Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo
Year: 2024
Type: article
Abstract: Importance: Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Identifying neuroimaging-based biomarkers might aid in defining the disease-related dimensions that characterize MDD and predict treatment response. Objective: To investigate the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressant or placebo. Design: Big data consortium (COORDINATE-MDD) sharing raw MRI data in first episode and recurrent MDD, deep clinical phenotyping, and state-of-the art machine learning analysis, involving harmonization of multi-center MRI data and the application of semi-supervised machine learning clustering, HYDRA, to regional brain volumes. Setting: International, multi-center, community-based MDD and healthy controls. Participants: International sample (N=1384), consisting of medication-free, first episode and recurrent MDD individuals (N=685) in a current depressive episode of moderate to severe intensity, that is not treatment resistant depression, and healthy controls (N=699). Prospective longitudinal treatment response data were available in a subset of MDD individuals (N=359 MDD). Treatments were SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Treatment duration was 6-8 weeks, and symptom severity was measured by clinician-rated scales. Main outcomes: First episode and recurrent MDD is optimally characterized by two neuroanatomical dimensions, which show distinct treatment effects to placebo and SSRI antidepressant medications. Results: Dimension 1 is characterized by preserved gray and white matter (N=290 MDD), whereas Dimension 2 is characterized by widespread subtle reductions in gray and white matter (N=395 MDD) relative to healthy controls. There are no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, but there is a significant dimension by treatment response interaction effect. Dimension 1 shows a significant decrease in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%), whereas Dimension 2 shows a comparable improvement to either SSRI (46.9%) or placebo (42.2%) (β=-18.3, 95% CI (-0.34 to -0.2), p=0.03). Conclusions and Relevance: Neuroimaging-based markers may aid in characterizing the MDD dimensions that predict treatment response. In an iterative process, we can characterize the disease-based dimensions that comprise MDD.
Source: Nature Mental Health
Authors: Cynthia H. Y. Fu, Mathilde Antoniades, Guray Erus, Jose A. Garcia, Yong Fan, Danilo Arnone, Stephen R. Arnott, Taolin Chen, Ki Sueng Choi, Cherise Chin Fatt, Benicio N. Frey, Vibe G. Frokjaer, Melanie Ganz, Beata R. Godlewska, Stefanie Hassel, Keith Ho, Andrew M. McIntosh, Kun Qin, Susan Rotzinger, Matthew D. Sacchet, Jonathan Savitz, Haochang Shou, Ashish Singh, Aleks Stolicyn, Irina Strigo, Stephen C. Strother, Duygu Tosun, Teresa A. Victor, Dongtao Wei, Toby Wise, Roland Zahn, Ian M. Anderson, W. Edward Craighead, J. F. William Deakin, Boadie W. Dunlop, Rebecca Elliott, Qiyong Gong, Ian H. Gotlib, Catherine J. Harmer, Sidney H. Kennedy, Gitte M. Knudsen, Helen S. Mayberg, Martin P. Paulus, Jiang Qiu, Madhukar H. Trivedi, Heather C. Whalley, Chao-Gan Yan, Allan H. Young, Christos Davatzikos
Institutions: University of East London, King's College London, University of Pennsylvania, Baycrest Hospital, Rotman Research Institute, Chinese Academy of Medical Sciences & Peking Union Medical College, Sichuan University, West China Hospital of Sichuan University, Icahn School of Medicine at Mount Sinai, The University of Texas Southwestern Medical Center, St. Joseph’s Healthcare Hamilton, McMaster University, University of Copenhagen, Copenhagen University Hospital, Rigshospitalet, Oxford Health NHS Foundation Trust, Warneford Hospital, University of Oxford, University of Calgary, Mathison Centre for Mental Health Research and Education, University Health Network, Royal Edinburgh Hospital, University of Edinburgh, Hubei University of Medicine, Taihe Hospital, Unity Health Toronto, Harvard University, Massachusetts General Hospital, Laureate Institute for Brain Research, University of California, San Francisco, University of Toronto, Southwest University, University of Manchester, Emory University, Stanford University, Chinese Academy of Sciences, Institute of Psychology, Chinese Academy of Sciences, South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital
Language: English
Subfield: Pharmacology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $12,290
