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BACKGROUND: In patients with relapsing remitting multiple sclerosis (RRMS) on low-efficacy disease modifying therapies (DMT), the optimal strategy on how to escalate treatment once needed, remains unknown. METHODS: We studied RRMS patients on low-efficacy DMTs listed in the Swiss National Treatment Registry, who underwent escalation to either medium- or high-efficacy DMTs. Propensity score-based matching was applied using 12 clinically relevant variables. Both groups were also separately matched with control subjects who did not escalate therapy. Time to relapse and to disability worsening were evaluated using Cox proportional hazard models. RESULTS: Of 1037 eligible patients, we 1:1 matched 450 MS patients who switched from low-efficacy to medium-efficacy (n = 225; 76.0% females, aged 42.4 ± 9.9 years [mean ± SD], median EDSS 3.0 [IQR 2-4]) or high-efficacy DMTs (n = 225; 72.4% females, aged 42.2 ± 10.6 years, median EDSS 3.0 [IQR 2-4]). Escalation to high-efficacy DMTs was associated with lower hazards of relapses than medium-efficacy DMTs (HR = 0.67, 95% CI 0.47-0.95, p = .027) or control subjects (HR = 0.61, 95% CI 0.44-0.84, p = .003). By contrast, escalation from low to medium-efficacy DMTs did not alter the hazard for relapses when compared to controls (i.e. patients on low-efficacy DMT who did not escalate DMT during follow-up) CONCLUSION: Our nationwide registry analysis suggests that, once escalation from a low-efficacy DMT is indicated, switching directly to a high-efficacy treatment is superior to a stepwise escalation starting with a moderate-efficacy treatment.

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

The involvement of the cerebellum in migraine pathophysiology is not well understood. We used a biparametric approach at high-field MRI (3T) to assess the structural integrity of the cerebellum in 15 migraineurs with aura (MWA), 23 migraineurs without aura (MWoA), and 20 healthy controls (HC). High-resolution T1 relaxation maps were acquired together with magnetization transfer images in order to probe microstructural and myelin integrity. Clusterwise analysis was performed on T1 and magnetization transfer ratio (MTR) maps of the cerebellum of MWA, MWoA, and HC using an ANOVA and a non-parametric clusterwise permutation F test, with age and gender as covariates and correction for familywise error rate. In addition, mean MTR and T1 in frontal regions known to be highly connected to the cerebellum were computed. Clusterwise comparison among groups showed a cluster of lower MTR in the right Crus I of MWoA patients vs. HC and MWA subjects (p = 0.04). Univariate and bivariate analysis on T1 and MTR contrasts showed that MWoA patients had longer T1 and lower MTR in the right and left pars orbitalis compared to MWA (p < 0.01 and 0.05, respectively), but no differences were found with HC. Lower MTR and longer T1 point at a loss of macromolecules and/or micro-edema in Crus I and pars orbitalis in MWoA patients vs. HC and vs. MWA. The pathophysiological implications of these findings are discussed in light of recent literature

Contains fulltext : 181742.pdf (publisher's version ) (Open Access), Objective: Quantitative and semi-quantitative MRI (qMRI) metrics provide complementary specificity and differential sensitivity to pathological brain changes compatible with brain inflammation, degeneration and repair. Moreover, advanced MRI metrics with overlapping elements amplify the true tissue-related information and limit measurement noise. In this work, we combined multiple advanced MRI parameters to assess focal and diffuse brain changes over 2 years in a group of early-stage relapsing-remitting MS patients. Methods: Thirty relapsing-remitting MS patients with less than 5-year disease duration and nine healthy subjects underwent 3T MRI at baseline and after 2 years including T1, T2, T2* relaxometry, and magnetization transfer imaging. To assess longitudinal changes in normal-appearing tissue and lesions, we used analyses of variance and Bonferroni correction for multiple comparisons. Multivariate linear regression was used to assess the correlation between clinical outcome and multiparametric MRI changes in lesions and normal appearing tissue. Results: In patients, we measured a significant longitudinal decrease of mean T2 relaxation times in normal appearing white matter (p = 0.005) and a decrease of T1 relaxation times in the pallidum (p < 0.05), which are compatible with edema reabsorption and/or iron deposition. No longitudinal changes in qMRI metrics were observed in controls. In MS lesions, we measured a decrease in T1 relaxation time (p-value < 2.2e-16) and a significant increase in MTR (p-value < 1e-6), suggesting repair mechanisms such as remyelination, increased axonal density and/or a gliosis. Last, the evolution of advanced MRI metrics - and not changes in lesions or brain volume - were correlated to motor and cognitive tests scores evolution (Adj-R2 > 0.4, p < 0.05). In summary, the combination of multiple advanced MRI provided evidence of changes compatible with focal and diffuse brain repair at early MS stages as suggested by histopathological

There is growing interest in understanding the role of the non-injured contra-lateral hemisphere in stroke recovery. In the experimental field, histological evidence has been reported that structural changes occur in the contra-lateral connectivity and circuits during stroke recovery. In humans, some recent imaging studies indicated that contra-lateral sub-cortical pathways and functional and structural cortical networks are remodeling, after stroke. Structural changes in the contra-lateral networks, however, have never been correlated to clinical recovery in patients. To determine the importance of the contra-lateral structural changes in post-stroke recovery, we selected a population of patients with motor deficits after stroke affecting the motor cortex and/or sub-cortical motor white matter. We explored i) the presence of Generalized Fractional Anisotropy (GFA) changes indicating structural alterations in the motor network of patientsâeuro? contra-lateral hemisphere as well as their longitudinal evolution ii) the correlation of GFA changes with patientsâeuro? clinical scores, stroke size and demographics data iii) and a predictive model.

Validation is the main bottleneck preventing theadoption of many medical image processing algorithms inthe clinical practice. In the classical approach,a-posteriori analysis is performed based on someobjective metrics. In this work, a different approachbased on Petri Nets (PN) is proposed. The basic ideaconsists in predicting the accuracy that will result froma given processing based on the characterization of thesources of inaccuracy of the system. Here we propose aproof of concept in the scenario of a diffusion imaginganalysis pipeline. A PN is built after the detection ofthe possible sources of inaccuracy. By integrating thefirst qualitative insights based on the PN withquantitative measures, it is possible to optimize the PNitself, to predict the inaccuracy of the system in adifferent setting. Results show that the proposed modelprovides a good prediction performance and suggests theoptimal processing approach.