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Spinal cord and brain atrophy patterns in neuromyelitis optica spectrum disorder and multiple sclerosis

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Abstract

Background

Spinal cord and brain atrophy are common in neuromyelitis optica spectrum disorder (NMOSD) and relapsing-remitting multiple sclerosis (RRMS) but harbor distinct patterns accounting for disability and cognitive impairment.

Methods

This study included 209 NMOSD and 304 RRMS patients and 436 healthy controls. Non-negative matrix factorization was used to parse differences in spinal cord and brain atrophy at subject level into distinct patterns based on structural MRI. The weights of patterns were obtained using a linear regression model and associated with Expanded Disability Status Scale (EDSS) and cognitive scores. Additionally, patients were divided into cognitive impairment (CI) and cognitive preservation (CP) groups.

Results

Three patterns were observed in NMOSD: (1) Spinal Cord-Deep Grey Matter (SC-DGM) pattern was associated with high EDSS scores and decline of visuospatial memory function; (2) Frontal-Temporal pattern was associated with decline of language learning function; and (3) Cerebellum-Brainstem pattern had no observed association. Patients with CI had higher weights of SC-DGM pattern than CP group. Three patterns were observed in RRMS: (1) DGM pattern was associated with high EDSS scores, decreased information processing speed, and decreased language learning and visuospatial memory functions; (2) Frontal-Temporal pattern was associated with overall cognitive decline; and (3) Occipital pattern had no observed association. Patients with CI trended to have higher weights of DGM and Frontal-Temporal patterns than CP group.

Conclusion

This study estimated the heterogeneity of spinal cord and brain atrophy patterns in NMOSD and RRMS patients at individual level, and evaluated the clinical relevance of these patterns, which may contribute to stratifying participants for targeted therapy.

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Data availability

The data can be made available upon reasonable request by a qualified researcher.

References

  1. Reich DS, Lucchinetti CF, Calabresi PA (2018) Multiple sclerosis. N Engl J Med 378:169–180. https://doi.org/10.1056/NEJMra1401483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Carnero Contentti E, Correale J (2021) Neuromyelitis optica spectrum disorders: from pathophysiology to therapeutic strategies. J Neuroinflammation 18:208. https://doi.org/10.1186/s12974-021-02249-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Jarius S, Paul F, Weinshenker BG, Levy M, Kim HJ, Wildemann B (2020) Neuromyelitis optica. Nat Rev Dis Primers 6:85. https://doi.org/10.1038/s41572-020-0214-9

    Article  PubMed  Google Scholar 

  4. Jarius S, Ruprecht K, Kleiter I, Borisow N, Asgari N, Pitarokoili K, Pache F, Stich O, Beume LA, Hümmert MW, Trebst C, Ringelstein M, Aktas O, Winkelmann A, Buttmann M, Schwarz A, Zimmermann H, Brandt AU, Franciotta D, Capobianco M, Kuchling J, Haas J, Korporal-Kuhnke M, Lillevang ST, Fechner K, Schanda K, Paul F, Wildemann B, Reindl M (2016) MOG-IgG in NMO and related disorders: a multicenter study of 50 patients. Part 1: frequency, syndrome specificity, influence of disease activity, long-term course, association with AQP4-IgG, and origin. J Neuroinflammation 13:279. https://doi.org/10.1186/s12974-016-0717-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Miller DH, Barkhof F, Frank JA, Parker GJ, Thompson AJ (2002) Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. Brain 125:1676–1695. https://doi.org/10.1093/brain/awf177

    Article  PubMed  Google Scholar 

  6. Sastre-Garriga J, Pareto D, Battaglini M, Rocca MA, Ciccarelli O, Enzinger C, Wuerfel J, Sormani MP, Barkhof F, Yousry TA, De Stefano N, Tintoré M, Filippi M, Gasperini C, Kappos L, Río J, Frederiksen J, Palace J, Vrenken H, Montalban X, Rovira À (2020) MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat Rev Neurol 16:171–182. https://doi.org/10.1038/s41582-020-0314-x