Funders: U.S. Department of Health and Human Services, William K. Warren Foundation, American Foundation for Suicide Prevention, Eli Lilly and Company, Patient-Centered Outcomes Research Institute, University of Pennsylvania, Canadian Institute for Advanced Research, University of East London, University of Texas Southwestern Medical Center, Ontario Brain Institute, South London and Maudsley NHS Foundation Trust, Sunovion, National Alliance for Research on Schizophrenia and Depression, McMaster University, Wellcome Trust, Valeant Pharmaceuticals International, Hersh Foundation, LivaNova, Neurocrine Biosciences, Sage Therapeutics, Government of Canada, Michael Smith Health Research BC, National Institute for Health and Care Research, King's College London, University of Oxford, Rosetrees Trust, Lister Institute of Preventive Medicine, National Natural Science Foundation of China, Rotman Research Institute, Baycrest, Lundbeckfonden, Sichuan University, Rigshospitalet, Chinese Academy of Medical Sciences, Natural Science Foundation of Chongqing, Otsuka Pharmaceutical, Faculty of Health and Medical Sciences, University of Western Australia, Fok Ying Tung Education Foundation, Servier, H. Lundbeck A/S, Otsuka Canada Pharmaceutical, Oxford Health NHS Foundation Trust, Victoria General Hospital Foundation, National Institutes of Health, Perelman School of Medicine, University of Pennsylvania, Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, NIHR Maudsley Biomedical Research Centre, Canadian Institutes of Health Research, Medical Research Council, Fok Ying Tong Education Foundation, Beijing Nova Program, Det Sundhedsvidenskabelige Fakultet, Københavns Universitet, Fundamental Research Funds for the Central Universities, West China Hospital, Sichuan University, National Institute on Aging, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of General Medical Sciences, National Institute of Biomedical Imaging and Bioengineering
Awards: G1033398352, G1061241866, G1066048361, G1267428316, G1274447166, G1301500047, G1401239860, G1515240546, G1700960499, Multimodal MRI Characteristics of Psychotherapy Response in Late Life Depression, G1817067656, Neuroimaging abnormalities in major depressive disorder: effect of inflammation, G1898893588, G1916470285, Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth, G2206710430, G2330506321, Disentangling the anatomical, functional and clinical heterogeneity of major depression, using machine learning methods, G2452653468, G248825482, G2513392025, G259633682, 5/21 ABCD-USA Consortium: Research Project Site at UC San Diego, G2701965430, Mapping Heterogeneity of Neuroanatomical Imaging Signatures of Psychosis via Pattern Analysis, Exploiting genomic approaches to identify the environmental basis of depression, G2985766253, Generation Scotland:NextGenScot, G3202677910, G321263122, G3258733268, G3277402143, G3294163712, G332757754, G3407965938, G3415959687, Generation Scotland:NextGenScot, G3738900353, Exploiting genomic approaches to identify the environmental basis of depression, G4097959210, G4103629697, G4104423096, G4114397373, G4283534842, Stratifying Resilience and Depression Longitudinally (STRADL), G4319640437, G4320070042, G4389421672, Neurocognitive signatures predicting risk of recurrent depression, G4541319330, G494712410, 青少年创造性发展的神经机制及其大脑可塑性：基于横断和纵向跟踪的研究, Fast and robust deep learning tools for analysis of neuroimaging data of Alzheimer's disease, G5425601344, Predictors of Treatment Response Relapse and Recurrence in Major Depression, G5797701037, G5959956424, G6009597264, G6054520753, G6081261122, G6161972580, Stratifying Resilience and Depression Longitudinally (STRADL), G6301362210, G6304496351, G6334848382, Psychobiological Mechanisms Underlying the Association Between Early Life Stress and Depression Across Adolescence, Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders, Predictors of Antidepressant Treatment Response: The Emory CIDAR, G6594664946, G6614686337, Individualized brain systems and depression, G6831603426, Inflammatory Transcripts, Genes and Positive Valence System Function in Anhedonia, G7362551106, G7518927107, G7795466796, G7808792923, G8147441241, G856368605, G8584063691, G8623287158, G8917468808, Establishing Moderators/Biosignatures of Antidepressant Response- Clinical Care, G9000008347, G9000018797, G9000033569, G9000043587, G9000052254, G9000054871, G9000087906, G9000110024, G9000130585, G9000163168, G9000318234, Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
OpenAlex ID: W4390791007