{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T03:52:11Z","timestamp":1791345131832,"version":"4.3.1"},"reference-count":62,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"unspecified","delay-in-days":279,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["SCHU3432\/2-1"],"award-info":[{"award-number":["SCHU3432\/2-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["CR 994\/2-1"],"award-info":[{"award-number":["CR 994\/2-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Psychol. Med."],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S003329172610587X_sec1\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accelerated brain aging has been associated with several neuropsychiatric disorders; however, findings remain inconsistent due to limited lifespan modeling and inadequate age-bias correction. This study aimed to establish normative brain-aging trajectories across the lifespan and quantify disorder-specific deviations in brain-predicted age difference (brain-PAD) in a large, age-diverse sample.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S003329172610587X_sec2\" sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Brain-age models were trained on structural MRI data from 25,425 healthy individuals (aged 2\u201395\u00a0years) and validated in 270 independent cross-sectional and 188 longitudinal participants from the Dallas Lifespan Brain Study. Clinical analyses included 1,737 patients with schizophrenia (SZ), major depressive disorder (MDD), bipolar disorder (BD), attention-deficit\/hyperactivity disorder (ADHD), chronic substance use disorder (CSUD), Alzheimer\u2019s disease (AD), and frontotemporal dementia (FTD), compared with 1,793 age-matched controls. Brain-PAD was computed after age-bias correction, regional morphometric differences were assessed using ANCOVA, and model interpretability was evaluated using SHapley Additive exPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S003329172610587X_sec3\" sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved a mean absolute error of ~5\u20137\u00a0years across validation and independent cohorts. Healthy individuals showed median brain-PAD values near zero, and longitudinal analyses tracked within-person aging. Significantly elevated \u0394brain-PAD values were observed in SZ (+7.64\u00a0years), FTD (+7.61\u00a0years), AD (+3.24\u00a0years), BD (+2.08\u00a0years), MDD (+2.76\u00a0years), and CSUD (+2.91\u00a0years), but not in ADHD. Regional analyses revealed shared and disorder-specific morphometric alterations, and SHAP highlighted key contributions from ventricular enlargement and fronto-parietal cortical features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S003329172610587X_sec4\" sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Brain-PAD captures biologically meaningful structural variation across disorders and provides a quantitative framework for investigating lifespan brain aging in psychiatric and neurodegenerative conditions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/s003329172610587x","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T03:46:46Z","timestamp":1791344806000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["Accelerated brain aging as a transdiagnostic biomarker: A lifespan MRI brain age study across seven disorders"],"prefix":"10.1017","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8404-5411","authenticated-orcid":false,"given":"Yubraj","family":"Gupta","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/035rzkx15","id-type":"ROR","asserted-by":"publisher"}],"name":"Jena University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6529-2679","authenticated-orcid":false,"given":"Feliberto","family":"de la Cruz","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/035rzkx15","id-type":"ROR","asserted-by":"publisher"}],"name":"Jena University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0321-2119","authenticated-orcid":false,"given":"Maria","family":"Geisler","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/035rzkx15","id-type":"ROR","asserted-by":"publisher"}],"name":"Jena University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5606-1515","authenticated-orcid":false,"given":"Katrin","family":"Rieger","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/035rzkx15","id-type":"ROR","asserted-by":"publisher"}],"name":"Jena University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5691-4325","authenticated-orcid":false,"given":"Andy","family":"Schumann","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/035rzkx15","id-type":"ROR","asserted-by":"publisher"}],"name":"Jena University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3861-5679","authenticated-orcid":false,"given":"Karl-J\u00fcrgen","family":"B\u00e4r","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/035rzkx15","id-type":"ROR","asserted-by":"publisher"}],"name":"Jena University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"S003329172610587X_r60","doi-asserted-by":"publisher","DOI":"10.1016\/j.comppsych.2017.12.002"},{"key":"S003329172610587X_r48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.119947"},{"key":"S003329172610587X_r15","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-022-01897-w"},{"key":"S003329172610587X_r38","doi-asserted-by":"publisher","DOI":"10.1093\/schbul\/sbt142"},{"key":"S003329172610587X_r26","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(23)00250-9"},{"key":"S003329172610587X_r31","doi-asserted-by":"publisher","DOI":"10.1016\/j.drugalcdep.2019.02.005"},{"key":"S003329172610587X_r19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdevneu.2011.02.013"},{"key":"S003329172610587X_r21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.01.021"},{"key":"S003329172610587X_r44","unstructured":"Lundberg, S. M. , & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"key":"S003329172610587X_r18","doi-asserted-by":"publisher","DOI":"10.1038\/s41386-021-00967-x"},{"key":"S003329172610587X_r45","