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Fengchun Wu,1,2,* Yue Zhang,2,3,* Yongzhe Yang,2–4 Xiaobing Lu,1,2 Ziyan Fang,1 Jianwei Huang,1 Lingyin Kong,3 Jun Chen,5,6 Yuping Ning,1,2 Xiaobo Li,7,8 Kai Wu2,3,5,6,9 1Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China; 2Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China; 3Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China; 4School of Medicine, South China University of Technology (SCUT), Guangzhou, China; 5Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China; 6National Engineering Research Center for Healthcare Devices, Guangzhou, China; 7Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, USA; 8Department of Electric and Computer Engineering, New Jersey Institute of Technology, NJ, USA; 9Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan *These authors contributed equally to this work 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: multimodal MRI, schizophrenia, support vector machine, SVM, classification

Supplemental Digital Content is available in the text, Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

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