[en] 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 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 <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 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.
Research Center/Unit :
GIGA CRC In vivo Imaging-Cognitive Neurosciences - ULiège
Disciplines :
Neurosciences & behavior
Author, co-author :
Guillemin, Camille ; Université de Liège - ULiège > Psychologie et Neuroscience Cognitives (PsyNCog) ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory
Hammad, Grégory ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Read, John ; Université de Liège - ULiège > Faculté de Psychologie, Logopédie et Sciences de l'Education > Master sc. psycho., à fin.
Requier, Florence ; Université de Liège - ULiège > Psychologie et Neuroscience Cognitives (PsyNCog)
Charonitis, Maëlle ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory
Delrue, Gaël ; Université de Liège - ULiège > Département de Psychologie > Neuropsychologie ; Centre Hospitalier Universitaire de Liège - CHU > > Service de médecine de l'appareil locomoteur
Vandeleene, Nora ; Université de Liège - ULiège > GIGA ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Neuroimaging, data acquisition and processing
LOMMERS, Emilie ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Maquet, Pierre ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie
Collette, Fabienne ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory ; Université de Liège - ULiège > Psychologie et Neuroscience Cognitives (PsyNCog) > Cognition et Langage
Language :
English
Title :
Pupil response speed as a marker of cognitive fatigue in early Multiple Sclerosis.
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Bibliography
Weiland, T.J., et al. Clinically significant fatigue: prevalence and associated factors in an international sample of adults with Multiple Sclerosis recruited via the internet. PLoS One, 10, 2015.
Bakshi, R., et al. Fatigue in Multiple Sclerosis and its relationship to depression and neurologic disability. Mult. Scler. 6 (2000), 181–185.
Fisk, J.D., Pontefract, A., Ritvo, P.G., Archibald, C.J., Murray, T.J., The impact of fatigue on patients with Multiple Sclerosis. Can. J. Neurol. Sci. 21 (1994), 9–14 /J. Can. des Sci. Neurol.
Sainz de la Maza, S., et al. Measuring productivity loss in early relapsing-remitting Multiple Sclerosis. Mult. Scler. Relat. Disord., 58, 2022, 103398.
Chen, J., et al. Estimating MS-related work productivity loss and factors associated with work productivity loss in a representative Australian sample of people with Multiple Sclerosis. Mult. Scler. J. 25:7 (2018), 1–11, 10.1177/1352458518781971.
Penner, I.K., Paul, F., Fatigue as a symptom or comorbidity of neurological diseases. Nat. Rev. Neurol. 13 (2017), 662–675.
Chaudhuri, A., Behan, P.O., Fatigue in neurological disorders. Lancet 363 (2004), 978–988.
Linnhoff, S., Fiene, M., Heinze, H.J., Zaehle, T., Cognitive fatigue in Multiple Sclerosis: an objective approach to diagnosis and treatment by transcranial electrical stimulation. Brain Sci. 9 (2019), 1–23.
Brochet, B., Ruet, A., Cognitive impairment in Multiple Sclerosis with regards to disease duration and clinical phenotypes. Front. Neurol. 10 (2019), 1–7.
Borragán, G., et al. Cognitive fatigue, sleep and cortical activity in Multiple Sclerosis disease. a behavioral, polysomnographic and functional near-infrared spectroscopy investigation. Front. Hum. Neurosci., 12, 2018, 378.
Agyemang, C., Berard, J.A., Walker, L.A.S., Cognitive fatigability in Multiple Sclerosis: how does performance decline over time on the paced auditory serial addition test?. Mult. Scler. Relat. Disord., 54, 2021, 103130.
Boksem, M.A.S., Tops, M., Mental fatigue: costs and benefits. Brain Res. Rev. 59 (2008), 125–139.
Joshi, S., Gold, J.I., Pupil size as a window on neural substrates of cognition. Trends Cogn. Sci. 24 (2020), 466–480.
Rodriguez, J.D., et al. Blink: characteristics, controls, and relation to dry eyes. Curr. Eye Res. 43 (2018), 52–66.
Pozzessere, G., et al. Autonomic involvement in Multiple Sclerosis: a pupillometric study. Clin. Auton. Res. 7 (1997), 315–319.
de Seze, J., et al. Pupillary disturbances in Multiple Sclerosis: correlation with MRI findings. J. Neurol. Sci. 188 (2001), 37–41.
de Rodez Benavent, S.A., et al. Fatigue and cognition: pupillary responses to problem-solving in early Multiple Sclerosis patients. Brain Behav., 7, 2017, e00717.
Surakka, J., et al. Pupillary function in early Multiple Sclerosis. Clin. Auton. Res. 18 (2008), 150–154.
Kurtzke, J.F., Rating neurologic impairment in Multiple Sclerosis: an Expanded Disability Status Scale (EDSS). Neurology 33 (1983), 1444–1452.
Thompson, A.J., et al. Diagnosis of Multiple Sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17 (2018), 162–173.
Borragán, G., Slama, H., Bartolomei, M., Peigneux, P., Cognitive fatigue: a time-based resource-sharing account. Cortex 89 (2017), 71–84.
