Academic Appointments

All Publications

  • 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


    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

  • The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ArXiv Poldrack, R. A., Markiewicz, C. J., Appelhoff, S., Ashar, Y. K., Auer, T., Baillet, S., Bansal, S., Beltrachini, L., Bénar, 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., 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. 2023


    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 10.21105/joss.01896

    View details for PubMedID 37744469

    View details for PubMedCentralID PMC10516110

  • Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn. bioRxiv : the preprint server for biology Wang, H. T., Meisler, S. L., Sharmarke, H., Clarke, N., Gensollen, N., Markiewicz, C. J., Paugam, F., Thirion, B., Bellec, P. 2023


    Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark is implemented in a fully reproducible framework, where the provided research objects enable readers to reproduce or modify core computations, as well as the figures of the article using the Jupyter Book project and the Neurolibre reproducible preprint server ( We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep software package. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods. Our reproducible benchmark infrastructure will facilitate such continuous evaluation in the future, and may also be applied broadly to different tools or even research fields.

    View details for DOI 10.1101/2023.04.18.537240

    View details for PubMedID 37131781

    View details for PubMedCentralID PMC10153168

  • 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

  • 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


    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

  • Open and reproducible neuroimaging: From study inception to publication NEUROIMAGE Niso, G., Botvinik-Nezer, R., Appelhoff, S., De la Vega, A., Esteban, O., Etzel, J. A., Finc, K., Ganz, M., Gau, R., Halchenko, Y. O., Herholz, P., Karakuzu, A., Keator, D. B., Markiewicz, C. J., Maumet, C., Pernet, C. R., Pestilli, F., Queder, N., Schmitt, T., Sojka, W., Wagner, A. S., Whitaker, K. J., Rieger, J. W. 2022; 263: 119623


    Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

    View details for DOI 10.1016/j.neuroimage.2022.119623

    View details for Web of Science ID 000880054900010

    View details for PubMedID 36100172

    View details for PubMedCentralID PMC10008521

  • ASL-BIDS, the brain imaging data structure extension for arterial spin labeling SCIENTIFIC DATA Clement, P., Castellaro, M., Okell, T. W., Thomas, D. L., Vandemaele, P., Elgayar, S., Oliver-Taylor, A., Kirk, T., Woods, J. G., Vos, S. B., Kuijer, J. A., Achten, E., van Osch, M. P., Detre, J. A., Lu, H., Alsop, D. C., Chappell, M. A., Hernandez-Garcia, L., Petr, J., Mutsaerts, H. M., BIDS Maintainers 2022; 9 (1): 543


    Arterial spin labeling (ASL) is a non-invasive MRI technique that allows for quantitative measurement of cerebral perfusion. Incomplete or inaccurate reporting of acquisition parameters complicates quantification, analysis, and sharing of ASL data, particularly for studies across multiple sites, platforms, and ASL methods. There is a strong need for standardization of ASL data storage, including acquisition metadata. Recently, ASL-BIDS, the BIDS extension for ASL, was developed and released in BIDS 1.5.0. This manuscript provides an overview of the development and design choices of this first ASL-BIDS extension, which is mainly aimed at clinical ASL applications. Discussed are the structure of the ASL data, focussing on storage order of the ASL time series and implementation of calibration approaches, unit scaling, ASL-related BIDS fields, and storage of the labeling plane information. Additionally, an overview of ASL-BIDS compatible conversion and ASL analysis software and ASL example datasets in BIDS format is provided. We anticipate that large-scale adoption of ASL-BIDS will improve the reproducibility of ASL research.

    View details for DOI 10.1038/s41597-022-01615-9

    View details for Web of Science ID 000850567100001

    View details for PubMedID 36068231

    View details for PubMedCentralID PMC9448788

  • 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


    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

  • qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data. Scientific data Karakuzu, A., Appelhoff, S., Auer, T., Boudreau, M., Feingold, F., Khan, A. R., Lazari, A., Markiewicz, C., Mulder, M., Phillips, C., Salo, T., Stikov, N., Whitaker, K., de Hollander, G. 2022; 9 (1): 517


    The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.

    View details for DOI 10.1038/s41597-022-01571-4

    View details for PubMedID 36002444

  • PET-BIDS, an extension to the brain imaging data structure for positron emission tomography. Scientific data Norgaard, M., Matheson, G. J., Hansen, H. D., Thomas, A., Searle, G., Rizzo, G., Veronese, M., Giacomel, A., Yaqub, M., Tonietto, M., Funck, T., Gillman, A., Boniface, H., Routier, A., Dalenberg, J. R., Betthauser, T., Feingold, F., Markiewicz, C. J., Gorgolewski, K. J., Blair, R. W., Appelhoff, S., Gau, R., Salo, T., Niso, G., Pernet, C., Phillips, C., Oostenveld, R., Gallezot, J. D., Carson, R. E., Knudsen, G. M., Innis, R. B., Ganz, M. 2022; 9 (1): 65

    View details for DOI 10.1038/s41597-022-01164-1

    View details for PubMedID 35236846

  • Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data. Frontiers in neuroscience Bourget, M., Kamentsky, L., Ghosh, S. S., Mazzamuto, G., Lazari, A., Markiewicz, C. J., Oostenveld, R., Niso, G., Halchenko, Y. O., Lipp, I., Takerkart, S., Toussaint, P., Khan, A. R., Nilsonne, G., Castelli, F. M., BIDS Maintainers, Cohen-Adad, J., Appelhoff, S., Blair, R., Earl, E., Feingold, F., Galassi, A., Gau, R., Markiewicz, C. J., Salo, T. 2022; 16: 871228


    The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.

