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Quantifying multiple sclerosis pathology in post mortem spinal cord using MRI

Schmierer, K; McDowell, A; Petrova, N; Carassiti, D; Thomas, DL; Miquel, ME; (2018) Quantifying multiple sclerosis pathology in post mortem spinal cord using MRI. Neuroimage 10.1016/j.neuroimage.2018.01.052. (In press). Green open access

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Multiple sclerosis (MS) is a common inflammatory, demyelinating and degenerative disease of the central nervous system. The majority of people with MS present with symptoms due to spinal cord damage, and in more advanced MS a clinical syndrome resembling that of progressive myelopathy is not uncommon. Significant efforts have been undertaken to predict MS-related disability based on short-term observations, for example, the spinal cord cross sectional area measured using MRI. The histo-pathological correlates of spinal cord MRI changes in MS are incompletely understood, however a surge of interest in tissue microstructure has recently led to new approaches to improve the precision with which MRI indices relate to underlying tissue features, such as myelin content, neurite density and orientation, among others. Quantitative MRI techniques including T1 and T2, magnetisation transfer (MT) and a number of diffusion-derived indices have all been successfully applied to post mortem MS spinal cord. Combining advanced quantification of histological features with quantitative - particularly diffusion-based - MRI techniques provide a new platform for high-quality MR/pathology data generation. To more accurately quantify grey matter pathology in the MS spinal cord, a key driver of physical disability in advanced MS, remains an important challenge of microstructural imaging.

Type: Article
Title: Quantifying multiple sclerosis pathology in post mortem spinal cord using MRI
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neuroimage.2018.01.052
Publisher version: http://doi.org/10.1016/j.neuroimage.2018.01.052
Language: English
Additional information: Copyright © 2018 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
URI: http://discovery.ucl.ac.uk/id/eprint/10043158
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