Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images

Xiaobing Lu, Yongzhe Yang, Fengchun Wu, Minjian Gao, Yong Xu, Yue Zhang, Yongcheng Yao, Xin Du, Chengwei Li, Lei Wu, Xiaomei Zhong, Yanling Zhou, Ni Fan, Yingjun Zheng, Dongsheng Xiong, Hongjun Peng, Javier Escudero, Biao Huang, Xiaobo Li, Yuping NingKai Wu

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

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.

Original languageEnglish (US)
Article number176
JournalMedicine (United States)
Volume95
Issue number30
DOIs
StatePublished - Jul 1 2016

All Science Journal Classification (ASJC) codes

  • Medicine(all)

Keywords

  • recursive feature elimination
  • region of interest
  • schizophrenia
  • support vector machine
  • voxel-based morphometry

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