Dr. Manish Saggar is an Assistant Professor in Computational Neuropsychiatry at Stanford University and currently directs the Brain Dynamics Lab. The overarching goal of his lab is to develop computational methods that could allow for anchoring psychiatric diagnosis into biological features (e.g., neural circuits, spatiotemporal neurodynamics). His lab is funded through an NIH Director’s New Innovator Award (DP2), an NIMH R01, and a faculty scholar award from Stanford’s Maternal and Child Health Research Institute. He has previously received a career development award (K99/R00) from the NIMH and BBRF’s NARSAD Young Investigator Award. His work has been recognized by several local and national and international awards. His lab excels in developing data-driven computational methods to generate clinically and behaviorally relevant insights from high-dimensional biological data (e.g., neuroimaging) without necessarily averaging the data at the outset. The lab also actively pursue developing novel technologies for experimental design and data collection for enhancing human cognition (e.g., creativity and collaboration). Dr. Saggar received his Ph.D. in Computer Science from the University of Texas at Austin and later received postdoctoral training in Psychiatry from Stanford University School of Medicine.

Honors & Awards

  • Tashia and John Morgridge Endowed Faculty Scholar in Pediatric Translational Medicine, Stanford Maternal & Child Health Research Institute (2020-25)
  • Travel Fellowship Award, Society of Biological Psychiatry (SOBP) (2020)
  • International Fellow, Institute for Scientific Interchange Foundation, Italy (2019-2022)
  • Annual Chairman's Award for Advancing Science, Department of Psychiatry & Behavioral Sciences, Stanford University (2019)
  • NIH Director's New Innovator Award (DP2), National Institute of Health (2018-2023)
  • NARSAD Young Investigator Grant, Brain & Behavior Research Foundation (2016-2018)
  • Innovator Grant, Department of Psychiatry & Behavioral Sciences, Stanford University (2016)
  • NIH Career Development Award (K99/R00), National Institute of Mental Health (2015-2020)
  • Child Health Research Institute (CHRI) Postdoctoral Grant, Lucile Packard Foundation for Children’s Health (LPFCH) (2013-2014)
  • Seed-grant Award, Stanford’s Center for Cognitive and Neurobiological Imaging (CNI). (2012-2013)
  • Francisco J. Varela Memorial Grant Award, Mind and Life Institute (2006-2011)
  • Merit Scholarship, Indian Institute of Information Technology, Allahabad (IIIT-A), India (2001-2005)

Boards, Advisory Committees, Professional Organizations

  • Editorial Board Member, NeuroImage (Elsevier) (2020 - Present)
  • Mentor, Organization of Human Brain Mapping Online Mentoring Program (2020 - Present)
  • Editorial Board Member, Scientific Reports (Nature Research Journal) (2017 - Present)
  • Executive Board Member, Society for the Neuroscience of Creativity (2017 - Present)

Professional Education

  • Faculty Fellow, Stanford Byers Center for Bio Design, Bio Design (2017)
  • Postdoctoral Fellowship, Stanford University School of Medicine, Psychiatry (2014)
  • Doctor of Philosophy, University of Texas at Austin, Computer Science (2011)
  • Master of Science, University of Texas at Austin, Computer Science (2009)
  • Bachelors in Technology, Indian Institute of Information Technology, Information Technology (2005)


  • Manish Saggar. "United States Patent 16/171,255 Systems and Methods for Mapping Neuronal Circuitry and Clinical Applications Thereof", Leland Stanford Junior University, Jan 1, 2019

Research Interests

  • Brain and Learning Sciences
  • Data Sciences
  • Psychology
  • Research Methods

Current Research and Scholarly Interests

Dr. Manish Saggar is an Assistant Professor in Computational Neuropsychiatry at Stanford University and currently directs the Brain Dynamics Lab. The overarching goal of his lab is to develop computational methods that could allow for anchoring psychiatric diagnosis into biological features (e.g., neural circuits, spatiotemporal neurodynamics). His lab is funded through an NIH Director’s New Innovator Award (DP2), an NIMH R01, and a faculty scholar award from Stanford’s Maternal and Child Health Research Institute. He has previously received a career development award (K99/R00) from the NIMH and BBRF’s NARSAD Young Investigator Award. His work has been recognized by several local (e.g., Department’s Innovator Award and Excellence in Advancing Science Award) and national and international awards (e.g., Institute for Scientific Interchange, Italy, Fellow). His lab excels in developing data-driven computational methods to generate clinically and behaviorally relevant insights from high-dimensional biological data (e.g., neuroimaging) without necessarily averaging the data at the outset. They also actively pursue developing novel technologies for experimental design and data collection for enhancing human cognition (e.g., creativity and collaboration). Dr. Saggar received his Ph.D. in Computer Science from the University of Texas at Austin and later received postdoctoral training in Psychiatry from Stanford University School of Medicine.

Clinical Trials

  • Examining the Effects of Regular Brief Internet-based Meditation Practice on Mental Health and Well Being Recruiting

    The study will examine the effects of online meditation training on stress and anxiety in healthy participants. It will also examine the dose-response relationship between the amount of daily focused attention meditation practice and established mental health outcome measures.

    View full details

2023-24 Courses

Stanford Advisees

All Publications

  • Increased anti-correlation between the left dorsolateral prefrontal cortex and the default mode network following Stanford Neuromodulation Therapy (SNT): analysis of a double-blinded, randomized, sham-controlled trial. Npj mental health research Gajawelli, N., Geoly, A. D., Batail, J. M., Xiao, X., Maron-Katz, A., Cole, E., Azeez, A., Kratter, I. H., Saggar, M., Williams, N. R. 2024; 3 (1): 35


    SNT is a high-dose accelerated intermittent theta-burst stimulation (iTBS) protocol coupled with functional-connectivity-guided targeting that is an efficacious and rapid-acting therapy for treatment-resistant depression (TRD). We used resting-state functional MRI (fMRI) data from a double-blinded sham-controlled randomized controlled trial1 to reveal the neural correlates of SNT-based symptom improvement. Neurobehavioral data were acquired at baseline, post-treatment, and 1-month follow-up. Our primary analytic objective was to investigate changes in seed-based functional connectivity (FC) following SNT and hypothesized that FC changes between the treatment target and the sgACC, DMN, and CEN would ensue following active SNT but not sham. We also investigated the durability of post-treatment observed FC changes at a 1-month follow-up. Study participants included transcranial magnetic stimulation (TMS)-naive adults with a primary diagnosis of moderate-to-severe TRD. Fifty-four participants were screened, 32 were randomized, and 29 received active or sham SNT. An additional 5 participants were excluded due to imaging artifacts, resulting in 12 participants per group (Sham: 5F; SNT: 5F). Although we did not observe any significant group × time effects on the FC between the individualized stimulation target (L-DLPFC) and the CEN or sgACC, we report an increased magnitude of negative FC between the target site and the DMN post-treatment in the active as compared to sham SNT group. This change in FC was sustained at the 1-month follow-up. Further, the degree of change in FC was correlated with improvements in depressive symptoms. Our results provide initial evidence for the putative changes in the functional organization of the brain post-SNT.

    View details for DOI 10.1038/s44184-024-00073-y

    View details for PubMedID 38971869

    View details for PubMedCentralID PMC11227523

  • Densely sampled stimulus-response map of human cortex with single pulse TMS-EEG and its relation to whole brain neuroimaging measures. bioRxiv : the preprint server for biology Sun, Y., Lucas, M. V., Cline, C. C., Menezes, M. C., Kim, S., Badami, F. S., Narayan, M., Wu, W., Daskalakis, Z. J., Etkin, A., Saggar, M. 2024


    Large-scale networks underpin brain functions. How such networks respond to focal stimulation can help decipher complex brain processes and optimize brain stimulation treatments. To map such stimulation-response patterns across the brain non-invasively, we recorded concurrent EEG responses from single-pulse transcranial magnetic stimulation (i.e., TMS-EEG) from over 100 cortical regions with two orthogonal coil orientations from one densely-sampled individual. We also acquired Human Connectome Project (HCP)-styled diffusion imaging scans (six), resting-state functional Magnetic Resonance Imaging (fMRI) scans (120 mins), resting-state EEG scans (108 mins), and structural MR scans (T1- and T2-weighted). Using the TMS-EEG data, we applied network science-based community detection to reveal insights about the brain's causal-functional organization from both a stimulation and recording perspective. We also computed structural and functional maps and the electric field of each TMS stimulation condition. Altogether, we hope the release of this densely sampled (n=1) dataset will be a uniquely valuable resource for both basic and clinical neuroscience research.

    View details for DOI 10.1101/2024.06.16.599236

    View details for PubMedID 38948696

    View details for PubMedCentralID PMC11212865

  • 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


    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

  • Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data. bioRxiv : the preprint server for biology Haşegan, D., Geniesse, C., Chowdhury, S., Saggar, M. 2023


    Capturing and tracking large-scale brain activity dynamics holds the potential to deepen our understanding of cognition. Previously, tools from Topological Data Analysis, especially Mapper, have been successfully used to mine brain activity dynamics at the highest spatiotemporal resolutions. Even though it is a relatively established tool within the field of Topological Data Analysis, Mapper results are highly impacted by parameter selection. Given that non-invasive human neuroimaging data (e.g., from fMRI) is typically fraught with artifacts and no gold standards exist regarding "true" state transitions, we argue for a thorough examination of Mapper parameter choices to better reveal their impact. Using synthetic data (with known transition structure) and real fMRI data, we explore a variety of parameter choices for each Mapper step, thereby providing guidance and heuristics for the field. We also release our parameter-exploration toolbox as a software package to make it easier for scientists to investigate and apply Mapper on any dataset.

    View details for DOI 10.1101/2023.10.13.562304

    View details for PubMedID 37904918

    View details for PubMedCentralID PMC10614807

  • Cross-attractor modeling of resting-state functional connectivity in psychiatric disorders. NeuroImage Sun, Y., Zhang, M., Saggar, M. 2023: 120302


    Resting-state functional connectivity (RSFC) is altered across various psychiatric disorders. Brain network modeling (BNM) has the potential to reveal the neurobiological underpinnings of such abnormalities by dynamically modeling the structure-function relationship and examining biologically relevant parameters after fitting the models with real data. Although innovative BNM approaches have been developed, two main issues need to be further addressed. First, previous BNM approaches are primarily limited to simulating noise-driven dynamics near a chosen attractor (or a stable brain state). An alternative approach is to examine multi(or cross)-attractor dynamics, which can be used to better capture non-stationarity and switching between states in the resting brain. Second, previous BNM work is limited to characterizing one disorder at a time. Given the large degree of co-morbidity across psychiatric disorders, comparing BNMs across disorders might provide a novel avenue to generate insights regarding the dynamical features that are common across (vs. specific to) disorders. Here, we address these issues by (1) examining the layout of the attractor repertoire over the entire multi-attractor landscape using a recently developed cross-attractor BNM approach; and (2) characterizing and comparing multiple disorders (schizophrenia, bipolar, and ADHD) with healthy controls using an openly available and moderately large multimodal dataset from the UCLA Consortium for Neuropsychiatric Phenomics. Both global and local differences were observed across disorders. Specifically, the global coupling between regions was significantly decreased in schizophrenia patients relative to healthy controls. At the same time, the ratio between local excitation and inhibition was significantly higher in the schizophrenia group than the ADHD group. In line with these results, the schizophrenia group had the lowest switching costs (energy gaps) across groups for several networks including the default mode network. Paired comparison also showed that schizophrenia patients had significantly lower energy gaps than healthy controls for the somatomotor and visual networks. Overall, this study provides preliminary evidence supporting transdiagnostic multi-attractor BNM approaches to better understand psychiatric disorders' pathophysiology.

    View details for DOI 10.1016/j.neuroimage.2023.120302

    View details for PubMedID 37579998

  • Brain mitochondrial diversity and network organization predict anxiety-like behavior in male mice. Nature communications Rosenberg, A. M., Saggar, M., Monzel, A. S., Devine, J., Rogu, P., Limoges, A., Junker, A., Sandi, C., Mosharov, E. V., Dumitriu, D., Anacker, C., Picard, M. 2023; 14 (1): 4726


    The brain and behavior are under energetic constraints, limited by mitochondrial energy transformation capacity. However, the mitochondria-behavior relationship has not been systematically studied at a brain-wide scale. Here we examined the association between multiple features of mitochondrial respiratory chain capacity and stress-related behaviors in male mice with diverse behavioral phenotypes. Miniaturized assays of mitochondrial respiratory chain enzyme activities and mitochondrial DNA (mtDNA) content were deployed on 571 samples across 17 brain areas, defining specific patterns of mito-behavior associations. By applying multi-slice network analysis to our brain-wide mitochondrial dataset, we identified three large-scale networks of brain areas with shared mitochondrial signatures. A major network composed of cortico-striatal areas exhibited the strongest mitochondria-behavior correlations, accounting for up to 50% of animal-to-animal behavioral differences, suggesting that this mito-based network is functionally significant. The mito-based brain networks also overlapped with regional gene expression and structural connectivity, and exhibited distinctmolecular mitochondrial phenotype signatures. This work provides convergent multimodal evidence anchored in enzyme activities, gene expression, and animal behavior that distinct, behaviorally-relevant mitochondrial phenotypes exist across the male mouse brain.

    View details for DOI 10.1038/s41467-023-39941-0

    View details for PubMedID 37563104

  • Network effects of Stanford Neuromodulation Therapy (SNT) in treatment-resistant major depressive disorder: a randomized, controlled trial. Translational psychiatry Batail, J., Xiao, X., Azeez, A., Tischler, C., Kratter, I. H., Bishop, J. H., Saggar, M., Williams, N. R. 2023; 13 (1): 240


    Here, we investigated the brain functional connectivity (FC) changes following a novel accelerated theta burst stimulation protocol known as Stanford Neuromodulation Therapy (SNT) which demonstrated significant antidepressant efficacy in treatment-resistant depression (TRD). In a sample of 24 patients (12 active and 12 sham), active stimulation was associated with significant pre- and post-treatment modulation of three FC pairs, involving the default mode network (DMN), amygdala, salience network (SN) and striatum. The most robust finding was the SNT effect on amygdala-DMN FC (group*time interaction F(1,22)=14.89, p<0.001). This FC change correlated with improvement in depressive symptoms (rho (Spearman) = -0.45, df=22, p=0.026). The post-treatment FC pattern showed a change in the direction of the healthy control group and was sustained at the one-month follow-up. These results are consistent with amygdala-DMN connectivity dysfunction as an underlying mechanism of TRD and bring us closer to the goal of developing imaging biomarkers for TMS treatment optimization.Trial registration: NCT03068715.

    View details for DOI 10.1038/s41398-023-02537-9

    View details for PubMedID 37400432

  • Temporal Mapper: Transition networks in simulated and real neural dynamics NETWORK NEUROSCIENCE Zhang, M., Chowdhury, S., Saggar, M. 2023; 7 (2): 431-460
  • Development of top-down cortical propagations in youth. Neuron Pines, A., Keller, A. S., Larsen, B., Bertolero, M., Ashourvan, A., Bassett, D. S., Cieslak, M., Covitz, S., Fan, Y., Feczko, E., Houghton, A., Rueter, A. R., Saggar, M., Shafiei, G., Tapera, T. M., Vogel, J., Weinstein, S. M., Shinohara, R. T., Williams, L. M., Fair, D. A., Satterthwaite, T. D. 2023


    Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.

    View details for DOI 10.1016/j.neuron.2023.01.014

    View details for PubMedID 36803653

  • Current opinions on the present and future use of functional near-infrared spectroscopy in psychiatry. Neurophotonics Li, R., Hosseini, H., Saggar, M., Balters, S. C., Reiss, A. L. 2023; 10 (1): 013505


    Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique for assessing human brain activity by noninvasively measuring the fluctuation of cerebral oxygenated- and deoxygenated-hemoglobin concentrations associated with neuronal activity. Owing to its superior mobility, low cost, and good tolerance for motion, the past few decades have witnessed a rapid increase in the research and clinical use of fNIRS in a variety of psychiatric disorders. In this perspective article, we first briefly summarize the state-of-the-art concerning fNIRS research in psychiatry. In particular, we highlight the diverse applications of fNIRS in psychiatric research, the advanced development of fNIRS instruments, and novel fNIRS study designs for exploring brain activity associated with psychiatric disorders. We then discuss some of the open challenges and share our perspectives on the future of fNIRS in psychiatric research and clinical practice. We conclude that fNIRS holds promise for becoming a useful tool in clinical psychiatric settings with respect to developing closed-loop systems and improving individualized treatments and diagnostics.

    View details for DOI 10.1117/1.NPh.10.1.013505

    View details for PubMedID 36777700

    View details for PubMedCentralID PMC9904322

  • Brief intensive social gaze training reorganizes functional brain connectivity in boys with fragile X syndrome. Cerebral cortex (New York, N.Y. : 1991) Saggar, M., Bruno, J. L., Hall, S. S. 2022


    Boys with fragile X syndrome (FXS), the leading known genetic cause of autism spectrum disorder (ASD), demonstrate significant impairments in social gaze and associated weaknesses in communication, social interaction, and other areas of adaptive functioning. Little is known, however, concerning the impact of behavioral treatments for these behaviors on functional brain connectivity in this population. As part of a larger study, boys with FXS (mean age 13.23±2.31years) and comparison boys with ASD (mean age 12.15±2.76years) received resting-state functional magnetic resonance imaging scans prior to and following social gaze training administered by a trained behavior therapist in our laboratory. Network-agnostic connectome-based predictive modeling of pretreatment resting-state functional connectivity data revealed a set of positive (FXS>ASD) and negative (FXS

    View details for DOI 10.1093/cercor/bhac411

    View details for PubMedID 36376964

  • Dynamic autonomic nervous system states arise during emotions and manifest in basal physiology. Psychophysiology Pasquini, L., Noohi, F., Veziris, C. R., Kosik, E. L., Holley, S. R., Lee, A., Brown, J. A., Roy, A. R., Chow, T. E., Allen, I., Rosen, H. J., Kramer, J. H., Miller, B. L., Saggar, M., Seeley, W. W., Sturm, V. E. 2022: e14218


    The outflow of the autonomic nervous system (ANS) is continuous and dynamic, but its functional organization is not well understood. Whether ANS patterns accompany emotions, or arise in basal physiology, remain unsettled questions in the field. Here, we searched for brief ANS patterns amidst continuous, multichannel physiological recordings in 45 healthy older adults. Participants completed an emotional reactivity task in which they viewed video clips that elicited a target emotion (awe, sadness, amusement, disgust, or nurturant love); each video clip was preceded by a pre-trial baseline period and followed by a post-trial recovery period. Participants also sat quietly for a separate 2-min resting period to assess basal physiology. Using principal components analysis and unsupervised clustering algorithms to reduce the second-by-second physiological data during the emotional reactivity task, we uncovered five ANS states. Each ANS state was characterized by a unique constellation of patterned physiological changes that differentiated among the trials of the emotional reactivity task. These ANS states emerged and dissipated over time, with each instance lasting several seconds on average. ANS states with similar structures were also detectable in the resting period but were intermittent and of smaller magnitude. Our results offer new insights into the functional organization of the ANS. By assembling short-lived, patterned changes, the ANS is equipped to generate a wide range of physiological states that accompany emotions and that contribute to the architecture of basal physiology.

    View details for DOI 10.1111/psyp.14218

    View details for PubMedID 36371680

  • Dysfunctional Cortical Gradient Topography in Treatment-Resistant Major Depressive Disorder. Biological psychiatry. Cognitive neuroscience and neuroimaging Pasquini, L., Fryer, S. L., Eisendrath, S. J., Segal, Z. V., Lee, A. J., Brown, J. A., Saggar, M., Mathalon, D. H. 2022


    Treatment-resistant depression (TRD) refers to patients with major depressive disorder who do not remit after 2 or more antidepressant trials. TRD is common and highly debilitating, but its neurobiological basis remains poorly understood. Recent neuroimaging studies have revealed cortical connectivity gradients that dissociate primary sensorimotor areas from higher-order associative cortices. This fundamental topography determines cortical information flow and is affected by psychiatric disorders. We examined how TRD impacts gradient-based hierarchical cortical organization.In this secondary study, we analyzed resting-state functional magnetic resonance imaging data from a mindfulness-based intervention enrolling 56 patients with TRD and 28 healthy control subjects. Using gradient extraction tools, baseline measures of cortical gradient dispersion within and between functional brain networks were derived, compared across groups, and associated with graph theoretical measures of network topology. In patients, correlation analyses were used to associate measures of cortical gradient dispersion with clinical measures of anxiety, depression, and mindfulness at baseline and following the intervention.Cortical gradient dispersion was reduced within major intrinsic brain networks in patients with TRD. Reduced cortical gradient dispersion correlated with increased network degree assessed through graph theory-based measures of network topology. Lower dispersion among default mode, control, and limbic network nodes related to baseline levels of trait anxiety, depression, and mindfulness. Patients' baseline limbic network dispersion predicted trait anxiety scores 24 weeks after the intervention.Our findings provide preliminary support for widespread alterations in cortical gradient architecture in TRD, implicating a significant role for transmodal and limbic networks in mediating depression, anxiety, and lower mindfulness in patients with TRD.

    View details for DOI 10.1016/j.bpsc.2022.10.009

    View details for PubMedID 36754677

  • Neural resources shift under Methylphenidate: a computational approach to examine anxiety-cognition interplay. NeuroImage Saggar, M., Bruno, J., Gaillard, C., Claudino, L., Ernst, M. 2022: 119686


    The reciprocal interplay between anxiety and cognition is well documented. Anxiety negatively impacts cognition, while cognitive engagement can down-regulate anxiety. The brain mechanisms and dynamics underlying such interplay are not fully understood. To study this question, we experimentally and orthogonally manipulated anxiety (using a threat of shock paradigm) and cognition (using methylphenidate; MPH). The effects of these manipulations on the brain and behavior were evaluated in 50 healthy participants (25 MPH, 25 placebo), using an n-back working memory fMRI task (with low and high load conditions). Behaviorally, improved response accuracy was observed as a main effect of the drug across all conditions. We employed two approaches to understand the neural mechanisms underlying MPH-based cognitive enhancement in safe and threat conditions. First, we performed a hypothesis-driven computational analysis using a mathematical framework to examine how MPH putatively affects cognitive enhancement in the face of induced anxiety across two levels of cognitive load. Second, we performed an exploratory data analysis using Topological Data Analysis (TDA)-based Mapper to examine changes in spatiotemporal brain activity across the entire cortex. Both approaches provided converging evidence that MPH facilitated greater differential engagement of neural resources (brain activity) across low and high working memory load conditions. Furthermore, load-based differential management of neural resources reflects enhanced efficiency that is most powerful during higher load and induced anxiety conditions. Overall, our results provide novel insights regarding brain mechanisms that facilitate cognitive enhancement under MPH and, in future research, may be used to help mitigate anxiety-related cognitive underperformance.

    View details for DOI 10.1016/j.neuroimage.2022.119686

    View details for PubMedID 36273770

  • Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest. Nature communications Saggar, M., Shine, J. M., Liegeois, R., Dosenbach, N. U., Fair, D. 2022; 13 (1): 4791


    In the absence of external stimuli, neural activity continuously evolves from one configuration to another. Whether these transitions or explorations follow some underlying arrangement or lack a predictable ordered plan remains to be determined. Here, using fMRI data from highly sampled individuals (~5hours of resting-state data per individual), we aimed to reveal the rules that govern transitions in brain activity at rest. Our Topological Data Analysis based Mapper approach characterized a highly visited transition state of the brain that acts as a switch between different neural configurations to organize the spontaneous brain activity. Further, while the transition state was characterized by a uniform representation of canonical resting-state networks (RSNs), the periphery of the landscape was dominated by a subject-specific combination of RSNs. Altogether, we revealed rules or principles that organize spontaneous brain activity using a precision dynamics approach.

