Магнитно-резонансная томография коннектома в оценке результатов нейрореабилитации у пациентов с рассеянным склерозом (обзор литературы)

Представленный обзор литературы посвящен оценке функционального состояния коннектома головного мозга при применении специализированных методик магнитно-резонансной томографии (МРТ) — функциональной МРТ покоя и МР-трактографии и возможностям их использования в реабилитации пациентов с рассеянным склерозом (РС).

РС занимает 1-е место среди причин нетравматической неврологической инвалидизации молодых взрослых пациентов в Российской Федерации, а его распространенность продолжает неуклонно расти. Применение комплексной нейрореабилитации — основа восстановления функционального статуса пациентов, страдающих данным заболеванием.

При этом в настоящее время в клинической практике оценка эффективности ее результатов производится сугубо на основании показателей клинических шкал. Применение методики функциональной магнитно-резонансной томографии покоя (фМРТ покоя) позволяет выявлять связи между различными функционально связанными регионами головного мозга без применения какой-либо внешней стимуляции, а также оценивать состояние коннектома в динамике.

В данной обзорной статье рассмотрены основные направления современных методик нейрореабилитации, оценены нарушения показателей ДТ и фМРТ покоя у пациентов с рассеянным склерозом по сравнению со здоровыми добровольцами, а также изменения данных показателей в динамике после применения различных методик нейрореабилитации, направленных на восстановление двигательных и когнитивных функций.

1. Мартынов М.Ю., Гусев Е.И., Бойко А.Н. и др. Рассеянный склероз, справочник. М.: Реал Тайм, 2009. Журнал неврологии и психиатрии им. С. С. Корсакова. Спецвыпуски. 2012;112(2–2):112–112.

2. Cotsapas C, Mitrovic M, Hafler D. Multiple sclerosis. Handbook of Clinical Neurology. 2018; 148:723–730.

3. Global, regional, and national burden of multiple sclerosis 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet neurology. 2019;18(3):269–285.

4. Cree BAC, Hauser SL. Multiple Sclerosis In: Jameson JL, Fauci AS, Kasper DL, eds. Harrison’s Principles of Internal Medicine, 20e New York, NY: McGraw-Hill Education; 2018.

5. Comi G, Radaelli M, Soelberg Sorensen P. Evolving concepts in the treatment of relapsing multiple sclerosis. Lancet. 2017; 389:1347–1356.

6. Oh J, Vidal-Jordana A, Montalban X. Multiple sclerosis. Current Opinion in Neurology. 2018; 31(6):752–759.

7. Motl RW. Exercise and Multiple Sclerosis. Adv Exp Med Biol. 2020;1228: 333–343.

8. Tavazzi E, Cazzoli M, Pirastru A, et al. Neuroplasticity and Motor Rehabilitation in Multiple Sclerosis: A Systematic Review on MRI Markers of Functional and Structural Changes. Front Neurosci. 2021 Oct 6; 15:707675.

9. Amatya B, Khan F, Galea M. Rehabilitation for people with multiple sclerosis: an overview of Cochrane Reviews. Cochrane Database Syst Rev. 2019 Jan 14;1(1):CD012732.

10. El-Sayes J, Harasym D, Turco CV, et al. ExerciseInduced neuroplasticity: a mechanistic model and prospects for promoting plasticity. Neuroscientist. 2019; 25, 65–85.

11. Lai B, Young HJ, Bickel CS, et al. Current trends in exercise intervention research, technology, and behavioral change strategies for people with disabilities: a scoping review. Am J Phys Med Rehabil. 2017; 96(10):748–761.

12. Motl RW, Barstow EA, Blaylock S, et al. Promotion of exercise in multiple sclerosis through healthcare providers. Exerc Sport Sci Rev. 2018; 46(2):105–111.

13. Amedoro A, Berardi A, Conte A, et al. The effect of aquatic physical therapy on patients with multiple sclerosis: A systematic review and meta-analysis. Mult Scler Relat Disord. 2020 Jun; 41:102–122.

14. Conroy SS, Zhan M, Culpepper WJ, et al. Selfdirected exercise in multiple sclerosis: Evaluation of a home automated tele-management system. J. Telemed. Telecare. 2018; 24:410–419.

