| Literature DB >> 33155763 |
Emilie Lommers1,2, Camille Guillemin1,3, Gilles Reuter1,4, Eve Fouarge1, Gaël Delrue2, Fabienne Collette1,3, Christian Degueldre1, Evelyne Balteau1, Pierre Maquet1,2, Christophe Phillips1,5.
Abstract
Despite robust postmortem evidence and potential clinical importance of gray matter (GM) pathology in multiple sclerosis (MS), assessing GM damage by conventional magnetic resonance imaging (MRI) remains challenging. This prospective cross-sectional study aimed at characterizing the topography of GM microstructural and volumetric alteration in MS using, in addition to brain atrophy measures, three quantitative MRI (qMRI) parameters-magnetization transfer (MT) saturation, longitudinal (R1), and effective transverse (R2*) relaxation rates, derived from data acquired during a single scanning session. Our study involved 35 MS patients (14 relapsing-remitting MS; 21 primary or secondary progressive MS) and 36 age-matched healthy controls (HC). The qMRI maps were computed and segmented in different tissue classes. Voxel-based quantification (VBQ) and voxel-based morphometry (VBM) statistical analyses were carried out using multiple linear regression models. In MS patients compared with HC, three configurations of GM microstructural/volumetric alterations were identified. (a) Co-localization of GM atrophy with significant reduction of MT, R1, and/or R2*, usually observed in primary cortices. (b) Microstructural modifications without significant GM loss: hippocampus and paralimbic cortices, showing reduced MT and/or R1 values without significant atrophy. (c) Atrophy without significant change in microstructure, identified in deep GM nuclei. In conclusion, this quantitative multiparametric voxel-based approach reveals three different spatially-segregated combinations of GM microstructural/volumetric alterations in MS that might be associated with different neuropathology.Entities:
Keywords: atrophy; demyelination; gray matter; multiple sclerosis; quantitative MRI; voxel-based analysis
Mesh:
Year: 2020 PMID: 33155763 PMCID: PMC7856642 DOI: 10.1002/hbm.25274
Source DB: PubMed Journal: Hum Brain Mapp ISSN: 1065-9471 Impact factor: 5.399