Diagnosis of Autism Spectrum Disorder Based on Eigenvalues of Brain Networks

Autism spectrum disorder (ASD) is a neuro dysfunction which causes the repetitive behavior and social instability of patients. Diagnosing ASD has been of great interest. However, due to the lack of discriminate differences between neuroimages of healthy persons and ASD patients, there has been no powerful diagnosis approach. In this study, we have designed brain network-based features for the diagnosis of ASD. Specifically, we have used the 264 regions based parcellation scheme to construct a brain network from a brain functional magnetic resonance imaging (fMRI). Then we have defined 264 raw brain features by the 264 eigenvalues of the Laplacian matrix of the brain network and another three features by network centralities. By applying a feature selection algorithm, we have obtained 64 discriminate features. Furthermore, we have trained several machine learning models for diagnosing ASD with our obtained features on ABIDE (Autism Brain Imaging Data Exchange) dataset. With our derived features, the linear discriminant analysis has achieved the classification accuracy of 77.7%, which is better than the state-of-the-art results.