Diagnosis of ASD from rs-fMRI Images Based on Brain Dynamic Networks.

The resting-state functional magnetic resonance imaging (rs-fMRI) as a non-invasive technique with the high spatial and temporal resolution can help characterize the pathogenesis of autism spectrum disorder (ASD). Some results have been achieved with machine learning techniques to diagnose ASD with rs-fMRI data. However, most of machine learning methods have neglected the temporal dependency of the time-series fMRI data. In this study, we propose a method for diagnosing ASD based on brain dynamic networks (BDNs) which are constructed with time series rs-fMRI brain image data to describe the dynamic relationship among multiple brain regions. The least squares method with the forward model selection method was used to establish BDNs, and the Bayesian information criterion (BIC) was adopted as the model selection criteria to avoid overfitting. The resulted DBNs are weighted directed networks. Then a feature extraction method was proposed to extract representative and discriminated features from BDNs. Lastly, machine learning classifiers were trained with the whole ABIDE I cohort to diagnose ASD. The accuracy of 88.8% was achieved, which is higher than any previously reported methods.