Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-11T10:14:56.030Z Has data issue: false hasContentIssue false
  • English
  • Français

Early detection of cognitive dysfunction in patients with multiple sclerosis: Implications on outcome

Published online by Cambridge University Press:  19 September 2019

Maged Abdel Naseer ,
Shereen Fathi ,
Dalia M. Labib ,
Dalia H. Khalil ,
Alshaimaa M. Aboulfotooh  and
Rehab Magdy
Maged Abdel Naseer
Affiliation:
Department of Neurology, Faculty of Medicine, Cairo University, Cairo, Egypt
Shereen Fathi
Affiliation:
Department of Neurology, Faculty of Medicine, Cairo University, Cairo, Egypt
Dalia M. Labib
Affiliation:
Department of Neurology, Faculty of Medicine, Cairo University, Cairo, Egypt
Dalia H. Khalil
Affiliation:
Department of Ophthalmology, Faculty of Medicine, Cairo University, Cairo, Egypt
Alshaimaa M. Aboulfotooh
Affiliation:
Department of Neurology, Faculty of Medicine, Cairo University, Cairo, Egypt
Rehab Magdy*
Affiliation:
Department of Neurology, Faculty of Medicine, Cairo University, Cairo, Egypt
*
*Corresponding author. Email: rehab.m.hassan@kasralainy.edu.eg
Get access

Abstract

Objective:

Cognitive impairment in multiple sclerosis (MS) has a complex relationship with disease progression and neurodegeneration. The aim of this study was to shed light on the importance of early detection of cognitive impairment in MS patients.

Methods:

The study comprised two groups of definite MS patients, relapsing remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS), each with 25 patients. Physical disability was assessed using the Expanded Disability Status Scale (EDSS), while the risk of secondary progression was assessed using the Bayesian Risk Estimate for Multiple Sclerosis (BREMS). Cognitive functions were assessed using the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) and Controlled Oral Word Association Test (COWAT). Assessment of neurodegeneration was done using optical coherence tomography (OCT) via quantification of retinal nerve fiber layer (RNFL).

Results:

MS patients with higher RNFL thickness demonstrated a larger learning effect size than patients who had lower values in RNFL thickness regardless of MS type. RRMS patients showed significant improvement in delayed recall after giving cues than SPMS. The symbol digit modalities test was the only neuropsychological test that showed a significant negative correlation with EDSS (P = 0.009). There was a statistically significant negative correlation between BREMS scores and performance in all neuropsychological tests.

Conclusion:

Inclusion of neurocognitive evaluation in the periodic assessment of MS patients is mandatory to detect patients at increased risk of secondary progression. The thickness of RNFL is suggested as a method to estimate the expected benefit of cognitive rehabilitation, regardless of MS type.

Keywords

Multiple sclerosissecondary progressive phasecognitive impairmentoptical coherence tomography
Type
Articles
Information
Brain Impairment , Volume 21 , Issue 2 , September 2020 , pp. 208 - 216
Copyright
© Australasian Society for the Study of Brain Impairment 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albanese, M., Zagaglia, S., Landi, D., Boffa, L., Nicoletti, C. G., Marciani, M. G., … Centonze, D. (2016). Cerebrospinal fluid lactate is associated with multiple sclerosis disease progression. Journal of Neuroinflammation, 13, 36. doi: 10.1186/s12974-016-0502-1.CrossRefGoogle ScholarPubMed
American Psychiatric Publishing. (2013). Diagnostic and statistical manual of mental disorders (DSM-5) (5th ed.). Arlington, VA: American Psychiatric Publishing.Google Scholar
Bartels, C., Wegrzyn, M., Wiedl, A., Ackermann, V., & Ehrenreich, H. (2010). Practice effects in healthy adults: A longitudinal study on frequent repetitive cognitive testing. BMC Neuroscience, 11, 118. doi: 10.1186/1471-2202-11-118.CrossRefGoogle ScholarPubMed
Benedict, R. (1997). Brief visuospatial memory test-revised professional manual. Odessa: Psychological Assessment Resources.Google Scholar
Benedict, R. H., Amato, M. P., Boringa, J., Brochet, B., Foley, F., Fredrikson, S., … Langdon, D. (2012). Brief International Cognitive Assessment for MS (BICAMS): International standards for validation. BMC Neurology, 12, 55. doi: 10.1186/1471-2377-12-55.CrossRefGoogle ScholarPubMed
Benton, A. L., Hamsher, S. K., & Sivan, A. B. (1983). Multilingual aplasia examination (2nd ed.). Iowa City, IA: AJA Associates.Google Scholar
Bergamaschi, R., Quaglini, S., Tavazzi, E., Amato, M. P., Paolicelli, D., Zipoli, V., … Trojano, M. (2016). Immunomodulatory therapies delay disease progression in multiple sclerosis. Multiple Sclerosis, 22(13), 1732–1740. doi: 10.1177/1352458512445941.CrossRefGoogle ScholarPubMed
Bergamaschi, R., Quaglini, S., Trojano, M., Amato, M. P., Tavazzi, E., Paolicelli, D., … Cosi, V. (2007). Early prediction of the long term evolution of multiple sclerosis: The Bayesian Risk Estimate for Multiple Sclerosis (BREMS) score. Journal of Neurology, Neurosurgery, and Psychiatry, 78(7), 757–759. doi: 10.1136/jnnp.2006.107052.CrossRefGoogle Scholar
Bonavita, S., Sacco, R., Della Corte, M., Esposito, S., Sparaco, M., d’Ambrosio, A., … Tedeschi, G. (2015). Computer-aided cognitive rehabilitation improves cognitive performances and induces brain functional connectivity changes in relapsing remitting multiple sclerosis patients: An exploratory study. Journal of Neurology, 262(1), 91–100. doi: 10.1007/s00415-014-7528-z.CrossRefGoogle Scholar
Brochet, B., Deloire, M. S., Bonnet, M., Salort-Campana, E., Ouallet, J. C., Petry, K. G., & Dousset, V. (2008). Should SDMT substitute for PASAT in MSFC? A 5-year longitudinal study. Multiple Sclerosis, 14(9), 1242–1249. doi: 10.1177/1352458508094398.CrossRefGoogle ScholarPubMed
Chiaravalloti, N. D., & DeLuca, J. (2008). Cognitive impairment in multiple sclerosis. Lancet Neurology, 7(12), 1139–1151. doi: 10.1016/s1474-4422(08)70259-x.CrossRefGoogle ScholarPubMed
Delis, D., Kramer, J., Kaplan, E., & Ober, B. (2000). The California verbal learning test (2nd ed.). San Antonio: Psychological Corporation. Adult Version.Google Scholar
Dendrou, C. A., Fugger, L., & Friese, M. A. (2015). Immunopathology of multiple sclerosis. Nature Reviews Immunology, 15(9), 545–558. doi: 10.1038/nri3871.CrossRefGoogle ScholarPubMed
Fiol, M. P., Ysrraelit, M. C., Gaitán, M. I., & Correale, J. (2016). Progressive multiple sclerosis: From pathogenic mechanisms to treatment. Brain, 140(3), 527–546. doi: 10.1093/brain/aww258.Google Scholar
Frau, J., Fenu, G., Signori, A., Coghe, G., Lorefice, L., Barracciu, M. A., … Cocco, E. (2018). A cross-sectional and longitudinal study evaluating brain volumes, RNFL, and cognitive functions in MS patients and healthy controls. BMC Neurology, 18(1), 67. doi: 10.1186/s12883-018-1065-9.CrossRefGoogle ScholarPubMed
Gold, R., Wolinsky, J. S., Amato, M. P., & Comi, G. (2010). Evolving expectations around early management of multiple sclerosis. Therapeutic Advances in Neurological Disorders, 3(6), 351–367. doi: 10.1177/1756285610385608.CrossRefGoogle ScholarPubMed
Hulst, H. E., Geurts, J. J. G., Meijer, K. A., Steenwijk, M. D., Schoonheim, M. M., van Geest, Q., … Uitdehaag, B. M. J. (2018). Predicting cognitive decline in multiple sclerosis: A 5-year follow-up study. Brain, 141(9), 2605–2618. doi: 10.1093/brain/awy202.Google Scholar
