Brain microstructure is linked to cognitive fatigue in early multiple sclerosis.

Cognitive fatigue is a major symptom of Multiple Sclerosis (MS), from the early stages of the disease. This study aims to detect if brain microstructure is altered early in the disease course and is associated with cognitive fatigue in people with MS (pwMS) compared to matched healthy controls (HC). Recently diagnosed pwMS (N = 18, age < 45 years old) with either a Relapsing-Remitting or a Clinically Isolated Syndrome course of the disease, and HC (N = 19) matched for sex, age and education were analyzed. Quantitative multiparameter maps (MTsat, PD, R1 and R2*) of pwMS and HC were calculated. Parameters were extracted within the normal appearing white matter, cortical grey matter and deep grey matter (NAWM, NACGM and NADGM, respectively). Bayesian T-test for independent samples assessed between-group differences in brain microstructure while associations between score at a cognitive fatigue scale and each parameter in each tissue class were investigated with Generalized Linear Mixed Models. Patients exhibited lower MTsat and R1 values within NAWM and NACGM, and higher R1 values in NADGM compared to HC. Cognitive fatigue was associated with PD measured in every tissue class and to MTsat in NAWM, regardless of group. Disease-specific negative correlations were found in pwMS in NAWM (R1, R2*) and NACGM (R1). These findings suggest that brain microstructure within normal appearing tissues is already altered in the very early stages of the disease. Moreover, additional microstructure alterations (e.g. diffuse and widespread demyelination or axonal degeneration) in pwMS may lead to disease-specific complaint of cognitive fatigue.

Using quantitative magnetic resonance imaging to track cerebral alterations in multiple sclerosis brain: A longitudinal study.

INTRODUCTION: Quantitative MRI quantifies tissue microstructural properties and supports the characterization of cerebral tissue damages. With an MPM protocol, 4 parameter maps are constructed: MTsat, PD, R1 and R2*, reflecting tissue physical properties associated with iron and myelin contents. Thus, qMRI is a good candidate for in vivo monitoring of cerebral damage and repair mechanisms related to MS. Here, we used qMRI to investigate the longitudinal microstructural changes in MS brain. METHODS: Seventeen MS patients (age 25-65, 11 RRMS) were scanned on a 3T MRI, in two sessions separated with a median of 30 months, and the parameters evolution was evaluated within several tissue classes: NAWM, NACGM and NADGM, as well as focal WM lesions. An individual annual rate of change for each qMRI parameter was computed, and its correlation to clinical status was evaluated. For WM plaques, three areas were defined, and a GLMM tested the effect of area, time points, and their interaction on each median qMRI parameter value. RESULTS: Patients with a better clinical evolution, that is, clinically stable or improving state, showed positive annual rate of change in MTsat and R2* within NAWM and NACGM, suggesting repair mechanisms in terms of increased myelin content and/or axonal density as well as edema/inflammation resorption. When examining WM lesions, qMRI parameters within surrounding NAWM showed microstructural modifications, even before any focal lesion is visible on conventional FLAIR MRI. CONCLUSION: The results illustrate the benefit of multiple qMRI data in monitoring subtle changes within normal appearing brain tissues and plaque dynamics in relation with tissue repair or disease progression.

Pupil response speed as a marker of cognitive fatigue in early Multiple Sclerosis☆.

CONTEXT: Cognitive fatigue (CF) is a disabling symptom frequently reported by patients with Multiple Sclerosis (pwMS). Whether pwMS in the early disease stages present an increased sensitivity to fatigue induction remains debated. Objective measures of CF have been validated neither for clinical nor research purposes. This study aimed at (i) assessing how fatigue induction by manipulation of cognitive load affects subjective fatigue and behavioural performance in newly diagnosed pwMS and matched healthy controls (HC); and (ii) exploring the relevance of eye metrics to describe CF in pwMS. METHODS: Nineteen pwMS with disease duration < 5 years and 19 matched HC participated to this study. CF was induced with a dual-task in two separate sessions with varying cognitive load (High and Low cognitive load conditions, HCL and LCL). Accuracy, reaction times (RTs), subjective fatigue and sleepiness states were assessed. Bayesian Analyses of Variance for repeated measures (rmANOVA) explored the effects of time, group and load condition on the assessed variables. Eye metrics (number of long blinks, pupil size and pupil response speed: PRS) were obtained during the CF task for a sub-sample (16 pwMS and 15 HC) and analysed with Generalized Linear Mixed Models (GLMM). RESULTS: Performance (accuracy and RTs) was lower in the HCL condition and accuracy decreased over time (BFsincl > 100) while RTs did not significantly vary. Performance over task and conditions followed the same pattern of evolution across groups (BFsincl < 0.08) suggesting that pwMS did not show increased alteration of performance during fatigue induction. Regarding subjective state, both fatigue and sleepiness increased following the task (BFsincl > 15), regardless of condition and group (BFsincl < 3). CF in pwMS seems to be associated with PRS, as PRS decreased during the task amongst pwMS only and especially in the HCL condition (all p < .05). A significant Condition*Group interaction was observed regarding long blinks (p < .0001) as well as an expected effect of cognitive load condition on pupil diameter (p < .01). CONCLUSION: These results suggest that newly diagnosed pwMS and HC behave similarly during fatigue induction, in terms of both performance decrement and accrued fatigue sensation. Eye metric data further reveal a susceptibility to CF in pwMS, which can be objectively measured.