Single-Node Wilson--Cowan Model Accounts for Speech-Evoked $\gamma$-Band Deficits in Schizophrenia

TL;DR

This study uses a Wilson-Cowan model coupled with speech processing to explain gamma-band deficits in schizophrenia, highlighting shifts in excitation-inhibition balance rather than input differences as the cause.

Contribution

It introduces a single-node Wilson-Cowan model integrated with speech input to simulate gamma responses, providing a mechanistic explanation for schizophrenia-related gamma deficits.

Findings

Healthy responses are higher than SCZ in speech and semantics conditions.

Gamma deficits in SCZ are linked to shifts in E/I operating point and gain.

Model predictions align with pharmacological effects on inhibition and excitation.

Abstract

Cortical gamma ($\gamma$)-band activity reflects local excitation-inhibition (E/I) balance. In schizophrenia (SCZ), reduced task-evoked gamma suggests altered E/I dynamics, but it is unclear whether differences stem from input properties or systematic shifts in E/I operating point and gain. We coupled a cochlear-inspired speech front end to a Wilson-Cowan E/I model to simulate gamma responses across three conditions: Healthy, SCZ-speech, and SCZ-semantics. Metrics included event-related spectral perturbation (ERSP$_\gamma$) and threshold-time fraction ($\gamma%$). A stable hierarchy emerged: Healthy(speech/semantics) $>$ SCZ(speech) $>$ SCZ(semantics), robust under equal-energy control and gain perturbations. Network dynamics coincided with single-node solutions, supporting interpretability. Pharmacological analogs showed bidirectional effects: reduced inhibition lowered $\gamma$, while reduced excitation increased $\gamma$, with no self-sustained oscillations. Findings indicate SCZ gamma deficits align more with shifts in E/I operating point and gain than input differences. This pipeline provides a testable, reusable mechanistic framework for speech-evoked gamma and a baseline for cross-population studies.