MMM: classification of schizophrenia using multi-modality multi-atlas feature representation and multi-kernel learning

Schizophrenia (SZ) is a complex neuropsychiatric disorder that seriously affects the daily life of patients. Therefore, accurate diagnosis of SZ is essential for patient care. Several T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) markers (e.g., cortical thickness (CT), mean diffusivity (MD)) for SZ have been identified by using some existing brain atlases, and have been used successfully to discriminate patients with SZ from healthy controls (HCs). Currently, these markers have mainly been used separately. Thus, the full potential of T1-weighted MRI images and DTI images for SZ diagnosis might not yet have been used comprehensively. Furthermore, the extraction of these markers based on single brain atlas might not yet be able to use the full potential of these images. Therefore, in this study, we propose a multi-modality multi-atlas feature representation and a multi-kernel learning method (MMM) to perform SZ/HC classification. Firstly, we extract 8 feature sets from T1-weighted MRI images and DTI images via 4 existing brain atlases and 4 markers. Then, a two-step feature selection method is proposed to select the most discriminative features of each feature set for SZ/HC classification. Finally, a multiple feature sets based multi-kernel SVM learning method (MFMK-SVM) is proposed to combine all feature sets for SZ/HC classification. Experimental results show that our proposed method achieves an accuracy of 91.28%, a sensitivity of 90.85%, a specificity of 92.17% and an AUC of 0.9485 for SZ/HC classification. Experimental results illustrate that our proposed classification method is efficient and promising for clinical diagnosis of SZ.