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Inter-regional ECoG correlations predicted by communication dynamics, geometry, and correlated gene expression
Preprint   Open access

Inter-regional ECoG correlations predicted by communication dynamics, geometry, and correlated gene expression

Richard F Betzel, John D Medaglia, Ari E Kahn, Jonathan Soffer, Daniel R Schonhaut and Danielle S Bassett
19 Jun 2017
DOI: https://doi.org/10.48550/arxiv.1706.06088
url
https://doi.org/10.48550/arxiv.1706.06088View
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute,  Open

Abstract

Quantitative Biology - Neurons and Cognition
Electrocorticography (ECoG) provides direct measurements of synchronized postsynaptic potentials at the exposed cortical surface. Patterns of signal covariance across ECoG sensors have been associated with diverse cognitive functions and remain a critical marker of seizure onset, progression, and termination. Yet, a systems level understanding of these patterns (or networks) has remained elusive, in part due to variable electrode placement and sparse cortical coverage. Here, we address these challenges by constructing inter-regional ECoG networks from multi-subject recordings, demonstrate similarities between these networks and those constructed from blood-oxygen-level-dependent signal in functional magnetic resonance imaging, and predict network topology from anatomical connectivity, interregional distance, and correlated gene expression patterns. Our models accurately predict out-of-sample ECoG networks and perform well even when fit to data from individual subjects, suggesting shared organizing principles across persons. In addition, we identify a set of genes whose brain-wide co-expression is highly correlated with ECoG network organization. Using gene ontology analysis, we show that these same genes are enriched for membrane and ion channel maintenance and function, suggesting a molecular underpinning of ECoG connectivity. Our findings provide fundamental understanding of the factors that influence interregional ECoG networks, and open the possibility for predictive modeling of surgical outcomes in disease.

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