Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks - Zhao, Feng; Chen, Zhiyuan; Rekik, Islem; Lee, Seong-Whan; Shen, Dinggang (Frontiers in Neuroscience, 2020)
The sliding-window-based dynamic functional connectivity networks (D-FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) are effective methods for diagnosing various neurological diseases, including autism spectrum disorder (ASD). However, traditional D-FCNs are low-order networks based on pairwise correlation between brain regions, thus overlooking high-level interactions across multiple ROIs. Moreover, D-FCNs suffer from the temporal mismatching issue, i.e., sub-networks in the same temporal window do not have temporal correspondence across different subjects. To address the above problems, we first construct a novel high-order D-FCNs based on the principle of “correlation’s correlation” for further exploring the higher-level and more complex interaction relationships among multiple ROIs. Further, we propose to use a central-moment method to extract temporal-invariance properties contained in either low-order or high-order D-FCNs. Finally, we design and train an ensemble classifier by fusing the features extracted from conventional FCN, low-order D-FCNs and high-order D-FCNs for the diagnosis of ASD and normal control subjects. Our method achieved the best ASD classification accuracy (83%) and our results revealed the features extracted from different networks fingerprinting the autistic brain at different connectional levels.