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We tested and compared machine learning\u2010based methods that predict individual\u2010level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA\u2010MDD consortium (<jats:italic>n<\/jats:italic>\u2009=\u2009262 MDD patients; age\u2009=\u200936.5\u2009\u00b1\u200915.3\u2009years; 154 (59%) female; mean response rate\u2009=\u200957%). Treatment response was defined as a \u2265\u200950% reduction in symptom severity score after 4\u201312\u2009weeks post\u2010initiation of antidepressant treatment. Structural MRI was acquired before, or &lt;\u200914\u2009days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel\u2010wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross\u2010validation methods (i.e., k\u2010fold and leave\u2010one\u2010site\u2010out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10\u2010fold cross\u2010validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non\u2010)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy\u2009=\u200950.5%; <jats:italic>p<\/jats:italic>\u2009=\u20090.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non\u2010)responders subpopulation (balanced accuracy\u2009=\u200963.9%, <jats:italic>p<\/jats:italic>\u2009=\u20090.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non\u2010)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response\u2010related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.<\/jats:p>","DOI":"10.1002\/hbm.70053","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T09:34:34Z","timestamp":1736156074000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting Antidepressant Treatment Response From Cortical Structure on <scp>MRI<\/scp>: A Mega\u2010Analysis From the <scp>ENIGMA<\/scp>\u2010<scp>MDD<\/scp> Working Group"],"prefix":"10.1002","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1937-7978","authenticated-orcid":false,"given":"Maarten G.","family":"Poirot","sequence":"first","affiliation":[{"name":"Amsterdam UMC, Department of Radiology and Nuclear Medicine University of Amsterdam  Amsterdam the Netherlands"},{"name":"Department of Biomedical Engineering and Physics Amsterdam UMC,University of Amsterdam  Amsterdam the Netherlands"},{"name":"Amsterdam Neuroscience, Brain Imaging  Amsterdam the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2976-769X","authenticated-orcid":false,"given":"Daphne E.","family":"Boucherie","sequence":"additional","affiliation":[{"name":"Amsterdam UMC, Department of Radiology and Nuclear Medicine University of Amsterdam  Amsterdam the Netherlands"},{"name":"Amsterdam Neuroscience, Brain Imaging  Amsterdam the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5162-8880","authenticated-orcid":false,"given":"Matthan W. A.","family":"Caan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Physics Amsterdam UMC,University of Amsterdam  Amsterdam the Netherlands"},{"name":"Division of Radiology and Nuclear Medicine, Computational Radiology and Artificial Intelligence (CRAI) Oslo University Hospital  Oslo Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7368-1332","authenticated-orcid":false,"given":"Roberto","family":"Goya\u2010Maldonado","sequence":"additional","affiliation":[{"name":"Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP\u2010Lab), Department of Psychiatry and Psychotherapy University Medical Center G\u00f6ttingen (UMG)  G\u00f6ttingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9036-2707","authenticated-orcid":false,"given":"Vladimir","family":"Belov","sequence":"additional","affiliation":[{"name":"Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP\u2010Lab), Department of Psychiatry and Psychotherapy University Medical Center G\u00f6ttingen (UMG)  G\u00f6ttingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9441-6079","authenticated-orcid":false,"given":"Emmanuelle","family":"Corruble","sequence":"additional","affiliation":[{"name":"MOODS Team, INSERM 1018, Centre de Recherche en Epid\u00e9miologie et Sant\u00e9 Des Populations Universit\u00e9 Paris\u2010Saclay, Facult\u00e9 de M\u00e9decine Paris\u2010Saclay, Le Kremlin Bic\u00eatre  Le Kremlin\u2010Bic\u00eatre France"},{"name":"Service Hospitalo\u2010Universitaire de Psychiatrie de Bic\u00eatre, Mood Center Paris Saclay, Assistance Publique\u2010H\u00f4pitaux de Paris H\u00f4pitaux Universitaires Paris\u2010Saclay, H\u00f4pital de Bic\u00eatre, Le Kremlin Bic\u00eatre  Le Kremlin\u2010Bic\u00eatre France"},{"name":"Paris\u2010Saclay University  Le Kremlin\u2010Bic\u00eatre France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romain","family":"Colle","sequence":"additional","affiliation":[{"name":"MOODS Team, INSERM 1018, Centre de Recherche en Epid\u00e9miologie et Sant\u00e9 Des Populations Universit\u00e9 Paris\u2010Saclay, Facult\u00e9 de M\u00e9decine Paris\u2010Saclay, Le Kremlin Bic\u00eatre  Le Kremlin\u2010Bic\u00eatre France"},{"name":"Service Hospitalo\u2010Universitaire de Psychiatrie de Bic\u00eatre, Mood Center Paris Saclay, Assistance Publique\u2010H\u00f4pitaux de Paris H\u00f4pitaux Universitaires Paris\u2010Saclay, H\u00f4pital de Bic\u00eatre, Le Kremlin Bic\u00eatre  Le Kremlin\u2010Bic\u00eatre France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0719-9302","authenticated-orcid":false,"given":"Baptiste","family":"Couvy\u2010Duchesne","sequence":"additional","affiliation":[{"name":"Institute for Molecular Bioscience the University of Queensland  St Lucia Queensland Australia"},{"name":"Sorbonne University Paris Brain Institute\u2014ICM, CNRS, Inria, Inserm, AP\u2010HP, H\u00f4pital de la Piti\u00e9 Salp\u00eatri\u00e8re  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshiharu","family":"Kamishikiryo","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Neurosciences. Graduate School of Biomedical &amp; Health Sciences Hiroshima University  Hiroshima Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hotaka","family":"Shinzato","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Neurosciences. Graduate School of Biomedical &amp; Health Sciences Hiroshima University  Hiroshima Japan"},{"name":"Department of Neuropsychiatry, Graduate School of Medicine University of the Ryukyus  Okinawa Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naho","family":"Ichikawa","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Neurosciences. 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