Browsing by Author "Kennedy, Sidney H."

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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
    (2023-01-23) Fu, Cynthia H. Y.; Erus, Guray; Fan, Yong; Antoniades, Mathilde; Arnone, Danilo; Arnott, Stephen R.; Chen, Taolin; Choi, Ki S.; Fatt, Cherise C.; 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. W.; 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 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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    Brain connectomes in youth at risk for serious mental illness: an exploratory analysis
    (2022-09-15) Metzak, Paul D.; Shakeel, Mohammed K.; Long, Xiangyu; Lasby, Mike; Souza, Roberto; Bray, Signe; Goldstein, Benjamin I.; MacQueen, Glenda; Wang, JianLi; Kennedy, Sidney H.; Addington, Jean; Lebel, Catherine
    Abstract Background Identifying early biomarkers of serious mental illness (SMI)—such as changes in brain structure and function—can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI. Methods Participants were classified as healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data. Results Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections. Conclusions Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset.