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In this study, brain-based predictors of major depressive disorder (MDD) were trained using machine-learning (Best Linear Unbiased Predictors [BLUP]) and deep-learning (ResNet3D) techniques applied to high-dimensional (voxel-wise) grey-matter structure extracted from T1-weighted structural MRI. The training sample comprised 987 MDD cases and 3934 controls from the UK Biobank. Predictors were evaluated in an independent sub-cohort of 483 MDD cases and 1939 controls from the UK Biobank and replicated in a clinical cohort (DEP-ARREST CLIN) of 64 cases and 32 controls. In the UK Biobank, the BLUP predictor showed a significant association with MDD status (AUC\u2009=\u20090.57; OR\u2009=\u20091.28 [1.15-1.43]; p-value\u2009=\u20091.1\u00d710\n                    <jats:sup>-5<\/jats:sup>\n                    ), which was confirmed in both males and females. By partitioning the BLUP predictor by brain regions of interest (ROI), we found nominal significance supporting the contribution of previously identified MDD-related ROIs (e.g. hippocampus and amygdala), though none passed multiple testing correction. The BLUP predictor overlapped partially with a polygenic score (PGS) of major depression (AUC\u2009=\u20090.65) but also captured a nominally significant signal that was not captured by the genetic score (combined AUC\u2009=\u20090.66, p-value\u2009=\u20090.024 when compared to PGS alone). No association passed multiple testing correction in the DEP-ARREST CLIN cohort, likely due to the small sample size. In contrast, the deep-learning predictor was not associated with MDD after multiple testing corrections. We estimated the morphometricity of MDD to be 0.061, implying limited potential of a brain-based predictor based on grey-matter structure (maximal AUC\u2009=\u20090.64). While the modest AUC values reiterate the challenge of developing brain-based MDD predictors for clinical applications, our predictors inform future research to explore brain-based relationships between MDD and comorbidities.\n                  <\/jats:p>","DOI":"10.1038\/s41398-026-03889-8","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T14:36:39Z","timestamp":1772030199000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Applying machine-learning and deep-learning to predict depression from brain MRI and identify depression-related brain biology"],"prefix":"10.1038","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3434-9983","authenticated-orcid":false,"given":"Jiayue-Clara","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camille","family":"Brianceau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elise","family":"Delzant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2549-4495","authenticated-orcid":false,"given":"Romain","family":"Colle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugo","family":"Bottemanne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuelle","family":"Corruble","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7421-3357","authenticated-orcid":false,"given":"Naomi R.","family":"Wray","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Colliot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5860-4526","authenticated-orcid":false,"given":"Sonia","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baptiste","family":"Couvy-Duchesne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"3889_CR1","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1016\/S0140-6736(20)30925-9","volume":"396","author":"T Vos","year":"2020","unstructured":"Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. 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Procedures are controlled by a dedicated Ethics and Guidance Council (\n                      \n                      ), with the Ethics and Governance Framework available at\n                      \n                      . IRB approval was also obtained from the North West Multi-centre Research Ethics Committee. Our research has been conducted using the UK Biobank Resource under Application Number 12505. This research is covered by The University of Queensland Human Research Ethics Committee approval (HREC number 2020\/HE002938). Written informed consent of the DEP-ARREST CLIN participants was obtained after the nature of the procedures had been fully explained.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Participant consent statement and ethics approval"}},{"value":"Competing financial interests related to the present article: none to disclose for all authors. Competing financial interests unrelated to the present article: Olivier Colliot reports having received consulting fees from Therapanacea. Olivier Colliot reports that other principal investigators affiliated to the team which he co-leads have received grants (paid to the institution) from Sanofi and Biogen. Olivier Colliot reports that his spouse was an employee of myBrainTechnologies and is an employee of DiamPark. Olivier Colliot holds a patent registered at the International Bureau of the World Intellectual Property Organization (PCT\/IB2016\/0526993, Allassonniere S, Colliot O, Durrleman S, Schiratti J-B, A method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2017). Olivier Colliot has submitted a declaration of invention to the European Patent Office (File Number: EP23306632, Ayache N, Colliot O, Hamzaoui M, Soulier T, Stankoff B, Devices for generation of synthetic 3D representation representative of myelin content, Submitted to the European Patent Office on September 28th, 2023).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"171"}}