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

Lo, Yi-Ping and O'Dea, Reuben D. and Crofts, Jonathan J. and Han, Cheol E. and Kaiser, Marcus (2015) A geometric network model of intrinsic grey-matter connectivity of the human brain. Scientific Reports, 5 . e15397. ISSN 2045-2322

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/763174
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: https://doi.org/10.1038/srep15397
Depositing User: O'Dea, Dr Reuben
Date Deposited: 28 Oct 2015 09:27
Last Modified: 04 May 2020 17:18
URI: http://eprints.nottingham.ac.uk/id/eprint/30230

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