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

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.

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, Article e15397. https://doi.org/10.1038/srep15397

• Journal Article Type: Article
• Publication Date: Oct 27, 2015
• Deposit Date: Oct 28, 2015
• Publicly Available Date: Oct 28, 2015
• Journal: Scientific Reports
• Electronic ISSN: 2045-2322
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Article Number: e15397
• DOI: https://doi.org/10.1038/srep15397
• Public URL: https://nottingham-repository.worktribe.com/output/763174
• Publisher URL: http://www.nature.com/articles/srep15397