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Neuroimaging predictors of longitudinal disability and cognition outcomes in multiple sclerosis patients: A systematic review and meta-analysis

  • Ashley R. Pike
    Correspondence
    Corresponding author.
    Affiliations
    Department of Neurobiology and Developmental Sciences, Brain Imaging Research Center, Psychiatric Research Institute, University of Arkansas for Medical Sciences, 4301W. Markham Street, #554, Little Rock, AR 72205, United States
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  • George A. James
    Affiliations
    Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR, United States

    Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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  • Paul D. Drew
    Affiliations
    Department of Neurobiology and Developmental Sciences, Brain Imaging Research Center, Psychiatric Research Institute, University of Arkansas for Medical Sciences, 4301W. Markham Street, #554, Little Rock, AR 72205, United States

    Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, United States

    Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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  • Robert L. Archer
    Affiliations
    Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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Published:December 06, 2021DOI:https://doi.org/10.1016/j.msard.2021.103452

      Highlights

      • Neuroimaging modalities predict longitudinal decline in Multiple Sclerosis patients.
      • The variability of methods in these relationships prompted a meta-analysis.
      • We found 64 PubMed publications investigating longitudinal predictor models.
      • Reported predictors overall are a medium predictor for longitudinal outcomes.
      • Standardizing study designs, analyzation methods, and thorough reporting is needed.

      Abstract

      Background

      Cross-sectional magnetic resonance imaging (MRI) studies have generated substantial evidence relating neuroimaging abnormalities to clinical and cognitive decline in multiple sclerosis (MS). Longitudinal neuroimaging studies may have additional value for predicting future cognitive deficits or clinical impairment, potentially leading to earlier interventions and better disease management. We conducted a meta-analysis of longitudinal studies using neuroimaging to predict cognitive decline (i.e. the Symbol Digits Modalities Test, SDMT) and disability outcomes (i.e. the Expanded Disability Status Scale, EDSS) in MS.

      Methods

      Our systematic literature search yielded 64 relevant publications encompassing 105 distinct sub-analyses. We performed a multilevel random-effects meta-analysis to estimate overall effect size for neuroimaging's ability to predict longitudinal cognitive and clinical decline, and a meta-regression to investigate the impact of distinct study factors on pooled effect size.

      Results

      In the EDSS analyses, the meta-analysis yielded a medium overall pooled effect size (Pearson's correlation coefficient r = 0.42, 95% CI [0.37; 0.46]). The meta-regression further indicated that analyses exclusively evaluating gray matter tissue had significantly stronger effect sizes than analyses of white matter tissue or whole brain analyses (p < 0.05). No other study factors significantly influenced the pooled effect size (all p > 0.05). In the SDMT analyses, the meta-analysis yielded a medium overall pooled effect size (r = 0.47, 95% CI [0.32; 0.60]). The meta-regression found no significant study factors influencing the pooled effect size.

      Conclusion

      The present findings indicate that brain imaging is a medium predictor of longitudinal change in both disability progression (EDSS) and cognitive decline (SDMT). These findings reinforce the need for further longitudinal studies standardizing methods, using multimodal approaches, creating data consortiums, and publishing more complete datasets investigating MRI modalities to predict longitudinal disability and cognitive decline.

      Keywords

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      References

        • Amato M.P.
        • et al.
        Multiple sclerosis-related cognitive changes: a review of cross-sectional and longitudinal studies.
        J. Neurol. Sci. 2006; 245 (Cognitive Decline in Multiple Sclerosis: Biological, Clinical and Therapeutic Aspects): 41-46https://doi.org/10.1016/j.jns.2005.08.019
        • Balduzzi S.
        • et al.
        How to perform a meta-analysis with R: a practical tutorial.
        Evid. Based Ment. Health. 2019; : 153-160https://doi.org/10.1136/ebmental-2019-300117
        • Benedict R.H.B.
        • et al.
        Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues.
        Lancet Neurol. 2020; 19: 860-871https://doi.org/10.1016/S1474-4422(20)30277-5
        • Bergendal G.
        • et al.
        Selective decline in information processing in subgroups of multiple sclerosis: an 8-year longitudinal study.
        Eur. Neurol. 2007; 57: 193-202https://doi.org/10.1159/000099158
        • Cheung M.W.L
        Modeling dependent effect sizes with three-level meta-analyses: a structural equation modeling approach.
        Psychol. Methods. 2014; 19: 211-229https://doi.org/10.1037/a0032968
        • Chiaravalloti N.D.
        • DeLuca J.
        Cognitive impairment in multiple sclerosis.
        Lancet Neurol. 2008; 7: 1139-1151https://doi.org/10.1016/S1474-4422(08)70259-X
        • DeLuca G.C.
        • et al.
        Cognitive impairment in multiple sclerosis: clinical, radiologic and pathologic insights.
        Brain Pathol. 2015; 25 (Zurich Switz): 79-98https://doi.org/10.1111/bpa.12220
        • DeLuca J.
        • et al.
        Is speed of processing or working memory the primary information processing deficit in multiple sclerosis?.
        J. Clin. Exp. Neuropsychol. 2004; 26: 550-562https://doi.org/10.1080/13803390490496641
        • Di Filippo M.
        • et al.
        Multiple sclerosis and cognition: synaptic failure and network dysfunction.
        Nat. Rev. Neurosci. 2018; 19: 599-609https://doi.org/10.1038/s41583-018-0053-9
      1. Harrer, M. et al. 2019. dmetar: companion R package for the guide “doing meta-analysis in R” [WWW Document]. URL http://dmetar.protectlab.org/.

