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

Camille Guillemin; Grégory Hammad; John Read; Florence Requier; Maëlle Charonitis; Gaël Delrue; Nora Vandeleene; Emilie Lommers; Pierre Maquet; Fabienne Collette

doi:10.1016/j.msard.2022.104001

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

Mult Scler Relat Disord. 2022 Sep:65:104001. doi: 10.1016/j.msard.2022.104001. Epub 2022 Jun 26.

Authors

Affiliations

¹ GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium; Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.
² GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.
³ Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium.
⁴ GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium; Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium.
⁵ GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium; Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium. Electronic address: f.collette@uliege.be.

PMID: 35803086
DOI: 10.1016/j.msard.2022.104001

Abstract

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 (BFs_incl > 100) while RTs did not significantly vary. Performance over task and conditions followed the same pattern of evolution across groups (BFs_incl < 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 (BFs_incl > 15), regardless of condition and group (BFs_incl < 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.

Keywords: Cognitive fatigue; Cognitive load; Early Multiple Sclerosis; Eye metrics; Pupillometry.

MeSH terms

Bayes Theorem
Cognition / physiology
Fatigue / complications
Fatigue / etiology
Humans
Multiple Sclerosis* / complications
Multiple Sclerosis* / diagnosis
Multiple Sclerosis* / psychology
Pupil
Reaction Time
Sleepiness