{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T20:27:36Z","timestamp":1787776056168,"version":"build-2784847793"},"reference-count":40,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Functional connectivity (FC), defined as the statistical reliance among different brain regions, has been an effective tool for studying cognitive brain functions. The majority of existing FC-based research has relied on the premise that networks are temporally stationary. However, there exist few research that support nonstationarity of FC which can be due to cognitive functioning. However, still there is a gap in tracking the dynamics of FC to gain a deeper understanding of how brain networks form and adapt in response to therapeutic interventions by identifying the change points that signify substantial shifts in network connectivity across the participants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>The proposed approach in this study is based on tensor representation of FC networks of the source signals of electroencephalogram (EEG) activities yielding a multi-mode tensor. Then analysis of variance has been used to investigate changing points in connectivity of brain activity in sources domain in different conditions of tasks, frequency bands, and among subjects in time. High-density EEG signals (256 channels) were acquired from 30 tinnitus patients under visual (positive emotion induction) and transcranial direct current stimulation (tDCS) stimuli.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The proposed method of this study could effectively identify the significant brain connectivity change points, indicating enhanced effectiveness in capturing connectivity shifts comparing to conventional methods. Findings in tinnitus patients suggest that visual stimulation alone may not significantly alter brain connectivity networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Based on the results, a combination of visual stimulation with simultaneous High-Definition tDCS is recommended, potentially informing optimal intervention strategies to enhance tinnitus treatment effectiveness.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmss.jmss_75_24","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T11:01:56Z","timestamp":1754478116000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["A New Method for Dynamic Brain Connectivity Analysis Based on Tensor Decomposition in Tinnitus Using High-density Electroencephalogram in Source 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