    View details for DOI 10.3389/fnins.2022.871228

    View details for PubMedID 35516811

  • The positron emission tomography brain imaging data structure (PET-BIDS) extension: A new standard for sharing PET data Norgaard, M., Matheson, G. J., Hansen, H. D., Thomas, A., Searle, G., Rizzo, G., Veronese, M., Giacomel, A., Yaqub, M., Tonietto, M., Funck, T., Gillman, A., Boniface, H., Routier, A., Dalenberg, J. R., Betthauser, T., Feingold, F., Markiewicz, C. J., Gorgolewski, K. J., Blair, R. W., Appelhoff, S., Gau, R., Salo, T., Niso, G., Pernet, C., Phillips, C., Oostenveld, R., Gallezot, J., Carson, R. E., Knudsen, G. M., Innis, R. B., Ganz, M. SAGE PUBLICATIONS INC. 2021: 117-119
  • 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


    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

  • 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
  • Brainhack: Developing a culture of open, inclusive, community-driven neuroscience NEURON Gau, R., Noble, S., Heuer, K., Bottenhorn, K. L., Bilgin, I. P., Yang, Y., Huntenburg, J. M., Bayer, J. M., Bethlehem, R. I., Rhoads, S. A., Vogelbacher, C., Borghesani, V., Levitis, E., Wang, H., Van den Bossche, S., Kobeleva, X., Legarreta, J., Guay, S., Atay, S., Varoquaux, G. P., Huijser, D. C., Sandstrom, M. S., Herholz, P., Nastase, S. A., Badhwar, A., Dumas, G., Schwab, S., Moia, S., Dayan, M., Bassil, Y., Brooks, P. P., Mancini, M., Shine, J. M., O'Connor, D., Xie, X., Poggiali, D., Friedrich, P., Heinsfeld, A. S., Riedl, L., Toro, R., Caballero-Gaudes, C., Eklund, A., Garner, K. G., Nolan, C. R., Demeter, D. V., Barrios, F. A., Merchant, J. S., McDevitt, E. A., Oostenveld, R., Craddock, R., Rokem, A., Doyle, A., Ghosh, S. S., Nikolaidis, A., Stanley, O. W., Urunuela, E., Brainhack Community 2021; 109 (11): 1769-1775


    Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

    View details for DOI 10.1016/j.neuron.2021.04.001

    View details for Web of Science ID 000657374800006

    View details for PubMedID 33932337

    View details for PubMedCentralID PMC9153215

  • In defense of decentralized research data management. Neuroforum Hanke, M., Pestilli, F., Wagner, A. S., Markiewicz, C. J., Poline, J., Halchenko, Y. O. 2021; 27 (1): 17-25


    Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.

    View details for DOI 10.1515/nf-2020-0037

    View details for PubMedID 36504549

  • The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging GIGASCIENCE Moreau, C. A., Jean-Louis, M., Blair, R., Markiewicz, C. J., Turner, J. A., Calhoun, V. D., Nichols, T. E., Pernet, C. R. 2020; 9 (10)


    Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they are interested in. Brain imaging genetics is at the intersection of two disciplines, each with dedicated dictionaries and ontologies facilitating data search and analysis. Here, we present the genetics Brain Imaging Data Structure extension, consisting of metadata files for human brain imaging data to which they are linked, and describe succinctly the genomic and transcriptomic data associated with them, which may be in different databases. This extension will facilitate identifying micro-scale molecular features that are linked to macro-scale imaging repositories, facilitating data aggregation across studies.

    View details for DOI 10.1093/gigascience/giaa104

    View details for Web of Science ID 000606074100006

    View details for PubMedID 33068112

    View details for PubMedCentralID PMC7568436

  • 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


    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 (, 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

  • 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-+
  • 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

  • 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


    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

  • 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


    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

  • Mapping the cortical representation of speech sounds in a syllable repetition task NEUROIMAGE Markiewicz, C. J., Bohland, J. W. 2016; 141: 174-190


    Speech repetition relies on a series of distributed cortical representations and functional pathways. A speaker must map auditory representations of incoming sounds onto learned speech items, maintain an accurate representation of those items in short-term memory, interface that representation with the motor output system, and fluently articulate the target sequence. A "dorsal stream" consisting of posterior temporal, inferior parietal and premotor regions is thought to mediate auditory-motor representations and transformations, but the nature and activation of these representations for different portions of speech repetition tasks remains unclear. Here we mapped the correlates of phonetic and/or phonological information related to the specific phonemes and syllables that were heard, remembered, and produced using a series of cortical searchlight multi-voxel pattern analyses trained on estimates of BOLD responses from individual trials. Based on responses linked to input events (auditory syllable presentation), predictive vowel-level information was found in the left inferior frontal sulcus, while syllable prediction revealed significant clusters in the left ventral premotor cortex and central sulcus and the left mid superior temporal sulcus. Responses linked to output events (the GO signal cueing overt production) revealed strong clusters of vowel-related information bilaterally in the mid to posterior superior temporal sulcus. For the prediction of onset and coda consonants, input-linked responses yielded distributed clusters in the superior temporal cortices, which were further informative for classifiers trained on output-linked responses. Output-linked responses in the Rolandic cortex made strong predictions for the syllables and consonants produced, but their predictive power was reduced for vowels. The results of this study provide a systematic survey of how cortical response patterns covary with the identity of speech sounds, which will help to constrain and guide theoretical models of speech perception, speech production, and phonological working memory.

    View details for DOI 10.1016/j.neuroimage.2016.07.023

    View details for Web of Science ID 000384074500016

    View details for PubMedID 27421186

  • Transparent Emergency Data Destruction Roberts, W., Johnson, C., Hale, J., Armistead, E. L. ACADEMIC CONFERENCES LTD. 2010: 271-278