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Updated: Sep 15, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
Published on: November 13, 2016
Concurrent multimodal imaging demonstrates that EEG-based excitation/inhibition balance reflects glutamate and GABA
Aaron Cochrane1, Luke Rosedahl1, Masako Tamaki2
1Brown University, Rhode Island, United States.
Abstract:
Neural excitation/inhibition (E/I) ratio is dynamically regulated on multiple timescales. Adaptive changes in E/I ratio can support healthy development, learning, and cognition, while disordered E/I ratio has been implicated in neurodevelopmental disorders, neurodegenerative disorders, and states of impaired vigilance. There has been growing interest in inferring E/I ratio from efficient and noninvasive measurements such as electroencephalography (EEG), and several algorithms have been proposed to estimate E/I ratio from EEG. Despite promising results, there has been a lack of validation studies testing the underlying neurochemical changes leading to increased or decreased EEG-based E/I ratio. Here, using concurrent EEG and magnetic resonance spectroscopy (MRS) for over an hour, we assessed which algorithm of EEG-based E/I ratio best matched with MRS-based E/I ratio in humans of both sexes. The MRS-based E/I ratio was obtained by the ratio of glutamate concentration to GABA concentration. We applied 10 candidate indices of EEG-based E/I ratio using four approaches in several spontaneous frequency bands. Uniquely, we quantified the associations between the EEG-based E/I ratio and MRS-based E/I ratio separately for between-subjects and within-subjects variations. We found that each EEG-based E/I algorithm showed reliable and positive associations with MRS-based E/I, and especially EEG-based E/I ratio in alpha band with a criticality theory based approach showed the best association to the MRS-based E/I ratio. While these associations were evident for between-subjects comparisons, they were quite weak for within-subjects comparisons. These results suggest that EEG-based E/I algorithms are likely to reflect, at least in part, relative concentrations of glutamate and GABA.
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