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Med."],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S0033291725101517_sec1\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Identifying key areas of brain dysfunction in mental illness is critical for developing precision diagnosis and treatment. This study aimed to develop region-specific brain aging trajectory prediction models using multimodal magnetic resonance imaging (MRI) to identify similarities and differences in abnormal aging between bipolar disorder (BD) and major depressive disorder (MDD) and pinpoint key brain regions of structural and functional change specific to each disorder.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0033291725101517_sec2\" sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Neuroimaging data from 340 healthy controls, 110 BD participants, and 68 MDD participants were included from the Taiwan Aging and Mental Illness cohort. We constructed 228 models using T1-weighted MRI, resting-state functional MRI, and diffusion tensor imaging data. Gaussian process regression was used to train models for estimating brain aging trajectories using structural and functional maps across various brain regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0033291725101517_sec3\" sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our models demonstrated robust performance, revealing accelerated aging in 66 gray matter regions in BD and 67 in MDD, with 13 regions common to both disorders. The BD group showed accelerated aging in 17 regions on functional maps, whereas no such regions were found in MDD. Fractional anisotropy analysis identified 43 aging white matter tracts in BD and 39 in MDD, with 16 tracts common to both disorders. Importantly, there were also unique brain regions with accelerated aging specific to each disorder.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0033291725101517_sec4\" sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings highlight the potential of brain aging trajectories as biomarkers for BD and MDD, offering insights into distinct and overlapping neuroanatomical changes. Incorporating region-specific changes in brain structure and function over time could enhance the understanding and treatment of mental illness.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/s0033291725101517","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T08:29:17Z","timestamp":1756456157000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":2,"title":["Identifying key brain pathology in bipolar and unipolar depression using a region-specific brain aging trajectories approach: Insights from the Taiwan Aging and Mental Illness Cohort"],"prefix":"10.1017","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0718-4087","authenticated-orcid":false,"given":"Jun-Ding","family":"Zhu","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/059ryjv25","id-type":"ROR","asserted-by":"publisher"}],"name":"College of Medical Science and Technology, Chung Shan Medical 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