Published in:

26-11-2023 | Multiple Sclerosis | Original Communication

Cognitive phenotypes in multiple sclerosis: mapping the spectrum of impairment

Authors: Damiano Mistri, Nicolò Tedone, Diana Biondi, Carmen Vizzino, Elisabetta Pagani, Maria A. Rocca, Massimo Filippi

Published in: Journal of Neurology | Issue 4/2024

Abstract

Background

Available criteria for cognitive phenotypes in multiple sclerosis (MS) do not consider the severity of impairment.

Objectives

To identify cognitive phenotypes with varying degrees of impairment in MS patients and describe their demographic, clinical and MRI characteristics.

Methods

Two hundred and forty-three MS patients and 158 healthy controls underwent neuropsychological tests to assess memory, attention, and executive function. For each domain, mild impairment was defined as performing 1.5 standard deviations below the normative mean on two tests, while the threshold for significant impairment was 2 standard deviations. Patients were classified into cognitive phenotypes based on severity of the impairment (mild/significant) and number of domains affected (one/more).

Results

Five cognitive phenotypes emerged: Preserved cognition (PC; 56%), Mild Single-Domain Impairment (MSD; 15%), Mild Multi-Domain Impairment (MMD; 9%), Significant Single-Domain Impairment (SSD; 12%), Significant Multi-Domain Impairment (SMD; 8%). Compared with PC, MSD patients were older, had longer disease duration (DD) and higher T2-hyperintense lesion volume (LV; all p ≤ 0.02); MMD patients were older, had longer DD, higher disability, higher T2 LV and lower thalamic volume (all p ≤ 0.01); SSD patients had longer DD and lower gray matter cortical volume, thalamic, caudate, putamen and accumbens volumes (all p ≤ 0.04); and SMD patients were older, had longer DD, higher disability and more extensive structural damage in all brain regions explored (all p ≤ 0.03), except white matter and amygdala volumes.

Conclusions

We identified five cognitive phenotypes with graded levels of impairment. These phenotypes were characterized by distinct demographic, clinical and MRI features, indicating potential variations in the neural substrates of dysfunction throughout disease stages.

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Rocca MA, Amato MP, De Stefano N, Enzinger C, Geurts JJ, Penner IK, Rovira A et al (2015) Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol 14(3):302–317. https://doi.org/10.1016/S1474-4422(14)70250-9CrossRefPubMed

Amato MP, Portaccio E, Goretti B, Zipoli V, Hakiki B, Giannini M, Pastò L et al (2010) Cognitive impairment in early stages of multiple sclerosis. Neurol Sci 31(S2):211–214. https://doi.org/10.1007/s10072-010-0376-4CrossRef

Benedict RHB, Amato MP, DeLuca J, Geurts JJG (2020) Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues. Lancet Neurol 19(10):860–871. https://doi.org/10.1016/s1474-4422(20)30277-5CrossRefPubMedPubMedCentral

Marchesi O, Bonacchi R, Valsasina P, Preziosa P, Pagani E, Cacciaguerra L, Meani A et al (2021) Functional and structural MRI correlates of executive functions in multiple sclerosis. Mult Scler J 28(5):742–756. https://doi.org/10.1177/13524585211033184CrossRef

Chalah MA, Ayache SS (2017) Deficits in social cognition: an unveiled signature of multiple sclerosis. J Int Neuropsychol Soc 23(3):266–286. https://doi.org/10.1017/s1355617716001156CrossRefPubMed

Achiron A, Polliack M, Rao SM, Barak Y, Lavie M, Appelboim N, Harel Y (2005) Cognitive patterns and progression in multiple sclerosis: construction and validation of percentile curves. J Neurol Neurosurg Psychiatry 76(5):744–749. https://doi.org/10.1136/jnnp.2004.045518CrossRefPubMedPubMedCentral

De Meo E, Portaccio E, Giorgio A, Ruano L, Goretti B, Niccolai C, Patti F et al (2021) Identifying the distinct cognitive phenotypes in multiple sclerosis. JAMA Neurol 78(4):414. https://doi.org/10.1001/jamaneurol.2020.4920CrossRefPubMed

Amato MP, Ponziani G, Siracusa G, Sorbi S (2001) Cognitive dysfunction in early-onset multiple sclerosis: a reappraisal after 10 years. Arch Neurol 58(10):1602–1606. https://doi.org/10.1001/archneur.58.10.1602CrossRefPubMed

Wojcik C, Fuchs TA, Tran H, Dwyer MG, Jakimovski D, Unverdi M, Weinstock-Guttman B et al (2022) Staging and stratifying cognitive dysfunction in multiple sclerosis. Mult Scler 28(3):463–471. https://doi.org/10.1177/13524585211011390CrossRefPubMed

10.