    View details for DOI 10.1038/s41467-022-32381-2

    View details for PubMedID 35970984

  • DISSOCIABLE SIGNATURES OF DYNAMIC AUTONOMIC ACTIVITY DURING EVOKED EMOTIONS AND REST Pasquini, L., Noohi, F., Veziris, C., Kosik, E., Lee, A., Brown, J., Holley, S., Miller, B., Saggar, M., Seeley, W., Sturm, V. WILEY. 2022: S141
  • Cross-attractor repertoire provides new perspective on structure-function relationship in the brain. NeuroImage Zhang, M., Sun, Y., Saggar, M. 2022: 119401


    The brain exhibits complex intrinsic dynamics, i.e., spontaneously arising activity patterns without any external inputs or tasks. Such intrinsic dynamics and their alteration are thought to play crucial roles in typical as well as atypical cognitive functioning. Linking the ever-changing intrinsic dynamics to the rather static anatomy is a challenging endeavor. Dynamical systems models are important tools for understanding how structure and function are linked in the brain. Here, we provide a novel modeling framework to examine how functional connectivity depends on structural connectivity in the brain. Existing modeling frameworks typically focus on noise-driven (or stochastic) dynamics near a single attractor. Complementing existing approaches, we examine deterministic features of the distribution of attractors, in particular, how regional states are correlated across all attractors - cross-attractor coordination. We found that cross-attractor coordination between brain regions better predict human functional connectivity than noise-driven single-attractor dynamics. Importantly, cross-attractor coordination better accounts for the nonlinear dependency of functional connectivity on structural connectivity. Our findings suggest that functional connectivity patterns in the brain may reflect transitions between attractors, which impose an energy cost. The framework may be used to predict transitions and energy costs associated with experimental or clinical interventions.

    View details for DOI 10.1016/j.neuroimage.2022.119401

    View details for PubMedID 35732244

  • NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization. Network neuroscience (Cambridge, Mass.) Geniesse, C., Chowdhury, S., Saggar, M. 2022; 6 (2): 467-498


    For better translational outcomes, researchers and clinicians alike demand novel tools to distill complex neuroimaging data into simple yet behaviorally relevant representations at the single-participant level. Recently, the Mapper approach from topological data analysis (TDA) has been successfully applied on noninvasive human neuroimaging data to characterize the entire dynamical landscape of whole-brain configurations at the individual level without requiring any spatiotemporal averaging at the outset. Despite promising results, initial applications of Mapper to neuroimaging data were constrained by (1) the need for dimensionality reduction and (2) lack of a biologically grounded heuristic for efficiently exploring the vast parameter space. Here, we present a novel computational framework for Mapper-designed specifically for neuroimaging data-that removes limitations and reduces computational costs associated with dimensionality reduction and parameter exploration. We also introduce new meta-analytic approaches to better anchor Mapper-generated representations to neuroanatomy and behavior. Our new NeuMapper framework was developed and validated using multiple fMRI datasets where participants engaged in continuous multitask experiments that mimic "ongoing" cognition. Looking forward, we hope our framework will help researchers push the boundaries of psychiatric neuroimaging toward generating insights at the single-participant level across consortium-size datasets.

    View details for DOI 10.1162/netn_a_00229

    View details for PubMedID 35733428

  • Brief, Intense Social Gaze Training Normalizes Functional Brain Connectivity in Boys With Fragile X Syndrome Bruno, J., Saggar, M., Hall, S. ELSEVIER SCIENCE INC. 2022: S152
  • Altered canonical and striatal-frontal resting state functional connectivity in children with pathogenic variants in the Ras/mitogen-activated protein kinase pathway. Molecular psychiatry Bruno, J. L., Shrestha, S. B., Reiss, A. L., Saggar, M., Green, T. 2022


    Mounting evidence supports the role of the Ras/mitogen-activated protein kinase (Ras/MAPK) pathway in neurodevelopmental disorders. Here, the authors used a genetics-first approach to examine how Ras/MAPK pathogenic variants affect the functional organization of the brain and cognitive phenotypes including weaknesses in attention and inhibition. Functional MRI was used to examine resting state functional connectivity (RSFC) in association with Ras/MAPK pathogenic variants in children with Noonan syndrome (NS). Participants (age 4-12 years) included 39 children with NS (mean age 8.44, SD = 2.20, 25 females) and 49 typically developing (TD) children (mean age 9.02, SD = 9.02, 33 females). Twenty-eight children in the NS group and 46 in the TD group had usable MRI data and were included in final analyses. The results indicated significant hyperconnectivity for the NS group within canonical visual, ventral attention, left frontoparietal and limbic networks (p < 0.05 FWE). Higher connectivity within canonical left frontoparietal and limbic networks positively correlated with cognitive function within the NS but not the TD group. Further, the NS group demonstrated significant group differences in seed-based striatal-frontal connectivity (Z > 2.6, p < 0.05 FWE). Hyperconnectivity within canonical brain networks may represent an intermediary phenotype between Ras/MAPK pathogenic variants and cognitive phenotypes, including weaknesses in attention and inhibition. Altered striatal-frontal connectivity corresponds with smaller striatal volume and altered white matter connectivity previously documented in children with NS. These results may indicate delayed maturation and compensatory mechanisms and they are important for understanding the pathophysiology underlying cognitive phenotypes in NS and in the broader population of children with neurodevelopmental disorders.

    View details for DOI 10.1038/s41380-021-01422-5

    View details for PubMedID 35087195

  • Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking. NeuroImage Xie, H., Beaty, R. E., Jahanikia, S., Geniesse, C., Sonalkar, N. S., Saggar, M. 2021: 118531


    Despite substantial progress in the quest of demystifying the brain basis of creativity, several questions remain open. One such issue concerns the relationship between two latent cognitive modes during creative thinking, i.e., deliberate goal-directed cognition and spontaneous thought generation. Although an interplay between deliberate and spontaneous thinking is often implicated in the creativity literature (e.g., dual-process models), a bottom-up data-driven validation of the cognitive processes associated with creative thinking is still lacking. Here, we attempted to capture the latent modes of creative thinking by utilizing a data-driven approach on a novel continuous multitask paradigm (CMP) that widely sampled a hypothetical two-dimensional cognitive plane of deliberate and spontaneous thinking in a single fMRI session. The CMP consisted of eight task blocks ranging from undirected mind wandering to goal-directed working memory task, while also included two widely-used creativity tasks, i.e., alternate uses task (AUT) and remote association task (RAT). Using eigen-connectivity (EC) analysis on the multitask whole-brain functional connectivity (FC) patterns, we embedded the multitask FCs into a low-dimensional latent space. The first two latent components, as revealed by the EC analysis, broadly mapped onto the two cognitive modes of deliberate and spontaneous thinking, respectively. Further, in this low-dimensional space, both creativity tasks were located in the upper right corner of high deliberate and spontaneous thinking (creative cognitive space). Neuroanatomically, the creative cognitive space was represented by not only increased intra-network connectivity within executive control and default mode network, but also by higher coupling between the two canonical brain networks. Further, individual differences reflected in the low-dimensional connectivity embeddings were related to differences in deliberate and spontaneous thinking abilities. Altogether, using a continuous multitask paradigm and a data-driven approach, we provide initial empirical evidence for the contribution of both deliberate and spontaneous modes of cognition during creative thinking.

    View details for DOI 10.1016/j.neuroimage.2021.118531

    View details for PubMedID 34469816

  • Creativity and the Brain: An Editorial Introduction to the Special Issue on the Neuroscience of Creativity. NeuroImage Saggar, M., Volle, E., Uddin, L. Q., Chrysikou, E. G., Green, A. E. 2021: 117836

    View details for DOI 10.1016/j.neuroimage.2021.117836

    View details for PubMedID 33549759

  • Thalamocortical connectivity is associated with autism symptoms in high-functioning adults with autism and typically developing adults. Translational psychiatry Ayub, R. n., Sun, K. L., Flores, R. E., Lam, V. T., Jo, B. n., Saggar, M. n., Fung, L. K. 2021; 11 (1): 93


    Alterations in sensorimotor functions are common in individuals with autism spectrum disorder (ASD). Such aberrations suggest the involvement of the thalamus due to its key role in modulating sensorimotor signaling in the cortex. Although previous research has linked atypical thalamocortical connectivity with ASD, investigations of this association in high-functioning adults with autism spectrum disorder (HFASD) are lacking. Here, for the first time, we investigated the resting-state functional connectivity of the thalamus, medial prefrontal, posterior cingulate, and left dorsolateral prefrontal cortices and its association with symptom severity in two matched cohorts of HFASD. The principal cohort consisted of 23 HFASD (mean[SD] 27.1[8.9] years, 39.1% female) and 20 age- and sex-matched typically developing controls (25.1[7.2] years, 30.0% female). The secondary cohort was a subset of the ABIDE database consisting of 58 HFASD (25.4[7.8] years, 37.9% female) and 51 typically developing controls (24.4[6.7] years, 39.2% female). Using seed-based connectivity analysis, between-group differences were revealed as hyperconnectivity in HFASD in the principal cohort between the right thalamus and bilateral precentral/postcentral gyri and between the right thalamus and the right superior parietal lobule. The former was associated with autism-spectrum quotient in a sex-specific manner, and was further validated in the secondary ABIDE cohort. Altogether, we present converging evidence for thalamocortical hyperconnectivity in HFASD that is associated with symptom severity. Our results fill an important knowledge gap regarding atypical thalamocortical connectivity in HFASD, previously only reported in younger cohorts.

    View details for DOI 10.1038/s41398-021-01221-0

    View details for PubMedID 33536431

  • Simplicial and topological descriptions of human brain dynamics. Network neuroscience (Cambridge, Mass.) Billings, J., Saggar, M., Hlinka, J., Keilholz, S., Petri, G. 2021; 5 (2): 549-568


    While brain imaging tools like functional magnetic resonance imaging (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the data's apparent self-organization. To clarify how patterns of brain activity support brain function, one might identify metric spaces that optimally distinguish brain states across experimentally defined conditions. Therefore, the present study considers the relative capacities of several metric spaces to disambiguate experimentally defined brain states. One fundamental metric space interprets fMRI data topographically, that is, as the vector of amplitudes of a multivariate signal, changing with time. Another perspective compares the brain's functional connectivity, that is, the similarity matrix computed between signals from different brain regions. More recently, metric spaces that consider the data's topology have become available. Such methods treat data as a sample drawn from an abstract geometric object. To recover the structure of that object, topological data analysis detects features that are invariant under continuous deformations (such as coordinate rotation and nodal misalignment). Moreover, the methods explicitly consider features that persist across multiple geometric scales. While, certainly, there are strengths and weaknesses of each brain dynamics metric space, wefind that those that track topological features optimally distinguish experimentally defined brain states.

    View details for DOI 10.1162/netn_a_00190

    View details for PubMedID 34189377

  • Activation Mutation in the Ras/MAPK Pathway Alters the Functional Resting-State Architecture Underlining Executive Function and Attention Bruno, J., Shrestha, S., Reiss, A., Saggar, M., Green, T. SPRINGERNATURE. 2020: 177–78
  • Sex Differences in Resting-State Functional Connectivity in High-Functioning Adults With Autism Sun, K., Ayub, R., Lam, V., Saggar, M., Fung, L. SPRINGERNATURE. 2020: 297–98
  • Finding the neural correlates of collaboration using a three-person fMRI hyperscanning paradigm. Proceedings of the National Academy of Sciences of the United States of America Xie, H., Karipidis, I. I., Howell, A., Schreier, M., Sheau, K. E., Manchanda, M. K., Ayub, R., Glover, G. H., Jung, M., Reiss, A. L., Saggar, M. 2020


    Humans have an extraordinary ability to interact and cooperate with others. Despite the social and evolutionary significance of collaboration, research on finding its neural correlates has been limited partly due to restrictions on the simultaneous neuroimaging of more than one participant (also known as hyperscanning). Several studies have used dyadic fMRI hyperscanning to examine the interaction between two participants. However, to our knowledge, no study to date has aimed at revealing the neural correlates of social interactions using a three-person (or triadic) fMRI hyperscanning paradigm. Here, we simultaneously measured the blood-oxygenation level-dependent signal from 12 triads (n = 36 participants), while they engaged in a collaborative drawing task based on the social game of Pictionary General linear model analysis revealed increased activation in the brain regions previously linked with the theory of mind during the collaborative phase compared to the independent phase of the task. Furthermore, using intersubject correlation analysis, we revealed increased synchronization of the right temporo-parietal junction (R TPJ) during the collaborative phase. The increased synchrony in the R TPJ was observed to be positively associated with the overall team performance on the task. In sum, our paradigm revealed a vital role of the R TPJ among other theory-of-mind regions during a triadic collaborative drawing task.

