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Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural
Xiaobing Lu1, Yongzhe Yang, Fengchun Wu
1Department of Psychiatry, Guangzhou Brain Hospital (GBH)/(Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China GBH-SCUT Joint Research Centre for Neuroimaging, Guangzhou, China School of Medicine, South China University of Technology (SCUT), Guangzhou, China Department of Clinical Psychology, Guangzhou Brain Hospital (GBH)/ (Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, US Department of Electric and Computer Engineering, New Jersey Institute of Technology, NJ, US Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY, US Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan Institute for Digital Communications, School of Engineering, The University of Edinburgh, Edinburgh EH9 3JL, UK.
Abstract:
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.
