Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jun, E. | - |
dc.contributor.author | Na, K.-S. | - |
dc.contributor.author | Kang, W. | - |
dc.contributor.author | Lee, J. | - |
dc.contributor.author | Suk, H.-I. | - |
dc.contributor.author | Ham, B.-J. | - |
dc.date.available | 2020-11-11T00:40:54Z | - |
dc.date.created | 2020-08-27 | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 1065-9471 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/78914 | - |
dc.description.abstract | Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting-state functional magnetic resonance imaging (rs-fMRI) and estimate functional connectivity for brain-disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph-based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole-brain data-driven manner from rs-fMRI. To distinguish drug-naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD. © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.relation.isPartOf | Human Brain Mapping | - |
dc.title | Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000560502300001 | - |
dc.identifier.doi | 10.1002/hbm.25175 | - |
dc.identifier.bibliographicCitation | Human Brain Mapping, v.41, no.17, pp.497 - 5014 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85089515996 | - |
dc.citation.endPage | 5014 | - |
dc.citation.startPage | 497 | - |
dc.citation.title | Human Brain Mapping | - |
dc.citation.volume | 41 | - |
dc.citation.number | 17 | - |
dc.contributor.affiliatedAuthor | Na, K.-S. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | effective connectivity | - |
dc.subject.keywordAuthor | graph convolutional networks (GCNs) | - |
dc.subject.keywordAuthor | major depressive disorder (MDD) | - |
dc.subject.keywordAuthor | resting-state functional magnetic resonance imaging (rs-fMRI) | - |
dc.subject.keywordAuthor | Sparse Group LASSO | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.