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Open Access 30-01-2024 | Multiple Sclerosis | Original Communication

Isolated cognitive impairment in people with multiple sclerosis: frequency, MRI patterns and its development over time

Authors: Piet M. Bouman, Maureen A. van Dam, Laura E. Jonkman, Martijn D. Steenwijk, Menno M. Schoonheim, Jeroen J. G. Geurts, Hanneke E. Hulst

Published in: Journal of Neurology | Issue 5/2024

Abstract

Objectives

To study the frequency of isolated (i.e., single-domain) cognitive impairments, domain specific MRI correlates, and its longitudinal development in people with multiple sclerosis (PwMS).

Methods

348 PwMS (mean age 48 ± 11 years, 67% female, 244RR/52SP/38PP) underwent neuropsychological testing (extended BRB-N) at baseline and at five-year follow-up. At baseline, structural MRI was acquired. Isolated cognitive impairment was defined as a Z-score of at least 1.5 SD below normative data in one domain only (processing speed, memory, executive functioning/working memory, and attention). Multi-domain cognitive impairment was defined as being affected in ≥ 2 domains, and cognitively preserved otherwise. For PwMS with isolated cognitive impairment, MRI correlates were explored using linear regression. Development of isolated cognitive impairment over time was evaluated based on reliable change index.

Results

At baseline, 108 (31%) PwMS displayed isolated cognitive impairment, 148 (43%) PwMS displayed multi-domain cognitive impairment. Most PwMS with isolated cognitive impairment were impaired on executive functioning/working memory (EF/WM; N = 37), followed by processing speed (IPS; N = 25), memory (N = 23), and attention (N = 23). Isolated IPS impairment was explained by a model of cortical volume and fractional anisotropy (adj. R² = 0.539, p < 0.001); memory by a model with cortical volume and hippocampal volume (adj. R² = 0.493, p = 0.002); EF/WM and attention were not associated with any MRI measure. At follow-up, cognitive decline was present in 11/16 (69%) of PwMS with isolated IPS impairment at baseline. This percentage varied between 18 and 31% of PwMS with isolated cognitive impairment in domains other than IPS at baseline.

Conclusion

Isolated cognitive impairment is frequently present in PwMS and can serve as a proxy for further decline, particularly when it concerns processing speed. Cortical and deep grey matter atrophy seem to play a pivotal role in isolated cognitive impairment. Timely detection and patient-tailored intervention, predominantly for IPS, may help to postpone further cognitive decline.

Benedict R, Amato MP, DeLuca J, Geurts JJG (2020) Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues. Lancet Neurol 19(10):860–871PubMedPubMedCentral

Kalb R et al (2018) Recommendations for cognitive screening and management in multiple sclerosis care. Mult Scler 24(13):1665–1680PubMedPubMedCentral

Sumowski JF et al (2018) Cognition in multiple sclerosis: State of the field and priorities for the future. Neurology 90(6):278–288PubMedPubMedCentral

Wojcik C et al (2022) Staging and stratifying cognitive dysfunction in multiple sclerosis. Mult Scler 28(3):463–471PubMed

Calabrese M et al (2009) Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol 66(9):1144–1150PubMed

Curti E et al (2018) Correlation between cortical lesions and cognitive impairment in multiple sclerosis. Brain Behav 8(6):e00955PubMedPubMedCentral

Daams M et al (2016) Multi-parametric structural magnetic resonance imaging in relation to cognitive dysfunction in long-standing multiple sclerosis. Mult Scler 22(5):608–619PubMed

Eijlers AJC et al (2018) Predicting cognitive decline in multiple sclerosis: a 5-year follow-up study. Brain 141(9):2605–2618PubMed

Benedict RH, Zivadinov R (2011) Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nat Rev Neurol 7:332–342PubMed

10.

