Brain microstructure is linked to cognitive fatigue in early multiple sclerosis.

Clinically isolated syndrome Cognitive fatigue Early multiple sclerosis Fatigue Quantitative MRI

Journal

Journal of neurology
ISSN: 1432-1459
Titre abrégé: J Neurol
Pays: Germany
ID NLM: 0423161

Informations de publication

Date de publication:
28 Mar 2024
Historique:
received: 29 12 2023
accepted: 08 03 2024
revised: 08 03 2024
medline: 28 3 2024
pubmed: 28 3 2024
entrez: 28 3 2024
Statut: aheadofprint

Résumé

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.

Identifiants

pubmed: 38538776
doi: 10.1007/s00415-024-12316-1
pii: 10.1007/s00415-024-12316-1
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.

Références

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
doi: 10.1371/journal.pone.0115541 pubmed: 25692993 pmcid: 4333355
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
doi: 10.1177/135245850000600308 pubmed: 10871830
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
doi: 10.3389/fneur.2015.00052
Chaudhuri A, Behan PO (2004) Fatigue in neurological disorders. Lancet 363:978–988. https://doi.org/10.1016/S0140-6736(04)15794-2
doi: 10.1016/S0140-6736(04)15794-2 pubmed: 15043967
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
doi: 10.1016/s0022-510x(00)00411-1 pubmed: 11054483
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
doi: 10.1016/j.jns.2019.03.007 pubmed: 30903860
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
doi: 10.1177/1352458519876032
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
doi: 10.3389/fneur.2016.00155 pubmed: 27708613 pmcid: 5030297
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
doi: 10.1177/1352458515599451 pubmed: 26238465
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
doi: 10.1016/j.nicl.2019.101879 pubmed: 31176293 pmcid: 6555891
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
doi: 10.1111/j.1552-6569.2007.00131.x
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
doi: 10.1016/j.neuroimage.2013.01.023 pubmed: 23357070
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
doi: 10.1111/j.1552-6569.2007.00132.x
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
doi: 10.1002/hbm.25274 pubmed: 33155763
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
doi: 10.1177/1352458509350306 pubmed: 19995847
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
doi: 10.1002/acn3.68 pubmed: 25356412 pmcid: 4184670
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
doi: 10.1136/jnnp.2003.021915 pubmed: 15201359 pmcid: 1739100
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
doi: 10.1002/nbm.3486 pubmed: 26820580
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
doi: 10.1007/pl00007864 pubmed: 11985386
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
doi: 10.1016/S1474-4422(17)30470-2 pubmed: 29275977
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
doi: 10.1212/wnl.33.11.1444 pubmed: 6685237
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
doi: 10.1177/1352458509348519 pubmed: 19995840
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
doi: 10.3758/s13428-016-0739-8 pubmed: 27325166
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
doi: 10.1016/j.neuroimage.2019.01.029 pubmed: 30677501
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
doi: 10.3389/fnins.2014.00278
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
doi: 10.1002/mrm.21776 pubmed: 19097220
Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26:839–851. https://doi.org/10.1016/j.neuroimage.2005.02.018
doi: 10.1016/j.neuroimage.2005.02.018 pubmed: 15955494
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
Jeffreys H (1961) Theory of probability, 3rd edn. Clarendon Press, Oxford
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
Jaeger BC, Edwards LJ, Das K, Sen PK (2017) An R
doi: 10.1080/02664763.2016.1193725
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
doi: 10.1016/j.neuroimage pubmed: 24607447
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
doi: 10.1016/j.neuroimage.2020.117561 pubmed: 33189927
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
doi: 10.1002/jmri.20984 pubmed: 17659567 pmcid: 2063415
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
doi: 10.1097/NEN.0b013e31827bced3 pubmed: 23242281
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
doi: 10.1212/WNL.0b013e318236ef0e pubmed: 21975210
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
doi: 10.1177/1352458514531085
Lommers E (2019) Multiparameter MRI quantification of microstructural brain alterations in multiple sclerosis. (ULiège–Université de Liège [Applied Sciences])
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
doi: 10.1016/j.msard.2019.05.028 pubmed: 31181540
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
doi: 10.1038/s41598-019-44615-3
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
doi: 10.1097/01.rmr.0000245459.82782.e4 pubmed: 17179895
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
doi: 10.1002/jmri.26561 pubmed: 30511803
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
doi: 10.1016/j.tins.2008.04.001 pubmed: 18538868 pmcid: 2486416
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
doi: 10.3389/fnhum.2021.662031
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
doi: 10.1016/j.jalz.2018.07.219
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
doi: 10.1136/bmjopen-2018-027070
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
doi: 10.1038/nrneurol.2017.117 pubmed: 29027539
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
doi: 10.3390/nu12010228
Haß U, Herpich C, Norman K (2019) Anti-inflammatory diets and fatigue. Nutrients 30:2315. https://doi.org/10.3390/nu11102315
doi: 10.3390/nu11102315
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
doi: 10.1016/j.jalz.2015.06.1888 pubmed: 26190494
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
doi: 10.1016/j.mri.2019.02.006 pubmed: 30776455 pmcid: 6477923
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
doi: 10.1016/j.jns.2023.120833
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
doi: 10.3389/fneur.2021.817256
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
doi: 10.1177/1352458519869185 pubmed: 31418637
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
doi: 10.1186/s40359-023-01235-x pubmed: 37452373 pmcid: 10349468
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
doi: 10.1371/journal.pone.0193839 pubmed: 29561895 pmcid: 5862438
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
doi: 10.1016/j.neuroimage.2018.06.007 pubmed: 29890327
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
doi: 10.1016/j.neurobiolaging.2014.03.039 pubmed: 24929968
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
doi: 10.1177/1352458519851247

Auteurs

Camille Guillemin (C)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.
Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.

Nora Vandeleene (N)

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

Maëlle Charonitis (M)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.
Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.

Florence Requier (F)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.
Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.

Gaël Delrue (G)

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

Emilie Lommers (E)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.
Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium.

Pierre Maquet (P)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.
Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium.

Christophe Phillips (C)

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

Fabienne Collette (F)

GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium. f.collette@uliege.be.
Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium. f.collette@uliege.be.

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