scholarly journals Iron in Multiple Sclerosis and Its Noninvasive Imaging with Quantitative Susceptibility Mapping

2016 ◽  
Vol 17 (1) ◽  
pp. 100 ◽  
Author(s):  
Carsten Stüber ◽  
David Pitt ◽  
Yi Wang
Keyword(s):  
Multiple Sclerosis ◽  
Noninvasive Imaging ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping

scholarly journals Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: Interpreting positive susceptibility and the presence of iron

2014 ◽  
Vol 74 (2) ◽  
pp. 564-570 ◽  
Author(s):  
Cynthia Wisnieff ◽  
Sriram Ramanan ◽  
John Olesik ◽  
Susan Gauthier ◽  
Yi Wang ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping

Improved Detection of Cortical Gray Matter Involvement in Multiple Sclerosis with Quantitative Susceptibility Mapping

2015 ◽  
Vol 22 (11) ◽  
pp. 1427-1432 ◽  
Author(s):  
Shingo Kakeda ◽  
Koichiro Futatsuya ◽  
Satoru Ide ◽  
Keita Watanabe ◽  
Mari Miyata ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Gray Matter ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping ◽  
Cortical Gray Matter

scholarly journals The Distributional Characteristics of Multiple Sclerosis Lesions on Quantitative Susceptibility Mapping and Their Correlation With Clinical Severity

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhuoxin Guo ◽  
Liu Long ◽  
Wei Qiu ◽  
Tingting Lu ◽  
Lina Zhang ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Wide Spectrum ◽  
Susceptibility Mapping ◽  
Clinical Severity ◽  
Imaging Features ◽  
Quantitative Susceptibility Mapping ◽  
Relapsing Remitting ◽  
Scale Scores ◽  
Susceptibility Contrast ◽  
Age Range

Background: Multiple sclerosis (MS) patients have a wide spectrum of severity and responses to therapy; the personalization of treatment relies on sensitive and specific biomarkers. Previous studies have suggested that susceptibility contrast in demyelinated plaques is associated with iron-related pathology in multiple sclerosis which may indicate clinical severity. The aims of this study were to characterize the spatial distribution of MS lesions with different iron patterns by using quantitative susceptibility mapping and to explore neuroradiological findings that correlate with poor clinical outcome.Methods: Twenty-six patients with relapsing–remitting MS [14 men, 12 women; mean age, 29 ± 8 (standard deviation) years; age range, 21–52 years] were included in this study. Differences in lesion number, T2 volume, and susceptibility were compared among lesions subcategorized by location and by the presence or absence of a hyperintense rim on quantitative susceptibility mapping. Associations between these imaging features and clinical outcomes including Expanded Disability Status Scale scores and annual relapse rates were investigated.Results: A total of 811 unifocal MS lesions were included, and their QSM patterns were nodular hyperintensity with no rim (rim–, 540, 67%) or with a hyperintense rim on the edge (rim+, 172, 21%) and nodular isointensity (99, 12%). Rim+ lesions had significantly larger volume (115 ± 142 vs. 166 ± 185 mm3, p < 0.001) and lower susceptibility (4 ± 15 vs. 8 ± 16 ppb, p < 0.05) than rim– lesions. More rim+ lesions were found in periventricular areas [median, 45%; interquartile range (IQR), 36%], whereas a larger proportion of rim– lesions were distributed in juxtacortical (median, 32%; IQR, 21%) and deep white matter (median, 38%; IQR, 22%) areas. The annual relapse rate was positively correlated with the proportion of periventricular rim+ lesions (p < 0.001, r = 0.65) and the proportion of subtentorial rim+ lesions (p < 0.05, r = 0.40). Additionally, a significant association was found between the burden of periventricular rim+ lesions (β = 0.64, p < 0.001) and the burden of subtentorial rim– lesions (β = 0.36, p < 0.05).Conclusions: A high number or lesion burden of periventricular rim+ lesions or subtentorial lesions is associated with frequent clinical relapses.

scholarly journals Structural disconnectivity from quantitative susceptibility mapping rim+ lesions is related to disability in people with multiple sclerosis

2020 ◽  
Author(s):  
Ceren Tozlu ◽  
Keith Jamison ◽  
Thanh Nguyen ◽  
Nicole Zinger ◽  
Ulrike Kaunzner ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Prediction Models ◽  
Structural Connectivity ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping ◽  
Adaptive Boosting ◽  
Lesion Location ◽  
Model Based ◽  
Structural Connectome ◽  
Left Thalamus

Background: Multiple Sclerosis (MS) is a disease characterized by inflammation, demyelination, and/or axonal loss that disrupts white matter pathways that constitute the brain's structural connectivity network. Individual disease burden and disability in patients with MS (pwMS) varies widely across the population, possibly due to heterogeneity of lesion location, size and subsequent disruption of the structural connectome. Chronic active MS lesions, which have a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) and a rim of iron-laden inflammatory cells, have been shown to be particularly detrimental to tissue concentration causing greater myelin damage compared to chronic silent MS lesions. How these rim+ lesions differentially impact structural connectivity and subsequently influence disability has not yet been explored. Objective: We characterize differences in the spatial location and structural disconnectivity patterns of rim+ lesions compared to rim- lesions. We test the hypothesis that rim+ lesions' disruption to the structural connectome are more predictive of disability compared to rim- lesions' disruption to the structural connectome. Finally, we quantify the most important regional structural connectome disruptions for disability prediction in pwMS. Methods: Ninety-six pwMS were included in our study (age: 40.25 ± 10.14, 67% female). Disability was assessed using Extended Disability Status Score (EDSS); thirty-seven pwMS had disability (EDSS ≥ 2). Regional structural disconnectivity patterns due to rim- and rim+ lesions were estimated using the Network Modification (NeMo) Tool. For each gray matter region, the NeMo Tool calculates a Change in Connectivity (ChaCo) score, i.e. the percent of connecting streamlines also passing through a lesion. Adaptive Boosting (ADA) classifiers were constructed based on demographics and the two sets of ChaCo scores (from rim+ and rim- lesions); performance was compared across the two models using the area under ROC curve (AUC). Finally, the importance of structural disconnectivity in each brain region in the disability prediction models was determined. Results: Rim+ lesions were much larger and tended to be more periventricular than rim- lesions. The model based on rim+ lesion structural disconnectivity measures had better disability classification performance (AUC = 0.67) than the model based on rim- lesion structural disconnectivity (AUC = 0.63). Structural disconnectivity, from both rim+ and rim- lesions, in the left thalamus and left cerebellum were most important for classifying pwMS into disability categories. Conclusion: Our findings suggest that, independent of the evidence that they have more damaging pathology, rim+ lesions also may be more influential on disability through their disruptions to the structural connectome. Furthermore, lesions of any type in the left cerebellum and left thalamus were especially important in classifying disability in pwMS. This analysis provides a deeper understanding of how lesion location/size and resulting disruption to the structural connectome can contribute to MS-related disability.

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