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  • Neuroinformatics
  • Open Access
  • National Science Foundation
  • CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks

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    Authors: Javier O. Garcia; Arian Ashourvan; Sarah F. Muldoon; Jean M. Vettel; +1 Authors

    ABSTRACTThe human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can be used to identify communities or modules in brain graphs: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the utility of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ bioRxivarrow_drop_down
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    bioRxiv
    Preprint . 2017
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    Europe PubMed Central
    Other literature type . 2018
    Data sources: PubMed Central
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    Proceedings of the IEEE
    Article . 2018 . Peer-reviewed
    License: IEEE Open Access
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      Europe PubMed Central
      Other literature type . 2018
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      Proceedings of the IEEE
      Article . 2018 . Peer-reviewed
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    Authors: Bassett, Danielle S.; Khambhati, Ankit N.; Grafton, Scott T.;

    Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales, and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems, by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems, from micro- to macroscales. We present examples of how human brain imaging data is being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers, and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights critical to the neuroengineer's toolkit. Comment: 17 pages, 6 figures. Manuscript accepted to the journal "Annual Review of Biomedical Engineering"

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    Europe PubMed Central
    Other literature type . 2017
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    https://doi.org/10.48550/arxiv...
    Article . 2016
    License: arXiv Non-Exclusive Distribution
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    Annual Review of Biomedical Engineering
    Article . 2017 . Peer-reviewed
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      Europe PubMed Central
      Other literature type . 2017
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      https://doi.org/10.48550/arxiv...
      Article . 2016
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      Annual Review of Biomedical Engineering
      Article . 2017 . Peer-reviewed
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    Authors: Ashourvan, Arian; Gu, Shi; Mattar, Marcelo G.; Vettel, Jean M.; +1 Authors

    Human brain dynamics can be viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing mental states. Many physically-inspired models of these dynamics define brain states based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. We study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions. We estimate the functional relationships between regions of interest (ROIs) by fitting a pair-wise maximum entropy model to each ROI's pattern of allegiance to functional modules. This process uses an information theoretic notion of energy (as opposed to a metabolic one) to produce an energy landscape in which local minima represent attractor states characterized by specific patterns of modular structure. The clustering of local minima highlights three classes of ROIs with similar patterns of allegiance to community states. Visual, attention, sensorimotor, and subcortical ROIs are well-characterized by a single functional community. The remaining ROIs affiliate with a putative executive control community or a putative default mode and salience community. We simulate the brain's dynamic transitions between these community states using a random walk process. We observe that simulated transition probabilities between basins are statistically consistent with empirically observed transitions in resting state fMRI data. These results offer a view of the brain as a dynamical system that transitions between basins of attraction characterized by coherent activity in groups of brain regions, and that the strength of these attractors depends on the ongoing cognitive computations.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ arXiv.org e-Print Ar...arrow_drop_down
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    NeuroImage
    Article . 2017
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    Europe PubMed Central
    Other literature type . 2017
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    NeuroImage
    Article . 2017 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2016
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      NeuroImage
      Article . 2017
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      Europe PubMed Central
      Other literature type . 2017
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      NeuroImage
      Article . 2017 . Peer-reviewed
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    Authors: Lia Papadopoulos; Christopher W. Lynn; Demian Battaglia; Danielle S. Bassett;

    At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity—in concert with network structure—affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions’ baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations. Author summary Stimulation can be used to alter brain activity and is a therapeutic option for certain neurological conditions. However, predicting the distributed effects of local perturbations is difficult. Previous studies show that responses to stimulation depend on anatomical (or structural) coupling. In addition to structure, here we consider how stimulation effects also depend on the brain’s collective dynamical (or functional) state, arising from the coordination of rhythmic activity across large-scale networks. In a whole-brain computational model, we show that global responses to regional stimulation can indeed be contingent upon and differ across various dynamical working points. Notably, depending on the network’s oscillatory regime, stimulation can accelerate the activity of the stimulated site, and lead to widespread effects at both the new, excited frequency, as well as in a much broader frequency range including areas’ baseline frequencies. While structural connectivity is a good predictor of “excited band” changes, in some states “baseline band” effects can be better predicted by functional connectivity, which depends upon the system’s oscillatory regime. By integrating and extending past efforts, our results thus indicate that dynamical—in additional to structural—brain organization plays a role in governing how focal stimulation modulates interactions between distributed network elements.

