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Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks

Authors
Zhao, FengChen, ZhiyuanRekik, IslemLee, Seong-WhanShen, Dinggang
Issue Date
28-4월-2020
Publisher
FRONTIERS MEDIA SA
Keywords
autism spectrum disorder; dynamic functional connectivity networks; resting-state functional MRI; central-moment features; conventional FC network
Citation
FRONTIERS IN NEUROSCIENCE, v.14
Indexed
SCIE
SCOPUS
Journal Title
FRONTIERS IN NEUROSCIENCE
Volume
14
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/56269
DOI
10.3389/fnins.2020.00258
ISSN
1662-4548
Abstract
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 regions of interest (ROIs). Moreover, D-FCNs suffer from the temporal mismatching issue, i.e., subnetworks 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" to further explore the higher level and more complex interaction relationships among multiple ROIs. Furthermore, we propose to use a central-moment method to extract temporal-invariance properties contained in either low- 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.
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