Global functional connectivity reorganization reflects cognitive processing speed deficits and fatigue in multiple sclerosis
Jazyk angličtina Země Velká Británie, Anglie Médium print-electronic
Typ dokumentu časopisecké články
Grantová podpora
260648/SVV/2024
Univerzita Karlova v Praze
programme Cooperatio (Neuroscience)
Univerzita Karlova v Praze
CZ.02.01.01/00/22_008/0004643
European Regional Development Fund
PubMed
39058296
PubMed Central
PMC11622266
DOI
10.1111/ene.16421
Knihovny.cz E-zdroje
- Klíčová slova
- biomarkers, cognitive processing speed, fMRI, fatigue, multiple sclerosis,
- MeSH
- dospělí MeSH
- kognitivní dysfunkce etiologie patofyziologie diagnostické zobrazování MeSH
- lidé středního věku MeSH
- lidé MeSH
- magnetická rezonanční tomografie * MeSH
- průřezové studie MeSH
- roztroušená skleróza * komplikace diagnostické zobrazování patofyziologie MeSH
- rychlost zpracování MeSH
- únava * patofyziologie etiologie diagnostické zobrazování MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND AND PURPOSE: Cognitive impairment (CI) in multiple sclerosis (MS) is associated with bidirectional changes in resting-state centrality measures. However, practicable functional magnetic resonance imaging (fMRI) biomarkers of CI are still lacking. The aim of this study was to assess the graph-theory-based degree rank order disruption index (kD) and its association with cognitive processing speed as a marker of CI in patients with MS (PwMS) in a secondary cross-sectional fMRI analysis. METHODS: Differentiation between PwMS and healthy controls (HCs) using kD and its correlation with CI (Symbol Digit Modalities Test) was compared to established imaging biomarkers (regional degree, volumetry, diffusion-weighted imaging, lesion mapping). Additional associations were assessed for fatigue (Fatigue Scale for Motor and Cognitive Functions), gait and global disability. RESULTS: Analysis in 56 PwMS and 58 HCs (35/27 women, median age 45.1/40.5 years) showed lower kD in PwMS than in HCs (median -0.30/-0.06, interquartile range 0.55/0.54; p = 0.009, Mann-Whitney U test), yielding acceptable yet non-superior differentiation (area under curve 0.64). kD and degree in medial prefrontal cortex (MPFC) correlated with CI (kD/MPFC Spearman's ρ = 0.32/-0.45, p = 0.019/0.001, n = 55). kD also explained fatigue (ρ = -0.34, p = 0.010, n = 56) but neither gait nor disability. CONCLUSIONS: kD is a potential biomarker of CI and fatigue warranting further validation.
Department of Cognitive Neuroscience Radboud University Medical Centre Nijmegen The Netherlands
Department of Neurology Faculty of Medicine and Dentistry Palacký University Olomouc Olomouc Czechia
Department of Neurology University Medicine Greifswald Greifswald Germany
Department of Rehabilitation 3rd Faculty of Medicine Charles University Prague Czechia
Zobrazit více v PubMed
Benedict RHB, Amato MP, DeLuca J, Geurts JJG. Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues. Lancet Neurol. 2020;19(10):860‐871. doi:10.1016/S1474-4422(20)30277-5 PubMed DOI PMC
Sumowski JF, Benedict R, Enzinger C, et al. Cognition in multiple sclerosis: state of the field and priorities for the future. Neurology. 2018;90(6):278‐288. doi:10.1212/WNL.0000000000004977 PubMed DOI PMC
Chard DT, Alahmadi AAS, Audoin B, et al. Mind the gap: from neurons to networks to outcomes in multiple sclerosis. Nat Rev Neurol. 2021;17(3):173‐184. doi:10.1038/s41582-020-00439-8 PubMed DOI
Rocca MA, Valsasina P, Meani A, Falini A, Comi G, Filippi M. Impaired functional integration in multiple sclerosis: a graph theory study. Brain Struct Funct. 2016;221(1):115‐131. doi:10.1007/s00429-014-0896-4 PubMed DOI
Carotenuto A, Valsasina P, Schoonheim MM, et al. Investigating functional network abnormalities and associations with disability in multiple sclerosis. Neurology. 2022;99(22):e2517‐e2530. doi:10.1212/WNL.0000000000201264 PubMed DOI
Dekker I, Schoonheim MM, Venkatraghavan V, et al. The sequence of structural, functional and cognitive changes in multiple sclerosis. Neuroimage Clin. 2021;29:102550. doi:10.1016/j.nicl.2020.102550 PubMed DOI PMC
Eijlers AJC, Wink AM, Meijer KA, Douw L, Geurts JJG, Schoonheim MM. Reduced network dynamics on functional MRI signals cognitive impairment in multiple sclerosis. Radiology. 2019;292(2):449‐457. doi:10.1148/radiol.2019182623 PubMed DOI
Eijlers AJC, Meijer KA, Wassenaar TM, et al. Increased default‐mode network centrality in cognitively impaired multiple sclerosis patients. Neurology. 2017;88(10):952‐960. doi:10.1212/WNL.0000000000003689 PubMed DOI
Huiskamp M, Eijlers AJC, Broeders TAA, et al. Longitudinal network changes and conversion to cognitive impairment in multiple sclerosis. Neurology. 2021;97(8):e794‐e802. doi:10.1212/WNL.0000000000012341 PubMed DOI PMC
Eijlers AJC, Meijer KA, van Geest Q, Geurts JJG, Schoonheim MM. Determinants of cognitive impairment in patients with multiple sclerosis with and without atrophy. Radiology. 2018;288(2):544‐551. doi:10.1148/radiol.2018172808 PubMed DOI
Mansour A, Baria AT, Tetreault P, et al. Global disruption of degree rank order: a hallmark of chronic pain. Sci Rep. 2016;6(1):34853. doi:10.1038/srep34853 PubMed DOI PMC
Achard S, Delon‐Martin C, Vértes PE, et al. Hubs of brain functional networks are radically reorganized in comatose patients. Proc Natl Acad Sci USA. 2012;109(50):20608‐20613. doi:10.1073/pnas.1208933109 PubMed DOI PMC
Bučková B, Kopal J, Řasová K, Tintěra J, Hlinka J. Open access: the effect of neurorehabilitation on multiple sclerosis—unlocking the resting‐state fMRI data. Front Neurosci. 2021;15:662784. doi:10.3389/fnins.2021.662784 PubMed DOI PMC
Rehák Bučková B, Mareš J, Škoch A, et al. Multimodal‐neuroimaging machine‐learning analysis of motor disability in multiple sclerosis. Brain Imaging Behav. 2023;17(1):18‐34. doi:10.1007/s11682-022-00737-3 PubMed DOI
Polman CH, Reingold SC, Banwell B, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69(2):292‐302. doi:10.1002/ana.22366 PubMed DOI PMC
Smith A. Symbol Digit Modalities Test. Western Psychological Services; 2017.