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-04492-9"},{"key":"S003329172610587X_r53","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2023.1266770"},{"key":"S003329172610587X_r46","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2021.3107372"},{"key":"S003329172610587X_r47","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awaf296"},{"key":"S003329172610587X_r6","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-04554-y"},{"key":"S003329172610587X_r17","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2020.102229"},{"key":"S003329172610587X_r23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.11.024"},{"key":"S003329172610587X_r10","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-020-01004-z"},{"key":"S003329172610587X_r30","doi-asserted-by":"publisher","DOI":"10.1093\/schbul\/sbx172"},{"key":"S003329172610587X_r16","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22913"},{"key":"S003329172610587X_r34","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2012.06678"},{"key":"S003329172610587X_r49","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-04847-7"},{"key":"S003329172610587X_r33","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.21334"},{"key":"S003329172610587X_r4","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.102063"},{"key":"S003329172610587X_r40","doi-asserted-by":"publisher","DOI":"10.12779\/dnd.2024.23.4.188"},{"key":"S003329172610587X_r11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2020.03.014"},{"key":"S003329172610587X_r59","unstructured":"Wilson, A. G. , Hu, Z. , Salakhutdinov, R. , & Xing, E. P. (2016). Deep kernel learning. Artificial Intelligence and Statistics, 370\u2013378. https:\/\/proceedings.mlr.press\/v51\/wilson16.html"},{"key":"S003329172610587X_r5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119330"},{"key":"S003329172610587X_r3","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2018.00252"},{"key":"S003329172610587X_r24","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.00789"},{"key":"S003329172610587X_r35","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxj037"},{"key":"S003329172610587X_r52","doi-asserted-by":"publisher","DOI":"10.1111\/add.16621"},{"key":"S003329172610587X_r13","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2017.62"},{"key":"S003329172610587X_r9","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"S003329172610587X_r12","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-018-0098-1"},{"key":"S003329172610587X_r7","doi-asserted-by":"publisher","DOI":"10.1038\/nrn.2017.125"},{"key":"S003329172610587X_r39","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2022.1019546"},{"key":"S003329172610587X_r43","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"key":"S003329172610587X_r57","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-47021-y"},{"key":"S003329172610587X_r28","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110484"},{"key":"S003329172610587X_r22","doi-asserted-by":"publisher","DOI":"10.1515\/REVNEURO.2010.21.3.187"},{"key":"S003329172610587X_r61","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.70689"},{"key":"S003329172610587X_r27","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.05.075"},{"key":"S003329172610587X_r37","doi-asserted-by":"publisher","DOI":"10.3389\/fncel.2017.00030"},{"key":"S003329172610587X_r56","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(24)00038-3"},{"key":"S003329172610587X_r8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0152983"},{"key":"S003329172610587X_r62","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2013.281053"},{"key":"S003329172610587X_r41","doi-asserted-by":"publisher","DOI":"10.1038\/s43587-022-00219-7"},{"key":"S003329172610587X_r2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101675"},{"key":"S003329172610587X_r58","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)61611-6"},{"key":"S003329172610587X_r42","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24588"},{"key":"S003329172610587X_r1","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-025-03411-6"},{"key":"S003329172610587X_r36","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-019-0471-7"},{"key":"S003329172610587X_r51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2016.06.040"},{"key":"S003329172610587X_r54","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000005163"},{"key":"S003329172610587X_r50","doi-asserted-by":"publisher","DOI":"10.1038\/s41596-022-00696-5"},{"key":"S003329172610587X_r20","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.neurobiolaging.2020.02.009","article-title":"Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging","volume":"91","author":"Feng","year":"2020","journal-title":"Neurobiology of Aging"},{"key":"S003329172610587X_r55","doi-asserted-by":"publisher","DOI":"10.1038\/s41386-018-0298-z"},{"key":"S003329172610587X_r25","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2020.00432"},{"key":"S003329172610587X_r29","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2022.913470"},{"key":"S003329172610587X_r14","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378"},{"key":"S003329172610587X_r32","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-020-0754-0"}],"container-title":["Psychological Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S003329172610587X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T03:46:48Z","timestamp":1791344808000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S003329172610587X\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":62,"alternative-id":["S003329172610587X"],"URL":"https:\/\/doi.org\/10.1017\/s003329172610587x","relation":{},"ISSN":["0033-2917","1469-8978"],"issn-type":[{"value":"0033-2917","type":"print"},{"value":"1469-8978","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"\u00a9 The Author(s), 2026. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:\/\/creativecommons.org\/licenses\/by\/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"e321"}}