        • Harrer M.
        • et al.
        Doing Meta-Analysis with R: A Hands-On Guide.
        Doing Meta-Analysis with R: A Hands-On Guide. 1st. Chapmann & Hall/CRC Press, Boca Raton, FL and London2022
        • Janculjak D.
        • et al.
        Changes of attention and memory in a group of patients with multiple sclerosis.
        Clin. Neurol. Neurosurg. 2002; 104: 221-227https://doi.org/10.1016/s0303-8467(02)00042-2
        • Kalb R.
        • et al.
        Recommendations for cognitive screening and management in multiple sclerosis care.
        Mult. Scler. Houndmills Basingstoke Engl. 2018; 24: 1665-1680https://doi.org/10.1177/1352458518803785
        • Kurtzke J.F.
        On the origin of EDSS.
        Mult. Scler. Relat. Disord. 2015; 4: 95-103https://doi.org/10.1016/j.msard.2015.02.003
      2. Lüdecke, 2019. ESC: effect size computation for meta analysis version 0.5.1 from CRAN [WWW Document]. URL https://cran.r-project.org/web/packages/esc/esc.pdf (accessed 11.9.21).

        • Louapre C
        • et al.
        Imaging markers of multiple sclerosis prognosis.
        Current Opinions in Neurology. 2017; 30: 231-236https://doi.org/10.1097/WCO.0000000000000456
        • Ontaneda D.
        • et al.
        Progressive multiple sclerosis: prospects for disease therapy, repair, and restoration of function.
        Lancet Lond. Engl. 2017; 389: 1357-1366https://doi.org/10.1016/S0140-6736(16)31320-4
        • Oset M.
        • et al.
        Cognitive dysfunction in the early stages of multiple sclerosis-how much and how important?.
        Curr. Neurol. Neurosci. Rep. 2020; 20: 22https://doi.org/10.1007/s11910-020-01045-3
        • Polman C.H.
        • et al.
        Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria.
        Ann. Neurol. 2011; 69: 292-302https://doi.org/10.1002/ana.22366
        • Rocca M.A.
        • et al.
        Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis.
        Lancet Neurol. 2015; 14: 302-317https://doi.org/10.1016/S1474-4422(14)70250-9
      3. RStudio: Integrated development for R., 2020. RStudio, PBC, Boston, MA.

        • Trapp B.D.
        • et al.
        Axonal transection in the lesions of multiple sclerosis.
        New England Journal of Medicine. 1998; 338: 278-285https://doi.org/10.1177/107385849900500107
        • Trapp B.D.
        • et al.
        Neurodegeneration in multiple sclerosis: relationship to neurological disability.
        Neurosci. 1999; 5: 48-57https://doi.org/10.1177/107385849900500107
        • Viechtbauer W.
        Conducting meta-analyses in R with the metafor package.
        J. Stat. Softw. 2010; 36: 1-48
        • Wattjes M.P.
        • et al.
        MRI in the diagnosis and monitoring of multiple sclerosis: an update.
        Clin. Neuroradiol. 2015; 25 (Suppl 2): 157-165https://doi.org/10.1007/s00062-015-0430-y
        • Wickham H.
        • et al.
        Welcome to the tidyverse.
        J. Open Source Softw. 2019; 4: 1686https://doi.org/10.21105/joss.01686
        • Yarkoni T.
        Big correlations in little studies: inflated fMRI correlations reflect low statistical power-commentary on Vul et al. (2009).
        Perspect. Psychol. Sci. J. Assoc. Psychol. Sci. 2009; 4: 294-298https://doi.org/10.1111/j.1745-6924.2009.01127.x