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

Research output: Contribution to journalArticlepeer-review

Cynthia H Y Fu, Guray Erus, Yong Fan, Mathilde Antoniades, Danilo Arnone, Stephen R Arnott, Taolin Chen, Ki Sueng Choi, Cherise Chin Fatt, Benicio N Frey, Vibe G Frokjaer, Melanie Ganz, Jose Garcia, Beata R Godlewska, Stefanie Hassel, Keith Ho, Andrew M McIntosh, Kun Qin, Susan Rotzinger, Matthew D Sacchet & 29 more Jonathan Savitz, Haochang Shou, Ashish Singh, Aleks Stolicyn, Irina Strigo, Stephen C Strother, Duygu Tosun, Teresa A Victor, Dongtao Wei, Toby Wise, Rachel D Woodham, Roland Zahn, Ian M Anderson, 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

Original languageEnglish
Article number59
Pages (from-to)59
JournalBMC Psychiatry
Issue number1
Published23 Jan 2023

Bibliographical note

Funding Information: AMM has received research support from Eli Lilly, Janssen, and The Sackler Trust. AMM has also received speaker fees from Illumina and Janssen. CHYF has received grant funding from MRC, Wellcome Trust, Brain and Behaviour NARSAD, Eli Lilly, Flow Neuroscience, Baszucki Brain Research Fund Milken Institute, Rosetrees Trust. Funding Information: MPP received funding from The William K. Warren Foundation, National Institute on Drug Abuse (U01 DA041089), and National Institute of General Medical Sciences Center Grant Award Number (1P20GM121312). Funding Information: MDS received funding from the National Institute of Mental Health (R01MH125850). Funding Information: DT received funding from the National Institute of Mental Health (5R01MH101472). Funding Information: TW received funding from the Anthony and Elizabeth Mellows Charitable Foundation. Funding Information: MT, CCF, CHYF and all authors wish to thank the participants, families, staff, and colleagues who made this project possible. BWD acknowledges W. Edward Craighead, PhD. The views expressed are those of the author(s) and not necessarily those of the funding agencies. Funding Information: GMK, MG & VGF received funding from the Lundbeck Foundation (R279–2018-1145 (BrainDrugs)). Funding Information: CD received funding from the National Institute of Health (R01 MH112070), HC (RF1-AG054409, R01-MH123550, U01-AG068057), IHG (R37MH101495) and YF (R01 AG066650 and R01EB022573). Funding Information: JS received funding from the National Institute of Mental Health (K01MH096077; R01MH098099). Funding Information: SCS received research support from Brain Canada, CIHR (Canadian Institutes of Health Research), Ontario Brain Institute and CFI (canadian Foundation for Innovation). SCS is a founder and share holder of ADMdx, Inc. Funding Information: IMA received funding from the Medical Research Council (MRC) (G0601526). DA received funding from the Academy of Medical Science (AMS-SGCL8) and National Institute for Health and Care Research (NIHR) PhD studentship. Funding Information: EMBARC study (NCT01407094) was supported by the National Institute of Mental Health of the National Institutes of Health under award numbers U01MH092221 (Trivedi, M.H.) and U01MH092250 (McGrath, P.J., Parsey, R.V., Weissman, M.M.), and in part by the Hersh Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Valeant Pharmaceuticals donated the Wellbutrin XL used in the study. This work was supported by the EMBARC National Coordinating Center at UT Southwestern Medical Center, Madhukar H. Trivedi, M.D., Coordinating PI, and the Data Center at Columbia and Stony Brook Universities. In addition, this work was funded in part by the Center for Depression Research and Clinical Care (PI: Madhukar Trivedi). Funding Information: HSM & BWD received funding from the National Institute of Mental Health (P50MH077083, 1RO1MH080880). Funding Information: SWU dataset was supported by the following: National Natural Science Foundation of China (31571137; 31500885), the National Outstanding young people plan, the Program for the Top Young Talents by Chongqing, the Fundamental Research Funds for the Central Universities (SWU1509383; SWU1509451; SWU1609177), Natural Science Foundation of Chongqing (cstc2015jcyjA10106), the Fok Ying Tung Education Foundation (151023) to JQ and DTW. Funding Information: CHYF received funding from the Medical Research Council (MRC) (G0802594) and Brain and Behaviour NARSAD Young Investigators Award. Funding Information: AHY reports declaration of interests: Paid lectures and advisory boards for the following companies: Astrazenaca, Eli Lilly, Lundbeck, Sunovion, Servier, Livanova, Janssen, Allegan, Bionomics, Sumitomo Dainippon Pharma, COMPASS, Sage, Novartis. Consultant to Johnson & Johnson and to Livanova. Received honoraria for attending advisory boards and presenting talks at meetings organised by LivaNova. Principal Investigator in the Restore-Life VNS registry study funded by LivaNova. UK Chief Investigator for Novartis MDD study MIJ821A12201. Principal Investigator on ESKETINTRD3004: “An Open-label, Long-term, Safety and Efficacy Study of Intranasal Esketamine in Treatment-resistant Depression”. Principal Investigator on “The Effects of Psilocybin on Cognitive Function in Healthy Participants”. Principal Investigator on “The Safety and Efficacy of Psilocybin in Participants with Treatment-Resistant Depression (P-TRD)”. No shareholdings in pharmaceutical companies. Deputy Editor of BJPsych Open. Grant funding (past and present): NIMH (USA); CIHR (Canada); NARSAD (USA); Stanley Medical Research Institute (USA); MRC (UK); Wellcome Trust (UK); Royal College of Physicians (Edin); BMA (UK); UBC-VGH Foundation (Canada); WEDC (Canada); CCS Depression Research Fund (Canada); MSFHR (Canada); NIHR (UK). Janssen (UK). Funding Information: CJH received support from the Medical Research Council (G0701421) and is supported by the Oxford Health NIHR Biomedical Research Centre. Funding Information: QG received funding from the National Natural Science Foundation of China (81820108018; 81621003). Funding Information: CGY was supported by the National Natural Science Foundation of China (82122035) and Beijing Nova Program of Science and Technology (Z191100001119104). Funding Information: AHY independent research is funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Funding Information: STRADL study was supported and funded by the Wellcome Trust Strategic Award, Stratifying Resilience and Depression Longitudinally (ref. 104036/Z/14/Z), and the Medical Research Council (MRC-MC/PC/17209). AMM received funding from the Wellcome Trust (220857/Z/20/Z, 216767/Z/19/Z). AS was funded as part of the STRADL study and indirectly through the Lister Institute of Preventive Medicine award ref. 173096. Data processing used the resources provided by the Edinburgh Compute and Data Facility (ECDF) ( ). Funding Information: RZ received funding from the Medical Research Council (MRC) (MR/T017538/1). Publisher Copyright: © 2023, The Author(s).

King's Authors


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.

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