    Article  PubMed  PubMed Central  Google Scholar 

  7. Liu Y, Wang J, Daams M, Weiler F, Hahn HK, Duan Y, Huang J, Ren Z, Ye J, Dong H, Vrenken H, Wattjes MP, Shi FD, Li K, Barkhof F (2015) Differential patterns of spinal cord and brain atrophy in NMO and MS. Neurology 84:1465–1472. https://doi.org/10.1212/wnl.0000000000001441

    Article  PubMed  Google Scholar 

  8. Duan Y, Liu Y, Liang P, Jia X, Yu C, Qin W, Sun H, Liao Z, Ye J, Li K (2012) Comparison of grey matter atrophy between patients with neuromyelitis optica and multiple sclerosis: a voxel-based morphometry study. Eur J Radiol 81:e110-114. https://doi.org/10.1016/j.ejrad.2011.01.065

    Article  PubMed  Google Scholar 

  9. Colato E, Stutters J, Tur C, Narayanan S, Arnold DL, Gandini Wheeler-Kingshott CAM, Barkhof F, Ciccarelli O, Chard DT, Eshaghi A (2021) Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes. J Neurol Neurosurg Psychiatry 92:995–1006. https://doi.org/10.1136/jnnp-2020-325610

    Article  PubMed  Google Scholar 

  10. Steenwijk MD, Geurts JJ, Daams M, Tijms BM, Wink AM, Balk LJ, Tewarie PK, Uitdehaag BM, Barkhof F, Vrenken H, Pouwels PJ (2016) Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain 139:115–126. https://doi.org/10.1093/brain/awv337

    Article  PubMed  Google Scholar 

  11. Bergsland N, Horakova D, Dwyer MG, Uher T, Vaneckova M, Tyblova M, Seidl Z, Krasensky J, Havrdova E, Zivadinov R (2018) Gray matter atrophy patterns in multiple sclerosis: a 10-year source-based morphometry study. Neuroimage Clin 17:444–451. https://doi.org/10.1016/j.nicl.2017.11.002

    Article  PubMed  Google Scholar 

  12. 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. Mult Scler 23:848–854. https://doi.org/10.1177/1352458516665496

    Article  PubMed  Google Scholar 

  13. Ma X, Kermode AG, Hu X, Qiu W (2020) Risk of relapse in patients with neuromyelitis optica spectrum disorder: recognition and preventive strategy. Mult Scler Relat Disord 46:102522. https://doi.org/10.1016/j.msard.2020.102522

    Article  PubMed  Google Scholar 

  14. Ciccarelli O, Cohen JA, Reingold SC, Weinshenker BG (2019) Spinal cord involvement in multiple sclerosis and neuromyelitis optica spectrum disorders. Lancet Neurol 18:185–197. https://doi.org/10.1016/s1474-4422(18)30460-5

    Article  PubMed  Google Scholar 

  15. Chien C, Scheel M, Schmitz-Hübsch T, Borisow N, Ruprecht K, Bellmann-Strobl J, Paul F, Brandt AU (2019) Spinal cord lesions and atrophy in NMOSD with AQP4-IgG and MOG-IgG associated autoimmunity. Mult Scler 25:1926–1936. https://doi.org/10.1177/1352458518815596

    Article  CAS  PubMed  Google Scholar 

  16. Tsagkas C, Parmar K, Pezold S, Barro C, Chakravarty MM, Gaetano L, Naegelin Y, Amann M, Papadopoulou A, Wuerfel J, Kappos L, Kuhle J, Sprenger T, Granziera C, Magon S (2021) Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance. Hum Brain Mapp 42:2399–2415. https://doi.org/10.1002/hbm.25375

