Cliques and cavities in the human connectome

By Ann E. Sizemore, Chad Giusti, Ari E. Kahn, Jean M. Vettel, Richard F. Betzel, D. Bassett. Journal of Computational Neuroscience, 2016

349 citations recorded by the source corpus.

Encoding brain regions and their connections as a network of nodes and edges captures many of the possible paths along which information can be transmitted as humans process and perform complex behaviors. Because cognitive processes involve large, distributed networks of brain areas, principled examinations of multi-node routes within larger connection patterns can offer fundamental insights into the complexities of brain function. Here, we investigate both densely connected groups of nodes that could perform local computations as well as larger patterns of interactions that would allow for parallel processing. Finding such structures necessitates that we move from considering exclusively pairwise interactions to capturing higher order relations, concepts naturally expressed in the language of algebraic topology. These tools can be used to study mesoscale network structures that arise from the arrangement of densely connected substructures called cliques in otherwise sparsely connected brain networks. We detect cliques (all-to-all connected sets of brain regions) in the average structural connectomes of 8 healthy adults scanned in triplicate and discover the presence of more large cliques than expected in null networks constructed via wiring minimization, providing architecture through which brain network can perform rapid, local processing. We then locate topological cavities of different dimensions, around which information may flow in either diverging or converging patterns. These cavities exist consistently across subjects, differ from those observed in null model networks, and – importantly – link regions of early and late evolutionary origin in long loops, underscoring their unique role in controlling brain function. These results offer a first demonstration that techniques from algebraic topology offer a novel perspective on structural connectomics, highlighting loop-like paths as crucial features in the human brain’s structural architecture.

Subfields: Cognitive Neuroscience, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/7b2396ba8da97cf4920b6a349ca0cddea38291b8

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In Cognitive Neuroscience

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Citations in the corpus

Higher order interactions in complex networks of phase oscillators promote abrupt synchronization switching (2019, background): “ote that in this Letter we have taken brain dynamics as our primary motivating example due to the existence of direct evidence of higher-order interactions in a system with synchronization properties [11, 12, 14, 17]. However, more general results suggest that higher-order interactions may be import”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “[636] described the white matter network of fibers between brain regions as a weighted network and then studied both its dense portions, in terms of cliques, and its cavities, in terms of homology.”