Browsing by Author "Arnott, Stephen R."

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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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    White matter hyperintensities and smaller cortical thickness are associated with neuropsychiatric symptoms in neurodegenerative and cerebrovascular diseases
    (2023-06-20) Ozzoude, Miracle; Varriano, Brenda; Beaton, Derek; Ramirez, Joel; Adamo, Sabrina; Holmes, Melissa F.; Scott, Christopher J. M.; Gao, Fuqiang; Sunderland, Kelly M.; McLaughlin, Paula; Goubran, Maged; Kwan, Donna; Roberts, Angela; Bartha, Robert; Symons, Sean; Tan, Brian; Swartz, Richard H.; Abrahao, Agessandro; Saposnik, Gustavo; Masellis, Mario; Lang, Anthony E.; Marras, Connie; Zinman, Lorne; Shoesmith, Christen; Borrie, Michael; Fischer, Corinne E.; Frank, Andrew; Freedman, Morris; Montero-Odasso, Manuel; Kumar, Sanjeev; Pasternak, Stephen; Strother, Stephen C.; Pollock, Bruce G.; Rajji, Tarek K.; Seitz, Dallas; Tang-Wai, David F.; Turnbull, John; Dowlatshahi, Dar; Hassan, Ayman; Casaubon, Leanne; Mandzia, Jennifer; Sahlas, Demetrios; Breen, David P.; Grimes, David; Jog, Mandar; Steeves, Thomas D. L.; Arnott, Stephen R.; Black, Sandra E.; Finger, Elizabeth; Rabin, Jennifer; Tartaglia, Maria C.
    Abstract Background Neuropsychiatric symptoms (NPS) are a core feature of most neurodegenerative and cerebrovascular diseases. White matter hyperintensities and brain atrophy have been implicated in NPS. We aimed to investigate the relative contribution of white matter hyperintensities and cortical thickness to NPS in participants across neurodegenerative and cerebrovascular diseases. Methods Five hundred thirteen participants with one of these conditions, i.e. Alzheimer’s Disease/Mild Cognitive Impairment, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia, Parkinson’s Disease, or Cerebrovascular Disease, were included in the study. NPS were assessed using the Neuropsychiatric Inventory – Questionnaire and grouped into hyperactivity, psychotic, affective, and apathy subsyndromes. White matter hyperintensities were quantified using a semi-automatic segmentation technique and FreeSurfer cortical thickness was used to measure regional grey matter loss. Results Although NPS were frequent across the five disease groups, participants with frontotemporal dementia had the highest frequency of hyperactivity, apathy, and affective subsyndromes compared to other groups, whilst psychotic subsyndrome was high in both frontotemporal dementia and Parkinson’s disease. Results from univariate and multivariate results showed that various predictors were associated with neuropsychiatric subsyndromes, especially cortical thickness in the inferior frontal, cingulate, and insula regions, sex(female), global cognition, and basal ganglia-thalamus white matter hyperintensities. Conclusions In participants with neurodegenerative and cerebrovascular diseases, our results suggest that smaller cortical thickness and white matter hyperintensity burden in several cortical-subcortical structures may contribute to the development of NPS. Further studies investigating the mechanisms that determine the progression of NPS in various neurodegenerative and cerebrovascular diseases are needed.