Hybrid High- order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Yu | - |
dc.contributor.author | Zhang, Han | - |
dc.contributor.author | Chen, Xiaobo | - |
dc.contributor.author | Lee, Seong-Whan | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-03T03:46:17Z | - |
dc.date.available | 2021-09-03T03:46:17Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-07-26 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/82787 | - |
dc.description.abstract | Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brain regions. A high-order FC based on "correlation's correlation" has emerged as a new approach for abnormality detection of brain disease. However, separate construction of the low-and high-order FC networks overlooks information exchange between the two FC levels. Such a higher-level relationship could be more important for brain diseases study. In this paper, we propose a novel framework, namely "hybrid high-order FC networks" by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis. For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated high-order network, by estimating the correlations between the topographical information of the high-order FC sub-network from one brain region and that of the loworder FC sub-network from another brain region. With multi-kernel learning, complementary features from multiple time-varying FC networks constructed at different levels are fused for eMCI classification. Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | NATURE PUBLISHING GROUP | - |
dc.subject | GRAPH-THEORETICAL ANALYSIS | - |
dc.subject | SCALE BRAIN NETWORKS | - |
dc.subject | ALZHEIMERS-DISEASE | - |
dc.subject | JOINT REGRESSION | - |
dc.subject | CLASSIFICATION | - |
dc.subject | PREDICTION | - |
dc.subject | SELECTION | - |
dc.subject | FMRI | - |
dc.title | Hybrid High- order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Seong-Whan | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1038/s41598-017-06509-0 | - |
dc.identifier.scopusid | 2-s2.0-85025174706 | - |
dc.identifier.wosid | 000406364600043 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, v.7 | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.citation.title | SCIENTIFIC REPORTS | - |
dc.citation.volume | 7 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | GRAPH-THEORETICAL ANALYSIS | - |
dc.subject.keywordPlus | SCALE BRAIN NETWORKS | - |
dc.subject.keywordPlus | ALZHEIMERS-DISEASE | - |
dc.subject.keywordPlus | JOINT REGRESSION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | FMRI | - |
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