NetDI: Methodology elucidating the role of power and dynamical brain network features that underpin word production

Abstract

Canonical language models describe eloquent function as the product of a series of cognitive processes, typically characterized by the independent activation profiles of focal brain regions. In contrast, more recent work has suggested that the interactions between these regions - the cortical networks of language - are critical for understanding speech production. We investigated the cortical basis of picture naming with human intracranial electrocorticography (ECoG) recordings and direct cortical stimulation (DCS), adjudicating between two competing hypotheses: Are task specific cognitive functions discretely computed within well-localized brain regions or rather by distributed networks? The time-resolution of ECoG allows direct comparison of intraregional activation measures (high-gamma power) with graph theoretic measures of interregional dynamics. We developed an analysis framework, “Network dynamics using Directed Information” (NetDI), using information and graph theoretic tools to reveal spatiotemporal dynamics at multiple scales: coarse, intermediate, and fine. Our analysis found novel relationships between the power profiles and network measures during the task. Furthermore, validation using DCS indicates that such network parameters combined with high-gamma power are more predictive than high-gamma power alone, for identifying critical language regions in the brain. NetDI reveals a high-dimensional space of network dynamics supporting cortical language function, and to account for disruptions to language function observed after neurosurgical resection, traumatic injury, and degenerative disease.

Significance Statement This work quantifies the network phenomena of distributed cortical substrates supporting language. First, estimated causality among brain regions was assessed with Directed Information. Second, a graph theoretic framework extracted task related dynamics from the causal estimates. Finally, we validated these functionally defined networks against the gold standard for causal inference - behavioral disruption with direct cortical stimulation. We demonstrate that the network measures combined with power have greater predictive capability for identifying critical language regions than discrete, regional power analyses alone.