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Brain microstructure is linked to cognitive fatigue in early multiple sclerosis

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Abstract

Cognitive fatigue is a major symptom of Multiple Sclerosis (MS), from the early stages of the disease. This study aims to detect if brain microstructure is altered early in the disease course and is associated with cognitive fatigue in people with MS (pwMS) compared to matched healthy controls (HC). Recently diagnosed pwMS (N = 18, age < 45 years old) with either a Relapsing–Remitting or a Clinically Isolated Syndrome course of the disease, and HC (N = 19) matched for sex, age and education were analyzed. Quantitative multiparameter maps (MTsat, PD, R1 and R2*) of pwMS and HC were calculated. Parameters were extracted within the normal appearing white matter, cortical grey matter and deep grey matter (NAWM, NACGM and NADGM, respectively). Bayesian T-test for independent samples assessed between-group differences in brain microstructure while associations between score at a cognitive fatigue scale and each parameter in each tissue class were investigated with Generalized Linear Mixed Models. Patients exhibited lower MTsat and R1 values within NAWM and NACGM, and higher R1 values in NADGM compared to HC. Cognitive fatigue was associated with PD measured in every tissue class and to MTsat in NAWM, regardless of group. Disease-specific negative correlations were found in pwMS in NAWM (R1, R2*) and NACGM (R1). These findings suggest that brain microstructure within normal appearing tissues is already altered in the very early stages of the disease. Moreover, additional microstructure alterations (e.g. diffuse and widespread demyelination or axonal degeneration) in pwMS may lead to disease-specific complaint of cognitive fatigue.

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References

  1. Weiland TJ, Jelinek GJ, Marck CH et al (2015) Clinically significant fatigue: prevalence and associated factors in an international sample of adults with multiple sclerosis recruited via the internet. PLoS ONE 10:e0115541. https://doi.org/10.1371/journal.pone.0115541

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Bakshi R, Shaikh ZA, Miletich RS et al (2000) Fatigue in multiple sclerosis and its relationship to depression and neurologic disability. Mult Scler 6:181–185. https://doi.org/10.1177/135245850000600308

    Article  CAS  PubMed  Google Scholar 

  3. Dobryakova E, Genova HM, DeLuca J, Wylie GR (2015) The dopamine imbalance hypothesis of fatigue in multiple sclerosis and other neurological disorders. Front Neurol 6:1–8. https://doi.org/10.3389/fneur.2015.00052

    Article  Google Scholar 

  4. Chaudhuri A, Behan PO (2004) Fatigue in neurological disorders. Lancet 363:978–988. https://doi.org/10.1016/S0140-6736(04)15794-2

    Article  PubMed  Google Scholar 

  5. Chaudhuri A, Behan PO (2000) Fatigue and basal ganglia. J Neurol Sci 179:34–42. https://doi.org/10.1016/s0022-510x(00)00411-1

    Article  CAS  PubMed  Google Scholar 

  6. Arm J, Ribbons K, Lechner-Scott J, Ramadan S (2019) Evaluation of MS related central fatigue using MR neuroimaging methods: scoping review. J Neurol Sci 400:52–71. https://doi.org/10.1016/j.jns.2019.03.007

    Article  PubMed  Google Scholar 

  7. Palotai M, Guttmann CRG (2020) Brain anatomical correlates of fatigue in multiple sclerosis. Mult Scler J 26:751–764. https://doi.org/10.1177/1352458519876032

    Article  Google Scholar 

  8. Sander C, Eling P, Hanken K, Klein J, Kastrup A, Hildebrandt H (2016) The impact of MS-related cognitive fatigue on future brain parenchymal loss and relapse: a 17-month follow-up study. Front Neurol 7:155. https://doi.org/10.3389/fneur.2016.00155

    Article  PubMed  PubMed Central  Google Scholar 

  9. Damasceno A, Damasceno BP, Cendes F (2016) Atrophy of reward-related striatal structures in fatigued MS patients is independent of physical disability. Mult Scler 22:822–829. https://doi.org/10.1177/1352458515599451

    Article  PubMed  Google Scholar 

  10. Lommers E, Simon J, Reuter G et al (2019) Multiparameter MRI quantification of microstructural tissue alterations in multiple sclerosis. NeuroImage Clin 23:101879. https://doi.org/10.1016/j.nicl.2019.101879

    Article  PubMed  PubMed Central  Google Scholar 

  11. Neema M, Stankiewicz J, Arora A et al (2007) T1- and T2-based MRI measures of diffuse gray matter and white matter damage in patients with multiple sclerosis. J Neuroimaging 17:16–21. https://doi.org/10.1111/j.1552-6569.2007.00131.x