    View details for DOI 10.1073/pnas.1917407117

    View details for PubMedID 32843342

  • Thalamic and prefrontal GABA concentrations but not GABAA receptor densities are altered in high-functioning adults with autism spectrum disorder. Molecular psychiatry Fung, L. K., Flores, R. E., Gu, M. n., Sun, K. L., James, D. n., Schuck, R. K., Jo, B. n., Park, J. H., Lee, B. C., Jung, J. H., Kim, S. E., Saggar, M. n., Sacchet, M. D., Warnock, G. n., Khalighi, M. M., Spielman, D. n., Chin, F. T., Hardan, A. Y. 2020


    The gamma aminobutyric acid (GABA) neurotransmission system has been implicated in autism spectrum disorder (ASD). Molecular neuroimaging studies incorporating simultaneous acquisitions of GABA concentrations and GABAA receptor densities can identify objective molecular markers in ASD. We measured both total GABAA receptor densities by using [18F]flumazenil positron emission tomography ([18F]FMZ-PET) and GABA concentrations by using proton magnetic resonance spectroscopy (1H-MRS) in 28 adults with ASD and 29 age-matched typically developing (TD) individuals. Focusing on the bilateral thalami and the left dorsolateral prefrontal cortex (DLPFC) as our regions of interest, we found no differences in GABAA receptor densities between ASD and TD groups. However, 1H-MRS measurements revealed significantly higher GABA/Water (GABA normalized by water signal) in the left DLPFC of individuals with ASD than that of TD controls. Furthermore, a significant gender effect was observed in the thalami, with higher GABA/Water in males than in females. Hypothesizing that thalamic GABA correlates with ASD symptom severity in gender-specific ways, we stratified by diagnosis and investigated the interaction between gender and thalamic GABA/Water in predicting Autism-Spectrum Quotient (AQ) and Ritvo Autism Asperger's Diagnostic Scale-Revised (RAADS-R) total scores. We found that gender is a significant effect modifier of thalamic GABA/Water's relationship with AQ and RAADS-R scores for individuals with ASD, but not for TD controls. When we separated the ASD participants by gender, a negative correlation between thalamic GABA/Water and AQ was observed in male ASD participants. Remarkably, in female ASD participants, a positive correlation between thalamic GABA/Water and AQ was found.

    View details for DOI 10.1038/s41380-020-0756-y

    View details for PubMedID 32376999

  • Pushing the boundaries of psychiatric neuroimaging to ground diagnosis in biology. eNeuro Saggar, M., Uddin, L. Q. 2019


    To accurately detect, track progression of, and develop novel treatments for mental illnesses, a diagnostic framework is needed that is grounded in biological features. Here we present the case for utilizing personalized neuroimaging, computational modeling, standardized computing, and ecologically valid neuroimaging to anchor psychiatric nosology in biology.Significance Statement There is a growing recognition that the boundaries of human neuroimaging data acquisition and analysis must be pushed to ground psychiatric diagnosis in biology. For successful clinical translations, we outline several proposals across the four identified domains of human neuroimaging, namely, (a) reliability of findings; (b) effective clinical translation at the individual subject level; (c) capturing mechanistic insights; and (d) enhancing ecological validity of lab findings. Advances across these domains will be necessary for further progress in psychiatric neuroimaging.

    View details for DOI 10.1523/ENEURO.0384-19.2019

    View details for PubMedID 31685674

  • Large expert-curated database for benchmarking document similarity detection in biomedical literature search DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION Brown, P., Tan, A., El-Esawi, M. A., Liehr, T., Blanck, O., Gladue, D. P., Almeida, G. F., Cernava, T., Sorzano, C. O., Yeung, A. K., Engel, M. S., Chandrasekaran, A., Muth, T., Staege, M. S., Daulatabad, S. V., Widera, D., Zhang, J., Meule, A., Honjo, K., Pourret, O., Yin, C., Zhang, Z., Cascella, M., Flegel, W. A., Goodyear, C. S., van Raaij, M. J., Bukowy-Bieryllo, Z., Campana, L. G., Kurniawan, N. A., Lalaouna, D., Huttner, F. J., Ammerman, B. A., Ehret, F., Cobine, P. A., Tan, E., Han, H., Xia, W., McCrum, C., Dings, R. M., Marinello, F., Nilsson, H., Nixon, B., Voskarides, K., Yang, L., Costa, V. D., Bengtsson-Palme, J., Bradshaw, W., Grimm, D. G., Kumar, N., Martis, E., Prieto, D., Sabnis, S. C., Amer, S. R., Liew, A. C., Perco, P., Rahimi, F., Riva, G., Zhang, C., Devkota, H. P., Ogami, K., Basharat, Z., Fierz, W., Siebers, R., Tan, K., Boehme, K. A., Brenneisen, P., Brown, J. L., Dalrymple, B. P., Harvey, D. J., Ng, G., Werten, S., Bleackley, M., Dai, Z., Dhariwal, R., Gelfer, Y., Hartmann, M. D., Miotla, P., Tamaian, R., Govender, P., Gurney-Champion, O. J., Kauppila, J. H., Zhang, X., Echeverria, N., Subhash, S., Sallmon, H., Tofani, M., Bae, T., Bosch, O., Cuiv, P. O., Danchin, A., Diouf, B., Eerola, T., Evangelou, E., Filipp, F. V., Klump, H., Kurgan, L., Smith, S. S., Terrier, O., Tuttle, N., Ascher, D. B., Janga, S. C., Schulte, L. N., Becker, D., Browngardt, C., Bush, S. J., Gaullier, G., Ide, K., Meseko, C., Werner, G. A., Zaucha, J., Al-Farha, A. A., Greenwald, N. F., Popoola, S. I., Rahman, M., Xu, J., Yang, S. Y., Hiroi, N., Alper, O. M., Baker, C. I., Bitzer, M., Chacko, G., Debrabant, B., Dixon, R., Forano, E., Gilliham, M., Kelly, S., Klempnauer, K., Lidbury, B. A., Lin, M. Z., Lynch, I., Ma, W., Maibach, E. W., Mather, D. E., Nandakumar, K. S., Ohgami, R. S., Parchi, P., Tressoldi, P., Xue, Y., Armitage, C., Barraud, P., Chatzitheochari, S., Coelho, L. P., Diao, J., Doxey, A. C., Gobet, A., Hu, P., Kaiser, S., Mitchell, K. M., Salama, M. F., Shabalin, I. G., Song, H., Stevanovic, D., Yadollahpour, A., Zeng, E., Zinke, K., Alimba, C. G., Beyene, T. J., Cao, Z., Chan, S. S., Gatchell, M., Kleppe, A., Piotrowski, M., Torga, G., Woldesemayat, A. A., Cosacak, M. I., Haston, S., Ross, S. A., Williams, R., Wong, A., Abramowitz, M. K., Effiong, A., Lee, S., Abid, M., Agarabi, C., Alaux, C., Albrecht, D. R., Atkins, G. J., Beck, C. R., Bonvin, A. J., Bourke, E., Brand, T., Braun, R. J., Bull, J. A., Cardoso, P., Carter, D., Delahay, R. M., Ducommun, B., Duijf, P. G., Epp, T., Eskelinen, E., Fallah, M., Farber, D. B., Fernandez-Triana, J., Feyerabend, F., Florio, T., Friebe, M., Furuta, S., Gabrielsen, M., Gruber, J., Grybos, M., Han, Q., Heinrich, M., Helantera, H., Huber, M., Jeltsch, A., Jiang, F., Josse, C., Jurman, G., Kamiya, H., de Keersmaecker, K., Kristiansson, E., de Leeuw, F., Li, J., Liang, S., Lopez-Escamez, J. A., Lopez-Ruiz, F. J., Marchbank, K. J., Marschalek, R., Martin, C. S., Miele, A. E., Montagutelli, X., Morcillo, E., Nicoletti, R., Niehof, M., O'Toole, R., Ohtomo, T., Oster, H., Palma, J., Paterson, R., Peifer, M., Portilla, M., Portillo, M. C., Pritchard, A. L., Pusch, S., Raghava, G. S., Roberts, N. J., Ross, K., Schuele, B., Sergeant, K., Shen, J., Stella, A., Sukocheva, O., Uversky, V. N., Vanneste, S., Villet, M. H., Viveiros, M., Vorholt, J. A., Weinstock, C., Yamato, M., Zabetakis, I., Zhao, X., Ziegler, A., Aizat, W. M., Atlas, L., Bridges, K. M., Chakraborty, S., Deschodt, M., Domingues, H. S., Esfahlani, S. S., Falk, S., Guisado, J. L., Kane, N. C., Kueberuwa, G., Lau, C. L., Liang, D., Liu, E., Luu, A. M., Ma, C., Ma, L., Moyer, R., Norris, A. D., Panthee, S., Parsons, J. R., Peng, Y., Pinto, I., Reschke, C. R., Sillanpaa, E., Stewart, C. J., Uhle, F., Yang, H., Zhou, K., Zhu, S., Ashry, M., Bergsland, N., Berthold, M., Chen, C., Colella, V., Cuypers, M., Eskew, E. A., Fan, X., Gajda, M., Gonzalezlez-Prendes, R., Goodin, A., Graham, E. B., Groen, E. N., Gutierrez-Sacristan, A., Habes, M., Heffler, E., Higginbottom, D. B., Janzen, T., Jayaraman, J., Jibb, L. A., Jongen, S., Kinyanjui, T., Koleva-Kolarova, R. G., Li, Z., Liu, Y., Lund, B. A., Lussier, A. A., Ma, L., Mier, P., Moore, M. D., Nagler, K., Orme, M. W., Pearson, J. A., Prajapati, A. S., Saito, Y., Troder, S. E., Uchendu, F., Verloh, N., Voutchkova, D. D., Abu-Zaid, A., Bakkach, J., Baumert, P., Dono, M., Hanson, J., Herbelet, S., Hobbs, E., Kulkarni, A., Kumar, N., Liu, S., Loft, N. D., Reddan, T., Senghore, T., Vindin, H., Xu, H., Bannon, R., Chen, B., Cheung, J. K., Cooper, J., Esnakul, A. K., Feghali, K. A., Ghelardi, E., Gnasso, A., Horbar, J., Lai, H. M., Li, J., Ma, L., Ma, R., Pan, Z., Peres, M. A., Pranata, R., Seow, E., Sydes, M., Testoni, I., Westermair, A. L., Yang, Y., Afnan, M., Albiol, J., Albuquerque, L. G., Amiya, E., Amorim, R. M., An, Q., Andersen, S. U., Aplin, J. D., Argyropoulos, C., Asmann, Y. W., Assaeed, A. M., Atanasov, A. G., Atchison, D. A., Avery, S. V., Avillach, P., Baade, P. D., Backman, L., Badie, C., Baldi, A., Ball, E., Bardot, O., Barnett, A. G., Basner, M., Batra, J., Bazanova, O. M., Beale, A., Beddoe, T., Bell, M. L., Berezikov, E., Berners-Price, S., Bernhardt, P., Berry, E., Bessa, T. B., Billington, C., Birch, J., Blakely, R. D., Blaskovich, M. T., Blum, R., Boelaert, M., Bogdanos, D., Bosch, C., Bourgoin, T., Bouvard, D., Boykin, L. M., Bradley, G., Braun, D., Brownlie, J., Bruhl, A., Burt, A., Butler, L. M., Byrareddy, S. N., Byrne, H. J., Cabantous, S., Calatayud, S., Candal, E., Carlson, K., Casillas, S., Castelvetro, V., Caswell, P. T., Cavalli, G., Cerovsky, V., Chagoyen, M., Chen, C., Chen, D. F., Chen, H., Chen, H., Chen, J., Chen, Y., Cheng, C., Cheng, J., Chinapaw, M., Chinopoulos, C., Cho, W. S., Chong, L., Chowdhury, D., Chwalibog, A., Ciresi, A., Cockcroft, S., Conesa, A., Cook, P. A., Cooper, D. N., Coqueret, O., Corea, E. M., Costa, E., Coupland, C., Crawford, S. Y., Cruz, A. D., Cui, H., Cui, Q., Culver, D. C., D'Angiulli, A., Dahms, T. S., Daigle, F., Dalgleish, R., Danielsen, H. E., Darras, S., Davidson, S. M., Day, D. A., Degirmenci, V., Demaison, L., Devriendt, K., Ding, J., Dogan, Y., Dong, X. C., Donner, C. F., Dressick, W., Drevon, C. A., Duan, H., Ducho, C., Dumaz, N., Dwarakanath, B. S., Ebell, M. H., Eisenhardt, S., Elkum, N., Engel, N., Erickson, T. B., Fairhead, M., Faville, M. J., Fejzo, M. S., Festa, F., Feteira, A., Flood-Page, P., Forsayeth, J., Fox, S. A., Franks, S. J., Frentiu, F. D., Frilander, M. J., Fu, X., Fujita, S., Galea, I., Galluzzi, L., Gani, F., Ganpule, A. P., Garcia-Alix, A., Gedye, K., Giordano, M., Giunta, C., Gleeson, P. A., Goarant, C., Gong, H., Gora, D., Gough, M. J., Goyal, R., Graham, K. E., Grande-Perez, A., Graves, P. M., Greidanus, H., Grice, D., Grunau, C., Gumulya, Y., Guo, Y., Gurevich, V. V., Gusev, O., Hacker, E., Hage, S. R., Hagen, G., Hahn, S., Haller, D. M., Hammerschmidt, S., Han, J., Han, R., Handfield, M., Hapuarachchi, H. C., Harder, T., Hardingham, J. E., Heck, M., Heers, M., Hew, K. F., Higuchi, Y., St Hilaire, C., Hilton, R., Hodzic, E., Hone, A., Hongoh, Y., Hu, G., Huber, H. P., Hueso, L. E., Huirne, J., Hurt, L., Idborg, H., Ikeo, K., Ingley, E., Jakeman, P. M., Jensen, A., Jia, H., Jia, H., Jia, S., Jiang, J., Jiang, X., Jin, Y., Jo, D., Johnson, A. M., Johnston, M., Jonscher, K. R., Jorens, P. G., Jorgensen, J. L., Joubert, J. W., Jung, S., Junior, A. M., Kahan, T., Kamboj, S. K., Kang, Y., Karamanos, Y., Karp, N. A., Kelly, R., Kenna, R., Kennedy, J., Kersten, B., Khalaf, R. A., Khalid, J. M., Khatlani, T., Khider, T., Kijanka, G. S., King, S. B., Kluz, T., Knox, P., Kobayashi, T., Koch, K., Kohonen-Corish, M. J., Kong, X., Konkle-Parker, D., Korpela, K. M., Kostrikis, L. G., Kraiczy, P., Kratz, H., Krause, G., Krebsbach, P. H., Kristensen, S. R., Kumari, P., Kunimatsu, A., Kurdak, H., Kwon, Y. D., Lachat, C., Lagisz, M., Laky, B., Lammerding, J., Lange, M., Larrosa, M., Laslett, A. L., LeClair, E. E., Lee, K., Lee, M., Lee, M., Li, G., Li, J., Lieb, K., Lim, Y. Y., Lindsey, M. L., Line, P., Liu, D., Liu, F., Liu, H., Liu, H., Lloyd, V. K., Lo, T., Locci, E., Loidl, J., Lorenzen, J., Lorkowski, S., Lovell, N. H., Lu, H., Lu, W., Lu, Z., Luengo, G. S., Lundh, L., Lysy, P. A., Mabb, A., Mack, H. G., Mackey, D. A., Mahdavi, S. R., Maher, P., Maher, T., Maity, S. N., Malgrange, B., Mamoulakis, C., Mangoni, A. A., Manke, T., Manstead, A. R., Mantalaris, A., Marsal, J., Marschall, H., Martin, F. L., Martinez-Raga, J., Martinez-Salas, E., Mathieu, D., Matsui, Y., Maza, E., McCutcheon, J. E., Mckay, G. J., McMillan, B., McMillan, N., Meads, C., Medina, L., Merrick, B., Metzger, D. W., Meunier, F. A., Michaelis, M., Micheau, O., Mihara, H., Mintz, E. M., Mizukami, T., Moalic, Y., Mohapatra, D. P., Monteiro, A., Montes, M., Moran, J. V., Morozov, S. Y., Mort, M., Murai, N., Murphy, D. J., Murphy, S. K., Murray, S. A., Naganawa, S., Nammi, S., Nasios, G., Natoli, R. M., Nguyen, F., Nicol, C., van Nieuwerburgh, F., Nilsen, E. B., Nobile, C. J., O'Mahony, M., Ohlsson, S., Olatunbosun, O., Olofsson, P., Ortiz, A., Ostrikov, K., Otto, S., Outeiro, T. F., Ouyang, S., Paganoni, S., Page, A., Palm, C., Paradies, Y., Parsons, M. H., Parsons, N., Pascal, P., Paul, E., Peckham, M., Pedemonte, N., Pellizzon, M. A., Petrelli, M., Pichugin, A., Pinto, C. C., Plevris, J. N., Pollesello, P., Polz, M., Ponti, G., Porcelli, P., Prince, M., Quinn, G. P., Quinn, T. J., Ramula, S., Rappsilber, J., Rehfeldt, F., Reiling, J. H., Remacle, C., Rezaei, M., Riddick, E. W., Ritter, U., Roach, N. W., Roberts, D. D., Robles, G., Rodrigues, T., Rodriguez, C., Roislien, J., Roobol, M. J., Rowe, J., Ruepp, A., van Ruitenbeek, J., Rust, P., Saad, S., Sack, G. H., Santos, M., Saudemont, A., Sava, G., Schrading, S., Schramm, A., Schreiber, M., Schuler, S., Schymkowitz, J., Sczyrba, A., Seib, K. L., Shi, H., Shimada, T., Shin, J., Shortt, C., Silveyra, P., Skinner, D., Small, I., Smeets, P. M., So, P., Solano, F., Sonenshine, D. E., Song, J., Southall, T., Speakman, J. R., Srinivasan, M. V., Stabile, L. P., Stasiak, A., Steadman, K. J., Stein, N., Stephens, A. W., Stewart, D. I., Stine, K., Storlazzi, C., Stoynova, N. V., Strzalka, W., Suarez, O. M., Sultana, T., Sumant, A. V., Summers, M. J., Sun, G., Tacon, P., Tanaka, K., Tang, H., Tanino, Y., Targett-Adams, P., Tayebi, M., Tayyem, R., Tebbe, C. C., Telfer, E. E., Tempel, W., Teodorczyk-Injeyan, J. A., Thijs, G., Thorne, S., Thrift, A. G., Tiffon, C., Tinnefeld, P., Tjahjono, D. H., Tolle, F., Toth, E., del Tredici, A. L., Tsapas, A., Tsirigotis, K., Turak, A., Tzotzos, G., Udo, E. E., Utsumi, T., Vaidyanathan, S., Vaillant, M., Valsesia, A., Vandenbroucke, R. E., Veiga, F. H., Vendrell, M., Vesk, P. A., Vickers, P., Victor, V. M., Villemur, R., Vohl, M., Voolstra, C. R., Vuillemin, A., Wakelin, S., Waldron, L., Walsh, L. J., Wang, A. Y., Wang, F., Wang, Y., Watanabe, Y., Weigert, A., Wen, J., Wham, C., White, E. P., Wiener, J., Wilharm, G., Wilkinson, S., Willmann, R., Wilson, C., Wirth, B., Wojan, T. R., Wolff, M., Wong, B. M., Wu, T., Wuerbel, H., Xiao, X., Xu, D., Xu, J. W., Xu, J., Xue, B., Yalcin, S., Yan, H., Yang, E., Yang, S., Yang, W., Ye, Y., Ye, Z., Yli-Kauhaluoma, J., Yoneyama, H., Yu, Y., Yuan, G., Yuh, C., Zaccolo, M., Zeng, C., Zevnik, B., Zhang, C., Zhang, L., Zhang, L., Zhang, Y., Zhang, Y., Zhang, Z., Zhang, Z., Zhao, Y., Zhou, M., Zuberbier, T., Aanei, C. M., Ahmad, R., Al-Lawama, M., Alanio, A., Allardyce, J., Alonso-Caneiro, D., Atack, J. M., Baier, D., Bansal, A., Benezeth, Y., Berbesque, C., Berrevoet, F., Biedermann, P. W., Bijleveld, E., Bittner, F., Blombach, F., Van den Bos, W., Boudreau, S. A., Bramoweth, A. D., Braubach, O., Cai, Y., Campbell, M., Cao, Z., Catry, T., Chen, X., Cheng, S., Chung, H., Chavez-Fumagalli, M. A., Conway, A., Costa, B. M., Cyr, N., Dean, L. T., Denzel, M. S., Dlamini, S. V., Dudley, K. J., Dufies, M., Ecke, T., Eckweiler, D., Eixarch, E., El-Adawy, H., Emmrich, J. V., Eustace, A. J., Falter-Wagner, C. M., Fuss, J., Gao, J., Gill, M. R., Gloyn, L., Goggs, R., Govinden, U., Greene, G., Greiff, V., Grundle, D. S., Gruneberg, P., Gumede, N., Haore, G., Harrison, P., Hoenner, X., Hojsgaard, D., Hori, H., Ikonomopoulou, M. P., Jeurissen, P., Johnson, D. M., Kabra, D., Kamagata, K., Karmakar, C., Kasian, O., Kaye, L. K., Khan, M. M., Kim, Y., Kish, J. K., Kobold, S., Kohanbash, G., Kohls, G., Kugler, J., Kumar, G., Lacy-Colson, J., Latif, A., Lauschke, V. M., Li, B., Lim, C. J., Liu, F., Liu, X., Lu, J., Lu, Q., Mahavadi, P., Marzocchi, U., McGarrigle, C. A., van Meerten, T., Min, R., Moal, I., Molari, M., Molleman, L., Mondal, S. R., Van de Mortel, T., Moss, W. N., Moultos, O. A., Mukherjee, M., Nakayama, K., Narayan, E., Navaratnarajah, Neumann, P., Nie, J., Nie, Y., Niemeyer, F., Fiona, Nwaiwu, O., Oldenmenger, W. H., Olumayede, E., Ou, J., Pallebage-Gamarallage, M., Pearce, S. P., Pelkonen, T., Pelleri, M. C., Pereira, J. L., Pheko, M., Pinto, K. A., Piovesan, A., Pluess, M., Podolsky, I. M., Prescott, J., Qi, D., Qi, X., Raikou, V. D., Ranft, A., Rhodes, J., Rotge, J., Rowe, A. D., Saggar, M., Schuon, R. A., Shahid, S., Shalchyan, V., Shirvalkar, P., Shiryayev, O., Singh, J., Smout, M. J., Soares, A., Song, C., Srivastava, K., Srivastava, R. K., Sun, J., Szabo, A., Szymanski, W., Tai, C. P., Takeuchi, H., Tanadini-Lang, S., Tang, F., Tao, W., Theron, G., Tian, C. F., Tian, Y., Tuttle, L. M., Valenti, A., Verlot, P., Walker, M., Wang, J., Welter, D., Winslade, M., Wu, D., Wu, Y., Xiao, H., Xu, B., Xu, J., Xu, Z., Yang, D., Yang, M., Yankilevich, P., You, Y., Yu, C., Zhan, J., Zhang, G., Zhang, K., Zhang, T., Zhang, Y., Zhao, G., Zhao, J., Zhou, X., Zhu, Z., Ajani, P. A., Anazodo, U. C., Bagloee, S. A., Bail, K., Bar, I., Bathelt, J., Benkeser, D., Bernier, M. L., Blanchard, A. M., Boakye, D. W., Bonatsos, V., Boon, M. H., Bouboulis, G., Bromfield, E., Brown, J., Bul, K. M., Burton, K. J., Butkowski, E. G., Carroll, G., Chao, F., Charrier, E. E., Chen, X., Chen, Y., Chenguang, Choi, J. R., Christoffersen, T., Comel, J. C., Cosse, C., Cui, Y., van Dessel, P., Dhaval, Diodato, D., Duffey, M., Dutt, A., Egea, L. G., El-Said, M., Faye, M., Fernandez-Fernandez, B., Foley, K. G., Founou, L. L., Fu, F., Gadelkareem, R. A., Galimov, E., Garip, G., Gemmill, A., Gouil, Q., Grey, J., Gridneva, Z., Grothe, M. J., Grebert, T., Guerrero, F., Guignard, L., Haenssgen, M. J., Hasler, D., Holgate, J. Y., Huang, A., Hulse-Kemp, A. M., Jean-Quartier, C., Jeon, S., Jia, Y., Jutzeler, C., Kalatzis, P., Karim, M., Karsay, K., Keitel, A., Kempe, A., Keown, J. R., Khoo, C. M., Khwaja, N., Kievit, R. A., Kosanic, A., Koutoukidis, D. A., Kramer, P., Kumar, D., Kirag, N., Lanza, G., Le, T. D., Leem, J. W., Leightley, D., Leite, A., Lercher, L., Li, Y., Lim, R., Lima, L. A., Lin, L., Ling, T., Liu, Y., Liu, Z., Lu, Y., Lum, F. M., Luo, H., Machhi, J., Macleod, A., Macwan, I., Madala, H. R., Madani, N., de Maio, N., Makowiecki, K., Mallinson, D. J., Margelyte, R., Maria, C., Markonis, Y., Marsili, L., Mavoa, S., McWilliams, L., Megersa, M., Mendes, C. M., Menichetti, J., Mercieca-Bebber, R., Miller, J. J., Minde, D. M., Minges, A., Mishra, E., Mishra, V. R., Moores, C., Morrice, N., Moskalensky, A. E., Navarin, N., Negera, E., Nolet, P., Nordberg, A., Norden, R., Nowicki, J. P., Olova, N., Olszewski, P., Onzima, R., Pan, C., Park, C., Park, D., Park, S., Patil, C. D., Pedro, S. A., Perry, S. R., Peter, J., Peterson, B. M., Pezzuolo, A., Pozdnyakov, I., Qian, S., Qin, L., Rafe, A., Raote, I., Raza, A., Rebl, H., Refai, O., Regan, T., Richa, T., Richardson, M. F., Robinson, K. R., Rossoni, L., Rouet, R., Safaei, S., Schneeberger, P. H., Schwotzer, D., Sebastian, A., Selinski, J., Seltmann, S., Sha, F., Shalev, N., Shang, J., Singer, J., Singh, M., Smith, T., Solomon-Moore, E., Song, L., Soraggi, S., Stanley, R., Steckhan, N., Strobl, F., Subissi, L., Supriyanto, I., Surve, C. R., Suzuki, T., Syme, C., Sorelius, K., Tang, Y., Tantawy, M., Tennakoon, S., Teseo, S., Toelzer, C., Tomov, N., Tovar, M., Tran, L., Tripathi, S., Tuladhar, A. M., Ukubuiwe, A. C., Ung, C. L., Valgepea, K., Vatanparast, H., Vidal, A., Wang, F., Wang, Q., Watari, R., Webster, R., Webster, R., Wei, J., Wibowo, D., Wingenbach, T. H., Xavier, R. M., Xiao, S., Xiong, P., Xu, S., Xu, S., Yao, R., Yao, W., Yin, Q., Yu, Y., Zaitsu, M., Zeineb, Z., Zhan, X., Zhang, J., Zhang, R., Zhang, W., Zhang, X., Zheng, S., Zhou, B., Zhou, X., Ahmad, H., Akinwumi, S. A., Albery, G. F., Alhowimel, A., Ali, J., Alshehri, M., Alsuhaibani, M., Anikin, A., Azubuike, S. O., Bach-Mortensen, A., Baltiansky, L., Bartas, M., Belachew, K. Y., Bhardwaj, V., Binder, K., Bland, N. S., Boah, M., Bullen, B., Calabro, G. E., Callahan, T. J., Cao, B., Chalmers, K., Chang, W., Che, Z., Chen, A. Y., Chen, H., Chen, H., Chen, Y., Chen, Z., Choi, Y., Chowdhury, M. K., Christensen, M. R., Cooke, R. C., Cottini, M., Covington, N. V., Cunningham, C., Delarocque, J., Devos, L., Dhar, A. R., Ding, K., Dong, K., Dong, Z., Dreyer, N., Ekstrand, C., Fardet, T., Feleke, B. E., Feurer, T., Freitas, A., Gao, T., Asefa, N. G., Giganti, F., Grabowski, P., Guerra-Mora, J. R., Guo, C., Guo, X., Gupta, H., He, S., Heijne, M., Heinemann, S., Hogrebe, A., Huang, Z., Iskander-Rizk, S., Iyer, L. M., Jahan, Y., James, A. S., Joel, E., Joffroy, B., Jegousse, C., Kambondo, G., Karnati, P., Kaya, C., Ke, A., Kelly, D., Kickert, R., Kidibule, P. E., Kieselmann, J. P., Kim, H. J., Kitazawa, T., Lamberts, A., Li, Y., Liang, H., Linn, S. N., Litfin, T., Liusuo, W., Lygirou, V., Mahato, A. K., Mai, Z., Major, R. W., Mali, S., Mallis, P., Mao, W., Mao, W., Marvin-Dowle, K., Marvin-Dowle, K., Mason, L. D., Merideth, B., Merino-Plaza, M. J., Merlaen, B., Messina, R., Mishra, A. K., Muhammad, J., Musinguzi, C., Nanou, A., Naqash, A., Nguyen, J. T., Nguyen, T. H., Ni, D., Nida, Notcovich, S., Ohst, B., Ollivier, Q. R., Osses, D. F., Peng, X., Plantinga, A., Pulia, M., Rafiq, M., Raman, A., Raucher-Chene, D., Rawski, R., Ray, A., Razak, L. A., Rudolf, K., Rusch, P., Sadoine, M. L., Schmidt, A., Schurr, R., Searles, S., Sharma, S., Sheehan, B., Shi, C., Shohayeb, B., Sommerlad, A., Strehlow, J., Sun, X., Sundar, R., Taherzadeh, G., Tahir, N. M., Tang, J., Testa, J., Tian, Z., Tingting, Q., Verheijen, G. P., Vickstrom, C., Wang, T., Wang, X., Wang, Z., Wei, P., Wilson, A., Wyart, Yassine, A., Yousefzadeh, A., Zare, A., Zeng, Z., Zhang, C., Zhang, H., Zhang, L., Zhang, T., Zhang, W., Zhang, Z., Zhou, J., Zhu, D., Adamo, V., Adeyemo, A. A., Aggelidou, M., Al-Owaifeer, A. M., Al-Riyami, A. Z., Alzghari, S. K., Andersen, V., Angus, K., Asaduzzaman, M., Asady, H., Ato, D., Bai, X., Baines, R. L., Ballantyne, M., Ban, B., Beck, J., Ben-Nafa, W., Black, E., Blancher, A., Blankstein, R., Bodagh, N., Borges, P. V., Brooks, A., Brox-Ponce, J., Brunetti, A., Canham, C. D., Carninci, P., Carvajal, R., Chang, S. C., Chao, J., Chatterjee, P., Chen, H., Chen, Y., Chhatriwalla, A. K., Chikowe, I., Chuang, T., Collevatti, R. G., Valera-Cornejo, D. A., Cuenda, A., Dao, M., Dauga, D., Deng, Z., Devkota, K., Doan, L. V., Elewa, Y. A., Fan, D., Faruk, M., Feifei, S., Ferguson, T. S., Fleres, F., Foster, E. J., Foster, C., Furer, T., Gao, Y., Garcia-Rivera, E. J., Gazdar, A., George, R. B., Ghosh, S., Gianchecchi, E., Gleason, J. M., Hackshaw, A., Hall, A., Hall, R., Harper, P., Hogg, W. E., Huang, G., Hunter, K. E., IJzerman, A. P., Jesus, C., Jian, G., Lewis, J. S., Kanj, S. S., Kaur, H., Kelly, S., Kheir, F., Kichatova, V. S., Kiyani, M., Klein, R., Kovesi, T., Kraschnewski, J. L., Kumar, A. P., Labutin, D., Lazo-Langner, A., Leclercq, G., Li, M., Li, Q., Li, T., Li, Y., Liao, W., Liao, Z., Lin, J., Lizer, J., Lobreglio, G., Lowies, C., Lu, C., Majeed, H., Martin, A., Martinez-Sobrido, L., Meresh, E., Middelveen, M., Mohebbi, A., Mota, J., Mozaheb, Z., Muyaya, L., Nandhakumar, A., Ng, S. X., Obeidat, M., Oh, D., Owais, M., Pace-Asciak, P., Panwar, A., Park, C., Patterson, C., Penagos-Tabaree, F., Pianosi, P. T., Pinzi, V., Pridans, C., Psaroulaki, A., Pujala, R., Pulido-Arjona, L., Qi, P., Rahman, P., Rai, N. K., Rassaf, T., Refardt, J., Ricciardi, W., Riess, O., Rovas, A., Sacks, F. M., Saleh, S., Sampson, C., Schmutz, A., Sepanski, R., Sharma, N., Singh, M., Spearman, P., Subramaniapillai, M., Swali, R., Tan, C. M., Tellechea, J. I., Thomas, L., Tong, X., Veys, R., Vitriol, V., Wang, H., Wang, J., Wang, J., Waugh, J., Webb, S. A., Williams, B. A., Workman, A. D., Xiang, T., Xie, L., Xu, J., Xu, T., Yang, C., Yoon, J. G., Yuan, C. M., Zaritsky, A., Zhang, Y., Zhao, H., Zuckerman, H., Lyu, R., Pullan, W., Zhou, Y., RELISH Consortium 2019
  • Creativity slumps and bumps: Examining the neurobehavioral basis of creativity development during middle childhood NEUROIMAGE Saggar, M., Xie, H., Beaty, R. E., Stankov, A. D., Schreier, M., Reiss, A. L. 2019; 196: 94–101
  • Implementing Evolutionary Optimization to Model Neural Functional Connectivity Maile, K., Saggar, M., Miikkulainen, R., ACM ASSOC COMPUTING MACHINERY. 2019: 1731–33
  • Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis NETWORK NEUROSCIENCE Geniesse, C., Sporns, O., Petri, G., Saggar, M. 2019; 3 (3): 763–78
  • Towards a new approach to reveal dynamical organization of the brain using topological data analysis NATURE COMMUNICATIONS Saggar, M., Sporns, O., Gonzalez-Castillo, J., Bandettini, P. A., Carlsson, G., Glover, G., Reiss, A. L. 2018; 9: 1399