Penner IK, Raselli C, Stöcklin M, Opwis K, Kappos L, Calabrese P. The Fatigue Scale for Motor and Cognitive Functions (FSMC): validation of a new instrument to assess multiple sclerosis‐related fatigue. Mult Scler. 2009;15(12):1509‐1517. doi:10.1177/1352458509348519 PubMed DOI
Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444‐1452. doi:10.1212/wnl.33.11.1444 PubMed DOI
Podsiadlo D, Richardson S. The timed ‘Up & Go’: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142‐148. doi:10.1111/j.1532-5415.1991.tb01616.x PubMed DOI
Whitfield‐Gabrieli S, Nieto‐Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2(3):125‐141. doi:10.1089/brain.2012.0073 PubMed DOI
Grothe M, Domin M, Hoffeld K, Nagels G, Lotze M. Functional representation of the symbol digit modalities test in relapsing remitting multiple sclerosis. Mult Scler Relat Disord. 2020;43:102159. doi:10.1016/j.msard.2020.102159 PubMed DOI
Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR‐hyperintense white‐matter lesions in multiple sclerosis. NeuroImage. 2012;59(4):3774‐3783. doi:10.1016/j.neuroimage.2011.11.032 PubMed DOI
Schoonheim MM, Geurts J, Wiebenga OT, et al. Changes in functional network centrality underlie cognitive dysfunction and physical disability in multiple sclerosis. Mult Scler. 2014;20(8):1058‐1065. doi:10.1177/1352458513516892 PubMed DOI
Tommasin S, De Giglio L, Ruggieri S, et al. Multi‐scale resting state functional reorganization in response to multiple sclerosis damage. Neuroradiology. 2020;62(6):693‐704. doi:10.1007/s00234-020-02393-0 PubMed DOI
Stellmann JP, Hodecker S, Cheng B, et al. Reduced rich‐club connectivity is related to disability in primary progressive MS. Neurol Neuroimmunol Neuroinflam. 2017;4(5):e375. doi:10.1212/NXI.0000000000000375 PubMed DOI PMC
Grothe M, Jochem K, Strauss S, et al. Performance in information processing speed is associated with parietal white matter tract integrity in multiple sclerosis. Front Neurol. 2022;13:982964. doi:10.3389/fneur.2022.982964 PubMed DOI PMC
Denissen S, Engemann DA, De Cock A, et al. Brain age as a surrogate marker for cognitive performance in multiple sclerosis. Eur J Neurol. 2022;29(10):3039‐3049. doi:10.1111/ene.15473 PubMed DOI PMC
Pike AR, James GA, Drew PD, Archer RL. Neuroimaging predictors of longitudinal disability and cognition outcomes in multiple sclerosis patients: a systematic review and meta‐analysis. Mult Scler Relat Disord. 2022;57:103452. doi:10.1016/j.msard.2021.103452 PubMed DOI
Has Silemek AC, Fischer L, Pöttgen J, et al. Functional and structural connectivity substrates of cognitive performance in relapsing remitting multiple sclerosis with mild disability. Neuroimage Clin. 2020;25:102177. doi:10.1016/j.nicl.2020.102177 PubMed DOI PMC
Bisecco A, Nardo FD, Docimo R, et al. Fatigue in multiple sclerosis: the contribution of resting‐state functional connectivity reorganization. Mult Scler. 2018;24(13):1696‐1705. doi:10.1177/1352458517730932 PubMed DOI
Penner IK, Paul F. Fatigue as a symptom or comorbidity of neurological diseases. Nature Rev Neurol. 2017;13(11):662‐675. doi:10.1038/nrneurol.2017.117 PubMed DOI
Manjaly ZM, Harrison NA, Critchley HD, et al. Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2019;90(6):642‐651. doi:10.1136/jnnp-2018-320050 PubMed DOI PMC
Fleischer V, Ciolac D, Gonzalez‐Escamilla G, et al. Subcortical volumes as early predictors of fatigue in multiple sclerosis. Ann Neurol. 2022;91(2):192‐202. doi:10.1002/ana.26290 PubMed DOI
Diedrichsen J, King M, Hernandez‐Castillo C, Sereno M, Ivry RB. Universal transform or multiple functionality? Understanding the contribution of the human cerebellum across task domains. Neuron. 2019;102(5):918‐928. doi:10.1016/j.neuron.2019.04.021 PubMed DOI PMC