Cortical quantitative MRI parameters are related to the cognitive status in patients with relapsing-remitting multiple sclerosis.

OBJECTIVES: We aimed to assess cortical damage in patients with relapsing-remitting multiple sclerosis (RRMS)/clinically isolated syndrome (CIS) with a multiparametric, surface-based quantitative MRI (qMRI) approach and to evaluate the correlation of imaging-derived parameters with cognitive scores, hypothesizing that qMRI parameters are correlated with cognitive abilities. METHODS: Multiparametric qMRI-data (T1, T2 and T2* relaxation times and proton density (PD)) were obtained from 34 patients/24 matched healthy control subjects. Cortical qMRI values were analyzed on the reconstructed cortical surface with Freesurfer. We tested for group differences of cortical microstructural parameters between the healthy and patient collectives and for partial Pearson correlations of qMRI parameters with cognitive scores, correcting for age. RESULTS: Cortical T2-/T2*-/PD values and four cognitive parameters differed between groups (p ≤ 0.046). These cognitive scores, reflecting information processing speed, verbal memory, visuospatial abilities, and attention, were correlated with cortical T2 (p ≤ 0.02) and T2* (p ≤ 0.03). Cortical changes appeared heterogeneous across the cortex and their distribution differed between the parameters. Vertex-wise correlation of T2 with neuropsychological parameters revealed specific patterns of cortical damage being related to distinct cognitive deficits. CONCLUSIONS: Microstructural changes are distributed heterogeneously across the cortex in RRMS/CIS. QMRI has the potential to provide surrogate parameters for the assessment of cognitive impairment in these patients for clinical studies. The characteristics of cognitive impairment in RRMS might depend on the distribution of cortical changes. KEY POINTS: • The goal of the presented study was to investigate cortical changes in RRMS/CIS and their relation to the cognitive status, using multiparametric quantitative MRI. • Cortical T2, T2*, and PD increases observed in patients appeared heterogeneous across the cortex and their distribution differed between the parameters. • Vertex-wise correlation of T2 with neuropsychological scores revealed specific patterns of cortical changes being related to distinct cognitive deficits.

Multimodal Quantitative MRI Reveals No Evidence for Tissue Pathology in Idiopathic Cervical Dystonia.

Background: While in symptomatic forms of dystonia cerebral pathology is by definition present, it is unclear so far whether disease is associated with microstructural cerebral changes in idiopathic dystonia. Previous quantitative MRI (qMRI) studies assessing cerebral tissue composition in idiopathic dystonia revealed conflicting results. Objective: Using multimodal qMRI, the presented study aimed to investigate alterations in different cerebral microstructural compartments associated with idiopathic cervical dystonia in vivo. Methods: Mapping of T1, T2, T 2 * , and proton density (PD) was performed in 17 patients with idiopathic cervical dystonia and 29 matched healthy control subjects. Statistical comparisons of the parametric maps between groups were conducted for various regions of interest (ROI), including major basal ganglia nuclei, the thalamus, white matter, and the cerebellum, and voxel-wise for the whole brain. Results: Neither whole brain voxel-wise statistics nor ROI-based analyses revealed significant group differences for any qMRI parameter under investigation. Conclusions: The negative findings of this qMRI study argue against the presence of overt microstructural tissue change in patients with idiopathic cervical dystonia. The results seem to support a common view that idiopathic cervical dystonia might primarily resemble a functional network disease.

Improved synthetic T1-weighted images for cerebral tissue segmentation in neurological diseases.

Structural cerebral MRI analysis in patients with neurological diseases usually requires T1-weighted datasets for tissue segmentation. For this purpose, synthetic T1-weighted images which are constructed from quantitative maps of the underlying tissue parameters such as the T1 relaxation time and the proton density (PD) may provide advantages over conventional datasets. However, in some cases synthetic images may suffer from specific artifacts, hampering accurate tissue segmentation. The goal was to improve a previously described method for the calculation of synthetic magnetization-prepared rapid gradient-echo (MP-RAGE) datasets from quantitative T1 and PD maps. Improvements comprise a B0-correction for the water-selective excitation pulses employed in T1-mapping and the use of T1-based pseudo-PD maps. Synthetic T1-weighted MP-RAGE datasets were calculated, using the standard and the improved algorithm, for 10 patients with focal epilepsy (caused by focal cortical dysplasia in 9), 10 patients with multiple sclerosis and 10 healthy control subjects and segmented with the Freesurfer toolbox. Visual inspection disclosed that segmentation of the standard synthetic datasets was inaccurate in 6 out of 10 patients with epilepsy, 7 out of 10 patients with multiple sclerosis and 7 out of 10 healthy control subjects, while the improved synthetic datasets resulted in adequate segmentation outcomes in the majority of cases. Only for one patient with multiple sclerosis and one with epilepsy, segmentation in basal temporal regions was not sufficient. Furthermore, data based on the standard algorithm showed strong signal non-uniformities in basal regions. This effect was not present in the improved synthetic datasets.