Deloire MS, Ruet A, Hamel D, Bonnet M, Dousset V, Brochet B (2011) MRI predictors of cognitive outcome in early multiple sclerosis. Neurology 76(13):1161–1167. https://doi.org/10.1212/WNL.0b013e318212a8beCrossRefPubMed

11.

Feuillet L, Reuter F, Audoin B, Malikova I, Barrau K, Cherif AA, Pelletier J (2007) Early cognitive impairment in patients with clinically isolated syndrome suggestive of multiple sclerosis. Mult Scler 13(1):124–127. https://doi.org/10.1177/1352458506071196CrossRefPubMed

12.

Schulz D, Kopp B, Kunkel A, Faiss JH (2006) Cognition in the early stage of multiple sclerosis. J Neurol 253(8):1002–1010. https://doi.org/10.1007/s00415-006-0145-8CrossRefPubMed

13.

Podda J, Ponzio M, Pedullà L, Monti Bragadin M, Battaglia MA, Zaratin P, Brichetto G et al (2021) Predominant cognitive phenotypes in multiple sclerosis: Insights from patient-centered outcomes. Multiple Scler Relat Disord 51:102919. https://doi.org/10.1016/j.msard.2021.102919CrossRef

14.

Hancock LM, Galioto R, Samsonov A, Busch RM, Hermann B, Matias-Guiu JA (2022) A proposed new taxonomy of cognitive phenotypes in multiple sclerosis: The International Classification of Cognitive Disorders in MS (IC-CoDiMS). Mult Scler J. https://doi.org/10.1177/13524585221127941CrossRef

15.

Leavitt VM, Tosto G, Riley CS (2018) Cognitive phenotypes in multiple sclerosis. J Neurol 265(3):562–566. https://doi.org/10.1007/s00415-018-8747-5CrossRefPubMed

16.

Preziosa P, Rocca MA, Pagani E, Stromillo ML, Enzinger C, Gallo A, Hulst HE et al (2016) Structural MRI correlates of cognitive impairment in patients with multiple sclerosis. Hum Brain Mapp 37(4):1627–1644. https://doi.org/10.1002/hbm.23125CrossRefPubMedPubMedCentral

17.

Rocca MA, Valsasina P, Leavitt VM, Rodegher M, Radaelli M, Riccitelli GC, Martinelli V et al (2017) Functional network connectivity abnormalities in multiple sclerosis: correlations with disability and cognitive impairment. Mult Scler J 24(4):459–471. https://doi.org/10.1177/1352458517699875CrossRef

18.

Diagnostic and statistical manual of mental disorders: DSM-5™. 5th edition. DSM-5. 2013, American Psychiatric Publishing, a division of American Psychiatric Association, Washington, DC

19.

Sachdev PS, Blacker D, Blazer DG, Ganguli M, Jeste DV, Paulsen JS, Petersen RC (2014) Classifying neurocognitive disorders: the DSM-5 approach. Nat Rev Neurol 10(11):634–642. https://doi.org/10.1038/nrneurol.2014.181CrossRefPubMed

20.

Hancock LM, Hermann B, Schoonheim MM, Hetzel SJ, Brochet B, DeLuca J (2022) Comparing diagnostic criteria for the diagnosis of neurocognitive disorders in multiple sclerosis. Mult Scler Relat Disord 58:103479. https://doi.org/10.1016/j.msard.2021.103479CrossRefPubMed

21.

Eijlers AJC, Meijer KA, Wassenaar TM, Steenwijk MD, Uitdehaag BMJ, Barkhof F, Wink AM et al (2017) Increased default-mode network centrality in cognitively impaired multiple sclerosis patients. Neurology 88(10):952–960. https://doi.org/10.1212/wnl.0000000000003689CrossRefPubMed

22.

Hawley CJ, Gale TM, Sivakumaran T (2002) Defining remission by cut off score on the MADRS: selecting the optimal value. J Affect Disord 72(2):177–184. https://doi.org/10.1016/s0165-0327(01)00451-7CrossRefPubMed

23.

Amato MP, Portaccio E, Goretti B, Zipoli V, Ricchiuti L, De Caro MF, Patti F et al (2006) The Rao’s Brief Repeatable Battery and Stroop Test: normative values with age, education and gender corrections in an Italian population. Mult Scler 12(6):787–793. https://doi.org/10.1177/1352458506070933CrossRefPubMed

24.

