Background: Structural and functional brain abnormalities in schizophrenia (SZ) have been widely reported. However, a few studies have investigated both structural and functional characteristics in SZ patients at different stages to understand the neuropathology of SZ.
Methods: In this study, we recruited 44 first-episode drug-naive SZ (FESZ) patients, 44 medicated chronic SZ (CSZ) patients, and 56 normal controls (NCs) and acquired their structural and resting-state functional magnetic resonance imaging (MRI). We then made group comparisons on structural and functional characteristics, including regional gray matter volume (GMV), regional homogeneity, amplitude of low-frequency fluctuation, and degree centrality. A linear support vector machine (SVM) combined with a recursive feature elimination (RFE) algorithm was implemented to discriminate three groups.
Results: Our results indicated that the regional GMV was significantly decreased in patients compared with that in NCs; CSZ patients have more diffused GMV decreases primarily involved in the frontal and temporal lobes when compared with FESZ patients. Both FESZ and CSZ patients showed significant functional alterations compared with NCs; when compared with FESZ patients, CSZ patients showed significant reductions in functional characteristics in several brain regions associated with auditory, visual processing, and sensorimotor functions. Moreover, a linear SVM combined with a RFE algorithm was implemented to discriminate three groups. The accuracies of the three classifiers were 79.80%, 83.16%, and 81.71%, respectively. The performance of classifiers in this study with multimodal MRI was better than that of previous discriminative analyses of SZ patients with single-modal MRI.
Conclusion: Our findings bring new insights into the understanding of the neuropathology of SZ and contribute to stage-specific biomarkers in diagnosis and interventions of SZ.
Keywords: SVM; classification; multimodal MRI; schizophrenia; support vector machine.