    Article  Google Scholar 

  12. Cohen-Adad J (2014) What can we learn from T2* maps of the cortex? Neuroimage 93:189–200. https://doi.org/10.1016/j.neuroimage.2013.01.023

    Article  PubMed  Google Scholar 

  13. Filippi M, Agosta F (2007) Magnetization transfer MRI in multiple sclerosis. J Neuroimaging 17:22–26. https://doi.org/10.1111/j.1552-6569.2007.00132.x

    Article  Google Scholar 

  14. Lommers E, Guillemin C, Reuter G et al (2021) Voxel-based quantitative MRI reveals spatial patterns of grey matter alteration in multiple sclerosis. Hum Brain Mapp 42:1003–1012. https://doi.org/10.1002/hbm.25274

    Article  PubMed  Google Scholar 

  15. Zellini F, Niepel G, Tench CR, Constantinescu CS (2009) Hypothalamic involvement assessed by T1 relaxation time in patients with relapsing–remitting multiple sclerosis. Mult Scler 15:1442–1449. https://doi.org/10.1177/1352458509350306

    Article  PubMed  Google Scholar 

  16. Bonnier G, Roche A, Romascano D et al (2014) Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis. Ann Clin Transl Neurol 1:423–432. https://doi.org/10.1002/acn3.68

    Article  PubMed  PubMed Central  Google Scholar 

  17. Davies GR, Ramio-Torrenta L, Hadjiprocopis A et al (2004) Evidence for grey matter MTR abnormality in minimally disabled patients with early relapsing–remitting multiple sclerosis. J Neurol Neurosurg Psychiatry 75:998–1002. https://doi.org/10.1136/jnnp.2003.021915

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gracien RM, Reitz SC, Hof S-M et al (2016) Assessment of cortical damage in early multiple sclerosis with quantitative T2 relaxometry. NMR Biomed 29:444–450. https://doi.org/10.1002/nbm.3486

    Article  CAS  PubMed  Google Scholar 

  19. Griffin CM, Chard DT, Parker GJ-M, Barker GJ, Thompson AJ, Miller DH (2002) The relationship between lesion and normal appearing brain tissue abnormalities in early relapsing remitting multiple sclerosis. J Neurol 249:193–199. https://doi.org/10.1007/pl00007864

    Article  PubMed  Google Scholar 

  20. Thompson AJ, Banwell BL, Barkhof F et al (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 

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

    Article  CAS  PubMed  Google Scholar 

  22. Penner IK, Raselli C, Stöcklin M, Opwis K, Kappos L, Calabrese P (2009) The fatigue scale for motor and cognitive functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue. Mult Scler 15:1509–1517. https://doi.org/10.1177/1352458509348519

    Article  CAS  PubMed  Google Scholar 

  23. Jamil T, Ly A, Morey RD, Love J, Marsman M, Wagenmakers E-J (2017) Default, “Gunel and Dickey” bayes factors for contingency tables. Behav Res Methods 49:638–652. https://doi.org/10.3758/s13428-016-0739-8

    Article  PubMed  Google Scholar 

  24. Tabelow K, Balteau E, Ashburner J et al (2019) hMRI—a toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage 194:191–210. https://doi.org/10.1016/j.neuroimage.2019.01.029

    Article  PubMed  Google Scholar 

  25. Weiskopf N, Callaghan MF, Josephs O, Lutti A, Mohammadi S (2014) Estimating the apparent transverse relaxation time (R2*) from images with different contrasts (ESTATICS) reduces motion artifacts. Front Neurosci 8:1–10. https://doi.org/10.3389/fnins.2014.00278

    Article  Google Scholar 

  26. Preibisch C, Deichmann R (2009) Influence of RF spoiling on the stability and accuracy of T1 mapping based on spoiled FLASH with varying flip angles. Magn Reson Med 61:125–135. https://doi.org/10.1002/mrm.21776

    Article  CAS  PubMed  Google Scholar 

  27. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26:839–851. https://doi.org/10.1016/j.neuroimage.2005.02.018

    Article  PubMed  Google Scholar 

  28. Phillips C, Lommers E, Pernet C (2017) Unifying lesion masking and tissue probability maps for improved segmentation and normalization. In: 23rd annual meeting of the organization for human brain mapping

  29. Jeffreys H (1961) Theory of probability, 3rd edn. Clarendon Press, Oxford

    Google Scholar 

  30. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300

    Google Scholar 

  31. Jaeger BC, Edwards LJ, Das K, Sen PK (2017) An R2 statistic for fixed effects in the generalized linear mixed model. J Appl Stat 44:1086–1105. https://doi.org/10.1080/02664763.2016.1193725

    Article  Google Scholar 

  32. Stüber C, Morawski M, Schäfer A et al (2014) Myelin and iron concentration in the human brain: a quantitative study of MRI contrast. Neuroimage 93:95–106. https://doi.org/10.1016/j.neuroimage

