Journal
BRAIN
Volume 144, Issue -, Pages 1974-1984Publisher
OXFORD UNIV PRESS
DOI: 10.1093/brain/awab132
Keywords
multiple sclerosis; deep grey matter; thalamus; atrophy; MRI
Categories
Funding
- Race to Erase MS Foundation
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Multiple sclerosis is not only a white matter disease, but also involves grey matter injury, especially in deep grey matter regions. The deep grey matter is uniquely suited for studying the mechanisms and clinical relevance of tissue injury in multiple sclerosis using magnetic resonance techniques. MRI characterization of deep grey matter properties has potential as clinical trial end points for neurodegenerative aspects of multiple sclerosis.
Although multiple sclerosis has traditionally been considered a white matter disease, extensive research documents the presence and importance of grey matter injury including cortical and deep regions. The deep grey matter exhibits a broad range of pathology and is uniquely suited to study the mechanisms and clinical relevance of tissue injury in multiple sclerosis using magnetic resonance techniques. Deep grey matter injury has been associated with clinical and cognitive disability. Recently, MRI characterization of deep grey matter properties, such as thalamic volume, have been tested as potential clinical trial end points associated with neurodegenerative aspects of multiple sclerosis. Given this emerging area of interest and its potential clinical trial relevance, the North American Imaging in Multiple Sclerosis (NAIMS) Cooperative held a workshop and reached consensus on imaging topics related to deep grey matter. Herein, we review current knowledge regarding deep grey matter injury in multiple sclerosis from an imaging perspective, including insights from histopathology, image acquisition and post-processing for deep grey matter. We discuss the clinical relevance of deep grey matter injury and specific regions of interest within the deep grey matter. We highlight unanswered questions and propose future directions, with the aim of focusing research priorities towards better methods, analysis, and interpretation of results.
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