{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T03:57:47Z","timestamp":1773460667901,"version":"3.50.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T00:00:00Z","timestamp":1629590400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1DP1DA048968\u201001"],"award-info":[{"award-number":["1DP1DA048968\u201001"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrics"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Group\u2010level brain connectome analysis has attracted increasing interest in neuropsychiatric research with the goal of identifying connectomic subnetworks (subgraphs) that are systematically associated with brain disorders. However, extracting disease\u2010related subnetworks from the whole brain connectome has been challenging, because no prior knowledge is available regarding the sizes and locations of the subnetworks. In addition, neuroimaging data are often mixed with substantial noise that can further obscure informative subnetwork detection. We propose a likelihood\u2010based adaptive dense subgraph discovery (ADSD) model to extract disease\u2010related subgraphs from the group\u2010level whole brain connectome data. Our method is robust to both false positive and false negative errors of edge\u2010wise inference and thus can lead to a more accurate discovery of latent disease\u2010related connectomic subnetworks. We develop computationally efficient algorithms to implement the novel ADSD objective function and derive theoretical results to guarantee the convergence properties. We apply the proposed approach to a brain fMRI study for schizophrenia research and identify well\u2010organized and biologically meaningful subnetworks that exhibit schizophrenia\u2010related salience network centered connectivity abnormality. Analysis of synthetic data also demonstrates the superior performance of the ADSD method for latent subnetwork detection in comparison with existing methods in various\u00a0settings.<\/jats:p>","DOI":"10.1111\/biom.13537","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T01:44:25Z","timestamp":1628559865000},"page":"1566-1578","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Extracting brain disease\u2010related connectome subgraphs by adaptive dense subgraph discovery"],"prefix":"10.1093","volume":"78","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5747-1899","authenticated-orcid":false,"given":"Qiong","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Mathematics University of Maryland College Park Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4410-9185","authenticated-orcid":false,"given":"Xiaoqi","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Mathematics Johns Hopkins University Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam J.","family":"Culbreth","sequence":"additional","affiliation":[{"name":"Maryland Psychiatric Research Center Department of Psychiatry, School of Medicine University of Maryland Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James A.","family":"Waltz","sequence":"additional","affiliation":[{"name":"Maryland Psychiatric Research Center Department of Psychiatry, School of Medicine University of Maryland Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"L. Elliot","family":"Hong","sequence":"additional","affiliation":[{"name":"Maryland Psychiatric Research Center Department of Psychiatry, School of Medicine University of Maryland Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Chen","sequence":"additional","affiliation":[{"name":"Maryland Psychiatric Research Center Department of Psychiatry, School of Medicine University of Maryland Baltimore Maryland USA"},{"name":"Division of Biostatistics and Bioinformatics Department of Epidemiology and Public Health School of Medicine University of Maryland Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,8,22]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24723"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-61422-2_127"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"Charikar M.(2000)Greedy approximation algorithms for finding dense components in a graph. 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