15. Maggio MG, Russo M, Cuzzola MF, et al. Virtual reality in multiple sclerosis rehabilitation: A review on cognitive and motor outcomes. Journal of Clinical Neuroscience. 2019; 65:106–111.

16. Molhemi F, Monjezi S, Mehravar M., et al. Effects of Virtual Reality vs Conventional Balance Training on Balance and Falls in People with Multiple Sclerosis: A Randomized Controlled Trial. Arch. Phys. Med. Rehabil. 2021; 102:290–299.

17. Yazgan YZ, Tarakci E, Tarakci D, et al. Comparison of the effects of two different exergaming systems on balance, functionality, fatigue, and quality of life in people with multiple sclerosis: A randomized controlled trial. Mult. Scler. Relat. Disord. 2019; 39:101902.

18. Donzé C, Massot C. Rehabilitation in multiple sclerosis in 2021. La Presse Médical. 2021; 50(2):104066.

19. Xie X, Sun H, Zeng Q, et al. Do Patients with Multiple Sclerosis Derive More Benefit from RobotAssisted Gait Training Compared with Conventional Walking Therapy Motor Function? A Meta-analysis. Front Neurol. 2017; 8:260.

20. Straudi S, Manfredini F, Lamberti N, et al. Robotassisted gait training is not superior to intensive overground walking in multiple sclerosis with severe disability (the RAGTIME study): A randomized controlled trial. Mult Scler. 2020;26(6):716–724.

21. Androwis GJ, Sandroff BM, Niewrzol P, et al. A pilot randomized controlled trial of robotic exoskeletonassisted exercise rehabilitation in multiple sclerosis. Multiple Sclerosis and Related Disorders. 2021; 51:102936.

22. Russo M, Dattola V, De Cola MC, et al. The role of robotic gait training coupled with virtual reality in boosting the rehabilitative outcomes in patients with multiple sclerosis. Int. J. Rehabil. Res. 2018; 41:166–172.

23. Etoom M, Khraiwesh Y, Lena F, et al. Effectiveness of Physiotherapy Interventions on Spasticity in People with Multiple Sclerosis. A Systematic Review and MetaAnalysis. American Journal of Physical Medicine & Rehabilitation. 2018; 97(11):793–807.

24. Khan F, Amatya B. Rehabilitation in Multiple Sclerosis: a Systematic Review of Systematic Reviews. Arch Phys Med Rehabil. 2017; 98: 353–67.

25. Leocani L, Chieffo R, Gentile A, et al. Beyond rehabilitation in MS: Insights from non-invasive brain stimulation. Mult Scler. 2019;25(10):1363–1371.

26. Stampanoni Bassi M, Buttari F, Gilio L, et al. Inflammation and Corticospinal Functioning in Multiple Sclerosis: A TMS Perspective. Frontiers in Neurology, 2020, 11:566.

27. Etoom M, Khraiwesh Y, Foti C. Transcutaneous Electrical Nerve Stimulation for Spasticity. Am J Phys Med Rehabil. 2017; 96: e198.

28. Camerota F, Celletti C, Di Sipio E, et al. Focal muscle vibration, an effective rehabilitative approach in severe gait impairment due to multiple sclerosis. J Neurol Sci. 2017;372:33–39.

29. Yahya AS, Khawaja S. Electroconvulsive Therapy in Multiple Sclerosis: A Review of Current Evidence. Prim Care Companion CNS Disord. 2021; 23(2):20r02717.

30. Lu W, Dong K, Cui D, el al. Quality assurance of human functional magnetic resonance imaging: a literature review. Quant Imaging Med Surg. 2019;9(6):1147–1162.

31. Puig J, Blasco G, Alberich-Bayarri A, et al. RestingState Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke. Stroke. 2018;49(10):2353–2360.

32. Rocca MA, Valsasina P, Leavitt VM, et al. Functional network connectivity abnormalities in multiple sclerosis: Correlations with disability and cognitive impairment. Mult Scler. 2018;24(4):459–471.

33. Charalambous T, Tur C, Prados F, et al. Structural network disruption markers explain disability in multiple sclerosis. J Neurol Neurosurg Psychiatry 2018; jnnp2018–318440.

34. Pasqua G, Tommasin S, Bharti K, et al. Restingstate functional connectivity of anterior and posterior cerebellar lobes is altered in multiple sclerosis. Mult Scler. 2021;27(4):539–548.

35. 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.