    Article  PubMed  PubMed Central  Google Scholar 

  17. Liu Y, Duan Y, Huang J, Ren Z, Liu Z, Dong H, Weiler F, Hahn HK, Shi FD, Butzkueven H, Barkhof F, Li K (2018) Different patterns of longitudinal brain and spinal cord changes and their associations with disability progression in NMO and MS. Eur Radiol 28:96–103. https://doi.org/10.1007/s00330-017-4921-x

    Article  PubMed  Google Scholar 

  18. Hyun JW, Park G, Kwak K, Jo HJ, Joung A, Kim JH, Lee SH, Kim S, Lee JM, Kim SH, Kim HJ (2017) Deep gray matter atrophy in neuromyelitis optica spectrum disorder and multiple sclerosis. Eur J Neurol 24:437–445. https://doi.org/10.1111/ene.13224

    Article  PubMed  Google Scholar 

  19. D’Ambrosio A, Pagani E, Riccitelli GC, Colombo B, Rodegher M, Falini A, Comi G, Filippi M, Rocca MA (2017) Cerebellar contribution to motor and cognitive performance in multiple sclerosis: an MRI sub-regional volumetric analysis. Mult Scler 23:1194–1203. https://doi.org/10.1177/1352458516674567

    Article  PubMed  Google Scholar 

  20. Schoonheim MM, Douw L, Broeders TA, Eijlers AJ, Meijer KA, Geurts JJ (2021) The cerebellum and its network: disrupted static and dynamic functional connectivity patterns and cognitive impairment in multiple sclerosis. Mult Scler 27:2031–2039. https://doi.org/10.1177/1352458521999274

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tona F, De Giglio L, Petsas N, Sbardella E, Prosperini L, Upadhyay N, Giannì C, Pozzilli C, Pantano P (2018) Role of cerebellar dentate functional connectivity in balance deficits in patients with multiple sclerosis. Radiology 287:267–275. https://doi.org/10.1148/radiol.2017170311

    Article  PubMed  Google Scholar 

  22. Liu Y, Jiang X, Butzkueven H, Duan Y, Huang J, Ren Z, Dong H, Shi FD, Barkhof F, Li K, Wang J (2018) Multimodal characterization of gray matter alterations in neuromyelitis optica. Mult Scler 24:1308–1316. https://doi.org/10.1177/1352458517721053

    Article  PubMed  Google Scholar 

  23. Han Y, Liu Y, Zeng C, Luo Q, Xiong H, Zhang X, Li Y (2020) Functional connectivity alterations in neuromyelitis optica spectrum disorder: correlation with disease duration and cognitive impairment. Clin Neuroradiol 30:559–568. https://doi.org/10.1007/s00062-019-00802-3

    Article  PubMed  Google Scholar 

  24. Yang Y, Li J, Li T, Li Z, Zhuo Z, Han X, Duan Y, Cao G, Zheng F, Tian D, Wang X, Zhang X, Li K, Zhou F, Huang M, Li Y, Li H, Li Y, Zeng C, Zhang N, Sun J, Yu C, Shi F, Asgher U, Muhlert N, Liu Y, Wang J (2023) Cerebellar connectome alterations and associated genetic signatures in multiple sclerosis and neuromyelitis optica spectrum disorder. J Transl Med 21:352. https://doi.org/10.1186/s12967-023-04164-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Eshaghi A, Young AL, Wijeratne PA, Prados F, Arnold DL, Narayanan S, Guttmann CRG, Barkhof F, Alexander DC, Thompson AJ, Chard D, Ciccarelli O (2021) Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun 12:2078. https://doi.org/10.1038/s41467-021-22265-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhuo Z, Li Y, Duan Y, Cao G, Zheng F, Ding J, Tian D, Wang X, Wang J, Zhang X, Li K, Zhou F, Huang M, Li Y, Li H, Zeng C, Zhang N, Sun J, Yu C, Han X, Haller S, Barkhof F, Shi F, Liu Y (2021) Subtyping relapsing-remitting multiple sclerosis using structural MRI. J Neurol 268:1808–1817. https://doi.org/10.1007/s00415-020-10376-7

