Multi-feature based network revealing the structural abnormalities in autism spectrum disorder

Abstract

Although morphological features have been used in the diagnosis of a variety of neurological and psychiatric disorders, these features did not show significant discriminative value in identifying Autism Spectrum Disorder (ASD), possibly due to the omission of changes in structural similarities among cortical regions. In this study, structural images from 66 high-functioning adults with ASD and 66 matched typically-developing controls (TDC) were used to test the hypothesis of the abnormality of cortico-cortical relationship in ASD. Seven morphological features of each of the 360 brain regions were extracted and elastic network was used to quantify the similarities between each target region and all other regions. The similarities were used to construct multi-feature-based networks (MFN), which were then submitted to a support vector machine classifier to classify the individuals of the two groups. Results showed MFN significantly improved the accuracy of discriminating patients with ASD from TDCs (78.63%) compared to using morphological features only (< 65%). The combination of MFN with morphological features and other high-level MFN properties did not further enhance the classification performance. Our findings demonstrate that the variations in cortico-cortical similarities are important in the etiology of ASD and can be used as biomarkers in the diagnostic process.