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.

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviours such as synchronization. Although descriptions of these behaviours are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behaviour. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain’s control performance by removing single edges in the network. Generally, our results ground the expectation of a control system’s behaviour in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

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.