Damasceno A, Pimentel-Silva LR, Damasceno BP, Cendes F (2020) Cognitive trajectories in relapsing-remitting multiple sclerosis: a longitudinal 6-year study. Mult Scler 26(13):1740–1751PubMed

11.

Polman CH et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302PubMedPubMedCentral

12.

Steenwijk MD et al (2014) What explains gray matter atrophy in long-standing multiple sclerosis? Radiology 272(3):832–842PubMed

13.

Schoonheim MM et al (2015) Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis. Neurology 84(8):776–783PubMed

14.

Eijlers AJC et al (2018) Determinants of cognitive impairment in patients with multiple sclerosis with and without atrophy. Radiology 288(2):544–551PubMed

15.

Eijlers AJC et al (2017) Increased default-mode network centrality in cognitively impaired multiple sclerosis patients. Neurology 88:952–960PubMed

16.

Meijer KA et al (2017) Increased connectivity of hub networks and cognitive impairment in multiple sclerosis. Neurology 88:2107–2114PubMed

17.

Verhage F (1964) Intelligentie En Leeftijd: Onderzoek bij Nederlanders Van Twaalf Tot Zevenenzeventig Jaar. Van Gorcum, Assen

18.

Kurtzke JF (1983) An expanded disability status scale (EDSS). Neurology 33:1444–1452PubMed

19.

Rao SM (1990) A manual for the brief repeatable battery of neuropsychological tests in multiple sclerosis. Medical College of Wisconsin, Milwaukee

20.

Buschke H (1973) Selective reminding for analysis of memory and learning. J Verbal Learn Verbal Behav 12:543–550

21.

Smith A (1982) The symbol digits modalities test manual, revised. Western Psychological Services, Los Angeles

22.

Stroop JR (1992) Studies of interference in serial verbal reactions. J Exp Psychol Gen 121:15–23

23.

Kane MJ, Engle RW (2003) Working-memory capacity and the control of attention: the contributions of goal neglect, response competition, and task set to Stroop interference. J Exp Psychol Gen 132(1):47–70PubMed

24.

Brueggemann P et al (2021) On the relationship between tinnitus distress, cognitive performance and aging. Prog Brain Res 262:263–285PubMed

25.

Jensen AR, Rohwer WD Jr (1966) The Stroop color-word test: a review. Acta Psychol (Amst) 25(1):36–93PubMed

26.

Van der Elst W, Van Boxtel MP, Breukelen GJ, Jolles J (2006) The concept shifting test: adult normative data. Psychol Assess 18:424–432PubMed

27.

Brand N, Jolles J (1987) Information processing in depression and anxiety. Psychol Med 17:263–267

28.

Benedict R (2009) Standards for sample composition and impairment classification in neuropsychological studies of multiple sclerosis. Mult Scler 15(7):777–778PubMed

29.

Fischer M et al (2014) How reliable is the classification of cognitive impairment across different criteria in early and late stages of multiple sclerosis? J Neurol Sci 343(1–2):91–99PubMed

30.

Weinstock Z et al (2021) Interpreting change on the Symbol Digit Modalities Test in people with relapsing multiple sclerosis using the reliable change methodology. Mult Scler 28(7):1101–1111. https://doi.org/10.1177/13524585211049397

31.

Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56(3):907–922PubMed

32.

Smith SM et al (2002) Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17(1):479–489PubMed

33.

Geurts JJG et al (2011) Consensus recommendations for MS cortical lesion detection scoring using double inversion recovery MRI. Neurology 76(5):418–424PubMed

34.

Podda J et al (2021) Predominant cognitive phenotypes in multiple sclerosis: Insights from patient-centered outcomes. Mult Scler Relat Disord 51:102919PubMed

35.

De Meo E et al (2021) Identifying the distinct cognitive phenotypes in multiple sclerosis. JAMA Neurol 78(4):414–425PubMed

36.

