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However, the dynamic coordination among these networks remains poorly understood, impeding comprehensive elucidation of the underlying neuropathology of SD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Resting\u2010state functional magnetic resonance imaging (fMRI) data were collected from subjects with SD (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200926) and healthy controls (HCs,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200933). A preclustering\u2010based co\u2010activation pattern method was developed to investigate the dynamic patterns of network coordination. Finally, machine learning analysis was conducted to evaluate the potential of network dynamics for clinical diagnosis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Subjects with SD exhibited decreased dwell time in the SN and increased transition frequency from the SN to DMN, which was positively correlated with depressive severity. Furthermore, an ensemble learning model based on SN\u2010DMN dynamic features achieved a classification accuracy of 96.44% in distinguishing SD from HC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings underscore the potential of altered SN\u2010DMN dynamics as candidates for future neuroimaging markers of SD and support a neurocognitive model whereby altered SN\u2010DMN dynamic coordination makes subjects with SD more prone to internal directed attention biases, thereby contributing to self\u2010related depressive symptoms like rumination.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cns.70736","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T15:19:08Z","timestamp":1770218348000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Altered Salience\u2010Default Mode Network Dynamics in Subclinical Depression: A Preclustering\u2010Based Co\u2010Activation Pattern Analysis"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9324-7074","authenticated-orcid":false,"given":"Bo","family":"Zhang","sequence":"first","affiliation":[{"name":"Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University  Tianjin China"},{"name":"State Key Laboratory of Advanced Medical Materials and Devices  Tianjin China"},{"name":"Haihe Laboratory of Brain\u2010Computer Interaction and Human\u2010Machine Integration  Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8987-1002","authenticated-orcid":false,"given":"Zhinan","family":"Yu","sequence":"additional","affiliation":[{"name":"Tianjin International Joint Research Center for Neural Engineering, Academy 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