Atrophic brain changes and cognitive impairment in multiple sclerosis

February 28, 2025
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УДК:  616.831-007.23:616.89-008.45/.48]-07:616-004
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The study included 93 patients with relapsing-remitting multiple sclerosis who underwent a comprehensive neurological, psychodiagnostic, and neuroimaging examination. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) test. The analysis revealed statistically significant correlations between the MoCA test results and morphometric indicators of brain atrophy, in particular, subcortical and cortical atrophy. The MoCA test showed high sensitivity for detecting cognitive changes, correlating with the duration of the disease, EDSS level, and neuroimaging indicators. This confirms its potential in the early detection of cognitive impairment that precedes more pronounced neurological symptoms. Thus, the results of the study indicate the feasibility of using the MoCA test as a tool for monitoring the cognitive status of patients with multiple sclerosis, as well as for predicting disease progression in the early stages.

References

  • 1. Wilcox O., Amin M., Hancock L. et al. (2024) Associations Between Cognitive Impairment and Neuroimaging in Patients with Multiple Sclerosis. Arch. Clin. Neuropsychol., 39(2): 196–203. doi: 10.1093/arclin/acad070.
  • 2. Tekin S., Bir L.S., Oncel C. et al. (2022) Evaluation of cognitive dysfunction by the clock drawing test in multiple sclerosis and clinically isolated syndrome patients: Correlation with other neuropsychological tests. Neurosciences, 27(4): 251–256.
  • 3. Bouman P.M., van Dam M.A., Jonkman L.E. et al. (2024) Isolated cognitive impairment in people with multiple sclerosis: frequency, MRI patterns and its development over time. J. Neurol., 271(5): 2159–2168.
  • 4. Modica C.M., Bergsland N., Dwyer M.G. et al. (2016) Cognitive reserve moderates the impact of subcortical gray matter atrophy on neuropsychological status in multiple sclerosis. Mult. Scler., 22(1): 36–42.
  • 5. Rogers J.M., Panegyres P.K. (2007) Cognitive impairment in multiple sclerosis: evidence-based analysis and recommendations. J. Clin. Neurosci., 14(10): 919–927. doi: 10.1016/j.jocn.2007.02.006.
  • 6. Moccia M., Lanzillo R., Palladino R. et al. (2016) Cognitive impairment at diagnosis predicts 10-year multiple sclerosis progression. Mult. Scler., 22(5): 659–667.
  • 7. Clough M., Dobbing J., Stankovich J. et al. (2020) Cognitive processing speed deficits in multiple sclerosis: Dissociating sensorial and motor processing changes from cognitive processing speed. Mult. Scler. Relat. Disord., 38: 101522.
  • 8. Calabrese M., Gajofatto A., Benedetti M.D. (2014) Therapeutic strategies for relapsing-remitting multiple sclerosis: a special focus on reduction of grey matter damage as measured by brain atrophy. Expert Rev. Neurother., 14(12): 1417–1428.
  • 9. Eshaghi A., Marinescu R.V., Young A.L. et al. (2018) Progression of regional grey matter atrophy in multiple sclerosis. Brain, 141(6): 1665–1677.
  • 10. Nabizadeh F., Balabandian M., Rostami M.R. et al. (2022) Association of cognitive impairment and quality of life in patients with multiple sclerosis: A cross-sectional study. Curr. J. Neurol., 21(3): 144–150. doi: 10.18502/cjn.v21i3.11106.
  • 11. R Core Team (2024) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. http://www.R-project.org.
  • 12. Kloke J.D., McKean J.W. (2012) Rfit: Rank-based Estimation for Linear Models. journal.r-project.org/archive/2012/RJ-2012-014/RJ-2012-014.pdf.
  • 13. Сіделковський О., Овсянніков О., Марусіченко В., Савчук М. (2022) Діагностичні шкали і тести в неврології, нейрохірургії і нейрореабілітації. Пабліш Про, Київ, 294 с.