A geometric network model of intrinsic grey-matter connectivity of the human brain.
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 (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.
School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK.|Department of Mathematics, National Taiwan University, Taipei, Taiwan 10617.
Yi-Ping
YP
Lo
Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.
Reuben
R
O'Dea
School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK.
Jonathan J
JJ
Crofts
Department of Brain and Cognitive Sciences, Seoul National University, South Korea.
Cheol E
CE
Han
Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.|Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK.
Marcus
M
Kaiser
D066128:Gray Matter / Q000503:physiopathology*; D006801:Humans; D008954:Models, Biological