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dc.contributor.authorSeguin, C
dc.contributor.authorTian, Y
dc.contributor.authorZalesky, A
dc.date.accessioned2020-12-09T22:34:25Z
dc.date.available2020-12-09T22:34:25Z
dc.date.issued2020-11-01
dc.identifierpii: netn_a_00161
dc.identifier.citationSeguin, 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.
dc.identifier.issn2472-1751
dc.identifier.urihttp://hdl.handle.net/11343/253005
dc.description.abstractThe 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.
dc.languageEnglish
dc.publisherMIT PRESS
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleNetwork communication models improve the behavioral and functional predictive utility of the human structural connectome
dc.typeJournal Article
dc.identifier.doi10.1162/netn_a_00161
melbourne.affiliation.departmentPsychiatry
melbourne.source.titleNetwork Neuroscience
melbourne.source.volume4
melbourne.source.issue4
melbourne.source.pages980-1006
melbourne.identifier.nhmrc1136649
dc.rights.licenseCC BY
melbourne.elementsid1480255
melbourne.contributor.authorZalesky, Andrew
melbourne.contributor.authorTian, Ye
melbourne.contributor.authorSeguin, Caio
dc.identifier.eissn2472-1751
melbourne.identifier.fundernameidNHMRC, 1136649
melbourne.accessrightsOpen Access


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