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A Canonical Microcircuit for Estimating Excitation/Inhibition (E/I) Balance

Hauke, D. J., Rodriguez-Sanchez, J., Oloye, H. , Berndt, L. C. S., Pinotsis, D. ORCID: 0000-0002-6865-8103, Friston, K. J., Mathalon, D. H. & Adams, R. A. (2026). A Canonical Microcircuit for Estimating Excitation/Inhibition (E/I) Balance. Translational Psychiatry,

Abstract

Excitation/inhibition (E/I) balance is crucial for maintaining healthy brain function and can be disrupted in various neurological and psychiatric disorders. Despite its importance, there are few tools to study E/I balance non-invasively in humans. Here, we propose a canonical microcircuit model to estimate E/I balance from non-invasive magnetoencephalography (MEG), electroencephalography (EEG) or optically pumped magnetometers (OPM) recordings by parameterising global pyramidal and inhibitory cell excitability. We first establish that E/I parameters are identifiable and recoverable. We then explore the effects of these new parameters and their interaction with other parameters in a series of simulations. To highlight the clinical relevance of this new model, we simulate changes in E/I balance and their impact on event-related potentials (ERPs) derived from paired-click, passive and active oddball paradigms, which are among the most robust clinical biomarkers of schizophrenia. Our simulations show that a loss of pyramidal cell excitability can explain reduced ERP amplitudes across all three paradigms, mirroring empirical findings in schizophrenia. This method may serve as a computational assay for estimating synaptopathy and E/I balance from non-invasive electrophysiological recordings across various clinical conditions thereby advancing efforts to develop personalised interventions to restore E/I balance.

Publication Type: Article
Additional Information: The version of record of this article, to be published in Translational Psychiatry, will be available online at Publisher’s website: https://www.nature.com/tp/
Publisher Keywords: E/I balance, dynamic causal modelling, predictive coding, biophysical modelling, EEG, MEG
Departments: School of Health & Medical Sciences
School of Health & Medical Sciences > Department of Psychology & Neuroscience
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