IBM. (2011). IBM SPSS statistics for windows (20.0 ed.). Armonk, NY: IBM Corp.Google Scholar
Kalmar, J. H., Gaudino, E. A., Moore, N. B., Halper, J., & Deluca, J. (2008). The relationship between cognitive deficits and everyday functional activities in multiple sclerosis. Neuropsychology, 22(4), 442–449. doi: 10.1037/0894-4105.22.4.442.CrossRefGoogle ScholarPubMed
Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33(11), 1444–1452.CrossRefGoogle Scholar
Matlach, J., Wagner, M., Malzahn, U., & Gobel, W. (2014). Repeatability of peripapillary retinal nerve fiber layer and inner retinal thickness among two spectral domain optical coherence tomography devices. Investigative Ophthalmology & Visual Science, 55(10), 6536–6546. doi: 10.1167/iovs.14-15072.CrossRefGoogle ScholarPubMed
Mattioli, F., Stampatori, C., Zanotti, D., Parrinello, G., & Capra, R. (2010). Efficacy and specificity of intensive cognitive rehabilitation of attention and executive functions in multiple sclerosis. Journal of the Neurological Sciences, 288(1–2), 101–105. doi: 10.1016/j.jns.2009.09.024.Google ScholarPubMed
Medaglia, J. D., Pasqualetti, F., Hamilton, R. H., Thompson-Schill, S. L., & Bassett, D. S. (2017). Brain and cognitive reserve: Translation via network control theory. Neuroscience & Biobehavioral Reviews, 75, 53–64. doi: 10.1016/j.neubiorev.2017.01.016.CrossRefGoogle ScholarPubMed
Moccia, M., Lanzillo, R., Palladino, R., Chang, K. C., Costabile, T., Russo, C., … Brescia Morra, V. (2016). Cognitive impairment at diagnosis predicts 10-year multiple sclerosis progression. Multiple Sclerosis, 22(5), 659–667. doi: 10.1177/1352458515599075.CrossRefGoogle ScholarPubMed
Muller, S., Saur, R., Greve, B., Melms, A., Hautzinger, M., Fallgatter, A. J., & Leyhe, T. (2013). Similar autobiographical memory impairment in long-term secondary progressive multiple sclerosis and Alzheimer’s disease. Multiple Sclerosis, 19(2), 225–232. doi: 10.1177/1352458512450352.CrossRefGoogle ScholarPubMed
Nutter-Upham, K. E., Saykin, A. J., Rabin, L. A., Roth, R. M., Wishart, H. A., Pare, N., & Flashman, L. A. (2008). Verbal fluency performance in amnestic MCI and older adults with cognitive complaints. Archives of Clinical Neuropsychology, 23(3), 229–241. doi: 10.1016/j.acn.2008.01.005.CrossRefGoogle ScholarPubMed
Pitteri, M., Romualdi, C., Magliozzi, R., Monaco, S., & Calabrese, M. (2017). Cognitive impairment predicts disability progression and cortical thinning in MS: An 8-year study. Multiple Sclerosis, 23(6), 848–854. doi: 10.1177/1352458516665496.CrossRefGoogle Scholar
Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., … Wolinsky, J. S. (2011). Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Annals of Neurology, 69(2), 292–302. doi: 10.1002/ana.22366.CrossRefGoogle ScholarPubMed
Rao, J. A., Jenkins, L. M., Hymen, E., Feigon, M., Weisenbach, S. L., Zubieta, J. K., & Langenecker, S. A. (2016). Differential resting state connectivity patterns and impaired semantically cued list learning test performance in early course remitted major depressive disorder. Journal of the International Neuropsychological Society, 22(2), 225–239. doi: 10.1017/s1355617716000011.CrossRefGoogle ScholarPubMed
Rouleau, I., Dagenais, E., Tremblay, A., Demers, M., Roger, E., Jobin, C., & Duquette, P. (2018). Prospective memory impairment in multiple sclerosis: A review. The Clinical Neuropsychologist, 32(5), 922–936. doi: 10.1080/13854046.2017.1361473.CrossRefGoogle ScholarPubMed
Sicotte, N. L., Kern, K. C., Giesser, B. S., Arshanapalli, A., Schultz, A., Montag, M., … Bookheimer, S. Y. (2008). Regional hippocampal atrophy in multiple sclerosis. Brain, 131(Pt. 4), 1134–1141. doi: 10.1093/brain/awn030.CrossRefGoogle ScholarPubMed
Smith, A. (2002). Symbol digit modalities test: Manual. Los Angeles, CA: Western Psychological Corporation: Western Psychological Services (Firm).Google Scholar
Turken, A., Whitfield-Gabrieli, S., Bammer, R., Baldo, J. V., Dronkers, N. F., & Gabrieli, J. D. (2008). Cognitive processing speed and the structure of white matter pathways: Convergent evidence from normal variation and lesion studies. Neuroimage, 42(2), 1032–1044. doi: 10.1016/j.neuroimage.2008.03.057.CrossRefGoogle ScholarPubMed
Wang, H. X., MacDonald, S. W., & Dekhtyar, S. (2017). Association of lifelong exposure to cognitive reserve-enhancing factors with dementia risk: A community-based cohort study. PLoS Medicine, 14(3), e1002251. doi: 10.1371/journal.pmed.1002251.CrossRefGoogle ScholarPubMed