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- Barletta, V, et al.
(author)
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Quantitative 7-Tesla Imaging of Cortical Myelin Changes in Early Multiple Sclerosis
- 2021
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In: Frontiers in neurology. - : Frontiers Media SA. - 1664-2295. ; 12, s. 714820-
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Journal article (peer-reviewed)abstract
- Cortical demyelination occurs early in multiple sclerosis (MS) and relates to disease outcome. The brain cortex has endogenous propensity for remyelination as proven from histopathology study. In this study, we aimed at characterizing cortical microstructural abnormalities related to myelin content by applying a novel quantitative MRI technique in early MS. A combined myelin estimation (CME) cortical map was obtained from quantitative 7-Tesla (7T) T2* and T1 acquisitions in 25 patients with early MS and 19 healthy volunteers. Cortical lesions in MS patients were classified based on their myelin content by comparison with CME values in healthy controls as demyelinated, partially demyelinated, or non-demyelinated. At follow-up, we registered changes in cortical lesions as increased, decreased, or stable CME. Vertex-wise analysis compared cortical CME in the normal-appearing cortex in 25 MS patients vs. 19 healthy controls at baseline and investigated longitudinal changes at 1 year in 10 MS patients. Measurements from the neurite orientation dispersion and density imaging (NODDI) diffusion model were obtained to account for cortical neurite/dendrite loss at baseline and follow-up. Finally, CME maps were correlated with clinical metrics. CME was overall low in cortical lesions (p = 0.03) and several normal-appearing cortical areas (p < 0.05) in the absence of NODDI abnormalities. Individual cortical lesion analysis revealed, however, heterogeneous CME patterns from extensive to partial or absent demyelination. At follow-up, CME overall decreased in cortical lesions and non-lesioned cortex, with few areas showing an increase (p < 0.05). Cortical CME maps correlated with processing speed in several areas across the cortex. In conclusion, CME allows detection of cortical microstructural changes related to coexisting demyelination and remyelination since the early phases of MS, and shows to be more sensitive than NODDI and relates to cognitive performance.
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| 2. |
- Granberg, T, et al.
(author)
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Corrigendum
- 2018
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In: Brain : a journal of neurology. - : Oxford University Press (OUP). - 1460-2156. ; 141:2, s. e14-
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Journal article (peer-reviewed)
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| 8. |
- Herranz, E, et al.
(author)
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Profiles of cortical inflammation in multiple sclerosis by 11C-PBR28 MR-PET and 7 Tesla imaging
- 2020
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In: Multiple sclerosis (Houndmills, Basingstoke, England). - : SAGE Publications. - 1477-0970 .- 1352-4585. ; 26:12, s. 1497-1509
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Journal article (peer-reviewed)abstract
- Neuroinflammation with microglia activation is thought to be closely related to cortical multiple sclerosis (MS) lesion pathogenesis. Objective: Using 11C-PBR28 and 7 Tesla (7T) imaging, we assessed in 9 relapsing–remitting multiple sclerosis (RRMS) and 10 secondary progressive multiple sclerosis (SPMS) patients the following: (1) microglia activation in lesioned and normal-appearing cortex, (2) cortical lesion inflammatory profiles, and (3) the relationship between neuroinflammation and cortical integrity. Methods: Mean 11C-PBR28 uptake was measured in focal cortical lesions, cortical areas with 7T quantitative T2* (q-T2*) abnormalities, and normal-appearing cortex. The relative difference in cortical 11C-PBR28 uptake between patients and 14 controls was used to classify cortical lesions as either active or inactive. Disease burden was investigated according to cortical lesion inflammatory profiles. The relation between q-T2* and 11C-PBR28 uptake along the cortex was assessed. Results: 11C-PBR28 uptake was abnormally high in cortical lesions in RRMS and SPMS; in SPMS, tracer uptake was significantly increased also in normal-appearing cortex. 11C-PBR28 uptake and q-T2* correlated positively in many cortical areas, negatively in some regions. Patients with high cortical lesion inflammation had worse clinical outcome and higher intracortical lesion burden than patients with low inflammation. Conclusion: 11C-PBR28 and 7T imaging reveal distinct profiles of cortical inflammation in MS, which are related to disease burden.
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| 10. |
- Mangeat, G., et al.
(author)
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Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis
- 2020
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In: Journal of Neuroimaging. - : Blackwell Publishing Inc.. - 1051-2284 .- 1552-6569. ; 30:5, s. 674-682
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Journal article (peer-reviewed)abstract
- BACKGROUND AND PURPOSE: Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. HDLS is a rare disorder compared to MS, which has led to occurrent misdiagnosis of HDLS as MS. That is problematic since their prognosis and treatment differ. Both disorders are investigated by MRI, which could help to identify patients with high probability of having HDLS, which could guide targeted genetic testing to confirm the HDLS diagnosis. METHODS: Here, we present a machine learning method based on quantitative MRI that can achieve a robust classification of HDLS versus MS. Four HDLS and 14 age-matched MS patients underwent a quantitative brain MRI protocol (synthetic MRI) at 3 Tesla (T) (scan time '7 minutes). We also performed a repeatability analysis of the predicting features to assess their generalizability by scanning a healthy control with five scan-rescans at 3T and 1.5T. RESULTS: Our predicting features were measured with an average confidence interval of 1.7% (P =.01), at 3T and 2.3% (P =.01) at 1.5T. The model gave a 100% correct classification of the cross-validation data when using 5-11 predicting features. When the maximum measurement noise was inserted in the model, the true positive rate of HDLS was 97.2%, while the true positive rate of MS was 99.6%. CONCLUSIONS: This study suggests that computer-assistance in combination with quantitative MRI may be helpful in aiding the challenging differential diagnosis of HDLS versus MS.
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