Heaton RK, Chelune GJ, Talley JL, Kay GG, Curtiss G (1993) Wisconsin card sorting test manual: revised and expanded. Psychological Assessment Resources Inc, Odessa

25.

Amato M, Morra V, Falautano M, Ghezzi A, Goretti B, Patti F, Riccardi A et al (2018) Cognitive assessment in multiple sclerosis-an Italian consensus. Neurol Sci 39(8):1317–1324. https://doi.org/10.1007/s10072-018-3427-xCrossRefPubMed

26.

Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33(11):1444–1452. https://doi.org/10.1212/wnl.33.11.1444CrossRefPubMed

27.

Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302. https://doi.org/10.1002/ana.22366CrossRefPubMedPubMedCentral

28.

Debernard L, Melzer TR, Van Stockum S, Graham C, Wheeler-Kingshott CA, Dalrymple-Alford JC, Miller DH et al (2013) Reduced grey matter perfusion without volume loss in early relapsing-remitting multiple sclerosis. J Neurol Neurosurg Psychiatry 85(5):544–551. https://doi.org/10.1136/jnnp-2013-305612CrossRefPubMed

29.

Fischer M, Kunkel A, Bublak P, Faiss JH, Hoffmann F, Sailer M, Schwab 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):91–99. https://doi.org/10.1016/j.jns.2014.05.042CrossRefPubMed

30.

Valverde S, Cabezas M, Roura E, Gonzalez-Villa S, Pareto D, Vilanova JC, Ramio-Torrenta L et al (2017) Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. Neuroimage 155:159–168. https://doi.org/10.1016/j.neuroimage.2017.04.034CrossRefPubMed

31.

Battaglini M, Jenkinson M, De Stefano N (2012) Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 33(9):2062–2071. https://doi.org/10.1002/hbm.21344CrossRefPubMed

32.

Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56(3):907–922. https://doi.org/10.1016/j.neuroimage.2011.02.046CrossRefPubMed

33.

Eshaghi A, Marinescu RV, Young AL, Firth NC, Prados F, Jorge Cardoso M, Tur C et al (2018) Progression of regional grey matter atrophy in multiple sclerosis. Brain 141(6):1665–1677. https://doi.org/10.1093/brain/awy088CrossRefPubMedPubMedCentral

34.

Rocca MA, Riccitelli GC, Meani A, Pagani E, Del Sette P, Martinelli V, Comi G et al (2018) Cognitive reserve, cognition, and regional brain damage in MS: a 2-year longitudinal study. Mult Scler J 25(3):372–381. https://doi.org/10.1177/1352458517750767CrossRef

35.

Aktas O, Achiron A, Chapman J, Magalashvili D, Dolev M, Lavie M, Bercovich E et al (2013) Modeling of cognitive impairment by disease duration in multiple sclerosis: a cross-sectional study. PLoS One 8(8):e71058. https://doi.org/10.1371/journal.pone.0071058CrossRef

36.

Ruano L, Portaccio E, Goretti B, Niccolai C, Severo M, Patti F, Cilia S et al (2017) Age and disability drive cognitive impairment in multiple sclerosis across disease subtypes. Mult Scler J 23(9):1258–1267. https://doi.org/10.1177/1352458516674367CrossRef

37.

Sacco R, Bisecco A, Corbo D, Della Corte M, d’Ambrosio A, Docimo R, Gallo A 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):1691–1697. https://doi.org/10.1007/s00415-015-7763-yCrossRefPubMed

38.

Summers M, Swanton J, Fernando K, Dalton C, Miller DH, Cipolotti L, Ron MA (2008) Cognitive impairment in multiple sclerosis can be predicted by imaging early in the disease. J Neurol Neurosurg Psychiatry 79(8):955–958. https://doi.org/10.1136/jnnp.2007.138685CrossRefPubMed

39.

Geurts JJG, Schoonheim MM, Douw L, Steenwijk MD, Meijer KA (2020) Long-range connections are more severely damaged and relevant for cognition in multiple sclerosis. Brain 143(1):150–160. https://doi.org/10.1093/brain/awz355CrossRefPubMed

40.

Brochet B, Ruet A (2019) Cognitive impairment in multiple sclerosis with regards to disease duration and clinical phenotypes. Front Neurol. https://doi.org/10.3389/fneur.2019.00261CrossRefPubMedPubMedCentral

41.

Saalmann YB, Kastner S (2015) The cognitive thalamus. Front Syst Neurosci. https://doi.org/10.3389/fnsys.2015.00039CrossRefPubMedPubMedCentral

42.

