The significance of thalamic nuclei degeneration in relapsing-remitting and secondary progressive multiple sclerosis: results of neuropsychological and morphometry studies

Cover Page

Abstract

Introduction. The thalamus is a 'transmitting organ' that is involved in a wide range of neurological functions. Its functional uniqueness and high sensitivity to damage during the earliest stages of multiple sclerosis (MS) make the thalamus a kind of barometer of diffuse brain damage in MS.

The aim of the study was to examine the structural and functional changes in the thalamus and its subregions using magnetic resonance morphometry and to determine their clinical significance in different types of MS.

Materials and methods. We examined 68 patients with relapsing-remitting (n = 40) and secondary progressive (n = 28) MS. The control group consisted of 10 healthy people matched for age and gender. The Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Severity Score (MSSS) were used to assess the patients' neurological status. The cognitive and mental domains were tested using the MMSE, FAB, MoCA, SDMT, Beck's test, and HADS. All patients underwent a brain MRI and morphometric evaluation of the obtained data using the FreeSurfer 6.0 software.

Results. The size of the thalamic pulvinar in relapsing-remitting MS was reduced on the left (M (anterior : posterior) = 186.6 : 149.4 mm3) compared with the controls (229.5 : 187.5 mm3) and on the right (219.5 : 187.1 mm3) compared with the controls (261.6 : 240.5 mm3; p < 0.05). The size of the left thalamic nuclei was significantly reduced in secondary progressive MS when compared with relapsing-remitting MS and the controls. EDSS was correlated with a decrease in the dimensions of the geniculate nucleus on the left (r = –0.48) and the pulvinar nuclei on the left (r = 0.46–0.54). Standard neuropsychological scales correlated with the size of the medial dorsal nucleus (r (MMSE:FAB:MoCA) = 0.51; 0.45; 0.59). The greatest correlation was between the SDMT test (written section) and the left ventral anterior nucleus (r = 0.71).

Conclusion. The obtained data indicate that thalamic nuclei atrophy plays a significant role in the progression of disability and cognitive disorders in MS. Mag- netic resonance morphometry of the thalamic nuclei can be considered an important marker and predictor of MS progression.

