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Deep grey matter injury in multiple sclerosis: a NAIMS consensus statement
Author(s) -
Daniel Ontaneda,
Praneeta Raza,
Kedar Mahajan,
Douglas L. Arnold,
Michael G. Dwyer,
Susan A. Gauthier,
Douglas N. Greve,
Daniel M. Harrison,
Roland G. Henry,
David K.B. Li,
Caterina Mainero,
Wayne Moore,
Sridar Narayanan,
Jiwon Oh,
Raihaan Patel,
Daniel Pelletier,
Alexander Rauscher,
William D. Rooney,
Nancy L. Sicotte,
Roger Tam,
Daniel S. Reich,
Christina Azevedo
Publication year - 2021
Publication title -
brain
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.142
H-Index - 336
eISSN - 1460-2156
pISSN - 0006-8950
DOI - 10.1093/brain/awab132
Subject(s) - grey matter , white matter , multiple sclerosis , magnetic resonance imaging , neuroscience , pathology , medicine , psychology , psychiatry , radiology
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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