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A geometric network model of intrinsic grey-matter connectivity of the human brain

Lo, Yi-Ping; O'Dea, Reuben D.; Crofts, Jonathan J.; Han, Cheol E.; Kaiser, Marcus

Authors

Yi-Ping Lo

Jonathan J. Crofts

Cheol E. Han

Marcus Kaiser



Abstract

Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuroscience is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (\emph{i.e.} white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct `shortcuts' through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections.

Journal Article Type Article
Publication Date Oct 27, 2015
Journal Scientific Reports
Electronic ISSN 2045-2322
Publisher Nature Publishing Group
Peer Reviewed Peer Reviewed
Volume 5
Article Number e15397
APA6 Citation Lo, Y., O'Dea, R. D., Crofts, J. J., Han, C. E., & Kaiser, M. (2015). A geometric network model of intrinsic grey-matter connectivity of the human brain. Scientific Reports, 5, doi:10.1038/srep15397
DOI https://doi.org/10.1038/srep15397
Publisher URL http://www.nature.com/articles/srep15397
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





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