{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T10:35:07Z","timestamp":1792924507669,"version":"4.5.0"},"reference-count":50,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"vor","delay-in-days":19,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>EEG functional\u2010connectivity studies in major depressive disorder (MDD) have produced inconsistent findings, partly because different estimators capture distinct aspects of neural coupling. We implemented a multi\u2010metric resting\u2010state EEG approach combining linear, phase\u2010based, nonlinear, and graph\u2010theoretical measures to characterize frequency\u2010dependent sensor\u2010level connectivity alterations and identify sensor\u2010level network patterns relevant to depression\u2010related dysconnectivity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>\n                      Resting\u2010state EEG from 30 DSM\u2010IV MDD participants and 28 healthy controls was analyzed using a 19\u2010channel 10\u201320 montage. After MNE\u2010Python preprocessing, including filtering, ICA\u2010based artifact removal, current source density (CSD) transformation, and symmetric orthogonalization, coherence, PLV, PLI, and MI were computed across theta, alpha, and beta bands. Edge\u2010wise differences were tested using Brunner\u2013Munzel with Holm correction, confirmed with Westfall\u2013Young maxT correction, and summarized using Hedges\u2019\n                      <jats:italic>g<\/jats:italic>\n                      and graph measures.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Finding<\/jats:title>\n                    <jats:p>MDD showed frequency\u2010 and metric\u2010dependent sensor\u2010level connectivity alterations. Theta coherence, PLV, and PLI were dominated by lower connectivity across frontal\u2010to\u2010posterior, fronto\u2010central, temporal\/posterior\u2010temporal, parieto\u2010occipital, and central\u2010to\u2010occipital sensor pairs, whereas theta MI showed few increased frontal\/fronto\u2010temporal effects. Alpha findings were more selective, with mainly lower coherence and PLV across frontopolar\/frontal\u2010to\u2010central, fronto\u2010midline, and posterior links, while alpha MI showed a limited mixed pattern. In beta, coherence, PLV, and PLI showed sparse robust reductions, whereas beta MI showed widespread increased statistical dependence. Graph\u2010theoretical summaries at 20% proportional density showed mainly lower mean weighted strength and global efficiency in theta\u2010 and alpha\u2010band networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings indicate frequency\u2010 and metric\u2010dependent resting\u2010state EEG sensor\u2010level connectivity alterations in MDD. The dominant pattern was lower theta\/alpha connectivity across distributed sensor pairs, alongside increased beta MI\u2010based dependence. The results provide corrected, effect\u2010size\u2010informed evidence of altered inter\u2010sensor coordination without establishing dysfunction of specific cortical regions or named networks. This framework may support future hypothesis\u2010driven research in independent, clinically annotated, higher\u2010density EEG or multimodal datasets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.71842","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T06:12:54Z","timestamp":1792563174000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010Metric EEG Connectivity and Graph Analysis Reveal Altered Resting\u2010State Networks in Major Depressive Disorder"],"prefix":"10.1002","volume":"16","author":[{"given":"Sushmit Jahan","family":"Rose","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering Varendra University  Rajshahi Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. H. M. Rahmatullah","family":"Imon","sequence":"additional","affiliation":[{"name":"Center For Interdisciplinary Research Varendra University  Rajshahi Bangladesh"},{"name":"Professor Emeritus of Mathematical Sciences Ball State University  Muncie Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rushdania","family":"Bushra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Varendra University  Rajshahi Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nafis","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Varendra University  Rajshahi Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moha. 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