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    Other literature type . Article . 2020
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    Europe PubMed Central
    Article . 2020
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    PLoS Computational Biology
    Article . 2020 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2020
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      Other literature type . Article . 2020
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      Europe PubMed Central
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      PLoS Computational Biology
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      PLoS Computational Biology
      Article . 2020 . Peer-reviewed
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    Authors: Richard F. Betzel; Danielle S. Bassett;

    Brain areas' functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections are short, reflecting spatial and energetic constraints. Nonetheless, a small number of connections span long distances, consistent with the notion that the functionality of these connections must outweigh their cost. While the precise function of these long-distance connections is not known, the leading hypothesis is that they act to reduce the topological distance between brain areas and facilitate efficient interareal communication. However, this hypothesis implies a non-specificity of long-distance connections that we contend is unlikely. Instead, we propose that long-distance connections serve to diversify brain areas' inputs and outputs, thereby promoting complex dynamics. Through analysis of five interareal network datasets, we show that long-distance connections play only minor roles in reducing average interareal topological distance. In contrast, areas' long-distance and short-range neighbors exhibit marked differences in their connectivity profiles, suggesting that long-distance connections enhance dissimilarity between regional inputs and outputs. Next, we show that -- in isolation -- areas' long-distance connectivity profiles exhibit non-random levels of similarity, suggesting that the communication pathways formed by long connections exhibit redundancies that may serve to promote robustness. Finally, we use a linearization of Wilson-Cowan dynamics to simulate the covariance structure of neural activity and show that in the absence of long-distance connections, a common measure of functional diversity decreases. Collectively, our findings suggest that long-distance connections are necessary for supporting diverse and complex brain dynamics. Comment: 18 pages, 8 figures

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    Proceedings of the National Academy of Sciences
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    Proceedings of the National Academy of Sciences
    Article . 2018 . Peer-reviewed
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      Proceedings of the National Academy of Sciences
      Article . 2018 . Peer-reviewed
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    Authors: Ankit N. Khambhati; Danielle S. Bassett; Brian S. Oommen; Stephanie Chen; +3 Authors

    AbstractHuman epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 hours of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (i) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (ii) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (iii) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that the epileptic network drives dysfunction by controlling dynamics of functional interactions between brain regions that generate seizures and those that underlie normal function.

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    Preprint . 2016
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    Europe PubMed Central
    Article . 2017
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    eNeuro
    Article . 2017 . Peer-reviewed
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      Preprint . 2016
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      Europe PubMed Central
      Article . 2017
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      eNeuro
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    Authors: Arian Ashourvan; Qawi K. Telesford; Timothy Verstynen; Jean M. Vettel; +1 Authors

    Community detection algorithms have been widely used to study the organization of complex systems like the brain. A principal appeal of these techniques is their ability to identify a partition of brain regions (or nodes) into communities, where nodes within a community are densely interconnected. In their simplest application, community detection algorithms are agnostic to the presence of community hierarchies, but a common characteristic of many neural systems is a nested hierarchy. To address this limitation, we exercise a multi-scale extension of a community detection technique known as modularity maximization, and we apply the tool to both synthetic graphs and graphs derived from human structural and functional imaging data. Our multi-scale community detection algorithm links a graph to copies of itself across neighboring topological scales, thereby becoming sensitive to conserved community organization across neighboring levels of the hierarchy. We demonstrate that this method allows for a better characterization of topological inhomogeneities of the graph's hierarchy by providing a local (node) measure of community stability and inter-scale reliability across topological scales. We compare the brain's structural and functional network architectures and demonstrate that structural graphs display a wider range of topological scales than functional graphs. Finally, we build a multimodal multiplex graph that combines structural and functional connectivity in a single model, and we identify the topological scales where resting state functional connectivity and underlying structural connectivity show similar versus unique hierarchical community architecture. Together, our results showcase the advantages of the multi-scale community detection algorithm in studying hierarchical community structure in brain graphs, and they illustrate its utility in modeling multimodal neuroimaging data.

