Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks - Gao, Jingjing; Chen, Mingren; Li, Yuanyuan; Gao, Yachun; Li, Yanling; Cai, Shimin; Wang, Jiaojian (Frontiers in Neuroscience, 2021)
Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to patients’ families and society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and not considering the covariance patterns of these features between regions. In this study, by combining convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in prefrontal cortex and cerebellum, which may be the early biomarkers for diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for diagnosis of ASD with individual structural covariance brain network.