Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography

Electrocorticography (ECoG) data can be used to estimate brain-wide connectivity patterns. Yet, the invasiveness of ECoG, incomplete cortical coverage, and variability in electrode placement across individuals make the network analysis of ECoG data challenging. Here, we show that the architecture of whole-brain ECoG networks and the factors that shape it can be studied by analysing whole-brain, interregional and band-limited ECoG networks from a large cohort—in this case, of individuals with medication-resistant epilepsy. Using tools from network science, we characterized the basic organization of ECoG networks, including frequency-specific architecture, segregated modules and the dependence of connection weights on interregional Euclidean distance. We then used linear models to explain variabilities in the connection strengths between pairs of brain regions, and to highlight the joint role, in shaping the brain-wide organization of ECoG networks, of communication along white matter pathways, interregional Euclidean distance and correlated gene expression. Moreover, we extended these models to predict out-of-sample, single-subject data. Our predictive models may have future clinical utility; for example, by anticipating the effect of cortical resection on interregional communication. Analyses of interregional neural networks from electrocorticography data from a large database of individuals with medication-resistant epilepsy highlight the structural, geometric and genetic factors that shape network organization.