Generative models of the human connectome

By Richard F. Betzel, Andrea Avena-Koenigsberger, Joaquín Goñi, Ye He, Marcel A. de Reus, Alessandra Griffa, Petra E. Vértes, Bratislav Misic, Jean-Philippe Thiran, Patric Hagmann and 4 more. NeuroImage, 2015

298 citations recorded by the source corpus.

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1fcdad6690b9cbfc3b131d108eed0d7746ed0bb1

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In Cognitive Neuroscience

In Statistical and Nonlinear Physics

Citations in the corpus

Gradients of structure–function tethering across neocortex (2019, unknown): “In constructing a consensus adjacency matrix, we sought to preserve 1) the density and 2) the edge-length distribution of the individual participant matrices (11, 25, 27).”
Small-World Brain Networks Revisited (2016, methodology): “Several variants of this approach have been published, exploring a range of diﬀerent topological relationships k i,j between nodes, for example, clustering and homophily [10–12].”
Communication dynamics in complex brain networks (2017, background): “Several computational studies have demonstrated that geometrically embedded networks that minimize wiring cost are characterized by physically short connections that have an overall topology that increases the mean path length 37,39,40 .”