Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhao, Feng | - |
dc.contributor.author | Chen, Zhiyuan | - |
dc.contributor.author | Rekik, Islem | - |
dc.contributor.author | Lee, Seong-Whan | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-08-31T01:50:08Z | - |
dc.date.available | 2021-08-31T01:50:08Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020-04-28 | - |
dc.identifier.issn | 1662-4548 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/56269 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | FRONTIERS MEDIA SA | - |
dc.subject | CLASSIFICATION | - |
dc.subject | ACTIVATION | - |
dc.subject | REGRESSION | - |
dc.subject | CORTEX | - |
dc.subject | RISK | - |
dc.title | Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Seong-Whan | - |
dc.identifier.doi | 10.3389/fnins.2020.00258 | - |
dc.identifier.scopusid | 2-s2.0-85084493360 | - |
dc.identifier.wosid | 000533427500001 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN NEUROSCIENCE, v.14 | - |
dc.relation.isPartOf | FRONTIERS IN NEUROSCIENCE | - |
dc.citation.title | FRONTIERS IN NEUROSCIENCE | - |
dc.citation.volume | 14 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | ACTIVATION | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | CORTEX | - |
dc.subject.keywordPlus | RISK | - |
dc.subject.keywordAuthor | autism spectrum disorder | - |
dc.subject.keywordAuthor | dynamic functional connectivity networks | - |
dc.subject.keywordAuthor | resting-state functional MRI | - |
dc.subject.keywordAuthor | central-moment features | - |
dc.subject.keywordAuthor | conventional FC network | - |
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