Inference of Whole Brain Electrophysiological Networks Through Multimodal Integration of Simultaneous Scalp and Intracranial EEG

Abstract

Brain imaging research has transitioned over the past decades from identifying isolated regions of task-evoked activation to characterizing the spatiotemporal dynamics of large-scale brain networks. Electrophysiological signals are the direct manifestation of brain activity; thus, characterizing whole-brain electrophysiological networks (WBEN) can serve as a fundamental tool for neuroscience studies and clinical applications. In this work, we introduce a framework for integrating scalp EEG and intracranial EEG (iEEG) for WBEN estimation through a principled state-space modeling approach, where an Expectation-Maximization (EM) algorithm is designed to infer the state variables and brain connectivity simultaneously. We validated the proposed method on synthetic data, and the results revealed improved performance compared to traditional two-step methods using scalp EEG only, demonstrating the importance of including iEEG signals for WBEN estimation. For real data with simultaneous EEG and iEEG, we applied the developed framework to understand the information flows during encoding and maintenance phases of a working memory task. The information flows between subcortical and cortical regions are delineated, highlighting more significant information flows from cortical to subcortical regions during encoding than during maintenance. The results are consistent with previous research findings, but from a whole-brain perspective, which underscores the unique utility of the proposed framework.