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

peer reviewedIntroduction: 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. Emilie Lommers and Christophe Phillips equally contributed to the work

Brain microstructure is linked to cognitive fatigue in early multiple sclerosis

peer reviewedCognitive 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

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

peer reviewedContext: 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 behavioral 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 analyzed 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 15), regardless of condition and group (BFsincl <3). CF in pwMS seems to be associated with PRS, as PRS decreased during the task among 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

Using quantitative MRI to track cerebral damage in multiple sclerosis: a longitudinal study

Contrary to conventional MRI (cMRI), quantitative MRI (qMRI) quantifies tissue physical microstructural properties and improves the characterization of cerebral damages in relation with various neurological diseases. With a multi-parameter mapping (MPM) protocol, 4 parameter maps are constructed: saturated magnetization transfer (MTsat), proton density (PD), longitudinal relaxation (R1) and effective transverse relaxation (R2*) rates, reflecting tissue physical properties associated with iron and myelin contents. Here, we used qMRI to investigate the microstructural changes happening over time in multiple sclerosis (MS). Seventeen MS patients (age 25-65, 11 RRMS) were scanned on a 3T MRI, with at least one year separation between two acquisition sessions, and the evolution of their parameters was evaluated within several tissue classes: normal appearing white matter (NAWM), normal appearing cortical and deep gray matter (NACGM and NADGM) as well as focal white matter (WM) lesions. Brain tissue segmentation was performed using US-with-Lesion, an adapted version of the Unified Segmentation (US) algorithm accounting for the lesion tissue class, based on qMRI and FLAIR images. An individual annual rate of change for each qMRI parameter was computed, and its correlation to clinical status was evaluated. As for WM plaques, three areas were defined within them. A Generalized Linear Mixed Model (GLMM) tested the effect of area and time points, as well as their interaction on each median qMRI parameter value. Patients with a better clinical evolution showed positive annual rate of change in MT and R2* within NAWM and NACGM, suggesting repair mechanisms in terms of increased myelin content and/or axonal density. When examining focal WM lesions, qMRI parameters within surrounding NAWM showed modification in terms of reduction in MT, R1 and R2* combined with increased of PD even before any focal lesion is visible on conventional FLAIR MRI. 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.Preprin

Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

peer reviewedArousals during sleep are transient accelerations of the EEG signal, considered to reflectsleep perturbations associated with poorer sleep quality. They are typically detected by visualinspection, which is time consuming, subjective, and prevents good comparability across scorers,studies and research centres. We developed a fully automatic algorithm which aims at detectingartefact and arousal events in whole-night EEG recordings, based on time-frequency analysis withadapted thresholds derived from individual data. We ran an automated detection of arousals over35 sleep EEG recordings in healthy young and older individuals and compared it against humanvisual detection from two research centres with the aim to evaluate the algorithm performance.Comparison across human scorers revealed a high variability in the number of detected arousals,which was always lower than the number detected automatically. Despite indexing more events,automatic detection showed high agreement with human detection as reflected by its correlationwith human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleepstage did not influence performance, while age may impact automatic detection, depending on thehuman rater considered as gold standard. We propose our freely available algorithm as a reliable andtime-sparing alternative to visual detection of arousals

Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

Arousals during sleep are transient accelerations of the EEG signal typically detected by visual inspection of the sleep recording. Such visual identification is a time-consuming, subjective process that prevents comparability across scorers, studies and research centres. We developed an algorithm, which automatically detects arousals in whole-night EEG recordings, based on time and frequency analysis with adapted thresholds derived from individual data. We performed automatic arousals detection over 35 sleep recordings of young (µ=24.07±3, N=18) and older (µ=61.38±6, N=17) healthy individuals, and compared it against human raters (HR) detection from two research centres. We assessed performance of the automatic algorithm using generalized linear mixed models with Cohen’s kappa as dependent variable. Performance of automatic detection was compared to a gold standard, composed of either all arousals found by any of the HR (inclusive detection – ID) or only those common to both HR (conservative detection – CD). Comparison between human scorers revealed a high variability in the number of arousals detected (µ=71±32 vs 111±50). Although many more arousals were automatically detected (µ=200 ± 43), agreement of automatic detection against human detection was high, as reflected by very large Cohen’s kappa values (κ=.93 for ID, .94 for CD). Importantly, automatic detection was correlated to human detection (r=.38, p=.025 for CD). Algorithm performance was not significantly influenced by sleep stage (p=.74 for ID; p=.97 for CD), age (p=.12 for ID; p=.91 for CD) or sex (p=.10 for ID; p=.21 for CD). We further found that relative power in the theta and alpha bands were, respectively, higher and lower (p<.0001) for arousals that were only detected by the algorithm, arguably making them less obvious for the human eye. Our results show that the automated algorithm is performing at least equally as well as HR. While the automatic method detects most of HR events, it finds many more events that bear the characteristics of AASM arousals, but are missed by visual inspection of the EEG. This is seen for other micro-events detectors such as spindle detectors. In conclusion, our algorithm a reliable tool for automatic detection of arousals