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.