Electrophysiological Brain Network Estimation with Simultaneous Scalp EEG and Intracranial EEG: Inference Algorithm and Applications

Abstract

AbstractActivity in the human brain is composed of complex firing patterns and interactions among neurons and neuronal circuits. The neuroimaging field underwent a paradigm shift over the past decades from mapping tasked evoked brain regions of activations towards identifying and characterizing the dynamic brain networks of coordinating brain regions. Electrophysiological signals are the direct manifestation of brain activities, thus characterizing the whole brain electrophysiological networks (WBEN) can serve as a fundamental tool for neuroscience studies and clinical applications. The electrophysiological network inferred from electroencephalogram (EEG) source imaging suffers from low accuracy limited by the Restricted Isometry Property (RIP), while the invasive EEG-derived electrophysiological networks can only characterize partial brain regions where invasive electrodes reside. In this work, we introduce the first framework for the integration of scalp EEG and intracranial EEG (iEEG) for WBEN estimation with a principled estimation framework based on state-space models, 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, which demonstrates the importance of the inclusion of iEEG signal for WBEN estimation. For real data with simultaneous EEG and iEEG, we applied the developed framework to understand the information flows of the encoding and maintenance phases during the working memory task. The information flows between the subcortical and cortical regions are delineated, which highlights more significant information flows from cortical to subcortical regions compared to maintenance phases. The results are consistent with previous research findings, however with the view of the whole brain scope, which underscores the unique utility of the proposed framework.