Aided Diagnosis of Autism Spectrum Disorder Based on a Mixed Neural Network Model

Abstract

The diagnosis of autism spectrum disorder (ASD) is a challenging task, especially for children. In order to determine whether a person has ASD or not, the conventional methods are questionnaires and behavioral observation, which may be subjective and cause misdiagnosis. In order to obtain an accurate diagnosis, we could explore the quantitative imaging biomarkers and leverage the machine learning to learn the classification model on these biomarkers for auxiliary ASD diagnosis. At present, many machine learning methods rely on resting-state fMRI data for feature extraction and auxiliary diagnosis. However, due to the heterogeneity of the data, there can be many noisy features that are adverse to diagnosis, and a lot of biometric information may be not fully explored. In this study, we designed a mixed neural network model of convolutional neural network (CNN) and graph neural network (GNN), termed as MCG-Net, to extract discriminative information from the brain functional connectivity based on the resting-state fMRI data. We used the F-score and KNN algorithms to remove the abundant connectivities in the functional connectivity matrix from global and local level. Besides, the brain gradient features were first introduced in the model. A datasets of 848 subjects from 17 sites on ABIDE datasets was adopted to evaluate the methods. The proposed method has achieved better diagnostic performance compared with other existing methods, with 4.56$\%$ improvement in accuracy.