    Article  PubMed  Google Scholar 

  27. Pontillo G, Penna S, Cocozza S, Quarantelli M, Gravina M, Lanzillo R, Marrone S, Costabile T, Inglese M, Morra VB, Riccio D, Elefante A, Petracca M, Sansone C, Brunetti A (2022) Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach. Eur Radiol 32:5382–5391. https://doi.org/10.1007/s00330-022-08610-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wolfers T, Doan NT, Kaufmann T, Alnæs D, Moberget T, Agartz I, Buitelaar JK, Ueland T, Melle I, Franke B, Andreassen OA, Beckmann CF, Westlye LT, Marquand AF (2018) Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiat 75:1146–1155. https://doi.org/10.1001/jamapsychiatry.2018.2467

    Article  Google Scholar 

  29. Rocca MA, Valsasina P, Meani A, Gobbi C, Zecca C, Rovira A, Sastre-Garriga J, Kearney H, Ciccarelli O, Matthews L, Palace J, Gallo A, Bisecco A, Lukas C, Bellenberg B, Barkhof F, Vrenken H, Preziosa P, Filippi M (2021) Association of gray matter atrophy patterns with clinical phenotype and progression in multiple sclerosis. Neurology 96:e1561–e1573. https://doi.org/10.1212/wnl.0000000000011494

    Article  CAS  PubMed  Google Scholar 

  30. Sotiras A, Resnick SM, Davatzikos C (2015) Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization. Neuroimage 108:1–16. https://doi.org/10.1016/j.neuroimage.2014.11.045

    Article  PubMed  Google Scholar 

  31. Chen P, Yao H, Tijms BM, Wang P, Wang D, Song C, Yang H, Zhang Z, Zhao K, Qu Y, Kang X, Du K, Fan L, Han T, Yu C, Zhang X, Jiang T, Zhou Y, Lu J, Han Y, Liu B, Zhou B, Liu Y (2023) Four distinct subtypes of Alzheimer’s disease based on resting-state connectivity biomarkers. Biol Psychiatry 93:759–769. https://doi.org/10.1016/j.biopsych.2022.06.019

    Article  CAS  PubMed  Google Scholar 

  32. Karan B, Sahu SS, Orozco-Arroyave JR, Mahto K (2021) Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson’s disease prediction. Comput Speech Lang 69:101216. https://doi.org/10.1016/j.csl.2021.101216

    Article  Google Scholar 

  33. Zhao S, Ji W, Shen Y, Fan Y, Huang H, Huang J, Lai G, Yuan K, Cheng C (2022) Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing. BMC Cancer 22:1274. https://doi.org/10.1186/s12885-022-10305-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wingerchuk DM, Banwell B, Bennett JL, Cabre P, Carroll W, Chitnis T, de Seze J, Fujihara K, Greenberg B, Jacob A, Jarius S, Lana-Peixoto M, Levy M, Simon JH, Tenembaum S, Traboulsee AL, Waters P, Wellik KE, Weinshenker BG (2015) International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology 85:177–189. https://doi.org/10.1212/wnl.0000000000001729

    Article  PubMed  PubMed Central  Google Scholar 

  35. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, Correale J, Fazekas F, Filippi M, Freedman MS, Fujihara K, Galetta SL, Hartung HP, Kappos L, Lublin FD, Marrie RA, Miller AE, Miller DH, Montalban X, Mowry EM, Sorensen PS, Tintoré M, Traboulsee AL, Trojano M, Uitdehaag BMJ, Vukusic S, Waubant E, Weinshenker BG, Reingold SC, Cohen JA (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17:162–173. https://doi.org/10.1016/s1474-4422(17)30470-2

