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Abstract #1279

A Multiblock Partial Least Squares Correlation Framework for Covariate Adjustment and Interpretation of Latent Associations in Multimodal Data

Warda T. Syeda1, Bjørn H. Ebdrup1,2,3, Cassandra M.J. Wannan1, Micah Cearns1, Rigas Soldatos1, Antonia Merritt1, Mahesh Jayaram 1, Andrew Zalesky 1, Jayachandra M. Raghava2,4, Birgitte Fagerlund 2, Egill Rostrup 2, Birte Glenthøj 2,3, Leigh A. Johnston5,6, Chad Bousman 1,7, Ian Everall8, Efstratios Skafidas 1,9, and Christos Pantelis1,9
1Melbourne Neuropsychiatry Centre, The University of Melbourne, Parkville, Australia, 2Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark, 3Faculty of Health and Medical Sciences, Department of Clinical Medicine, University of Copenhagen, Denmark, Denmark, 4Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Glostrup, Denmark, 5Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia, 6Melbourne Brain Centre Imaging Unit, The University of Melbourne, Parkville, Australia, 7Departments of Medical Genetics, Psychiatry, and Physiology & Pharmacology, University of Calgary, Calgary, AB, Canada, 8Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom, 9Electrical and Electronic Engineering Department, The University of Melbourne, Parkville, Australia

Partial least squares (PLS) methods enable identification of multimodal patterns of latent associations between neuroimaging, cognitive, functional and clinical measures. Here, we propose a multiblock PLS correlation (MB-PLS-C) technique to enable covariate representation in the latent space and an interpretation framework to assess results from PLS-C analyses in clinical contexts. We investigate latent structure-cognition patterns in a multivariate dataset of individuals with treatment-resistant schizophrenia and healthy controls using the proposed MB-PLS-C method, and compare with standard PLS-C with and without covariate adjustment through residualization.

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