    Little is known about how our brains dynamically adapt for efficient functioning. Most previous work has focused on analyzing changes in co-fluctuations between a set of brain regions over several temporal segments of the data. We argue that by collapsing data in space or time, we stand to lose useful information about the brain's dynamical organization. Here we use Topological Data Analysis to reveal the overall organization of whole-brain activity maps at a single-participant level-as an interactive representation-without arbitrarily collapsing data in space or time. Using existing multitask fMRI datasets, with the known ground truth about the timing of transitions from one task-block to next, our approach tracks both within- and between-task transitions at a much faster time scale (~4-9 s) than before. The individual differences in the revealed dynamical organization predict task performance. In summary, our approach distills complex brain dynamics into interactive and behaviorally relevant representations.

    View details for PubMedID 29643350

  • Creativity in the Twenty-first Century: The Added Benefit of Training and Cooperation DESIGN THINKING RESEARCH: MAKING DISTINCTIONS: COLLABORATION VERSUS COOPERATION Mayseless, N., Saggar, M., Hawthorne, G., Reiss, A., Plattner, H., Meinel, C., Leifer, L. 2018: 239–49
  • Identification of biotypes in Attention-Deficit/Hyperactivity Disorder, a report from a randomized, controlled trial. Personalized medicine in psychiatry Leikauf, J. E., Griffiths, K. R., Saggar, M., Hong, D. S., Clarke, S., Efron, D., Tsang, T. W., Hermens, D. F., Kohn, M. R., Williams, L. M. 2017; 3: 8-17


    Attention-Deficit/Hyperactivity Disorder (ADHD) is a heterogeneous disorder. Current subtypes lack longitudinal stability or prognostic utility. We aimed to identify data-driven biotypes using multiple cognitive measures, then to validate these biotypes using EEG, ECG, and clinical response to atomoxetine as external validators. Study design was a double-blind, randomized, placebo-controlled crossover trial of atomoxetine including 116 subjects ages 6 through 17 with diagnosis of ADHD and 56 typically developing controls. Initial features for unsupervised machine learning included a cognitive battery with 20 measures affected in ADHD. External validators included baseline mechanistic validators (using electroencephalogram/EEG and electrocardiogram/ECG) and clinical response (ADHD Rating Scale and correlation with cognitive change). One biotype, labeled impulsive cognition, was characterized by increased errors of commission and shorter reaction time, had greater EEG slow wave (theta/delta) power and greater resting heart rate. The second biotype, labeled inattentive cognition, was characterized by longer/more variable reaction time and errors of omission, had lower EEG fast wave (beta) power, resting heart rate that did not differ from controls, and a strong correlation (r = -0.447, p < 0.001) between clinical response to atomoxetine and improvement in verbal memory immediate recall. ADHD comprises at least two biotypes that cut across current subtype criteria and that may reflect distinct arousal mechanisms. The findings provide evidence that further investigation of cognitive subtypes may be at least as fruitful as symptom checklist-based subtypes for development of biologically-based diagnostics and interventions for ADHD.

