe early in learning. However, this dependence rapidly declined with training, as predicted by previous computational models of associative learning.

  View details for Web of Science ID 000172405900048

  View details for PubMedID 11734855

• Recovering meaning: Left prefrontal cortex guides controlled semantic retrieval NEURON Wagner, A. D., Pare-Blagoev, E. J., Clark, J., Poldrack, R. A. 2001; 31 (2): 329-338

  Abstract

  Prefrontal cortex plays a central role in mnemonic control, with left inferior prefrontal cortex (LIPC) mediating control of semantic knowledge. One prominent theory posits that LIPC does not mediate semantic retrieval per se, but rather subserves the selection of task-relevant knowledge from amidst competing knowledge. The present event-related fMRI study provides evidence for an alternative hypothesis: LIPC guides controlled semantic retrieval irrespective of whether retrieval requires selection against competing representations. With selection demands held constant, LIPC activation increased with semantic retrieval demands and with the level of control required during retrieval. LIPC mediates a top-down bias signal that is recruited to the extent that the recovery of meaning demands controlled retrieval. Selection may reflect a specific instantiation of this mechanism.

  View details for Web of Science ID 000170277700016

  View details for PubMedID 11502262

• Priming of new associations in reading time: What is learned? PSYCHONOMIC BULLETIN & REVIEW Poldrack, R. A., COHEN, N. J. 1997; 4 (3): 398-402

  View details for Web of Science ID A1997XY31800013

• Fluency and response speed in recognition judgments MEMORY & COGNITION Poldrack, R. A., Logan, G. D. 1997; 25 (1): 1-10

  Abstract

  Previous research has suggested that perceptual fluency can contribute to recognition judgments. In this study, we examined whether fluency in recognition is based upon the speed of preceding operations, as suggested by studies of perceptual fluency. Subjects studied items in both lexical decision and naming tasks, and were then tested on two blocks of lexical decision trials with probe recognition trials. Jacoby's process dissociation procedure was used, and results from this procedure suggested that recognition judgments in the task were based largely upon familiarity. However, the estimated discriminability available from response time distributions was significantly less than the observed recognition discriminability. Simulated memory operating characteristics confirmed this under determination of recognition by response times. The results demonstrate, contrary to previous suggestions, that fluency in recognition is not based upon speed.

  View details for Web of Science ID A1997WG18600001

  View details for PubMedID 9046865

• Memory for items and memory for relations in the procedural/declarative memory framework MEMORY COHEN, N. J., Poldrack, R. A., EICHENBAUM, H. 1997; 5 (1-2): 131-178

  Abstract

  A major area of research in memory and amnesia concerns the item specificity of implicit memory. In this paper we address several issues about the nature of implicit memory phenomena and about what constitutes an "item", using the procedural/declarative memory theory to guide us. We consider the nature of memory for items and of memory for relations among items, within the context of the procedural/declarative framework, providing us with the foundation necessary to analyse the basis for item-specific implicit memory phenomena. We review recent work from our laboratories demonstrating the fundamentally relational and flexible nature of declarative memory representation, in both humans and animals, and the essential role of the hippocampal system in relational memory processing. We show, further, that the memory representations supporting implicit memory phenomena are inflexible and nonrelational, and are tied to specific processing modules. Finally, we introduce empirical approaches that blur the distinction between skill learning and repetition priming, and show computational modelling results that demonstrate how these two implicit memory phenomena can be mediated by a single incremental learning mechanism, in accord with the claims of the procedural-declarative theory. Taken together, these various analyses of memory for items and memory for relations help to illuminate the nature of the functional deficit in amnesia and the memory systems of the brain.

  View details for Web of Science ID A1997WV18700010

  View details for PubMedID 9156097

• ON THE REPRESENTATIONAL COMPUTATIONAL PROPERTIES OF MULTIPLE MEMORY-SYSTEMS BEHAVIORAL AND BRAIN SCIENCES Poldrack, R. A., COHEN, N. J. 1994; 17 (3): 416-417

  View details for Web of Science ID A1994PH24500025