Schoonheim MM et al (2014) Sex-specific extent and severity of white matter damage in multiple sclerosis: implications for cognitive decline. Hum Brain Mapp 35(5):2348–2358PubMed

37.

Rocca MA et al (2018) The hippocampus in multiple sclerosis. Lancet Neurol 17(10):918–926PubMed

38.

Sacco R et al (2015) Cognitive impairment and memory disorders in relapsing-remitting multiple sclerosis: the role of white matter, gray matter and hippocampus. J Neurol 262(7):1691–1697PubMed

39.

Tremblay A et al (2018) Thalamic and hippocampal volume associated with memory functions in multiple sclerosis. Brain Cogn 125:61–68PubMed

40.

Faria CA, Alves HVD, Charchat-Fichman H (2015) The most frequently used tests for assessing executive functions in aging. Dement Neuropsychol 9(2):149–155PubMedPubMedCentral

41.

Marchesi O et al (2022) Functional and structural MRI correlates of executive functions in multiple sclerosis. Mult Scler 28(5):742–756PubMed

42.

Bryan J, Luszcz MA (2000) Measurement of executive function: considerations for detecting adult age differences. J Clin Exp Neuropsychol 22(1):40–55PubMed

43.

Bergsland N et al (2016) Localized atrophy of the thalamus and slowed cognitive processing speed in MS patients. Mult Scler 22(10):1327–1336PubMed

44.

Carotenuto A et al (2022) Cognitive trajectories in multiple sclerosis: a long-term follow-up study. Neurol Sci 43(2):1215–1222PubMed

45.

Colato E et al (2021) Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes. J Neurol Neurosurg Psychiatry 92(9):995–1006PubMed

46.

Sandry J et al (2021) The Symbol Digit Modalities Test (SDMT) is sensitive but non-specific in MS: lexical access speed, memory, and information processing speed independently contribute to SDMT performance. Mult Scler Relat Disord 51:102950PubMed

47.

Van Schependom J et al (2014) The Symbol Digit Modalities Test as sentinel test for cognitive impairment in multiple sclerosis. Eur J Neurol 21(9):1219–1225 (e71–2)PubMed

48.

Prouskas SE et al (2022) A randomized trial predicting response to cognitive rehabilitation in multiple sclerosis: is there a window of opportunity? Mult Scler 28(13):2124–2136. https://doi.org/10.1177/13524585221103134

49.

Chiaravalloti ND, DeLuca J (2008) Cognitive impairment in multiple sclerosis. Lancet Neurol 7:1139–1151PubMed

50.

Langdon DW et al (2012) Recommendations for a brief international cognitive assessment for multiple sclerosis (BICAMS). Mult Scler 18(6):891–898PubMedPubMedCentral

51.

Gromisch ES, Portnoy JG, Foley FW (2018) Comparison of the abbreviated minimal assessment of cognitive function in multiple sclerosis (aMACFIMS) and the brief international cognitive assessment for multiple sclerosis (BICAMS). J Neurol Sci 388:70–75PubMed

52.

van Dongen L et al (2020) Introducing multiple screener: an unsupervised digital screening tool for cognitive deficits in MS. Mult Scler Relat Disord 38:101479PubMed

Title: Isolated cognitive impairment in people with multiple sclerosis: frequency, MRI patterns and its development over time
Authors: Piet M. Bouman
Maureen A. van Dam
Laura E. Jonkman
Martijn D. Steenwijk
Menno M. Schoonheim
Jeroen J. G. Geurts
Hanneke E. Hulst
Publication date: 30-01-2024
Publisher: Springer Berlin Heidelberg
Keyword: Multiple Sclerosis
Published in: Journal of Neurology / Issue 5/2024
Print ISSN: 0340-5354
Electronic ISSN: 1432-1459
DOI: https://doi.org/10.1007/s00415-024-12185-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Isolated cognitive impairment in people with multiple sclerosis: frequency, MRI patterns and its development over time

Abstract

Objectives

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

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