36. Pinter D, Beckmann CF, Fazekas F, et al. Morphological MRI phenotypes of multiple sclerosis differ in resting-state brain function. Sci Rep. 2019 7;9(1):16221.

37. Tauhid S, Neema M, Healy BC, et al. MRI phenotypes based on cerebral lesions and atrophy in patients with multiple sclerosis. J. Neurol. Sci. 2014; 346:250–254.

38. Tozlu C, Jamison K, Gu Z, et al. Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups. Neuroimage Clin. 2021;32:102827.

39. Fling BW, Martini DN, Zeeboer E, el al. Neuroplasticity of the sensorimotor neural network associated with walking aid training in people with multiple sclerosis. Mult Scler Relat Disord. 2019;31:1–4.

40. Rocca MA, Meani A, Fumagalli S, et al. Functional and structural plasticity following action observation training in multiple sclerosis. Multiple Sclerosis Journal. 2019;25(11):1472–1487.

41. Tavazzi E, Bergsland N, Cattaneo D, et al. Effects of motor rehabilitation on mobility and brain plasticity in multiple sclerosis: a structural and functional MRI study. J Neurol. 2018;265(6):1393–1401.

42. Sandroff BM, Wylie GR, Sutton BP, el al. Treadmill walking exercise training and brain function in multiple sclerosis: Preliminary evidence setting the stage for a network-based approach to rehabilitation. Mult Scler J Exp Transl Clin. 2018 Feb 21;4(1):2055217318760641.

43. Huiskamp M, Moumdjian L, van Asch P, et al. A pilot study of the effects of running training on visuospatial memory in MS: A stronger functional embedding of the hippocampus in the default-mode network? Mult Scler. 2020;26(12):1594–1598.

44. Manca R, Mitolo M, Wilkinson ID, et al. A networkbased cognitive training induces cognitive improvements and neuroplastic changes in patients with relapsingremitting multiple sclerosis: an exploratory case-control study. Neural Regen Res. 2021;16(6):1111–1120.

45. Akbar N, Sandroff BM, Wylie GR, et al. Progressive resistance exercise training and changes in resting-state functional connectivity of the caudate in persons with multiple sclerosis and severe fatigue: A proof-of-concept study. Neuropsychol Rehabil. 2020;30(1):54–66.

46. Dobryakova E, Hulst HE, Spirou A, et al. Frontostriatal network activation leads to less fatigue in multiple sclerosis. Mult Scler. 2018;24(9):1174–1182.

47. Androwis GJ, Sandroff BM, Niewrzol P. A pilot randomized controlled trial of robotic exoskeletonassisted exercise rehabilitation in multiple sclerosis. Mult Scler Relat Disord. 2021;51:102936.

48. Bonzano L, Pedullà L, Tacchino A, et al. Upper limb motor training based on task-oriented exercises induces functional brain reorganization in patients with multiple sclerosis. Neuroscience. 2019; 410:150–159.

49. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010; 52, 1059–1069.

50. Fujimori J, Uryu K, Fujihara K, et al. Measurements of the corpus callosum index and fractional anisotropy of the corpus callosum and their cutoff values are useful to assess global brain volume loss in multiple sclerosis. Mult Scler Relat Disord. 2020;45:102388.

51. Bergsland N, Dwyer MG, Jakimovski D, et al. Diffusion tensor imaging reveals greater microstructure damage in lesional tissue that shrinks into cerebrospinal fluid in multiple sclerosis. J Neuroimaging. 2021;31(5):995–1002.

52. Granberg T, Fan Q, Treaba CA, et al. In vivo characterization of cortical and white matter neuroaxonal pathology in early multiple sclerosis. Brain. 2017; 140: 2912–2926.

53. Barghi A, Allendorfer JB, Taub E, et al. Phase II Randomized Controlled Trial of Constraint-Induced Movement Therapy in Multiple Sclerosis. Part 2: Effect on White Matter Integrity. Neurorehabil Neural Repair. 2018;32(3):233–241.

54. Laura G, Silvia T, Nikolaos P, et al. The role of fMRI in the assessment of neuroplasticity in MS: a systematic review. Neural Plast. 2018:3419871.

55. Prosperini L, Di Filippo M. Beyond clinical changes: Rehabilitation-induced neuroplasticity in MS. Multiple Sclerosis Journal. 2019; 25(10): 1348–1362.