{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:21:36Z","timestamp":1791516096627,"version":"4.3.4"},"reference-count":77,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T00:00:00Z","timestamp":1605744000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Dynamic functional connectivity (DFC) analysis can capture time\u2010varying properties of connectivity. However, studies on large samples using DFC to investigate transdiagnostic dysconnectivity across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are rare. In this study, we used resting\u2010state functional magnetic resonance imaging and a sliding\u2010window method to study DFC in a total of 610 individuals (150 with SZ, 100 with BD, 150 with MDD, and 210 healthy controls [HC]) at a single site. Using k\u2010means clustering, DFCs were clustered into three functional connectivity states: one was a more frequent state with moderate positive and negative connectivity (State 1), and the other two were less frequent states with stronger positive and negative connectivity (State 2 and State 3). Significant 4\u2010group differences (SZ, BD, MDD, and HC groups;\n                    <jats:italic>q<\/jats:italic>\n                    \u2009&lt;\u2009.05, false\u2010discovery rate [FDR]\u2010corrected) in DFC were nearly only in State 1. Post hoc analyses (\n                    <jats:italic>q<\/jats:italic>\n                    \u2009&lt;\u2009.05, FDR\u2010corrected) in State 1 showed that transdiagnostic dysconnectivity patterns among SZ, BD and MDD featured consistently decreased connectivity within most networks (the visual, somatomotor, salience and frontoparietal networks), which was most obvious in both range and extent for SZ. Our findings suggest that there is more common dysconnectivity across SZ, BD and MDD than we previously expected and that such dysconnectivity is state\u2010dependent, which provides new insights into the pathophysiological mechanism of major psychiatric disorders.\n                  <\/jats:p>","DOI":"10.1002\/hbm.25285","type":"journal-article","created":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T16:30:11Z","timestamp":1605803411000},"page":"1182-1196","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Transdiagnostic time\u2010varying dysconnectivity across major psychiatric disorders"],"prefix":"10.1002","volume":"42","author":[{"given":"Chao","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of China Medical University  Shenyang China"},{"name":"Brain Function Research Section The First Affiliated Hospital of China Medical University  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