Autism spectrum disorder recognition based on multi-view ensemble learning with multi-site fMRI

Autism spectrum disorders (ASD) is a neurodevelopmental disorder that causes repetitive stereotyped behavior and social difficulties, early diagnosis and intervention are beneficial to improve treatment effect. Although multi-site data expand sample size, they suffer from inter-site heterogeneitys, which degrades the performance of identitying ASD from normal controls (NC). To solve the problem, in this paper a multi-view ensemble learning network based on deep learning is proposed to improve the classification performance with multi-site functional MRI (fMRI). Specifically, the LSTM-Conv model was firstly proposed to obtain dynamic spatiotemporal features of the mean time series of fMRI data; then the low/high-level brain functional connectivity features of the brain functional network were extracted by principal component analysis algorithm and a 3-layer stacked denoising autoencoder; finally, feature selection and ensemble learning were carried out for the above three brain functional features, and a classification accuracy of 72% was obtained on multi-site data of ABIDE dataset. The experimental result illustrates that the proposed method can effectively improve the classification performance of ASD and NC. Compared with single-view learning, multi-view ensemble learning can mine various brain functional features of fMRI data from different perspectives and alleviate the problems caused by data heterogeneity. In addition, this study also employed leave-one-out cross validation to test the single-site data, and the results showed that the proposed method has strong generalization capability, in which the highest classification accuracy of 92.9% was obtained at the CMU site.