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Detecting microstructural deviations in individuals with deep diffusion MRI tractometry

Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Shastin, Dmitri, Koller, Kristin, Raven, Erika P., Cunningham, Adam, Doherty, Joanne, van den Bree, Marianne B.M, Parker, Greg D., Hamandi, Khalid, Gray, William P. and Jones, Derek K. 2021. Detecting microstructural deviations in individuals with deep diffusion MRI tractometry. Nature Computational Science 1 , pp. 598-606.

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Abstract

Most diffusion magnetic resonance imaging studies of disease rely on statistical comparisons between large groups of patients and healthy participants to infer altered tissue states in the brain; however, clinical heterogeneity can greatly challenge their discriminative power. There is currently an unmet need to move away from the current approach of group-wise comparisons to methods with the sensitivity to detect altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalized medicine in translational imaging. To this end, Detect was developed to advance diffusion magnetic resonance imaging tractometry towards single-patient analysis. By operating on the manifold of white-matter pathways and learning normative microstructural features, our framework captures idiosyncrasies in patterns along white-matter pathways. Our approach paves the way from traditional group-based comparisons to true personalized radiology, taking microstructural imaging from the bench to the bedside.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Publisher: Springer
ISSN: 2662-8457
Funders: Wellcome Trust
Date of First Compliant Deposit: 10 August 2021
Date of Acceptance: 9 August 2021
Last Modified: 27 Sep 2021 08:16
URI: http://orca.cardiff.ac.uk/id/eprint/143263

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