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However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting\u2010state functional magnetic resonance imaging (rs\u2010fMRI) and estimate functional connectivity for brain\u2010disease 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\u2010based 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\u2010brain data\u2010driven manner from rs\u2010fMRI. To distinguish drug\u2010na\u00efve 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.<\/jats:p>","DOI":"10.1002\/hbm.25175","type":"journal-article","created":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T01:43:45Z","timestamp":1598579025000},"page":"4997-5014","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Identifying <scp>resting\u2010state<\/scp> effective connectivity abnormalities in <scp>drug\u2010na\u00efve<\/scp> major depressive disorder diagnosis via graph convolutional networks"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3121-7734","authenticated-orcid":false,"given":"Eunji","family":"Jun","sequence":"first","affiliation":[{"name":"Department of Brain and Cognitive Engineering Korea University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0148-9827","authenticated-orcid":false,"given":"Kyoung\u2010Sae","family":"Na","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Gachon University Gil Medical Center  Incheon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4733-027X","authenticated-orcid":false,"given":"Wooyoung","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences Korea University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8400-2729","authenticated-orcid":false,"given":"Jiyeon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Brain and Cognitive Engineering Korea University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7019-8962","authenticated-orcid":false,"given":"Heung\u2010Il","family":"Suk","sequence":"additional","affiliation":[{"name":"Department of Brain and Cognitive Engineering Korea University  Seoul Republic of Korea"},{"name":"Department of Artificial Intelligence Korea University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0108-2058","authenticated-orcid":false,"given":"Byung\u2010Joo","family":"Ham","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Korea University Anam Hospital, Korea University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,8,19]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nn.2921"},{"key":"e_1_2_13_3_1","first-page":"133","article-title":"Diagnostic and statistical manual of mental disorders","volume":"17","author":"American Psychiatric Association","year":"2013","journal-title":"BMC Medicine"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2005.02.021"},{"issue":"1","key":"e_1_2_13_5_1","first-page":"101","article-title":"Patterns of interregional correlations of cerebral glucose metabolic rates in patients with dementia of the Alzheimer type","volume":"1","author":"Azari N.","year":"1992","journal-title":"Neurodegeneration"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"e_1_2_13_7_1","first-page":"1803","article-title":"How to explain individual classification decisions","volume":"11","author":"Baehrens D.","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2009.08.009"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2016.02.018"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-45528-0"},{"key":"e_1_2_13_11_1","doi-asserted-by":"crossref","unstructured":"Brosch T. 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