Penner, I.K., et al. The Fatigue Scale for Motor and Cognitive Functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue. Mult. Scler. 15 (2009), 1509–1517.
Buysse, D.J., Reynolds, C.F., Monk, T.H., Berman, S.R., Kupfer, D.J., The pittsburg sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 28 (1998), 193–213.
Johns, M.W., A new method for measuring daytime sleepiness: the epworth sleepiness scale. Sleep 14 (1991), 540–545.
Spielberger, C.D., Gorsuch, R.L., Ushene, R., Vagg, P.R., Jacobs, G.A., Manual for the State-Trait Anxiety Inventory. 1983, Consulting Psychologists Press.
Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J., An inventory for measuring depression. Arch. Gen. Psychiatry, 4, 1961, 561.
Kirchner, W.K., Age differences in short-term retention of rapidly changing information. J. Exp. Psychol. 55 (1958), 352–358.
Åkerstedt, T., Gillberg, M., Subjective and objective sleepiness in the active individual. Int. J. Neurosci. 52 (1990), 29–37.
Lee, K.A., Hicks, G., Nino-Murcia, G., Validity and reliability of a scale to assess fatigue. Psychiatry Res. 36 (1991), 291–298.
Keysers, C., Gazzola, V., Wagenmakers, E.J., Using Bayes factor hypothesis testing in neuroscience to establish evidence of absence. Nat. Neurosci. 23 (2020), 788–799.
Jeffreys, H., Theory of Probability. 3rd ed., 1961, Clarendon Press.
Kenward, M.G., Roger, J.H., An improved approximation to the precision of fixed effects from restricted maximum likelihood. Comput. Stat. Data Anal. 53 (2009), 2583–2595.
Jaeger, B.C., Edwards, L.J., Das, K., Sen, P.K., An R2 statistic for fixed effects in the generalized linear mixed model. J. Appl. Stat. 44 (2017), 1086–1105.
Rooney, S., Wood, L., Moffat, F., Paul, L., Prevalence of fatigue and its association with clinical features in progressive and non-progressive forms of Multiple Sclerosis. Mult. Scler. Relat. Disord. 28 (2019), 276–282.
Galland-Decker, C., Marques-Vidal, P., Vollenweider, P., Prevalence and factors associated with fatigue in the Lausanne middle-aged population: a population-based, cross-sectional survey. BMJ Open 9 (2019), 1–10.
Cellini, N., et al. Changes in sleep timing and subjective sleep quality during the COVID-19 lockdown in Italy and Belgium: age, gender and working status as modulating factors. Sleep Med. 77 (2021), 112–119.
Torrente, F., et al. Psychological symptoms, mental fatigue and behavioural adherence after 72 continuous days of strict lockdown during the COVID-19 pandemic in Argentina. BJPsych Open, 8, 2022.
Boeschoten, R.E., et al. Prevalence of depression and anxiety in Multiple Sclerosis: a systematic review and meta-analysis. J. Neurol. Sci. 372 (2017), 331–341.
Sakkas, G.K., Giannaki, C.D., Karatzaferi, C., Manconi, M., Sleep abnormalities in Multiple Sclerosis. Curr. Treat. Options Neurol., 21, 2019, 4.
Guillemin, C., et al. The complex interplay between trait fatigue and cognition in Multiple Sclerosis. Psychol. Belg., 62, 2022, 108.
Diamond, B.J., Johnson, S.K., Kaufman, M., Graves, L., Relationships between information processing, depression, fatigue and cognition in Multiple Sclerosis. Arch. Clin. Neuropsychol. 23 (2008), 189–199.
Pokryszko-Dragan, A., et al. Cognitive performance, fatigue and event-related potentials in patients with clinically isolated syndrome. Clin. Neurol. Neurosurg. 149 (2016), 68–74.
Andreasen, A.K., et al. Structural and cognitive correlates of fatigue in progressive Multiple Sclerosis. Neurol. Res. 41 (2019), 168–176.
Sandry, J., Genova, H.M., Dobryakova, E., DeLuca, J., Wylie, G., Subjective cognitive fatigue in Multiple Sclerosis depends on task length. Front. Neurol. 5 (2014), 1–7.
Kahneman, D., Beatty, J., Pupil diameter and load on memory. Science 154 (1966), 1583–1585 80-.
Bafna, T., Hansen, J.P., Mental fatigue measurement using eye metrics: a systematic literature review. Psychophysiology 58 (2021), 1–23.
Merkelbach, S., et al. Multiple Sclerosis and the autonomic nervous system. J. Neurol. 253 (2006), 21–25.
Schoonheim, M.M., Meijer, K.A., Geurts, J.J.G, Network collapse and cognitive impairment in Multiple Sclerosis. Front. Neurol., 6, 2015, 82.
Jamil, T., et al. Default “Gunel and Dickey” Bayes factors for contingency tables. Behav. Res. Methods 49 (2017), 638–652.
Mathôt, S., Pupillometry: psychology, physiology, and function. J. Cogn. 1:1 (2018), 1–23.
Watson, C., Kirkcaldie, M., Paxinos, G., The Brain. 2010, Elsevier Inc., 10.1016/C2009-0-01579-7 The Brain.
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