    View details for DOI 10.1016/j.pmip.2017.02.001

    View details for PubMedID 35637915

    View details for PubMedCentralID PMC9148272

  • Compensatory Hyperconnectivity in Developing Brains of Young Children With Type 1 Diabetes DIABETES Saggar, M., Tsalikian, E., Mauras, N., Mazaika, P., White, N. H., Weinzimer, S., Buckingham, B., Hershey, T., Reiss, A. L. 2017; 66 (3): 754-762


    Sustained dysregulation of blood glucose (hyper or hypoglycemia) associated with type 1 diabetes (T1D) has been linked to cognitive deficits and altered brain anatomy and connectivity. However, a significant gap remains with respect to how T1D affects spontaneous at-rest connectivity in young developing brains. Here, using a large multi-site study, resting state functional Magnetic Resonance Imaging (rsfMRI) data were examined in young children with T1D (N=57, mean age=7.88 years; 27F) as compared to age-matched non-diabetic controls (N=26, mean age=7.43 years; 14F). Using both model-driven seed-based analysis and model-free independent component analysis (ICA) and controlling for age, site and sex, converging results were obtained suggesting increased connectivity in young children with T1D as compared to non-diabetic controls. Further, increased connectivity in children with T1D was observed to be positively associated with cognitive functioning. The observed positive association of connectivity with cognitive functioning in T1D, without overall group differences in cognitive function, suggests a putative compensatory role of hyper-intrinsic connectivity in the brain in children with this condition. Altogether, our study attempts to fill a critical gap in knowledge regarding how dysglycemia in T1D might affect the brain's intrinsic connectivity at very young ages.

    View details for DOI 10.2337/db16-0414

    View details for Web of Science ID 000394634100020

  • Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics CEREBRAL CORTEX Bruno, J. L., Hosseini, S. M., Saggar, M., Quintin, E., Raman, M. M., Reiss, A. L. 2017; 27 (3): 2249-2259


    Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, is associated with significant behavioral, social, and neurocognitive deficits. Understanding structural brain network topology in FXS provides an important link between neurobiological and behavioral/cognitive symptoms of this disorder. We investigated the connectome via whole-brain structural networks created from group-level morphological correlations. Participants included 100 individuals: 50 with FXS and 50 with typical development, age 11-23 years. Results indicated alterations in topological properties of structural brain networks in individuals with FXS. Significantly reduced small-world index indicates a shift in the balance between network segregation and integration and significantly reduced clustering coefficient suggests that reduced local segregation shifted this balance. Caudate and amygdala were less interactive in the FXS network further highlighting the importance of subcortical region alterations in the neurobiological signature of FXS. Modularity analysis indicates that FXS and typically developing groups' networks decompose into different sets of interconnected sub networks, potentially indicative of aberrant local interconnectivity in individuals with FXS. These findings advance our understanding of the effects of fragile X mental retardation protein on large-scale brain networks and could be used to develop a connectome-level biological signature for FXS.

    View details for DOI 10.1093/cercor/bhw055

    View details for Web of Science ID 000397636600043

  • X-Chromosome Effects on Attention Networks: Insights from Imaging Resting-State Networks in Turner Syndrome. Cerebral cortex (New York, N.Y. : 1991) Green, T. n., Saggar, M. n., Ishak, A. n., Hong, D. S., Reiss, A. L. 2017: 1–8


    Attention deficit hyperactivity disorder (ADHD) is strongly affected by sex, but sex chromosomes' effect on brain attention networks and cognition are difficult to examine in humans. This is due to significant etiologic heterogeneity among diagnosed individuals. In contrast, individuals with Turner syndrome (TS), who have substantially increased risk for ADHD symptoms, share a common genetic risk factor related to the absence of the X-chromosome, thus serving as a more homogeneous genetic model. Resting-state functional MRI was employed to examine differences in attention networks between girls with TS (n = 40) and age- sex- and Tanner-matched controls (n = 33). We compared groups on resting-state functional connectivity measures from data-driven independent components analysis (ICA) and hypothesis-based seed analysis. Using ICA, reduced connectivity was observed in both frontoparietal and dorsal attention networks. Similarly, using seeds in the bilateral intraparietal sulcus (IPS), reduced connectivity was observed between IPS and frontal and cerebellar regions. Finally, we observed a brain-behavior correlation between IPS-cerebellar connectivity and cognitive attention measures. These findings indicate that X-monosomy contributes affects to attention networks and cognitive dysfunction that might increase risk for ADHD. Our findings not only have clinical relevance for girls with TS, but might also serve as a biological marker in future research examining the effects of the intervention that targets attention skills.

    View details for PubMedID 28981595

  • Changes in Brain Activation Associated with Spontaneous Improvization and Figural Creativity After Design-Thinking-Based Training: A Longitudinal fMRI Study. Cerebral cortex Saggar, M., Quintin, E., Bott, N. T., Kienitz, E., Chien, Y., Hong, D. W., Liu, N., Royalty, A., Hawthorne, G., Reiss, A. L. 2016


    Creativity is widely recognized as an essential skill for entrepreneurial success and adaptation to daily-life demands. However, we know little about the neural changes associated with creative capacity enhancement. For the first time, using a prospective, randomized control design, we examined longitudinal changes in brain activity associated with participating in a five-week design-thinking-based Creative Capacity Building Program (CCBP), when compared with Language Capacity Building Program (LCBP). Creativity, an elusive and multifaceted construct, is loosely defined as an ability to produce useful/appropriate and novel outcomes. Here, we focus on one of the facets of creative thinking-spontaneous improvization. Participants were assessed pre- and post-intervention for spontaneous improvization skills using a game-like figural Pictionary-based fMRI task. Whole-brain group-by-time interaction revealed reduced task-related activity in CCBP participants (compared with LCBP participants) after training in the right dorsolateral prefrontal cortex, anterior/paracingulate gyrus, supplementary motor area, and parietal regions. Further, greater cerebellar-cerebral connectivity was observed in CCBP participants at post-intervention when compared with LCBP participants. In sum, our results suggest that improvization-based creative capacity enhancement is associated with reduced engagement of executive functioning regions and increased involvement of spontaneous implicit processing.

    View details for PubMedID 27307467

  • Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning SCIENTIFIC REPORTS Baker, J. M., Liu, N., Cui, X., Vrticka, P., Saggar, M., Hosseini, S. M., Reiss, A. L. 2016; 6


    Researchers from multiple fields have sought to understand how sex moderates human social behavior. While over 50 years of research has revealed differences in cooperation behavior of males and females, the underlying neural correlates of these sex differences have not been explained. A missing and fundamental element of this puzzle is an understanding of how the sex composition of an interacting dyad influences the brain and behavior during cooperation. Using fNIRS-based hyperscanning in 111 same- and mixed-sex dyads, we identified significant behavioral and neural sex-related differences in association with a computer-based cooperation task. Dyads containing at least one male demonstrated significantly higher behavioral performance than female/female dyads. Individual males and females showed significant activation in the right frontopolar and right inferior prefrontal cortices, although this activation was greater in females compared to males. Female/female dyad's exhibited significant inter-brain coherence within the right temporal cortex, while significant coherence in male/male dyads occurred in the right inferior prefrontal cortex. Significant coherence was not observed in mixed-sex dyads. Finally, for same-sex dyads only, task-related inter-brain coherence was positively correlated with cooperation task performance. Our results highlight multiple important and previously undetected influences of sex on concurrent neural and behavioral signatures of cooperation.

    View details for DOI 10.1038/srep26492

    View details for Web of Science ID 000377330900001

    View details for PubMedID 27270754

    View details for PubMedCentralID PMC4897646

  • Surface-based morphometry reveals distinct cortical thickness and surface area profiles in Williams syndrome AMERICAN JOURNAL OF MEDICAL GENETICS PART B-NEUROPSYCHIATRIC GENETICS Green, T., Fierro, K. C., Raman, M. M., Saggar, M., Sheau, K. E., Reiss, A. L. 2016; 171 (3): 402-413


    Morphometric investigations of brain volumes in Williams syndrome (WS) consistently show significant reductions in gray matter volume compared to controls. Cortical thickness (CT) and surface area (SA) are two constituent parts of cortical gray matter volume that are considered genetically distinguishable features of brain morphology. Yet, little is known about the independent contribution of cortical CT and SA to these volumetric differences in WS. Thus, our objectives were: (i) to evaluate whether the microdeletion in chromosome 7 associated with WS has a distinct effect on CT and SA, and (ii) to evaluate age-related variations in CT and SA within WS. We compared CT and SA values in 44 individuals with WS to 49 age- and sex-matched typically developing controls. Between-group differences in CT and SA were evaluated across two age groups: young (age range 6.6-18.9 years), and adults (age range 20.2-51.5 years). Overall, we found contrasting effects of WS on cortical thickness (increases) and surface area (decreases). With respect to brain topography, the between-group pattern of CT differences showed a scattered pattern while the between-group surface area pattern was widely distributed throughout the brain. In the adult subgroup, we observed a cluster of increases in cortical thickness in WS across the brain that was not observed in the young subgroup. Our findings suggest that extensive early reductions in surface area are the driving force for the overall reduction in brain volume in WS. The age-related cortical thickness findings might reflect delayed or even arrested development of specific brain regions in WS. © 2016 Wiley Periodicals, Inc.

    View details for DOI 10.1002/ajmg.b.32422

    View details for Web of Science ID 000373029100011

  • Understanding the influence of personality on dynamic social gesture processing: An fMRI study. Neuropsychologia Saggar, M., Vrticka, P., Reiss, A. L. 2016; 80: 71-78


    This fMRI study aimed at investigating how differences in personality traits affect the processing of dynamic and natural gestures containing social versus nonsocial intent. We predicted that while processing gestures with social intent extraversion would be associated with increased activity within the reticulothalamic-cortical arousal system (RTCS), while neuroticism would be associated with increased activity in emotion processing circuits. The obtained findings partly support our hypotheses. We found a positive correlation between bilateral thalamic activity and extraversion scores while participants viewed social (versus nonsocial) gestures. For neuroticism, the data revealed a more complex activation pattern. Activity in the bilateral frontal operculum and anterior insula, extending into bilateral putamen and right amygdala, was moderated as a function of actor-orientation (i.e., first versus third-person engagement) and face-visibility (actor faces visible versus blurred). Our findings point to the existence of factors other than emotional valence that can influence social gesture processing in particular, and social cognitive affective processing in general, as a function of personality.

    View details for DOI 10.1016/j.neuropsychologia.2015.10.039

    View details for PubMedID 26541443

    View details for PubMedCentralID PMC4698311

  • Estimating individual contribution from group-based structural correlation networks. NeuroImage Saggar, M., Hosseini, S. M., Bruno, J. L., Quintin, E., Raman, M. M., Kesler, S. R., Reiss, A. L. 2015; 120: 274-284


    Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders.

