Validation of an electroencephalography data assimilation-based computational approach for estimating cortical excitation-inhibition balance

Abstract

The excitation and inhibition (E/I) balance of neural circuits is a crucial index of neurophysiological homeostasis associated with healthy brain functioning. Although several cutting-edge methods exist to assess E/I balance in an intact brain, they have inherent limitations, such as difficulties in tracking changes in E/I balance over time. To address this, we introduced neural-mass-model-based tracking using a data assimilation (DA) approach. While we previously demonstrated that sleep-dependent E/I changes could be estimated from electroencephalography (EEG) data, the neurophysiological validity of this method had not been directly evaluated. In this study, we developed an enhanced DA-based method and compared its E/I estimates with the concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) based measures. Our results revealed significant correlations between the DA-based estimates and TMS-EEG indices of E/I balance in the dorsolateral prefrontal cortex. These findings indicate that our computational approach provides neurophysiologically valid estimations of time-varying E/I balance.