Original Research ARTICLE
Diagnosis of autism spectrum disorder using central-moment features from low-and-high-order dynamic resting-state functional connectivity networks
- 1Shandong Institute of Business and Technology, China
- 2University of Dundee, United Kingdom
- 3Department of Brain and Cognitive Engineering, Korea University, South Korea
- 4University of North Carolina at Chapel Hill, United States
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.
Keywords: Autism spectrum disorder (ASD), high-order dynamic functional connectivity network (Ho-D-FCN), Conventional FC network(C-FCN), Resting-state fMRI (rs-fMRI), Central-moment feature
Received: 05 Dec 2019;
Accepted: 09 Mar 2020.
Copyright: © 2020 Zhao, Chen, Rekik, Lee and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Dinggang Shen, University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, United States, email@example.com