    Article  PubMed  Google Scholar 

  36. Rudick RA, Kappos L (2010) Measuring disability in relapsing-remitting MS. Neurology 75:296–297. https://doi.org/10.1212/WNL.0b013e3181ecf815

    Article  PubMed  Google Scholar 

  37. Boringa JB, Lazeron RH, Reuling IE, Adèr HJ, Pfennings L, Lindeboom J, de Sonneville LM, Kalkers NF, Polman CH (2001) The brief repeatable battery of neuropsychological tests: normative values allow application in multiple sclerosis clinical practice. Mult Scler 7:263–267. https://doi.org/10.1177/135245850100700409

    Article  CAS  PubMed  Google Scholar 

  38. Mesaros S, Rocca MA, Kacar K, Kostic J, Copetti M, Stosic-Opincal T, Preziosa P, Sala S, Riccitelli G, Horsfield MA, Drulovic J, Comi G, Filippi M (2012) Diffusion tensor MRI tractography and cognitive impairment in multiple sclerosis. Neurology 78:969–975. https://doi.org/10.1212/WNL.0b013e31824d5859

    Article  CAS  PubMed  Google Scholar 

  39. Liu Y, Fu Y, Schoonheim MM, Zhang N, Fan M, Su L, Shen Y, Yan Y, Yang L, Wang Q, Zhang N, Yu C, Barkhof F, Shi FD (2015) Structural MRI substrates of cognitive impairment in neuromyelitis optica. Neurology 85:1491–1499. https://doi.org/10.1212/wnl.0000000000002067

    Article  PubMed  Google Scholar 

  40. Ten Kate M, Dicks E, Visser PJ, van der Flier WM, Teunissen CE, Barkhof F, Scheltens P, Tijms BM (2018) Atrophy subtypes in prodromal Alzheimer’s disease are associated with cognitive decline. Brain 141:3443–3456. https://doi.org/10.1093/brain/awy264

    Article  PubMed  PubMed Central  Google Scholar 

  41. Brunet JP, Tamayo P, Golub TR, Mesirov JP (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci U S A 101:4164–4169. https://doi.org/10.1073/pnas.0308531101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cacciaguerra L, Valsasina P, Mesaros S, Martinelli V, Drulovic J, Filippi M, Rocca MA (2020) Spinal cord atrophy in neuromyelitis optica spectrum disorders is spatially related to cord lesions and disability. Radiology 297:154–163. https://doi.org/10.1148/radiol.2020192664

    Article  PubMed  Google Scholar 

  43. Duan Y, Liu Y, Liang P, Jia X, Ye J, Dong H, Li K (2014) White matter atrophy in brain of neuromyelitis optica: a voxel-based morphometry study. Acta Radiol 55:589–593. https://doi.org/10.1177/0284185113501815

    Article  PubMed  Google Scholar 

  44. Kawachi I, Lassmann H (2017) Neurodegeneration in multiple sclerosis and neuromyelitis optica. J Neurol Neurosurg Psychiatry 88:137–145. https://doi.org/10.1136/jnnp-2016-313300

    Article  PubMed  Google Scholar 

  45. Pittock SJ, Weinshenker BG, Lucchinetti CF, Wingerchuk DM, Corboy JR, Lennon VA (2006) Neuromyelitis optica brain lesions localized at sites of high aquaporin 4 expression. Arch Neurol 63:964–968. https://doi.org/10.1001/archneur.63.7.964

    Article  PubMed  Google Scholar 

  46. Masuda H, Mori M, Hirano S, Uzawa A, Uchida T, Muto M, Ohtani R, Aoki R, Kuwabara S (2022) Silent progression of brain atrophy in aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder. J Neurol Neurosurg Psychiatry 93:32–40. https://doi.org/10.1136/jnnp-2021-326386

    Article  PubMed  Google Scholar 

  47. Lucchinetti CF, Guo Y, Popescu BF, Fujihara K, Itoyama Y, Misu T (2014) The pathology of an autoimmune astrocytopathy: lessons learned from neuromyelitis optica. Brain Pathol 24:83–97. https://doi.org/10.1111/bpa.12099

