Quantitative neuroimaging measures of myelin in the healthy brain and in multiple sclerosis. Issue 7 (15th January 2019)

• Record Type: Journal Article
• Authors: O'Muircheartaigh, Jonathan; Vavasour, Irene; Ljungberg, Emil; Li, David K. B.; Rauscher, Alexander; Levesque, Victoria; Garren, Hideki; Clayton, David; Tam, Roger; Traboulsee, Anthony; Kolind, Shannon
• Is Part Of: Human brain mapping. Volume 40:Issue 7(2019)
• Page Start: 2104
• Page End: 2116
• Publication Date: 2019-01-15
• Subjects: individual differences -- magnetization transfer ratio -- multiple sclerosis -- myelin -- myelin water imaging -- quantitative MRI -- relaxometry; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24510
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Quantitative magnetic resonance imaging (MRI) techniques have been developed as imaging biomarkers, aiming to improve the specificity of MRI to underlying pathology compared to conventional weighted MRI. For assessing the integrity of white matter (WM), myelin, in particular, several techniques have been proposed and investigated individually. However, comparisons between these methods are lacking. In this study, we compared four established myelin‐sensitive MRI techniques in 56 patients with relapsing–remitting multiple sclerosis (MS) and 38 healthy controls. We used T2‐relaxation with combined GRadient And Spin Echoes (GRASE) to measure myelin water fraction (MWF‐G), multi‐component driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) to measure MWF‐D, magnetization‐transfer imaging to measure magnetization‐transfer ratio (MTR), and T1 relaxation to measure quantitative T1 (qT1 ). Using voxelwise Spearman correlations, we tested the correspondence of methods throughout the brain. All four methods showed associations that varied across tissue types; the highest correlations were found between MWF‐D and qT1 (median ρ across tissue classes 0.8) and MWF‐G and MWF‐D (median ρ = 0.59). In eight WM tracts, all measures showed differences ( p < 0.05) between MS normal‐appearing WM and healthy control WM, with qT1 showing the highest number of different regions (8), followed by MWF‐D and MTR (6), and MWF‐G ( n = 4). Comparing the methods in terms of their statistical sensitivity to MS lesions in WM, MWF‐D demonstrated the best accuracy ( p < 0.05, after multiple comparison correction). To aid future power analysis, we provide the average and standard deviation volumes of the four techniques, estimated from the healthy control sample.