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Cited 1 time in webofscience Cited 2 time in scopus
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Identifyingresting-stateeffective connectivity abnormalities indrug-naivemajor depressive disorder diagnosis via graph convolutional networks

Authors
Jun, EunjiNa, Kyoung-SaeKang, WooyoungLee, JiyeonSuk, Heung-IlHam, Byung-Joo
Issue Date
12월-2020
Publisher
WILEY
Keywords
effective connectivity; deep learning; graph convolutional networks (GCNs); major depressive disorder (MDD); resting-state functional magnetic resonance imaging (rs-fMRI); Sparse Group LASSO
Citation
HUMAN BRAIN MAPPING, v.41, no.17, pp.4997 - 5014
Indexed
SCIE
SCOPUS
Journal Title
HUMAN BRAIN MAPPING
Volume
41
Number
17
Start Page
4997
End Page
5014
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/51284
DOI
10.1002/hbm.25175
ISSN
1065-9471
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-naive 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.
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