Abstract
Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the 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 do not consider the covariance patterns of these features between regions. In this study, by combining the 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 the 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 the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.
Highlights
Autism spectrum disorder (ASD) encompasses a range of neurodevelopmental disorders
To evaluate our results obtained with the deep convolutional neural network, the performance of our model is compared with the results of classifiers trained using random forest (RF) (Vapnik, 1998), support vector machine (SVM) (Ho, 1995), XgBoost (XGB) (Chen and Guestrin, 2016), and autoencoder (AE)
Our proposed framework has the best performances in classifying ASD from typical controls (TC) with the highest ACC, SEN, F1 score, and area under the curve (AUC) values compared with the other methods
Summary
Autism spectrum disorder (ASD) encompasses a range of neurodevelopmental disorders. The core symptoms of ASD comprise abnormal emotional regulation and social interactions, restricted interest, repetitive behaviors, and hypo-/hyperreactivity to sensory stimuli (Guze, 1995). Many individuals with autism spectrum disorder usually exhibit impairments in learning, development, CNN Classifies ASD control, and interaction, as well as some daily life skills. ASD causes heavy economic burden for the patients’ families and the society. It is urgent to establish an early and accurate diagnosis framework to identify ASD patients from typical controls (TC).
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