AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale
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Published:2023-01-23
Issue:1
Volume:23
Page:
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ISSN:1471-244X
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Container-title:BMC Psychiatry
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language:en
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Short-container-title:BMC Psychiatry
Author:
Fu Cynthia H. Y.,Erus Guray,Fan Yong,Antoniades Mathilde,Arnone Danilo,Arnott Stephen R.,Chen Taolin,Choi Ki Sueng,Fatt Cherise Chin,Frey Benicio N.,Frokjaer Vibe G.,Ganz Melanie,Garcia Jose,Godlewska Beata R.,Hassel Stefanie,Ho Keith,McIntosh Andrew M.,Qin Kun,Rotzinger Susan,Sacchet Matthew D.,Savitz Jonathan,Shou Haochang,Singh Ashish,Stolicyn Aleks,Strigo Irina,Strother Stephen C.,Tosun Duygu,Victor Teresa A.,Wei Dongtao,Wise Toby,Woodham Rachel D.,Zahn Roland,Anderson Ian M.,Deakin J. F. William,Dunlop Boadie W.,Elliott Rebecca,Gong Qiyong,Gotlib Ian H.,Harmer Catherine J.,Kennedy Sidney H.,Knudsen Gitte M.,Mayberg Helen S.,Paulus Martin P.,Qiu Jiang,Trivedi Madhukar H.,Whalley Heather C.,Yan Chao-Gan,Young Allan H.,Davatzikos Christos
Abstract
Abstract
Background
Efforts 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.
Methods
We 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.
Results
We 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.
Conclusion
We 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.
Funder
Medical Research Council
National Institutes of Health
Ontario Brain Institute
Canadian Institutes of Health Research
National Natural Science Foundation of China
Hersh Foundation
EMBARC National Coordinating Center at UT Southwestern Medical Center
Data Center at Columbia and Stony Brook Universities
Center for Depression Research and Clinical Care
National Institute of Mental Health of the National Institutes of Health
Lundbeckfonden
Wellcome Trust
National Institute of Mental Health
Lister Institute of Preventive Medicine
Fundamental Research Funds for the Central Universities
National Outstanding young people plan
Program for the Top Young Talents by Chongqing
Natural Science Foundation of Chongqing
Fok Ying Tung Education Foundation
Anthony and Elizabeth Mellows Charitable Foundation
National Institute for Health and Care Research
Oxford Health NIHR Biomedical Research Centre
William K. Warren Foundation
National Institute on Drug Abuse
National Institute of General Medical Sciences
Beijing Nova Program of Science and Technology
NIHR Maudsley Biomedical Research Centre
King's College London
Publisher
Springer Science and Business Media LLC
Subject
Psychiatry and Mental health
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