Houtchens MK, Benedict RHB, Killiany R, Sharma J, Jaisani Z, Singh B, Weinstock-Guttman B et al (2007) Thalamic atrophy and cognition in multiple sclerosis. Neurology 69(12):1213–1223. https://doi.org/10.1212/01.wnl.0000276992.17011.b5CrossRefPubMed

43.

Bisecco A, Capuano R, Caiazzo G, d’Ambrosio A, Docimo R, Cirillo M, Russo A et al (2019) Regional changes in thalamic shape and volume are related to cognitive performance in multiple sclerosis. Mult Scler J 27(1):134–138. https://doi.org/10.1177/1352458519892552CrossRef

44.

Minagar A, Barnett MH, Benedict RHB, Pelletier D, Pirko I, Sahraian MA, Frohman E et al (2013) The thalamus and multiple sclerosis: modern views on pathologic, imaging, and clinical aspects. Neurology 80(2):210–219. https://doi.org/10.1212/WNL.0b013e31827b910bCrossRefPubMedPubMedCentral

45.

Pettigrew C, Soldan A (2019) Defining cognitive reserve and implications for cognitive aging. Curr Neurol Neurosci Rep. https://doi.org/10.1007/s11910-019-0917-zCrossRefPubMedPubMedCentral

46.

Eijlers AJC, Dekker I, Steenwijk MD, Meijer KA, Hulst HE, Pouwels PJW, Uitdehaag BMJ et al (2019) Cortical atrophy accelerates as cognitive decline worsens in multiple sclerosis. Neurology 93(14):e1348–e1359. https://doi.org/10.1212/wnl.0000000000008198CrossRefPubMed

47.

Randolph JJ, Wishart HA, Saykin AJ, McDonald BC, Schuschu KR, Macdonald JW, Mamourian AC et al (2005) FLAIR lesion volume in multiple sclerosis: relation to processing speed and verbal memory. J Int Neuropsychol Soc. https://doi.org/10.1017/s1355617705050253CrossRefPubMed

48.

Preziosa P, Pagani E, Mesaros S, Riccitelli GC, Dackovic J, Drulovic J, Filippi M et al (2017) Progression of regional atrophy in the left hemisphere contributes to clinical and cognitive deterioration in multiple sclerosis: a 5-year study. Hum Brain Mapp 38(11):5648–5665. https://doi.org/10.1002/hbm.23755CrossRefPubMedPubMedCentral

49.

Azzimonti M, Preziosa P, Pagani E, Valsasina P, Tedone N, Vizzino C, Rocca MA et al (2023) Functional and structural brain MRI changes associated with cognitive worsening in multiple sclerosis: a 3-year longitudinal study. J Neurol. https://doi.org/10.1007/s00415-023-11778-zCrossRefPubMed

50.

Kalb R, Beier M, Benedict RH, Charvet L, Costello K, Feinstein A, Gingold J et al (2018) Recommendations for cognitive screening and management in multiple sclerosis care. Mult Scler 24(13):1665–1680. https://doi.org/10.1177/1352458518803785CrossRefPubMedPubMedCentral

51.

Sandry J, Simonet DV, Brandstadter R, Krieger S, Katz Sand I, Graney RA, Buchanan AV 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 51:102950. https://doi.org/10.1016/j.msard.2021.102950CrossRefPubMed

52.

Tombaugh T (2006) A comprehensive review of the paced auditory serial addition test (PASAT). Arch Clin Neuropsychol 21(1):53–76. https://doi.org/10.1016/j.acn.2005.07.006CrossRefPubMed

53.

Benedict RHB, DeLuca J, Phillips G, LaRocca N, Hudson LD, Rudick R (2017) Validity of the symbol digit modalities test as a cognition performance outcome measure for multiple sclerosis. Mult Scler J 23(5):721–733. https://doi.org/10.1177/1352458517690821CrossRef

Title: Cognitive phenotypes in multiple sclerosis: mapping the spectrum of impairment
Authors: Damiano Mistri
Nicolò Tedone
Diana Biondi
Carmen Vizzino
Elisabetta Pagani
Maria A. Rocca
Massimo Filippi
Publication date: 26-11-2023
Publisher: Springer Berlin Heidelberg
Keyword: Multiple Sclerosis
Published in: Journal of Neurology / Issue 4/2024
Print ISSN: 0340-5354
Electronic ISSN: 1432-1459
DOI: https://doi.org/10.1007/s00415-023-12102-5

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Abstract

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