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    Europe PubMed Central
    Article . 2019
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    Article . 2019
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    PLoS ONE
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    https://doi.org/10.48550/arxiv...
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      https://doi.org/10.48550/arxiv...
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    Authors: Richard F. Betzel; John D. Medaglia; Danielle S. Bassett;

    The brain's functional diversity is reflected in the meso-scale architecture of its connectome, i.e. its division into clusters and communities of topologically-related brain regions. The dominant view, and one that is reinforced by current analysis techniques, is that communities are strictly assortative and segregated from one another, purportedly for the purpose of carrying out specialized information processing. Such a view, however, precludes the possibility of non-assortative communities that could engender a richer functional repertoire by allowing for a more complex set of inter-community interactions. Here, we use weighted stochastic blockmodels to uncover the meso-scale architecture of \emph{Drosophila}, mouse, rat, macaque, and human connectomes. We confirm that while many communities are assortative, others form core-periphery and disassortative structures, which in the human better recapitulate observed patterns of functional connectivity and in the mouse better recapitulate observed patterns of gene co-expression than other community detection techniques. We define a set of network measures for quantifying the diversity of community types in which brain regions participate. Finally, we show that diversity is peaked in control and subcortical systems in humans, and that individual differences in diversity within those systems predicts cognitive performance on Stroop and Navon tasks. In summary, our report paints a more diverse portrait of connectome meso-scale structure and demonstrates its relevance for cognitive performance. 36 pages, 8 figures (main text), 8 figures (supplementary materials)

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    Europe PubMed Central
    Article . 2018
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    Nature Communications
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    https://doi.org/10.48550/arxiv...
    Article . 2017
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      Europe PubMed Central
      Article . 2018
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      Nature Communications
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      Article . 2018
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      Nature Communications
      Article . 2018
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      https://doi.org/10.48550/arxiv...
      Article . 2017
      License: arXiv Non-Exclusive Distribution
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Betzel, Richard F.; Bassett, Danielle S.;

    The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales -- of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure -- irrespective of species, imaging modality, or spatial resolution. 12 pages, 3 figures, review article

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    NeuroImage
    Article . 2017 . 2016 . Peer-reviewed
    License: CC BY NC ND
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    Europe PubMed Central
    Other literature type . 2016
    Data sources: PubMed Central
    https://doi.org/10.48550/arxiv...
    Article . 2016
    License: arXiv Non-Exclusive Distribution
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      NeuroImage
      Article . 2017 . 2016 . Peer-reviewed
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      Europe PubMed Central
      Other literature type . 2016
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      https://doi.org/10.48550/arxiv...
      Article . 2016
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    Authors: Mattar, Marcelo G.; Wymbs, Nicholas F.; Bock, Andrew S.; Aguirre, Geoffrey K.; +2 Authors

    AbstractHuman behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain’s baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution.HighlightsSensorimotor autonomy at rest predicts faster motor learning in the future.Connection between calcarine and superior precentral sulci form strongest predictor.Sensorimotor autonomy is a relatively stable individual trait.

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    Europe PubMed Central
    Article . 2018
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    bioRxiv
    Preprint . 2016
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    NeuroImage
    Article . Preprint . 2018 . Peer-reviewed
    License: CC BY NC ND
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      Article . 2018
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      bioRxiv
      Preprint . 2016
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      NeuroImage
      Article . Preprint . 2018 . Peer-reviewed
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    Authors: Javier O. Garcia; Arian Ashourvan; Sarah F. Muldoon; Jean M. Vettel; +1 Authors