Automatic classification of first-episode, drug-naive schizophrenia with multi-modal magnetic resonance imaging

作者：Yang, Yongzhe; Zhang, Yue; Wu, Fengchun; Lu, Xiaobing; Ning, Yuping; Huang, Biao; Du, Xin; Li, Chengwei; Wang, Kaixi; Wu, Xiaoming; Wu, Kai^*

来源：Journal of Biomedical Engineering, 2017, 34(5): 674-680.

DOI：10.7507/1001-5515.201607084

• Automatic classification
• Discriminative features
• First episodes
• Low Frequency Fluctuations
• Magnetic Resonance Imaging (MRI)
• Multi-modal
• Recursive feature elimination
• Resting state functional mris

A great number of studies have demonstrated the structural and functional abnormalities in chronic schizophrenia (SZ) patients. However, few studies analyzed the differences between first-episode, drug-naive SZ (FESZ) patients and normal controls (NCs). In this study, we recruited 44 FESZ patients and 56 NCs, and acquired their multimodal magnetic resonance imaging (MRI) data, including structural and resting-state functional MRI data. We calculated gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), and degree centrality (DC) of 90 brain regions, basing on an automated anatomical labeling (AAL) atlas. We then applied these features into support vector machine (SVM) combined with recursive feature elimination (RFE) to discriminate FESZ patients from NCs. Our results showed that the classifier using the combination of ReHo and ALFF as input features achieved the best performance (an accuracy of 96.97%). Moreover, the most discriminative features for classification were predominantly located in the frontal lobe. Our findings may provide potential information for understanding the neuropathological mechanism of SZ and facilitate the development of biomarkers for computer-aided diagnosis of SZ patients.

• 出版日期2017-10-25
• 单位华南理工大学