Comparing baseline qMRI measures in stable & transitioning regions in/around MS white matter lesions
Poster No:
116
Submission Type:
Abstract Submission
Authors:
Kaihim Wong1, Md Nasir Uddin2, Teresa Figley1, Jennifer Kornelsen1, John Fisk3, Ruth Ann Marrie4, Chase Figley1
Institutions:
1University of Manitoba, Winnipeg, Manitoba, CA, 2University of Rochester, Rochester, New York, US, 3Nova Scotia Health, Halifax, Nova Scotia, CA, 4Dalhousie University, Halifax, Nova Scotia, CA
First Author:
Co-Author(s):
Introduction:
White matter lesions (WMLs) are a hallmark of multiple sclerosis (MS) (Haki et al., 2024; Lassmann, 2018). Prior studies have demonstrated microscopic heterogeneity within WMLs due to de/remyelination, inflammation, and other MS pathobiology (Ellen et al., 2023; Lucchinetti et al., 2000) involved in longitudinal lesion evolution. Quantitative MRI (qMRI) techniques are thought to have superior sensitivity compared to conventional MRI for detecting early microstructural tissue changes (Granziera et al., 2021; Tranfa et al., 2022)., This study aimed to investigate whether baseline qMRI metrics differed in the stable and transitioning regions in and around WMLs over the subsequent 2 years.
Methods:
Baseline and 2-year follow-up qMRI maps and WML masks were calculated for an adult MS cohort from Comorbidity and Cognition in MS (CCOMS) Study (Uddin et al., 2022). Axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD), fractional anisotropy (FA), myelin water fraction (MWF), intra- and extra-cellular water fraction (IEWF), geometric mean T2 (gT2), and calibrated T1w/T2w ratio were available for the full cohort [n(F)=90(75), median EDSS(IQR)=3.5(1.5), mean age(std)=51.0(12.6) years]. R2* relaxation and quantitative susceptibility mapping (QSM) data were available for a sub-cohort [n(F)=35(28), median EDSS(IQR)=3.5(1.4), mean age(std)=47.5(10.9) years]. Individual lesions at baseline and 2-year follow-up were separated and labeled using the Automatic Follow-Up of Individual Lesions (AFIL) algorithm with default settings (Köhler et al., 2019), which identified three primary WML types (Fig. 1A): i) newly appearing WMLs, ii) vanishing WMLs, and iii) enduring WMLs (with "expanding", "contracting", and "persisting" subregions). Perilesional normal appearing white matter (pNAWM) regions were also identified for each enduring WML (Fig 1B). After dilating baseline enduring WMLs by 2 mm and excluding non-WM regions (as well as other WMLs and their associated pNAWM shells), voxels within the pNAWM mask regions were defined as either "stable" or "transitioning" pNAWM by excluding or overlapping the "expanding" subregion of the enduring WML, respectively. Average values for each of the baseline qMRI metrics were extracted from each region and subregion. Comparisons were made among primary WML types, among enduring WML subregions, as well as between regions/subregions that underwent similar tissue transitions or had identical tissue class labels at baseline or follow-up. Statistical tests involving primary WML types (with different sample sizes) were performed using non-parametric Wilcoxon rank sum tests, while tests between enduring WML subregions (with paired samples for the same lesions) were performed using Wilcoxon signed rank tests (all tests two-tailed and FDR-corrected).
Results:
Across the full (partial) MS cohort, 2123(711) unique cortical WMLs were identified, with 623(252) newly appearing WMLs, 519(169) vanishing WMLs, 978(350) enduring WMLs. Most baseline qMRI measures showed significant differences between enduring WMLs and both newly appearing WMLs and vanishing WMLs (Fig. 2). By contrast, only T1w/T2w values differed between newly appearing and vanishing WMLs. 6/10 baseline qMRI measures showed differences between vanishing WMLs and contracting WML subregions, while 8/10 showed differences between newly appearing WMLs and expanding WML subregions. Overall, significant differences were found for 53/60 comparisons across enduring WML subregions with baseline qMRI measures.
Conclusions:
Baseline qMRI measures (particularly T1w/T2w ratio and diffusivity metrics) exhibited significant differences between newly appearing WMLs, vanishing WMLs, and enduring WMLs (as well as enduring WML subregions) over a 2-year span. The demonstrated sensitivity of baseline qMRI measures to WM heterogeneity among these regions suggests that they can potentially be exploited to predict the morphological evolution of WMLs.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Other Methods
Novel Imaging Acquisition Methods:
Diffusion MRI
Multi-Modal Imaging 2
Imaging Methods Other
Keywords:
Data analysis
MRI
Myelin
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - quantitative MRI;Multiple sclerosis;Lesions
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
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Köhler, C., Wahl, H., Ziemssen, T., Linn, J., & Kitzler, H. H. (2019). Exploring individual multiple sclerosis lesion volume change over time: Development of an algorithm for the analyses of longitudinal quantitative MRI measures. NeuroImage: Clinical, 21, 101623.
Lassmann, H. (2018). Multiple Sclerosis Pathology. Cold Spring Harbor Perspectives in Medicine, 8(3), a028936.
Lucchinetti, C., Brück, W., Parisi, J., Scheithauer, B., Rodriguez, M., & Lassmann, H. (2000). Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination. Annals of Neurology, 47(6), 707–717.
Tranfa, M., Pontillo, G., Petracca, M., Brunetti, A., Tedeschi, E., Palma, G., & Cocozza, S. (2022). Quantitative MRI in Multiple Sclerosis: From Theory to Application. American Journal of Neuroradiology, 43(12), 1688–1695.
Uddin, M. N., Figley, T. D., Kornelsen, J., Mazerolle, E. L., Helmick, C. A., O’Grady, C. B., Pirzada, S., Patel, R., Carter, S., Wong, K., Essig, M. R., Graff, L. A., Bolton, J. M., Marriott, J. J., Bernstein, C. N., Fisk, J. D., Marrie, R. A., & Figley, C. R. (2022). The Comorbidity and Cognition in Multiple Sclerosis (CCOMS) neuroimaging protocol: study rationale, MRI acquisition, and minimal image processing pipelines. Frontiers in Neuroimaging.