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


Journal

Multiple sclerosis and related disorders
ISSN: 2211-0356
Titre abrégé: Mult Scler Relat Disord
Pays: Netherlands
ID NLM: 101580247

Informations de publication

Date de publication:
Sep 2022
Historique:
received: 20 05 2022
revised: 10 06 2022
accepted: 24 06 2022
pubmed: 9 7 2022
medline: 8 9 2022
entrez: 8 7 2022
Statut: ppublish

Résumé

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. 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). Performance (accuracy and RTs) was lower in the HCL condition and accuracy decreased over time (BFs 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.

Identifiants

pubmed: 35803086
pii: S2211-0348(22)00511-9
doi: 10.1016/j.msard.2022.104001
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

104001

Informations de copyright

Copyright © 2022 Elsevier B.V. All rights reserved.

Auteurs

Camille Guillemin (C)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium; Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.

Grégory Hammad (G)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.

John Read (J)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.

Florence Requier (F)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium; Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.

Maëlle Charonitis (M)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium; Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.

Gaël Delrue (G)

Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium.

Nora Vandeleene (N)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.

Emilie Lommers (E)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium; Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium.

Pierre Maquet (P)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium; Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium.

Fabienne Collette (F)

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.

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Classifications MeSH