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 (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.
Highlights
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
We study in detail the network that arises from a sequence of increasingly smooth cortical surfaces, and compare networks arising from our method against similar structures constructed according to Euclidean or geodesic distance only—the former not adhering to physiological connectivity constraints in the grey matter, the latter, not considering shortcuts
To characterise in more detail the link between local network dynamics and cortical geometry, we studied the network activation times associated with initiation sites centred on each node in the network, and compared these against the local Gyrification Index, which provides a measure of the cortical folding locally to each point in the network; see Network characterisation
Summary
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. 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 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. Local cortico-cortical connectivity, in particular, has been proposed as a possible explanation for the folded, three-dimensional structure of the cerebral cortex, with axonal tensions between densely/sparsely connected sets of neurons hypothesised to form gyri/sulci[6,7] Such connections are well-documented in histological studies, and may extend for distances of 4–5 millimetres[8,9,10]. There is emerging evidence that alterations in cortical folding are present within a variety of brain disorders[18,19,20], and investigations into the relation between surface morphology and brain connectivity could provide novel insights into these disorders
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