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Visual evoked potential latency predicts cognitive function in people with multiple sclerosis

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Abstract

Prior studies have reported an association between visual evoked potentials (VEPs) and cognitive performance in people with multiple sclerosis (PwMS), but the specific mechanisms that account for this relationship remain unclear. We examined the relationship between VEP latency and cognitive performance in a large sample of PwMS, hypothesizing that VEP latency indexes not only visual system functioning but also general neural efficiency. Standardized performance index scores were obtained for the domains of memory, executive function, visual-spatial processing, verbal function, attention, information processing speed, and motor skills, as well as global cognitive performance (NeuroTrax battery). VEP P100 component latency was obtained using a standard checkerboard pattern-reversal paradigm. Prolonged VEP latency was significantly associated with poorer performance in multiple cognitive domains, and with the number of cognitive domains in which performance was ≥ 1 SD below the normative mean. Relationships between VEP latency and cognitive performance were significant for information processing speed, executive function, attention, motor skills, and global cognitive performance after controlling for disease duration, visual acuity, and inter-ocular latency differences. This study provides evidence that VEP latency delays index general neural inefficiency that is associated with cognitive disturbances in PwMS.

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References

  1. Barton JL, Garber JY, Klistorner A et al (2019) The electrophysiological assessment of visual function in multiple sclerosis. CNP 4:90–96

    PubMed  PubMed Central  Google Scholar 

  2. Odom JV, Bach M, Brigell et al (2016) ISCEV standard for clinical visual evoked potentials: (2016 update). Doc Ophthalmol 133(1):1–9

    Article  Google Scholar 

  3. Chirapapaisan N, Laotaweerungsawat S, Chuenkongkaew et al (2015) Diagnostic value of visual evoked potentials for clinical diagnosis of multiple sclerosis. Doc Ophtalmol 130(1):25–30

    Article  Google Scholar 

  4. You Y, Klistorner A, Thie J et al (2011) Latency delay of visual evoked potential is a real measurement of demyelination in a rat model of optic neuritis. Investig Opthalmol Vis Sci 52(9):6911–6918

    Article  Google Scholar 

  5. Backner Y, Petrou P, Glick-Shames H et al (2019) Vision and vision-related measures in progressive multiple sclerosis. Front Neurol 10:455

    Article  Google Scholar 

  6. Crnošija L, Gabelić T, Barun B et al (2020) Evoked potentials can predict future disability in people with clinically isolated syndrome. Eur J Neurol 27:437–444

    Article  Google Scholar 

  7. Fuhr P, Borggrefe-Chappius A, Schindler C et al (2001) Visual and motor evoked potentials in the course of multiple sclerosis. Brain 124:2162–2168

    Article  CAS  Google Scholar 

  8. Leocani L, Rovaris M, Boneschi FM et al (2006) Multimodal evoked potentials to assess the evolution of multiple sclerosis: a longitudinal study. J Neurol Neurosurg Psychiat 77:1030–1035

    Article  CAS  Google Scholar 

  9. Weinstock-Guttman B, Baier M, Stockton et al (2003) Pattern reversal visual evoked potentials as a measure of visual pathway pathology in multiple sclerosis. Mult Scler 9:529–534

    Article  CAS  Google Scholar 

  10. Green A, Gelfand J, Cree BA et al (2017) Clemastine fumarate as a remyelinating therapy for multiple sclerosis (ReBuild): a randomized, controlled, double-blind, crossover trial. Lancet 390:2481–2489

    Article  CAS  Google Scholar 

  11. Koziolek MJ, Tampe D, Bӓhr M et al (2012) Immunoadsorption therapy in patients with multiple sclerosis with steroid-refractory optical neuritis. J Neuroinflammation 9:80

    Article  CAS  Google Scholar 

  12. Ontaneda D, Thompson AJ, Fox RJ et al (2017) Progressive multiple sclerosis: prospects for disease therapy, repair, and restoration of function. Lancet 389:1357–1366

    Article  Google Scholar 

  13. Cadavid D, Balcer L, Galetta S et al (2017) Safety and efficacy of opicinumab in acute optic neuritis (RENEW): a randomized, placebo-controlled, phase 2 trial. Lancet Neurol 16:189–199

    Article  CAS  Google Scholar 

  14. Canham LJW, Kane N, Oware A et al (2015) Multimodal neurophysiological evaluation of primary progressive multiple sclerosis—an increasingly valid biomarker with limits. Mult Scler Relat Dis 4:607–613

    Article  CAS  Google Scholar 

  15. Tinnefeld M, Treitz FH, Haase CG et al (2005) Attention and memory dysfunctions in mild multiple sclerosis. Eur Arch Psy Clin N 255:319–326

    Google Scholar 

  16. Covey TJ, Shucard JL, Shucard DW (2017) Event-related brain potential indices of cognitive function and brain resource reallocation during working memory in patients with multiple sclerosis. Clin Neurophysiol 128:604–621

    Article  Google Scholar 

  17. Pelosi L, Geesken JM, Holly M et al (1997) Working memory impairment in early multiple sclerosis. Brain 120:2039–2058

    Article  Google Scholar 

  18. Lisicki M, D’Ostilio, Coppola G et al (2018) Brain correlates of single trial visual evoked potentials in migraine: more than meets the eye. Front Neurol 9:393

    Article  Google Scholar 

  19. Costa SL, Genova HM, DeLuca J et al (2017) Information processing speed in multiple sclerosis: past, present, and future. Mult Scler J 23(6):772–789

