Integrating Multi-scale Feature Representation and Ensemble Learning for Schizophrenia Diagnosis

Resting-state functional magnetic resonance imaging (rs-fMRI) images have been widely used for diagnosis of schizophrenia. With rs-fMRI, most existing schizophrenia diagnostic methods have revealed schizophrenia’s functional abnormalities from the following three scales, i.e., regional neural activity alterations, functional connectivity abnormalities and brain network dysfunctions. However, many schizophrenia diagnosis methods do not consider the fusion of features from the three scales. In this study, we propose a schizophrenia diagnostic method based on multi-scale feature representation and ensemble learning. Firstly, features including the three scales (region, connectivity and network) are extracted from rs-fMRI images using the brainnetome atlas. For each scale, feature selection, i.e., least absolute shrinkage and selection operator, is applied to identify effective sub-features related to schizophrenia classification by a grid search. Then the selected sub-features of each scale are input to support vector machine with linear kernel to classify schizophrenia patients and healthy controls respectively. To further improve the schizophrenia diagnostic performance, an ensemble learning framework named E-RCN is proposed to average the probabilities obtained by the classifiers of each scale in decision level. By leave-one-out cross-validation on the center for biomedical research excellence dataset (COBRE), our proposed method achieves encouraging diagnosis performance, outperforming several state-of-the-art methods. In addition, ranked by the occurence frequency of each brain region within the leave-one-out cross-validation experiments, some brain regions related to schizophrenia, i.e., thalamus and middle temporal gyrus, and important elaborate subregions, i.e., Tha_L_8_8, MTG_L_4_4 and MTG_R_4_4, are found.