    View details for DOI 10.1016/j.neuroimage.2015.07.006

    View details for PubMedID 26162553

  • Neural Correlates of Self-Injurious Behavior in Prader-Willi Syndrome HUMAN BRAIN MAPPING Klabunde, M., Saggar, M., Hustyi, K. M., Hammond, J. L., Reiss, A. L., Hall, S. S. 2015; 36 (10): 4135-4143


    Individuals with Prader-Willi syndrome (PWS), a genetic disorder caused by mutations to the q11-13 region on chromosome 15, commonly show severe skin-picking behaviors that can cause open wounds and sores on the body. To our knowledge, however, no studies have examined the potential neural mechanisms underlying these behaviors. Seventeen individuals with PWS, aged 6-25 years, who showed severe skin-picking behaviors, were recruited and scanned on a 3T scanner. We used functional magnetic resonance imaging (fMRI) while episodes of skin picking were recorded on an MRI-safe video camera. Three participants displayed skin picking continuously throughout the scan, three participants did not display skin picking, and the data for one participant evidenced significant B0 inhomogeneity that could not be corrected. The data for the remaining 10 participants (six male, four female) who displayed a sufficient number of picking and nonpicking episodes were subjected to fMRI analysis. Results showed that regions involved in interoceptive, motor, attention, and somatosensory processing were activated during episodes of skin-picking behavior compared with nonpicking episodes. Scores obtained on the Self-Injury Trauma scale were significantly negatively correlated with mean activation within the right insula and left precentral gyrus. These data indicate that itch and pain processes appear to underlie skin-picking behaviors in PWS, suggesting that interoceptive disturbance may contribute to the severity and maintenance of abnormal skin-picking behaviors in PWS. Implications for treatments are discussed. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.

    View details for DOI 10.1002/hbm.22903

    View details for Web of Science ID 000364219100031

    View details for PubMedID 26173182

  • Examining the neural correlates of emergent equivalence relations in fragile X syndrome PSYCHIATRY RESEARCH-NEUROIMAGING Klabunde, M., Saggar, M., Hustyi, K. M., Kelley, R. G., Reiss, A. L., Hall, S. S. 2015; 233 (3): 373-379


    The neural mechanisms underlying the formation of stimulus equivalence relations are poorly understood, particularly in individuals with specific learning impairments. As part of a larger study, we used functional magnetic resonance imaging (fMRI) while participants with fragile X syndrome (FXS), and age- and IQ-matched controls with intellectual disability, were required to form new equivalence relations in the scanner. Following intensive training on matching fractions to pie charts (A=B relations) and pie charts to decimals (B=C relations) outside the scanner over a 2-day period, participants were tested on the trained (A=B, B=C) relations, as well as emergent symmetry (i.e., B=A and C=B) and transitivity/equivalence (i.e., A=C and C=A) relations inside the scanner. Eight participants with FXS (6 female, 2 male) and 10 controls, aged 10-23 years, were able to obtain at least 66.7% correct on the trained relations in the scanner and were included in the fMRI analyses. Across both groups, results showed that the emergence of symmetry relations was correlated with increased brain activation in the left inferior parietal lobule, left postcentral gyrus, and left insula, broadly supporting previous investigations of stimulus equivalence research in neurotypical populations. On the test of emergent transitivity/equivalence relations, activation was significantly greater in individuals with FXS compared with controls in the right middle temporal gyrus, left superior frontal gyrus and left precuneus. These data indicate that neural execution was significantly different in individuals with FXS than in age- and IQ-matched controls during stimulus equivalence formation. Further research concerning how gene-brain-behavior interactions may influence the emergence of stimulus equivalence in individuals with intellectual disabilities is needed.

    View details for DOI 10.1016/j.pscychresns.2015.06.009

    View details for Web of Science ID 000360563800010

  • Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training NEUROIMAGE Saggar, M., Zanesco, A. P., King, B. G., Bridwell, D. A., MacLean, K. A., Aichele, S. R., Jacobs, T. L., Wallace, B. A., Saron, C. D., Miikkulainen, R. 2015; 114: 88-104


    Meditation training has been shown to enhance attention and improve emotion regulation. However, the brain processes associated with such training are poorly understood and a computational modeling framework is lacking. Modeling approaches that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and anatomy into account, to formally model brain processes associated with intensive meditation training. The observed changes in model parameters inform theoretical accounts of attention training through meditation, and may motivate future study on the use of meditation in a variety of clinical populations.

    View details for DOI 10.1016/j.neuroimage.2015.03.073

    View details for Web of Science ID 000355002900007

    View details for PubMedID 25862265

  • Pictionary-based fMRI paradigm to study the neural correlates of spontaneous improvisation and figural creativity SCIENTIFIC REPORTS Saggar, M., Quintin, E., Kienitz, E., Bott, N. T., Sun, Z., Hong, W., Chien, Y., Liu, N., Dougherty, R. F., Royalty, A., Hawthorne, G., Reiss, A. L. 2015; 5


    A novel game-like and creativity-conducive fMRI paradigm is developed to assess the neural correlates of spontaneous improvisation and figural creativity in healthy adults. Participants were engaged in the word-guessing game of Pictionary(TM), using an MR-safe drawing tablet and no explicit instructions to be "creative". Using the primary contrast of drawing a given word versus drawing a control word (zigzag), we observed increased engagement of cerebellum, thalamus, left parietal cortex, right superior frontal, left prefrontal and paracingulate/cingulate regions, such that activation in the cingulate and left prefrontal cortices negatively influenced task performance. Further, using parametric fMRI analysis, increasing subjective difficulty ratings for drawing the word engaged higher activations in the left pre-frontal cortices, whereas higher expert-rated creative content in the drawings was associated with increased engagement of bilateral cerebellum. Altogether, our data suggest that cerebral-cerebellar interaction underlying implicit processing of mental representations has a facilitative effect on spontaneous improvisation and figural creativity.

    View details for DOI 10.1038/srep10894

    View details for Web of Science ID 000355548100001

    View details for PubMedID 26018874

    View details for PubMedCentralID PMC4446895

  • Early signs of anomalous neural functional connectivity in healthy offspring of parents with bipolar disorder BIPOLAR DISORDERS Singh, M. K., Chang, K. D., Kelley, R. G., Saggar, M., Reiss, A. L., Gotlib, I. H. 2014; 16 (7): 678-689


    Bipolar disorder (BD) has been associated with dysfunctional brain connectivity and with family chaos. It is not known whether aberrant connectivity occurs before illness onset, representing vulnerability for developing BD amidst family chaos. We used resting-state functional magnetic resonance imaging (fMRI) to examine neural network dysfunction in healthy offspring living with parents with BD and healthy comparison youth.Using two complementary methodologies [data-driven independent component analysis (ICA) and hypothesis-driven region-of-interest (ROI)-based intrinsic connectivity], we examined resting-state fMRI data in 8-17-year-old healthy offspring of a parent with BD (n = 24; high risk) and age-matched healthy youth without any personal or family psychopathology (n = 25; low risk).ICA revealed that, relative to low-risk youth, high-risk youth showed increased connectivity in the ventrolateral prefrontal cortex (VLPFC) subregion of the left executive control network (ECN), which includes frontoparietal regions important for emotion regulation. ROI-based analyses revealed that high-risk versus low-risk youth had decreased connectivities between the left amygdala and pregenual cingulate, between the subgenual cingulate and supplementary motor cortex, and between the left VLPFC and left caudate. High-risk youth showed stronger connections in the VLPFC with age and higher functioning, which may be neuroprotective, and weaker connections between the left VLPFC and caudate with more family chaos, suggesting an environmental influence on frontostriatal connectivity.Healthy offspring of parents with BD show atypical patterns of prefrontal and subcortical intrinsic connectivity that may be early markers of resilience to or vulnerability for developing BD. Longitudinal studies are needed to determine whether these patterns predict outcomes.

    View details for DOI 10.1111/bdi.12221

    View details for Web of Science ID 000344373100002

  • Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility NEUROIMAGE Saggar, M., Shelly, E. W., Lepage, J., Hoeft, F., Reiss, A. L. 2014; 84: 648-656


    Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks was found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus was found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) was partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom.

    View details for DOI 10.1016/j.neuroimage.2013.09.046

    View details for Web of Science ID 000328868600059

    View details for PubMedID 24084068

    View details for PubMedCentralID PMC3903510

  • Creativity training enhances goal-directed attention and information processing THINKING SKILLS AND CREATIVITY Bott, N., Quintin, E., Saggar, M., Kienitz, E., Royalty, A., Hong, D. W., Liu, N., Chien, Y., Hawthorne, G., Reiss, A. L. 2014; 13: 120-128
  • Targeted intervention to increase creative capacity and performance: A randomized controlled pilot study THINKING SKILLS AND CREATIVITY Kienitz, E., Quintin, E., Saggar, M., Bott, N. T., Royalty, A., Hong, D. W., Liu, N., Chien, Y., Hawthorne, G., Reiss, A. L. 2014; 13: 57-66
  • Intensive training induces longitudinal changes in meditation state-related EEG oscillatory activity FRONTIERS IN HUMAN NEUROSCIENCE Saggar, M., King, B. G., Zanesco, A. P., MacLean, K. A., Aichele, S. R., Jacobs, T. L., Bridwell, D. A., Shaver, P. R., Rosenberg, E. L., Sahdra, B. K., Ferrer, E., Tang, A. C., Mangun, G. R., Wallace, B. A., Miikkulainen, R., Saron, C. D. 2012; 6


    The capacity to focus one's attention for an extended period of time can be increased through training in contemplative practices. However, the cognitive processes engaged during meditation that support trait changes in cognition are not well characterized. We conducted a longitudinal wait-list controlled study of intensive meditation training. Retreat participants practiced focused attention (FA) meditation techniques for three months during an initial retreat. Wait-list participants later undertook formally identical training during a second retreat. Dense-array scalp-recorded electroencephalogram (EEG) data were collected during 6 min of mindfulness of breathing meditation at three assessment points during each retreat. Second-order blind source separation, along with a novel semi-automatic artifact removal tool (SMART), was used for data preprocessing. We observed replicable reductions in meditative state-related beta-band power bilaterally over anteriocentral and posterior scalp regions. In addition, individual alpha frequency (IAF) decreased across both retreats and in direct relation to the amount of meditative practice. These findings provide evidence for replicable longitudinal changes in brain oscillatory activity during meditation and increase our understanding of the cortical processes engaged during meditation that may support long-term improvements in cognition.

    View details for DOI 10.3389/fnhum.2012.00256

    View details for Web of Science ID 000309107100001

    View details for PubMedID 22973218

    View details for PubMedCentralID PMC3437523

  • Behavioral, neuroimaging, and computational evidence for perceptual caching in repetition priming BRAIN RESEARCH Saggar, M., Miikkulainen, R., Schnyer, D. M. 2010; 1315: 75-91


    Repetition priming (RP) is a form of learning, whereby classification or identification performance is improved with item repetition. Various theories have been proposed to understand the basis of RP, including alterations in the representation of an object and associative stimulus-response bindings. There remain several aspects of RP that are still poorly understood, and it is unclear whether previous theories only apply to well-established object representations. This paper integrates behavioral, neuroimaging, and computational modeling experiments in a new RP study using novel objects. Behavioral and neuroimaging results were inconsistent with existing theories of RP, thus a new perceptual memory-based caching mechanism is formalized using computational modeling. The model instantiates a viable neural mechanism that not only accounts for the pattern seen in this experiment but also provides a plausible explanation for previous results that demonstrated residual priming after associative linkages were disrupted. Altogether, the current work helps advance our understanding of how brain utilizes repetition for faster information processing.

    View details for DOI 10.1016/j.brainres.2009.11.074

    View details for Web of Science ID 000275131300009

    View details for PubMedID 20005215

  • Memory Processes in Perceptual Decision Making Proceedings of the 30th Annual Conference of the Cognitive Science Society, Nashville, TN Saggar M., Miikkulainen R., Schnyer D. M. 2008
  • A computational model of the motivation-learning interface Proceedings of the 29th Annual Conference of the Cognitive Science Society, Nashville, TN Saggar M., Markman A.B., Maddox W.T., Miikkulainen R. 2007
  • Autonomous learning of stable quadruped locomotion ROBOCUP 2006: ROBOT SOCCER WORLD CUP X Saggar, M., D'Silva, T., Kohl, N., Stone, P. 2007; 4434: 98-109
  • System identification for the Hodgkin-Huxley model using artificial neural networks 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 Saggar, M., Mericli, T., Andoni, S., Miikkulainen, R. 2007: 2239-2244
  • Optimization of association rule mining using improved genetic algorithms IEEE International Conference on Systems, Man and Cybernetics Saggar M, Agrawal, A.K. , Lad, A. 2004; 4434/2007: 3725 - 3729