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    Network communication models improve the behavioral and functional predictive utility of the human structural connectome

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    Author
    Seguin, C; Tian, Y; Zalesky, A
    Date
    2020-11-01
    Source Title
    Network Neuroscience
    Publisher
    MIT PRESS
    University of Melbourne Author/s
    Zalesky, Andrew
    Affiliation
    Psychiatry
    Metadata
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    Document Type
    Journal Article
    Citations
    Seguin, C., Tian, Y. & Zalesky, A. (2020). Network communication models improve the behavioral and functional predictive utility of the human structural connectome. NETWORK NEUROSCIENCE, 4 (4), pp.980-1006. https://doi.org/10.1162/netn_a_00161.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/253005
    DOI
    10.1162/netn_a_00161
    Abstract
    The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.

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