AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

BMC Psychiatry

Research

Chao-Gan Yan¹  Guray Erus²  Ashish Singh²  Christos Davatzikos²  Mathilde Antoniades²  Yong Fan²  Jose Garcia²  Haochang Shou³  Roland Zahn⁴  Danilo Arnone⁵  Allan H. Young⁶  Toby Wise⁷  Boadie W. Dunlop⁸  Benicio N. Frey⁹  Madhukar H. Trivedi^1,10  Cherise Chin Fatt^1,10  Keith Ho^1,11  Susan Rotzinger^1,12  Sidney H. Kennedy^1,13  Catherine J. Harmer^1,14  Beata R. Godlewska^1,15  Rachel D. Woodham^1,16  Cynthia H. Y. Fu^1,17  Ian H. Gotlib^1,18  Duygu Tosun^1,19  Irina Strigo^1,19  J. F. William Deakin^2,20  Rebecca Elliott^2,20  Ian M. Anderson^2,20  Andrew M. McIntosh^2,21  Heather C. Whalley^2,21  Aleks Stolicyn^2,21  Taolin Chen^2,22  Qiyong Gong^2,22  Kun Qin^2,22  Martin P. Paulus^2,23  Jonathan Savitz^2,23  Teresa A. Victor^2,23  Stefanie Hassel^2,24  Matthew D. Sacchet^2,25  Helen S. Mayberg^2,26  Ki Sueng Choi^2,26  Gitte M. Knudsen^2,27  Vibe G. Frokjaer^2,28  Melanie Ganz^2,29  Stephen R. Arnott^3,30  Stephen C. Strother^3,31  Dongtao Wei^3,32  Jiang Qiu^3,32 

[1] CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China;Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA;Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA;Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA;Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK;Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK;Department of Psychiatry and Behavioral Science, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates;Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK;South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK;Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK;Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA;Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada;Mood Disorders Treatment and Research Centre and Women’s Health Concerns Clinic, St Joseph’s Healthcare Hamilton, Hamilton, Canada;Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA;Department of Psychiatry, University Health Network, Toronto, Canada;Department of Psychiatry, University Health Network, Toronto, Canada;Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada;Department of Psychiatry, University Health Network, Toronto, Canada;Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada;Unity Health Toronto, Toronto, Canada;Department of Psychiatry, University of Oxford, Oxford, UK;Department of Psychiatry, University of Oxford, Oxford, UK;Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK;Department of Psychological Sciences, University of East London, London, UK;Department of Psychological Sciences, University of East London, London, UK;Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK;Department of Psychology, Stanford University, Stanford, USA;Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA;Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK;Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK;Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China;Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China;Laureate Institute for Brain Research, Tulsa, USA;Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Canada;Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada;Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, USA;Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA;Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark;Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark;Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark;Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark;Department of Computer Science, University of Copenhagen, Copenhagen, Denmark;Rotman Research Institute, Baycrest Centre, Toronto, Canada;Rotman Research Institute, Baycrest Centre, Toronto, Canada;Department of Medical Biophysics, University of Toronto, Toronto, Canada;School of Psychology, Southwest University, Chongqing, China;

关键词: Classification;    Biomarkers;    Deep learning;    Neuroimaging;    Depression;    Harmonization;    Predictors;    MRI;   

DOI  :  10.1186/s12888-022-04509-7

 received in 2022-04-29, accepted in 2022-12-29,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundEfforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.MethodsWe describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level.International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.ResultsWe are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.ConclusionWe describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

【 授权许可】

CC BY   
© The Author(s) 2023

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