    Article  PubMed  Google Scholar 

  33. van der Weijden CWJ, García DV, Borra RJH et al (2021) Myelin quantification with MRI: a systematic review of accuracy and reproducibility. Neuroimage 226:117561. https://doi.org/10.1016/j.neuroimage.2020.117561

    Article  PubMed  Google Scholar 

  34. Schmierer K, Tozer DJ, Scaravilli F et al (2007) Quantitative magnetization transfer imaging in postmortem multiple sclerosis brain. J Magn Reson Imaging 26:41–51. https://doi.org/10.1002/jmri.20984

    Article  PubMed  PubMed Central  Google Scholar 

  35. Laule C, Pavlova V, Leung E et al (2013) Diffusely abnormal white matter in multiple sclerosis: further histologic studies provide evidence for a primary lipid abnormality with neurodegeneration. J Neuropathol Exp Neurol 72:42–52. https://doi.org/10.1097/NEN.0b013e31827bced3

    Article  CAS  PubMed  Google Scholar 

  36. Khalil M, Langkammer C, Ropele S et al (2011) Determinants of brain iron in multiple sclerosis: a quantitative 3T MRI study. Neurology 77:1691–1697. https://doi.org/10.1212/WNL.0b013e318236ef0e

    Article  CAS  PubMed  Google Scholar 

  37. Ropele S, Kilsdonk ID, Wattjes MP et al (2014) Determinants of iron accumulation in deep grey matter of multiple sclerosis patients. Mult Scler J 20:1692–1698. https://doi.org/10.1177/1352458514531085

    Article  CAS  Google Scholar 

  38. Lommers E (2019) Multiparameter MRI quantification of microstructural brain alterations in multiple sclerosis. (ULiège–Université de Liège [Applied Sciences])

  39. Elkady AM, Cobzas D, Sun H, Seres P, Blevins G, Wilman AH (2019) Five year iron changes in relapsing–remitting multiple sclerosis deep gray matter compared to healthy controls. Mult Scler Relat Disord 33:107–115. https://doi.org/10.1016/j.msard.2019.05.028

    Article  PubMed  Google Scholar 

  40. Andica C, Hagiwara A, Kamagata K et al (2019) Gray matter alterations in early and late relapsing–remitting multiple sclerosis evaluated with synthetic quantitative magnetic resonance imaging. Sci Rep 9:1–10. https://doi.org/10.1038/s41598-019-44615-3

    Article  CAS  Google Scholar 

  41. Brass SD, Chen N, Mulkern RV, Bakshi R (2006) Magnetic resonance imaging of iron deposition in neurological disorders. Top Magn Reson Imaging 17:31–40. https://doi.org/10.1097/01.rmr.0000245459.82782.e4

    Article  PubMed  Google Scholar 

  42. Hernández-Torres E, Wiggermann V, Machan L et al (2019) Increased mean R2* in the deep gray matter of multiple sclerosis patients: have we been measuring atrophy? J Magn Reson Imaging 50:201–208. https://doi.org/10.1002/jmri.26561

    Article  PubMed  Google Scholar 

  43. Fields RD (2008) White matter in learning, cognition and psychiatric disorders. Trends Neurosci 31:361–370. https://doi.org/10.1016/j.tins.2008.04.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Buyanova IS, Arsalidou M (2021) Cerebral white matter myelination and relations to age, gender, and cognition: a selective review. Front Hum Neurosci 15:1–22. https://doi.org/10.3389/fnhum.2021.662031

    Article  Google Scholar 

  45. Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D et al (2020) Whitepaper: defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer’s Dement 16:1305–1311. https://doi.org/10.1016/j.jalz.2018.07.219

    Article  Google Scholar 

  46. Galland-Decker C, Marques-Vidal P, Vollenweider P (2019) Prevalence and factors associated with fatigue in the Lausanne middle-aged population: a population-based, cross-sectional survey. BMJ Open 9:1–10. https://doi.org/10.1136/bmjopen-2018-027070

    Article  Google Scholar 

  47. Penner IK, Paul F (2017) Fatigue as a symptom or comorbidity of neurological diseases. Nat Rev Neurol 13:662–675. https://doi.org/10.1038/nrneurol.2017.117

    Article  PubMed  Google Scholar 

  48. Tardy AL, Pouteau E, Marquez D, Yilmaz C, Scholey A (2020) Vitamins and minerals for energy, fatigue and cognition: a narrative review of the biochemical and clinical evidence. Nutrients 16:228. https://doi.org/10.3390/nu12010228

    Article  CAS  Google Scholar 

  49. Haß U, Herpich C, Norman K (2019) Anti-inflammatory diets and fatigue. Nutrients 30:2315. https://doi.org/10.3390/nu11102315

