Dual-Pathway Inhibitory Dysfunction in Schizophrenia: MEG and Behavioral Markers for Risk Stratification

Background: Inhibitory control deficits are a core feature of schizophrenia, yet their spectral and temporal characteristics remain poorly defined. This study investigates fast-response inhibition in first-episode schizophrenia (FES) by focusing on low gamma-band (30–50 Hz) oscillations within the right inferior frontal gyrus (rIFG)–pre-supplementary motor area (preSMA) circuit. Methods: We conducted a magnetoencephalography (MEG) study involving FES, their first-degree relatives (FDR), and healthy controls (HCs), examining post-stimulus low gamma (30–50 Hz) and beta-band activity across rIFG–preSMA and preSMA–lM1 circuits. Power spectral density (PSD) and functional connectivity (FC) were segmented into middle (150–250 ms) and later (250–400 ms) stages. Behavioral data (BIS-11, DSST) were integrated with MEG features in two canonical discriminant models (Model 1: multimodal; Model 2: behavior-only), with independent temporal validation. Results: FES patients showed early low gamma power reductions in rIFG and preSMA, followed by increased rIFG–preSMA connectivity during later stages—suggesting phase-specific disruption and compensatory adaptation. Model 1 achieved 95.2% classification accuracy (80.6% cross-validated), identifying rIFG–preSMA low gamma connectivity as the strongest neural discriminator. Model 2 retained 88.5% accuracy on temporally independent samples using only BIS and DSST measures. FDR occupied an intermediate position in both models, supporting a dimensional liability continuum. Limitations: The modest sample size, especially for the MEG recordings, may limit generalizability. Medication effects in the FES group and the cross-sectional design may also confound causal interpretation. Conclusion: This study delineates a frequency- and phase-specific disruption in fast-response inhibition in schizophrenia, centered on rIFG–preSMA low gamma desynchronization. The identified neural and behavioral signatures support a dimensional risk framework and may inform early detection strategies in clinical and familial populations.