Association of Brain Iron Concentration and Demyelination in Multiple Sclerosis
Poster No:
111
Submission Type:
Abstract Submission
Authors:
Fang Liu1, Quy Cao1, Andrew Leroux2, Ying Jin2, Thanh Nguyen3, Yi Wang3, Susan Gauthier3, Ciprian Crainiceanu4
Institutions:
1University of Pennsylvania, Philadelphia, PA, 2Colorado School of Public Health, Aurora, CO, 3Weill Cornell Medicine, New York, NY, 4Johns Hopkins University, Baltimore, MD
First Author:
Co-Author(s):
Introduction:
Multiple sclerosis (MS) is a neurological condition that affects the central nervous system and affects more than 2.9 million people worldwide. Magnetic resonance imaging (MRI) plays an integral role in diagnosing MS. With recent advances in technology, new modalities have been introduced to capture different aspects of lesion progression. For instance, quantitative susceptibility mapping (QSM) has been used to characterize patterns of iron deposition (Liu et al., 2015; Kaunzner et al., 2019) while myelin water fraction (MWF) has been used as a quantitative in vivo marker for myelin (MacKay et al., 2016). Combining information across different modalities can help uncover complex patterns that single modalities might miss and offer novel insights into disease mechanisms. Using a cohort of MS patients with longitudinal MRI follow-up, we aim to explore the association between early QSM around month 3 and MWF around year 1 and apply a bootstrap framework to quantify the level of uncertainty for our point estimates. This analysis will inform whether early indicators of lesion inflammation, as captured by QSM, could predict future myelination levels, potentially providing insights into treatment planning for MS patients.
Methods:
This study used 38 T1-weighted gadolinium-enhancing lesions from 12 MS patients. Lesions were only included if QSM data around 3 months (85 - 180 days) and MWF data around 1 year (270 - 450 days) is available. Pearson's correlation is used to assess the association between QSM and MWF. Two levels of association were examined and compared: 1) a lesion-level association where QSM and MWF intensities were aggregated across all voxels of a lesion and the average was used, and 2) a voxel-level association where the original voxel level data was used. Additionally, to account for spatial correlation among voxels and to quantify the level of uncertainty in our correlation estimates, we applied a nonparametric bootstrap framework where lesions were resampled with replacement, and the confidence intervals (CI) were reported using the percentile method (i.e., 2.5th to 97.5th percentiles). The p-value for the bootstrap was computed against a null hypothesis of a correlation of zero and was also reported.
Results:
There is a nonsignificant correlation between 3-month QSM and 1-year MWF when we use the average lesion data (R = -0.11, p = .52). This correlation is stronger and statistically significant when we look at the more granular voxel-level association (R = -0.31, p<.001). A comparison of the lesion average vs. voxel-level association is presented in Figure 1. From the lesion-level bootstrapping results (Fig. 1c), the median correlation between QSM and MWF is R = -0.30 (95% CI: -0.51, -0.09; p<.001).
Conclusions:
Overall, there is a negative association between QSM and MWF: higher QSM around 3 months is associated with lower levels of myelin at year 1. These findings align with those in the literature, where lesions with high QSM values (e.g., paramagnetic rim lesions or PRLs) have been shown to have lower whole-lesion MWF (Yao et al., 2018). When using the averaged lesion-level approach, we are underpowered to observe a significant association between QSM and MWF (Fig.1a), likely due to lesion heterogeneity. However, the voxel-level approach and bootstrap results (Fig. 1b&1c) reveal a significant association, highlighting the need for more granular analytical approaches over simple averages. With the recommended inclusion of PRLs in the updated 2024 MS diagnostic criteria, we expect to see more longitudinal studies utilizing newer MRI modalities such as QSM for PRL identification and disease prediction in the future, which could shine more light on the mechanisms of lesion demyelination. In sum, demyelination in MS is a complex process that is at least partially influenced by gradual iron accumulation, and more granular statistical methodologies should be examined to optimize the use of data and help uncover meaningful patterns in MS progression.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Methods Development
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 2
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals
Keywords:
Data analysis
Degenerative Disease
Demyelinating
Design and Analysis
Modeling
MRI
Myelin
Statistical Methods
STRUCTURAL MRI
Other - multiple sclerosis
1|2Indicates the priority used for review
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2. Liu, C., Wei, H., Gong, N. J., Cronin, M., Dibb, R., & Decker, K. (2015). Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications. Tomography (Ann Arbor, Mich.), 1(1), 3–17. https://doi.org/10.18383/j.tom.2015.00136
3. MacKay, A. L., & Laule, C. (2016). Magnetic Resonance of Myelin Water: An in vivo Marker for Myelin. Brain plasticity (Amsterdam, Netherlands), 2(1), 71–91. https://doi.org/10.3233/BPL-160033
4. Yao, Y., Nguyen, T. D., Pandya, S., Zhang, Y., Hurtado Rúa, S., Kovanlikaya, I., Kuceyeski, A., Liu, Z., Wang, Y., & Gauthier, S. A. (2018). Combining Quantitative Susceptibility Mapping with Automatic Zero Reference (QSM0) and Myelin Water Fraction Imaging to Quantify Iron-Related Myelin Damage in Chronic Active MS Lesions. AJNR. American journal of neuroradiology, 39(2), 303–310. https://doi.org/10.3174/ajnr.A5482