    Article  CAS  Google Scholar 

  50. Pelletier A, Barul C, Féart C et al (2015) Mediterranean diet and preserved brain structural connectivity in older subjects. Alzheimers Dement 11:1023–1031. https://doi.org/10.1016/j.jalz.2015.06.1888

    Article  PubMed  Google Scholar 

  51. Torres-Velázquez M, Sawin EA, Anderson JM, Yu JJ (2019) Refractory diet-dependent changes in neural microstructure: implications for microstructural endophenotypes of neurologic and psychiatric disease. Magn Reson Imaging 58:148–155. https://doi.org/10.1016/j.mri.2019.02.006

    Article  PubMed  PubMed Central  Google Scholar 

  52. Hechenberger S, Helmlinger B, Penner IK et al (2023) Psychological factors and brain magnetic resonance imaging metrics associated with fatigue in persons with multiple sclerosis. J Neurol Sci 15:120833. https://doi.org/10.1016/j.jns.2023.120833

    Article  Google Scholar 

  53. Tarasiuk J, Kapica-Topczewska K, Czarnowska A, Chorązy M, Kochanowicz J, Kułakowska A (2022) Co-occurrence of fatigue and depression in people with multiple sclerosis: a mini-review. Front Neurol 12:1–8. https://doi.org/10.3389/fneur.2021.817256

    Article  Google Scholar 

  54. Palotai M, Cavallari M, Koubiyr I et al (2020) Microstructural fronto-striatal and temporo-insular alterations are associated with fatigue in patients with multiple sclerosis independent of white matter lesion load and depression. Mult Scler 26:1708–1718. https://doi.org/10.1177/1352458519869185

    Article  CAS  PubMed  Google Scholar 

  55. Biasi MM, Manni A, Pepe I et al (2023) Impact of depression on the perception of fatigue and information processing speed in a cohort of multiple sclerosis patients. BMC Psychol 11:208. https://doi.org/10.1186/s40359-023-01235-x

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bagnato F, Hametner S, Boyd E et al (2018) Untangling the R2* contrast in multiple sclerosis: a combined MRI-histology study at 7.0 tesla. PLoS ONE 13:e0193839. https://doi.org/10.1371/journal.pone.0193839

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Hametner S, Endmayr V, Deistung A et al (2018) The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation—a biochemical and histological validation study. Neuroimage 179:117–133. https://doi.org/10.1016/j.neuroimage.2018.06.007

    Article  CAS  PubMed  Google Scholar 

  58. Stankiewicz JM, Neema M, Ceccarelli A (2014) Iron and multiple sclerosis. Neurobiol Aging 35:S51–S58. https://doi.org/10.1016/j.neurobiolaging.2014.03.039

    Article  CAS  PubMed  Google Scholar 

  59. Capone F, Collorone S, Cortese R, Di Lazzaro V, Moccia M (2020) Fatigue in multiple sclerosis: the role of thalamus. Mult Scler J 26:6–16. https://doi.org/10.1177/1352458519851247

    Article  Google Scholar 

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Acknowledgements

This work was conducted at the GIGA-CRC In vivo Imaging platform of ULiège, Belgium. We want to thank all the participants who took part of the present study. This work was supported by Fonds National de la Recherche Scientifique (FNRS), University of Liège and the Fauconnier and Sallets Fund from the King Baudouin Fundation (KBS-FRB). C.G. was supported by University of Liège. N.V., C.P., F.C. were supported by Fonds National de la Recherche Scientifique (FRS-FNRS), Belgium. None of the funding sources had an involvement in study design; in the collection, analysis, interpretation of data; in the writing of the report; in the decision to submit the article for publication.

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Fonds De La Recherche Scientifique—FNRS, Koning Boudewijnstichting, Université de Liège

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  1. Camille Guillemin and Nora Vandeleene contributed equally to this work.

  2. Christophe Phillips and Fabienne Collette contributed equally to this work.

Authors and Affiliations

  1. GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium

    Camille Guillemin, Nora Vandeleene, Maëlle Charonitis, Florence Requier, Emilie Lommers, Pierre Maquet, Christophe Phillips & Fabienne Collette

  2. Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium

    Camille Guillemin, Maëlle Charonitis, Florence Requier & Fabienne Collette

  3. Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium

    Gaël Delrue, Emilie Lommers & Pierre Maquet

  4. GIGA In Silico Medicine, University of Liège, Liège, Belgium

    Christophe Phillips

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  1. Camille Guillemin
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Guillemin, C., Vandeleene, N., Charonitis, M. et al. Brain microstructure is linked to cognitive fatigue in early multiple sclerosis. J Neurol (2024). https://doi.org/10.1007/s00415-024-12316-1

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