About the authors

Artem G. Trufanov

S.M. Kirov Military Medical Academy, St. Petersburg

Author for correspondence.
Email: platonova@neurology.ru
Russian Federation

Gennadiy N. Bisaga

V.A. Almazov National Medical Research Centre, St. Petersburg

Email: platonova@neurology.ru
Russian Federation

Dmitriy I. Skulyabin

S.M. Kirov Military Medical Academy, St. Petersburg

Email: platonova@neurology.ru
Russian Federation

Alexandr V. Tyomniy

S.M. Kirov Military Medical Academy, St. Petersburg

Email: platonova@neurology.ru
Russian Federation

Anton A. Yurin

S.M. Kirov Military Medical Academy, St. Petersburg

Email: platonova@neurology.ru
Russian Federation

Maria O. Poplyak

S.M. Kirov Military Medical Academy, St. Petersburg

Email: platonova@neurology.ru
Russian Federation

Iliya D. Poltavskiy

S.M. Kirov Military Medical Academy, St. Petersburg

Email: platonova@neurology.ru
Russian Federation

Igor V. Litvinenko

S.M. Kirov Military Medical Academy, St. Petersburg

Email: platonova@neurology.ru
Russian Federation

Miroslav M. Odinak

S.M. Kirov Military Medical Academy, St. Petersburg

Email: platonova@neurology.ru
Russian Federation

Dmitriy A. Tarumov

S.M. Kirov Military Medical Academy, St. Petersburg

Email: platonova@neurology.ru
Russian Federation

References

  1. Lassmann H., Brück W., Lucchinetti C.F. The immunopathology of multiple sclerosis: an overview. Brain Pathol 2007; 17: 210–218. doi: 10.1111/j.1750- 3639.2007.00064.x. PMID: 17388952.
  2. Lucchinetti C., Brück W., Parisi J. et al. Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination. Ann Neurol 2000; 47: 707–717. doi: 10.1002/1531-8249(200006)47:6<707::aid-ana3>3.0.co;2-q. PMID: 10852536.
  3. Lucchinetti C.F., Popescu B.F., Bunyan R.F. et al. Inflammatory cortical demyelination in early multiple sclerosis. N Engl J Med 2011; 365: 2188–2197. doi: 10.1056/NEJMoa1100648. PMID: 22150037.
  4. Boyko A.N., Boyko O.V., Gusev E.I. [The choice of the optimal drug for pathogenic treatment of multiple sclerosis: a current state of the problem (a review)]. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova. 2014; 114(10-2): 77–91. (In Russ.)
  5. Pozdnyakov A.V., Bisaga G.N., Gaikova O.N. et al. [Multiple sclerosis: from morphology to pathogenesis]. St. Petersburg, 2015. 104 p. (In Russ.)
  6. Whitwell J.L., Jack C.R.Jr., Boeve B.F. et al. Voxel-based morphometry pat- terns of atrophy in FTLD with mutations in MAPT or PGRN. Neurology 2009; 72: 813–820. doi: 10.1212/01.wnl.0000343851.46573.67. PMID: 19255408.
  7. Tao G., Datta S., He R. et al. Deep gray matter atrophy in multiple sclerosis: a tensor based morphometry. J Neurol Sci 2009; 282: 39–46. DOI: 10.1016/j. jns.2008.12.035. PMID: 19168189.
  8. Matías-Guiu J.A., Cortés-Martínez A., Montero P. et al. Identification of cortical and subcortical correlates of cognitive performance in multiple sclerosis using voxel-based morphometry. Front Neurol 2018; 9: 920. DOI: 10.3389/ fneur.2018.00920. PMID: 30420834.
  9. Bergsland N., Horakova D., Dwyer M.G. et al. Gray matter atrophy patterns in multiple sclerosis: a 10-year source-based morphometry study. Neuroimage Clin 2017; 17: 444–451. doi: 10.1016/j.nicl.2017.11.002. PMID: 29159057.
  10. Louapre C., Govindarajan S.T., Giannì C. et al. Heterogeneous pathological processes account for thalamic degeneration in multiple sclerosis: insights from 7 T imaging. MultScler 2018; 24: 1433–1444. doi: 10.1177/1352458517726382. PMID: 28803512.
  11. Minagar A., Barnett M.H., Benedict R.H.B. et al. The thalamus and mul- tiple sclerosis: modern views on pathologic, imaging, and clinical aspects. Neu- rology 2013; 80: 210–219. doi: 10.1212/WNL.0b013e31827b910b. PMID: 23296131.
  12. Krotenkova I.A., Bryukhov V.V., Peresedova A.V., Krotenkova M.V. [Cen- tral nervous system atrophy in multiple sclerosis: MRI morphometry data]. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova. Spetzvypuski 2014; 114(10): 50–56. (In Russ.)
  13. Krotenkova I.A., Bryukhov V.V., Zakharova M.N. et al. [Brain and spine at- rophy in relapsing remitting multiple sclerosis: a 3-year follow up study]. Luchevaya diagnostika i terapiya 2017; (1): 35–39. doi: 10.22328/2079-5343-2017-1- 35-39. (In Russ.)
  14. Iglesias J.E., Insausti R., Lerma-Usabiaga G. et al. A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. Neuroimage 2018; 183: 314–326. doi: 10.1016/j.neuroimage.2018.08.012. PMID: 30121337.
  15. Thompson A.J., Banwell B.L., Barkhof F. et al. Diagnosis of multiple sclero- sis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018; 17: 162–173. doi: 10.1016/S1474-4422(17)30470-2. PMID: 29275977.
  16. Lublin F.D., Reingold S.C., Cohen J.A. et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 2014; 83: 278–86. doi: 10.1212/WNL.0000000000000560. PMID: 24871874.
  17. Kurtzke J.F. Rating neurologic impairment in multiple sclerosis: an Ex- panded Disability Status Scale (EDSS). Neurology 1983; 33: 1444–1452. doi: 10.1212/wnl.33.11.1444. PMID: 6685237.
  18. Roxburgh R.H.S.R., Seaman S.R., Masterman T. et al. Multiple sclerosis severity score: using disability and disease duration to rate disease severity. Neurolo- gy 2005; 64: 1144–1151. doi: 10.1212/01.WNL.0000156155.19270.F8. PMID: 15824338.
