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Quantitative neuroimaging measures of myelin in the healthy brain and in multiple sclerosis

Research output: Contribution to journalArticle

Jonathan O'Muircheartaigh, Irene Vavasour, Emil Albert Ljungberg, David Li, Alexander Rauscher, Victoria Levesque, Hideki Garren, David Clayton, Roger Tam, Anthony Traboulsee, Shannon Kolind

Original languageEnglish
Pages (from-to)2104-2116
JournalHuman Brain Mapping
Volume40
Issue number7
Early online date15 Jan 2019
DOIs
Publication statusPublished - May 2019

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Abstract

Quantitative magnetic resonance imaging (MRI) techniques have been developed as imaging biomarkers, aiming to improve 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 4 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 MWFD, 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 8 white matter 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 white matter, MWF-D demonstrated the best accuracy (p<0.05, after multiple comparison correction). To aide future power analysis, we provide the average and standard deviation volumes of the 4 techniques, estimated from the healthy control sample.

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