    Article  CAS  PubMed  Google Scholar 

  48. Nico B, Ribatti D, Frigeri A, Nicchia GP, Corsi P, Svelto M, Roncali L (2002) Aquaporin-4 expression during development of the cerebellum. Cerebellum 1:207–212. https://doi.org/10.1080/14734220260418439

    Article  CAS  PubMed  Google Scholar 

  49. Matthews PM (2019) Chronic inflammation in multiple sclerosis - seeing what was always there. Nat Rev Neurol 15:582–593. https://doi.org/10.1038/s41582-019-0240-y

    Article  CAS  PubMed  Google Scholar 

  50. Minagar A, Barnett MH, Benedict RH, Pelletier D, Pirko I, Sahraian MA, Frohman E, Zivadinov R (2013) The thalamus and multiple sclerosis: modern views on pathologic, imaging, and clinical aspects. Neurology 80:210–219. https://doi.org/10.1212/WNL.0b013e31827b910b

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Abdolalizadeh A, Mohammadi S, Aarabi MH (2022) The forgotten tract of vision in multiple sclerosis: vertical occipital fasciculus, its fiber properties, and visuospatial memory. Brain Struct Funct 227:1479–1490. https://doi.org/10.1007/s00429-022-02464-3

    Article  PubMed  Google Scholar 

  52. Kim SM, Waters P, Woodhall M, Kim YJ, Kim JA, Cheon SY, Lee S, Jo SR, Kim DG, Jung KC, Lee KW, Sung JJ, Park KS (2017) Gender effect on neuromyelitis optica spectrum disorder with aquaporin4-immunoglobulin G. Mult Scler 23:1104–1111. https://doi.org/10.1177/1352458516674366

    Article  CAS  PubMed  Google Scholar 

  53. Ruggieri S, Petracca M, Miller A, Krieger S, Ghassemi R, Bencosme Y, Riley C, Howard J, Lublin F, Inglese M (2015) Association of deep gray matter damage with cortical and spinal cord degeneration in primary progressive multiple sclerosis. JAMA Neurol 72:1466–1474. https://doi.org/10.1001/jamaneurol.2015.1897

    Article  PubMed  Google Scholar 

  54. Wolff M, Vann SD (2019) The cognitive thalamus as a gateway to mental representations. J Neurosci 39:3–14. https://doi.org/10.1523/jneurosci.0479-18.2018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Cacciaguerra L, Valsasina P, Meani A, Riccitelli GC, Radaelli M, Rocca MA, Filippi M (2021) Volume of hippocampal subfields and cognitive deficits in neuromyelitis optica spectrum disorders. Eur J Neurol 28:4167–4177. https://doi.org/10.1111/ene.15073

    Article  PubMed  Google Scholar 

  56. Lee CY, Mak HK, Chiu PW, Chang HC, Barkhof F, Chan KH (2018) Differential brainstem atrophy patterns in multiple sclerosis and neuromyelitis optica spectrum disorders. J Magn Reson Imaging 47:1601–1609. https://doi.org/10.1002/jmri.25866

    Article  PubMed  Google Scholar 

  57. Weier K, Eshaghi A, Magon S, Andelova M, Radue EW, Kappos L, Azimi AR, Sahraian MA, Sprenger T (2015) The role of cerebellar abnormalities in neuromyelitis optica–a comparison with multiple sclerosis and healthy controls. Mult Scler 21:757–766. https://doi.org/10.1177/1352458514554051

    Article  PubMed  Google Scholar 

  58. Magon S, Tsagkas C, Gaetano L, Patel R, Naegelin Y, Amann M, Parmar K, Papadopoulou A, Wuerfel J, Stippich C, Kappos L, Chakravarty MM, Sprenger T (2020) Volume loss in the deep gray matter and thalamic subnuclei: a longitudinal study on disability progression in multiple sclerosis. J Neurol 267:1536–1546. https://doi.org/10.1007/s00415-020-09740-4