  19. Folstein M.F., Folstein S.E., McHugh P.R. «Mini-mental state». A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: 189–198. doi: 10.1016/0022-3956(75)90026-6. PMID: 1202204.
  20. Dubois B., Slachevsky A., Litvan I., Pillon B. The FAB: a Frontal As- sessment Battery at bedside. Neurology 2000; 55: 1621–1626. DOI: 10.1212/ wnl.55.11.1621. PMID: 11113214.
  21. Nasreddine Z.S., Phillips N.A., Bédirian V. et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53: 695–699. doi: 10.1111/j.1532-5415.2005.53221.x. PMID: 15817019.
  22. Langdon D.W., Amato M.P., Boringa J. et al. Recommendations for a Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS). MultScler 2012; 18: 891–898. doi: 10.1177/1352458511431076. PMID: 22190573.
  23. Beck A.T., Ward C.H., Mendelson M. et al. An inventory for measur- ing depression. Arch Gen Psychiatry 1961; 4: 561–571. doi: 10.1001/arch- psyc.1961.01710120031004. PMID: 13688369.
  24. Zigmond A.S., Snaith R.P. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983; 67: 361–370. doi: 10.1111/j.1600-0447.1983.tb09716.x. PMID: 6880820.
  25. Assaf Y., Pasternak O. Diffusion Tensor Imaging (DTI)-based white mat- ter mapping in brain research: a review. J Mol Neurosci 2008; 34: 51–61. doi: 10.1007/s12031-007-0029-0. PMID: 18157658.
  26. O’Donnell L.J., Westin C.F. An introduction to diffusion tensor image ana- lysis. Neurosurg Clin N Am 2011; 22: 185–196. doi: 10.1016/j.nec.2010.12.004. PMID: 21435570.
  27. Alexander A.L., Lee J.E., Lazar M., Field A.S. Diffusion tensor imaging of the brain. Neurotherapeutics 2007; 4: 316–329. doi: 10.1016/j.nurt.2007.05.011. PMID: 17599699.
  28. Fischl B. FreeSurfer. Neuroimage 2012; 62: 774–781. doi: 10.1016/j.neuroimage.2012.01.021. PMID: 22248573.
  29. Culpepper L. Neuroanatomy and physiology of cognition. J. Clin. Psychiatry
  30. ; 76:e900. doi: 10.4088/JCP.13086tx3c. PMID: 26231020.
  31. Beatty W.W., Goodkin D.E. Screening for cognitive impairment in multiple sclerosis: an evaluation of the Mini-Mental State Examination. Arch Neurol 1990; 47: 297–301. doi: 10.1001/archneur.1990.00530030069018. PMID: 2310313.
  32. Creavin S.T., Wisniewski S., Noel-Storr A.H. et al. Mini-Mental State Exami- nation (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations. Cochrane Data- base Syst Rev 2016; (1): CD011145. doi: 10.1002/14651858.CD011145.pub2. PMID: 26760674.
  33. Benedict R.H.B., DeLuca J., Enzinger C. et al. Neuropsychology of multi- ple sclerosis: looking back and moving forward. J Int Neuropsychol Soc 2017; 23: 832–842. doi: 10.1017/S1355617717000959. PMID: 29198279.
  34. Charvet L.E., Taub E., Cersosimo B. et al. The Montreal Cognitive Assess- ment (MoCA) in multiple sclerosis: relation to clinical features. J Mult Scler 2015; 2: 135. doi: 10.4172/2376-0389.1000135. PMID: 27791389.
  35. Pirkhaefi A. Evaluation of cognitive abilities of different groups of sclerosis patients and its comparison with healthy people. PCP 2018; 6: 111–118. DOI: 10.292526.2.111.
  36. Estiasari R., Fajrina Y., Lastri D.N. et al. Validity and reliability of Brief In- ternational Cognitive Assessment for Multiple Sclerosis (BICAMS) in Indonesia and the correlation with quality of life. Neurol Res Int 2019; 2019: 4290352. doi: 10.1155/2019/4290352. PMID: 31263596.
  37. Greeke E.E., Chua A.S., Healy B.C. et al. Depression and fatigue in pa- tients with multiple sclerosis. J Neurol Sci 2017; 380: 236–241. DOI: 10.1016/j. jns.2017.07.047. PMID: 28870578.
  38. Jones S.M.W., Salem R., Amtmann D. Somatic symptoms of depression and anxiety in people with multiple sclerosis. Int J MS Care 2018; 20: 145–152. doi: 10.7224/1537-2073.2017-069. PMID: 29896052.
  39. Ntoskou K., Messinis L., Nasios G. et al. Cognitive and language deficits in multiple sclerosis: comparison of relapsing remitting and secondary progressive subtypes. Open Neurol J 2018; 12: 19–30. doi: 10.2174/1874205X01812010019. PMID: 29576812.
  40. Berman R.A., Wurtz R.H. Functional identification of a pulvinar path from superior colliculus to cortical area MT. J Neurosci 2010; 30: 6342–6354. doi: 10.1523/JNEUROSCI.6176-09.2010. PMID: 20445060.
  41. Berman R.A., Wurtz R.H. Signals conveyed in the pulvinar pathway from superior colliculus to cortical area MT. J Neurosci 2011; 31: 373–384. doi: 10.1523/JNEUROSCI.4738-10.2011. PMID: 21228149.
  42. Cappe C., Morel A., Barone P., Rouiller E.M. The thalamocortical projec- tion systems in primate: an anatomical support for multisensory and sensorimotor interplay. Cereb Cortex 2009; 19: 2025–2037. doi: 10.1093/cercor/bhn228. PMID: 19150924.
  43. Arcaro M.J., Pinsk M.A., Chen J., Kastner S. Organizing principles of pul- vino-cortical functional coupling in humans. Nat Commun 2018; 9: 5382. doi: 10.1038/s41467-018-07725-6. PMID: 30568159.
  44. Van der Werf Y.D., Witter M.P., Groenewegen H.J. The intralaminar and midline nuclei of the thalamus. Anatomical and functional evidence for participation in processes of arousal and awareness. Brain Res Brain Res Rev 2002; 39: 107–140. doi: 10.1016/s0165-0173(02)00181-9. PMID: 12423763.

Statistics

Views

Abstract: 359

PDF (Russian): 238

Article Metrics

Metrics Loading ...

Dimensions

PlumX


Copyright (c) 2020 Trufanov A.G., Bisaga G.N., Skulyabin D.I., Tyomniy A.V., Yurin A.A., Poplyak M.O., Poltavskiy I.D., Litvinenko I.V., Odinak M.M., Tarumov D.A.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies