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Abstract #1081

A multivariate machine learning framework for psychosis: integrating diffusion and structural MRI

Vasiliki Chatzi1, Rui Pedro A.G.Teixeira2, and Jacques Donald Tournier2

1Psychosis Studies, King's College London, London, United Kingdom, 2Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London

Machine learning is increasingly being used in psychiatric research for patient classification and stratification. Current machine learning approaches are largely univariate with main focus on structural MRI and do not account for the significant white matter connectivity alterations associated with the disorder. With the steady growth of multi-centre multi-modal neuroimaging studies there is a need for multivariate machine learning frameworks that can integrate these different types of information. Here, we propose an automated multivariate machine learning pipeline to integrate state-of-the-art structural and diffusion features, based on well-established widely-available software packages to keep implementation and replication as simple as possible.

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