    Article  Google Scholar 

  20. Holladay JT (1997) Proper method for calculating average visual acuity. J Refract Surg 13(4):388–391

    Article  CAS  Google Scholar 

  21. Achiron A, Doniger GM, Harel Y et al (2007) Prolonged response times characterize cognitive performance in multiple sclerosis. Eur J Neurol 14:1102–1108

    Article  CAS  Google Scholar 

  22. Golan D, Wilken J, Doniger GM et al (2019) Validity of a multi-domain computerized cognitive assessment battery for patients with multiple sclerosis. Mult Scler Relat Dis 30:154–162

    Article  Google Scholar 

  23. Lebrun, C, Blanc, F, Brassat, et al. on behalf of CFSEP (2010) Cognitive function in radiologically isolated syndrome. Mult Scler 16(8):919–925

    Article  Google Scholar 

  24. Benedict, RHB, DeLuca, J, Phillips, G, et al. and Multiple Sclerosis Outcome Assessments Consortium (2017) Validity of the symbol digit modalities test as a cognition performance outcome measure for multiple sclerosis. Mult Scler J 23(5):721–733

    Article  Google Scholar 

  25. Van Schependom J, D’hooghe MB, Cleynhens K et al (2014) The symbol digit modalities test as a sentinel test for cognitive impairment in multiple sclerosis. Eur J Neurol 21:1219–1225

    Article  Google Scholar 

  26. Drew MA, Starkey NJ, Isler RB (2009) Examining the link between information processing speed and executive function in multiple sclerosis. Arch Clin Neuropsych 24:47–58

    Article  Google Scholar 

  27. Llufriu S, Martinez-Heras E, Solana E et al (2017) Structural networks involved in attention and executive functions in multiple sclerosis. NeuroImage Clin 13:288–296

    Article  Google Scholar 

  28. Di Russo F, Martínez A, Sereno MI et al (2001) Cortical sources of the early components of the visual evoked potential. Hum Brain Mapp 15:95–111

    Article  Google Scholar 

  29. Cooray GK, Sundgren M, Brismar T (2020) Mechanism of visual network dysfunction in relapsing-remitting multiple sclerosis and its relation to cognition. Clin Neurophysiol 131(2):361–367

    Article  Google Scholar 

  30. Lobsien D, Ettrich B, Sotiriou K et al (2014) Whole-brain diffusion tensor imaging in correlation to visual-evoked potentials in multiple sclerosis: a tract-based spatial statistics analysis. Am J Neuroradiol 35(11):2076–2081

    Article  CAS  Google Scholar 

  31. Batista S, Zivadinov R, Hoogs M et al (2012) Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. J Neurol 259:139–146

    Article  Google Scholar 

  32. Bisecco A, Stamenova S, Caiazzo G et al (2018) Attention and processing speed performance in multiple sclerosis is mostly related to thalamic volume. Brain Imaging Behav 12:20–28

    Article  Google Scholar 

  33. Houtchens MK, Benedict RH, Killiany R et al (2007) Thalamic atrophy and cognition in multiple sclerosis. Neurology 69(12):1213–1223

    Article  CAS  Google Scholar 

  34. Golan D, Doniger GM, Srinivasan J et al (2020) The association between MRI brain volumes and computerized cognitive scores of people with multiple sclerosis. Brain Cogn 145:105614

    Article  Google Scholar 

  35. Sundgren M, Nikulin VV, Maurex L, Wahlin A, Piehl F, Brismar T (2015) P300 amplitude and response speed relate to preserved cognitive function in relapsing-remitting multiple sclerosis. Clin Neurophysiol 126(4):689–697

    Article  Google Scholar 

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Funding

The authors received no financial support for this research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Division of Cognitive and Behavioral Neurosciences, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University At Buffalo, Sherman Hall Annex Room 114, Buffalo, NY, 14214, USA

    Thomas J. Covey

  2. Neuroscience Program, Jacobs School of Medicine and Biomedical Sciences, University At Buffalo, Buffalo, NY, USA

    Thomas J. Covey

  3. Department of Neurology and Multiple Sclerosis Center, Lady Davis Carmel Medical Center, Haifa, Israel

    Daniel Golan

  4. Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel

    Daniel Golan

  5. Department of Clinical Research, NeuroTrax Corporation, Modiin, Israel

    Glen M. Doniger

  6. Wills Eye Institute, Philadelphia, PA, USA

    Robert Sergott

  7. South Shore Neurologic Associates, 712 Main Street, Islip, Patchogue, NY, USA

    Myassar Zarif, Jared Srinivasan, Barbara Bumstead, Marijean Buhse, Samson Mebrahtu & Mark Gudesblatt

  8. Washington Neuropsychology Research Group, Fairfax, VA, USA

    Jeffrey Wilken

  9. Department of Neurology, Georgetown University, Washington, DC, USA

    Jeffrey Wilken

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  1. Thomas J. Covey
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  2. Daniel Golan
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  3. Glen M. Doniger
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  7. Barbara Bumstead
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  8. Jeffrey Wilken
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  11. Mark Gudesblatt
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Corresponding authors

Correspondence to Thomas J. Covey or Mark Gudesblatt.

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Conflicts of interest

Glen M. Doniger is an employee of NeuroTrax Corporation. The authors declare no other potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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The use of de-identified data was approved by a central Institutional Review Board (IRB).

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Covey, T.J., Golan, D., Doniger, G.M. et al. Visual evoked potential latency predicts cognitive function in people with multiple sclerosis. J Neurol 268, 4311–4320 (2021). https://doi.org/10.1007/s00415-021-10561-2

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  • DOI: https://doi.org/10.1007/s00415-021-10561-2

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