Возможности нейровизуализационных и нейрофизиологических методов исследования для объективизации реабилитационного потенциала у пациентов, перенесших ишемический инсульт (аналитический обзор литературы)

Церебральный инсульт (ЦИ) остается важнейшей медико- социальной проблемой. По имеющимся данным, лишь 25% лиц, перенесших инсульт, возвращаются к преморбидному уровню повседневной или трудовой активности, у большинства пациентов остаются резидуальные неврологические нарушения различной степени выраженности. Эффективная реабилитация пациентов с ЦИ требует не только своевременного начала лечения, но и индивидуального выбора реабилитационной программы. Для оптимизации реабилитационной стратегии в каждом конкретном случае необходимо ставить цели и задачи с учетом реабилитационного потенциала (РП) и прогноза восстановления пациента. В настоящей работе приводится определение РП и способы его описания. Рассматриваются существующие нейрофизиологические методы оценки РП функционального восстановления после ЦИ, такие как электроэнцефалография, вызванные потенциалы и диагностическая транскраниальная магнитная стимуляция (ТМС). Представлены сведения о нейровизуализационных методах диагностики – компьютерной (КТ) и магнитно- резонансной томографии (МРТ) в контексте определения РП. Подробно освещены возможности функциональной и диффузионно- тензорной МРТ головного мозга для оценки РП в различные периоды заболевания. Рассматриваются и другие возможные предикторы восстановления нарушенных функций: объем и локализация поражения, возраст пациента, когнитивные функции и лабораторные показатели. Описываются современные комплексные подходы формирования алгоритмов количественной оценки РП. В частности, описаны актуальные алгоритмы оценки РП – PREP2 для верхней конечности и TWIST для прогнозирования восстановления нарушений функции ходьбы. В настоящий момент не существует общепринятых методов для определения и квантификации РП. Предложенные для этого инструменты недостаточно чувствительны и специфичны либо не подходят для рутинной клинической практики.

1. Feigin V.L., Stark B.A., Johnson C.O., Roth G.A., Bisignano C., Abady G.G. et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurology. 2021;20(10):795–820. https://doi.org/10.1016/s1474-4422(21)00252-0.

2. Wafa H.A., Wolfe C.D.A., Emmett E., Roth G.A., Johnson C.O., Wang Y. Burden of Stroke in Europe. Stroke. 2020;51(8):2418–2427. https://doi.org/10.1161/strokeaha.120.029606.

3. Foreman K.J., Marquez N., Dolgert A., Fukutaki K., Fullman N., McGaughey M. et al. Forecasting life expectancy, years of life lost, and allcause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet. 2018;392(10159):2052–2090. https://doi.org/10.1016/s01406736(18)31694-5.

4. Feigin V.L., Krishnamurthi R.V., Barker- Collo S., McPherson K.M., Barber P.A., Parag V. et al.; ARCOS IV Group. 30-Year Trends in Stroke Rates and Outcome in Auckland, New Zealand (1981–2012): A Multi- Ethnic Population- Based Series of Studies. PLoS ONE. 2015;10(8):e0134609. https://doi.org/10.1371/journal.pone.0134609.

5. Pearson- Stuttard J., Guzman- Castillo M., Penalvo J.L., Rehm C.D., Afshin A., Danaei G. et al. Modeling Future Cardiovascular Disease Mortality in the United States. Circulation. 2016;133(10):967–978. https://doi.org/10.1161/circulationaha.115.019904.

6. Пирадов М.А., Максимова М.Ю., Танашян М.М. Инсульт: пошаговая инструкция. Руководство для врачей. 2-е изд. перераб. и доп. М.: ГЭОТАР-Медиа; 2020. 288 с.

7. Гусев Е.И., Коновалова А.Н., Скворцова В.И., Гехт А.Б. Неврология: Национальное руководство. М.: ГЭОТАР-Медиа; 2018. Т. 1. 880 с.

8. Nijland R.H.M., van Wegen E.E.H., Harmeling-van der Wel B.C., Kwakkel G. Accuracy of Physical Therapists’ Early Predictions of Upper-L imb Function in Hospital Stroke Units: The EPOS Study. Phys Ther. 2013;93(4):460–469. https://doi.org/10.2522/ptj.20120112.

9. Alawieh A., Zhao J., Feng W. Factors affecting post-stroke motor recovery: Implications on neurotherapy after brain injury. Behavioural Brain Research. 2018;340:94–101. https://doi.org/10.1016/j.bbr.2016.08.029.

