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

autism spectrum disorder central-moment features conventional FC network dynamic functional connectivity networks resting-state functional MRI

Journal

Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481

Informations de publication

Date de publication:
2020
Historique:
received: 07 12 2019
accepted: 09 03 2020
entrez: 16 5 2020
pubmed: 16 5 2020
medline: 16 5 2020
Statut: epublish

Résumé

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.

Identifiants

pubmed: 32410930
doi: 10.3389/fnins.2020.00258
pmc: PMC7198826
doi:

Types de publication

Journal Article

Langues

eng

Pagination

258

Informations de copyright

Copyright © 2020 Zhao, Chen, Rekik, Lee and Shen.

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Auteurs

Feng Zhao (F)

School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China.

Zhiyuan Chen (Z)

School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China.

Islem Rekik (I)

BASIRA Lab, CVIP Group, Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom.

Seong-Whan Lee (SW)

Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.

Dinggang Shen (D)

Department of Radiology and Biomedical Research Imaging Central, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.

Classifications MeSH