TY - JOUR

T1 - AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder

T2 - COORDINATE-MDD consortium design and rationale

AU - Fu, Cynthia H Y

AU - Erus, Guray

AU - Fan, Yong

AU - Antoniades, Mathilde

AU - Arnone, Danilo

AU - Arnott, Stephen R

AU - Chen, Taolin

AU - Choi, Ki Sueng

AU - Fatt, Cherise Chin

AU - Frey, Benicio N

AU - Frokjaer, Vibe G

AU - Ganz, Melanie

AU - Garcia, Jose

AU - Godlewska, Beata R

AU - Hassel, Stefanie

AU - Ho, Keith

AU - McIntosh, Andrew M

AU - Qin, Kun

AU - Rotzinger, Susan

AU - Sacchet, Matthew D

AU - Savitz, Jonathan

AU - Shou, Haochang

AU - Singh, Ashish

AU - Stolicyn, Aleks

AU - Strigo, Irina

AU - Strother, Stephen C

AU - Tosun, Duygu

AU - Victor, Teresa A

AU - Wei, Dongtao

AU - Wise, Toby

AU - Woodham, Rachel D

AU - Zahn, Roland

AU - Anderson, Ian M

AU - Deakin, J F William

AU - Dunlop, Boadie W

AU - Elliott, Rebecca

AU - Gong, Qiyong

AU - Gotlib, Ian H

AU - Harmer, Catherine J

AU - Kennedy, Sidney H

AU - Knudsen, Gitte M

AU - Mayberg, Helen S

AU - Paulus, Martin P

AU - Qiu, Jiang

AU - Trivedi, Madhukar H

AU - Whalley, Heather C

AU - Yan, Chao-Gan

AU - Young, Allan H

AU - Davatzikos, Christos

N1 - © 2023. The Author(s).

PY - 2023/1/23

Y1 - 2023/1/23

N2 - 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.

AB - 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.

KW - Humans

KW - Depressive Disorder, Major/diagnosis

KW - Prospective Studies

KW - Reproducibility of Results

KW - Brain

KW - Neuroimaging

KW - Magnetic Resonance Imaging/methods

KW - Artificial Intelligence

U2 - 10.1186/s12888-022-04509-7

DO - 10.1186/s12888-022-04509-7

M3 - Journal article

C2 - 36690972

VL - 23

JO - B M C Psychiatry

JF - B M C Psychiatry

SN - 1471-244X

IS - 1

M1 - 59

ER -