    Article  CAS  PubMed  Google Scholar 

  59. Bonnet MC, Deloire MS, Salort E, Dousset V, Petry KG, Brochet B (2006) Evidence of cognitive compensation associated with educational level in early relapsing-remitting multiple sclerosis. J Neurol Sci 251:23–28. https://doi.org/10.1016/j.jns.2006.08.002

    Article  PubMed  Google Scholar 

  60. Borghi M, Cavallo M, Carletto S, Ostacoli L, Zuffranieri M, Picci RL, Scavelli F, Johnston H, Furlan PM, Bertolotto A, Malucchi S (2013) Presence and significant determinants of cognitive impairment in a large sample of patients with multiple sclerosis. PLoS ONE 8:e69820. https://doi.org/10.1371/journal.pone.0069820

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Chiaravalloti ND, DeLuca J (2008) Cognitive impairment in multiple sclerosis. Lancet Neurol 7:1139–1151. https://doi.org/10.1016/s1474-4422(08)70259-x

    Article  PubMed  Google Scholar 

  62. Dong X, Xu G, Wang J, Yin N, Meng N (2022) Clinical and MRI predictors of cognitive decline in patients with relapsing-remitting multiple sclerosis: a 2-year longitudinal study. Mult Scler Relat Disord 65:103838. https://doi.org/10.1016/j.msard.2022.103838

    Article  PubMed  Google Scholar 

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Acknowledgements

We acknowledge the contribution of colleagues and patients who participated in this multicenter study.

Funding

National Science Foundation of China, Grant/Award Numbers: 82202084; Beijing Hospital Management Center Young Talents, Grant/Award Numbers: QML20210505; Young Scientists Program of Beijing Tiantan Hospital, Capital Medical University; National Science Foundation of China, Grant/Award Numbers: 82330057.

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Yaou Liu was the guarantor of integrity of the entire study and contributed to study concepts; Tiantian Hua and Houyou Fan contributed to study design, statistical analyses, and manuscript preparation; Yaou Liu, Tiantian Hua, Zhizheng Zhuo, Houyou Fan, Zhenpeng Chen, and Yutong Bai contributed to definition of intellectual content; Yunyun Duan created the lesion mask. Tiantian Hua, Zhizheng Zhuo, and Yaou Liu contributed to literature research; Xiaolu Xu, Yuna Li, Ningnannan Zhang, Jie Sun, Haiqing Li, Yuxin Li, Yongmei Li, Chun Zeng, Xuemei Han, Fuqing Zhou, Muhua Huang, Siyao Xu, Ying Jin, and Hongfang Li, contributed to data acquisition; Zhizheng Zhuo and Houyou Fan contributed to data analysis; Tiantian Hua, and Zhizheng Zhuo contributed to manuscript editing; Yaou Liu and Xinghu Zhang contributed to manuscript review.

Corresponding author

Correspondence to Yaou Liu.

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The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Tiantian Hua, Houyou Fan, Yunyun Duan, Decai Tian, Zhenpeng Chen, Xiaolu Xu, Yutong Bai, Yuna Li, Ningnannan Zhang, Jie Sun, Haiqing Li, Yuxin Li, Yongmei Li, Chun Zeng, Xuemei Han, Fuqing Zhou, Muhua Huang, Siyao Xu, Ying Jin, Hongfang Li, Zhizheng Zhuo, Xinghu Zhang, and Yaou Liu declare that there is no conflict of interest.

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Hua, T., Fan, H., Duan, Y. et al. Spinal cord and brain atrophy patterns in neuromyelitis optica spectrum disorder and multiple sclerosis. J Neurol (2024). https://doi.org/10.1007/s00415-024-12281-9

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