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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dc.contributor.authorFu, Cynthia H. Y.
dc.contributor.authorErus, Guray
dc.contributor.authorFan, Yong
dc.contributor.authorAntoniades, Mathilde
dc.contributor.authorArnone, Danilo
dc.contributor.authorArnott, Stephen R.
dc.contributor.authorChen, Taolin
dc.contributor.authorChoi, Ki S.
dc.contributor.authorFatt, Cherise C.
dc.contributor.authorFrey, Benicio N.
dc.contributor.authorFrokjaer, Vibe G.
dc.contributor.authorGanz, Melanie
dc.contributor.authorGarcia, Jose
dc.contributor.authorGodlewska, Beata R.
dc.contributor.authorHassel, Stefanie
dc.contributor.authorHo, Keith
dc.contributor.authorMcIntosh, Andrew M.
dc.contributor.authorQin, Kun
dc.contributor.authorRotzinger, Susan
dc.contributor.authorSacchet, Matthew D.
dc.contributor.authorSavitz, Jonathan
dc.contributor.authorShou, Haochang
dc.contributor.authorSingh, Ashish
dc.contributor.authorStolicyn, Aleks
dc.contributor.authorStrigo, Irina
dc.contributor.authorStrother, Stephen C.
dc.contributor.authorTosun, Duygu
dc.contributor.authorVictor, Teresa A.
dc.contributor.authorWei, Dongtao
dc.contributor.authorWise, Toby
dc.contributor.authorWoodham, Rachel D.
dc.contributor.authorZahn, Roland
dc.contributor.authorAnderson, Ian M.
dc.contributor.authorDeakin, J. F. W.
dc.contributor.authorDunlop, Boadie W.
dc.contributor.authorElliott, Rebecca
dc.contributor.authorGong, Qiyong
dc.contributor.authorGotlib, Ian H.
dc.contributor.authorHarmer, Catherine J.
dc.contributor.authorKennedy, Sidney H.
dc.contributor.authorKnudsen, Gitte M.
dc.contributor.authorMayberg, Helen S.
dc.contributor.authorPaulus, Martin P.
dc.contributor.authorQiu, Jiang
dc.contributor.authorTrivedi, Madhukar H.
dc.contributor.authorWhalley, Heather C.
dc.contributor.authorYan, Chao-Gan
dc.contributor.authorYoung, Allan H.
dc.contributor.authorDavatzikos, Christos
dc.date.accessioned2023-01-29T01:02:21Z
dc.date.available2023-01-29T01:02:21Z
dc.date.issued2023-01-23
dc.date.updated2023-01-29T01:02:20Z
dc.description.abstractAbstract 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.
dc.identifier.citationBMC Psychiatry. 2023 Jan 23;23(1):59
dc.identifier.doihttps://doi.org/10.1186/s12888-022-04509-7
dc.identifier.urihttp://hdl.handle.net/1880/115779
dc.identifier.urihttps://doi.org/10.11575/PRISM/45960
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dc.titleAI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale
dc.typeJournal Article
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