Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.
Autor: | Poirot MG; Amsterdam UMC, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, the Netherlands.; Department of Biomedical Engineering and Physics, Amsterdam UMC,University of Amsterdam, Amsterdam, the Netherlands.; Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands., Boucherie DE; Amsterdam UMC, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, the Netherlands.; Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands., Caan MWA; Department of Biomedical Engineering and Physics, Amsterdam UMC,University of Amsterdam, Amsterdam, the Netherlands.; Division of Radiology and Nuclear Medicine, Computational Radiology and Artificial Intelligence (CRAI), Oslo University Hospital, Oslo, Norway., Goya-Maldonado R; Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Göttingen, Germany., Belov V; Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Göttingen, Germany., Corruble E; MOODS Team, INSERM 1018, Centre de Recherche en Epidémiologie et Santé Des Populations, Université Paris-Saclay, Faculté de Médecine Paris-Saclay, Le Kremlin Bicêtre, Le Kremlin-Bicêtre, France.; Service Hospitalo-Universitaire de Psychiatrie de Bicêtre, Mood Center Paris Saclay, Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Paris-Saclay, Hôpital de Bicêtre, Le Kremlin Bicêtre, Le Kremlin-Bicêtre, France.; Paris-Saclay University, Le Kremlin-Bicêtre, France., Colle R; MOODS Team, INSERM 1018, Centre de Recherche en Epidémiologie et Santé Des Populations, Université Paris-Saclay, Faculté de Médecine Paris-Saclay, Le Kremlin Bicêtre, Le Kremlin-Bicêtre, France.; Service Hospitalo-Universitaire de Psychiatrie de Bicêtre, Mood Center Paris Saclay, Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Paris-Saclay, Hôpital de Bicêtre, Le Kremlin Bicêtre, Le Kremlin-Bicêtre, France., Couvy-Duchesne B; Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia.; Sorbonne University, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France., Kamishikiryo T; Department of Psychiatry and Neurosciences. Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan., Shinzato H; Department of Psychiatry and Neurosciences. Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan.; Department of Neuropsychiatry, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan., Ichikawa N; Department of Psychiatry and Neurosciences. Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan.; Deloitte Analytics R&D, Deloitte Touche Tohmatsu LLC, Tokyo, Japan., Okada G; Department of Psychiatry and Neurosciences. Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan., Okamoto Y; Department of Psychiatry and Neurosciences. Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan., Harrison BJ; Department of Psychiatry, The University of Melbourne, Melbourne, Australia., Davey CG; Department of Psychiatry, The University of Melbourne, Melbourne, Australia., Jamieson AJ; Department of Psychiatry, The University of Melbourne, Melbourne, Australia., Cullen KR; University of Minnesota, Minneapolis, Minnesota, USA., Başgöze Z; University of Minnesota, Minneapolis, Minnesota, USA., Klimes-Dougan B; University of Minnesota, Minneapolis, Minnesota, USA., Mueller BA; University of Minnesota, Minneapolis, Minnesota, USA., Benedetti F; Division of Neuroscience, Psychiatry & Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy.; Vita-Salute San Raffaele University, Milano, Italy., Poletti S; Division of Neuroscience, Psychiatry & Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy., Melloni EMT; Division of Neuroscience, Psychiatry & Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy.; Vita-Salute San Raffaele University, Milano, Italy., Ching CRK; Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA., Zeng LL; Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China., Radua J; IDIBAPS, CIBERSAM, Instituto de Salud Carlos III, Barcelona, Spain., Han LKM; Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia.; Orygen, Parkville, Victoria, Australia., Jahanshad N; Orygen, Parkville, Victoria, Australia., Thomopoulos SI; Orygen, Parkville, Victoria, Australia., Pozzi E; Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia.; Orygen, Parkville, Victoria, Australia., Veltman DJ; Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam, the Netherlands., Schmaal L; Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia.; Orygen, Parkville, Victoria, Australia., Thompson PM; Orygen, Parkville, Victoria, Australia., Ruhe HG; Amsterdam UMC, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, the Netherlands.; Department of Psychiatry, Nijmegen, the Netherlands.; Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands., Reneman L; Amsterdam UMC, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, the Netherlands.; Department of Biomedical Engineering and Physics, Amsterdam UMC,University of Amsterdam, Amsterdam, the Netherlands.; Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands., Schrantee A; Amsterdam UMC, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, the Netherlands.; Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands. |
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Jazyk: | angličtina |
Zdroj: | Human brain mapping [Hum Brain Mapp] 2025 Jan; Vol. 46 (1), pp. e70053. |
DOI: | 10.1002/hbm.70053 |
Abstrakt: | Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days 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-wise 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-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation 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-)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 = 50.5%; p = 0.66) 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-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). 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-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD. (© 2025 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.) |
Databáze: | MEDLINE |
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