Work

A multimodal fusion analysis of structural and functional abnormalities in acute onset treatment-resistant schizophrenia

Year: 2026

Type: article

Abstract: BACKGROUND: Patients with treatment-resistant schizophrenia (TRS) experience more severe clinical symptoms, which may derive from greater structural and functional brain abnormalities compared to non-TRS. However, the neural underpinnings of TRS remain poorly understood, and conventional unimodal neuroimaging approaches are limited in capturing cross-modal interactions. Therefore, we used multimodal fusion via multiset canonical correlation and joint independent component analysis (mCCA+jICA) to integrate MRI data to examine shared and modality-specific features in TRS. Methods65 TRS patients, 65 non-TRS patients, and 54 healthy controls (HCs) underwent 3 T MRI. Structural MRI and resting-state functional MRI data were preprocessed using DPARSFA, and grey matter volume (GMV), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) maps were extracted. The mCCA+jICA was performed to derive joint independent components (ICs) and subject-specific mixing coefficients. Group differences in mixing coefficients were assessed via MANCOVA, and clinical correlations were evaluated using partial correlations. RESULTS: Patients showed significant differences in mixing coefficients for one modality-specific group-discriminative IC (GMV-IC3) and two joint group-discriminative ICs (ReHo-IC6, and GMV-IC6) compared to HCs. These components also differentiated TRS from non-TRS, with abnormalities concentrated in the precuneus in both ReHo and GMV. Partial correlation analysis revealed significant positive associations between the mixing coefficients of GMV-IC3, GMV-IC6, and total PANSS scores and CPZ equivalents specifically in the TRS group. CONCLUSIONS: This study demonstrates that co-existing structural and functional brain alterations are associated with the pathophysiology of TRS, highlighting their potential as novel multimodal biomarkers.

Source: Schizophrenia Research

DOI: 10.1016/j.schres.2026.01.008