10. Разумов А.Н., Мельникова Е.А. Основные показатели реабилитационного прогноза у больных, перенесших инсульт. Доктор. Ру. 2016;(12-2):16–22. Режим доступа: https://journaldoctor.ru/catalog/meditsinskaya-reabilitatsiya/osnovnye-pokazateli-reabilit. Razumov A.N., Mel’nikova E.A. The main indicators of rehabilitation prognosis in stroke patients. Doctor.Ru. 2016;(12-2):16–22. (In Russ.) Available at: https://journaldoctor.ru/catalog/meditsinskaya-reabilitatsiya/osnovnye-pokazateli-reabilit.

11. Corbetta M., Ramsey L., Callejas A., Baldassarre A., Hacker Carl D., Siegel Joshua S. et al. Common Behavioral Clusters and Subcortical Anatomy in Stroke. Neuron. 2015;85(5):927–941. https://doi.org/10.1016/j.neuron.2015.02.027.

12. Bigourdan A., Munsch F., Coupé P., Guttmann C.R.G., Sagnier S., Renou P. et al. Early Fiber Number Ratio Is a Surrogate of Corticospinal Tract Integrity and Predicts Motor Recovery After Stroke. Stroke. 2016;47(4):1053–1059. https://doi.org/10.1161/strokeaha.115.011576.

13. Jung Y.J., Jang S.H. The Fate of Injured Corticospinal Tracts in Patients with Intracerebral Hemorrhage: Diffusion Tensor Imaging Study. AJNR Am J Neuroradiol. 2012;33(9):1775–1778. https://doi.org/10.3174/ajnr.a3027.

14. Lin D.J., Cloutier A.M., Erler K.S., Cassidy J.M., Snider S.B., Ranford J. et al. Corticospinal Tract Injury Estimated From Acute Stroke Imaging Predicts Upper Extremity Motor Recovery After Stroke. Stroke. 2019;50(12):3569–3577. https://doi.org/10.1161/strokeaha.119.025898.

15. Nakashima A., Moriuchi T., Mitsunaga W., Yonezawa T., Kataoka H., Nakashima R. et al. Prediction of prognosis of upper- extremity function following stroke-related paralysis using brain imaging. Phys Ther Sci. 2017;29(8):1438–1443. https://doi.org/10.1589/jpts.29.1438.

16. Moura L.M., Luccas R., de Paiva J.P.Q., Amaro E., Leemans A., Leite C. da C. et al. Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review. Front Neurol. 2019;10:445. https://doi.org/10.3389/fneur.2019.00445.

17. Bhasin A., Srivastava P., Kumaran S.S. Correlation of DTI-Derived Measures to Therapy- Mediated Recovery after Stroke: Preliminary Findings. Neurol. India. 2021;69:1210–1216. https://doi.org/10.4103/0028-3886.329584.

18. Takenobu Y., Hayashi T., Moriwaki H., Nagatsuka K., Naritomi H., Fukuyama H. Motor recovery and microstructural change in rubro-s pinal tract in subcortical stroke. Neuroimage Clin. 2014;4:201–208. https://doi.org/10.1016/j.nicl.2013.12.003.

19. Paul T., Cieslak M., Hensel L., Wiemer V.M., Grefkes C., Grafton S.T. et al. The role of corticospinal and extrapyramidal pathways in motor impairment after stroke. Brain Commun. 2022;5(1):fcac301. https://doi.org/10.1093/braincomms/fcac301.

20. Doughty C., Wang J., Feng W., Hackney D., Pani E., Schlaug G. Detection and Predictive Value of Fractional Anisotropy Changes of the Corticospinal Tract in the Acute Phase of a Stroke. Stroke. 2016;47(6):1520–1526. https://doi.org/10.1161/strokeaha.115.012088.

21. Boers A.M.M., Jansen I.G.H., Beenen L.F.M., Devlin T.G., San Roman L., Heo J.H. et al. Association of follow-up infarct volume with functional outcome in acute ischemic stroke: a pooled analysis of seven randomized trials. J Neurointerv Surg. 2018;10(12):1137–1142. https://doi.org/10.1136/neurintsurg-2017-013724.

22. Бархатов Ю.Д., Кадыков А.С. Факторы, влияющие на восстановление двигательных функций у больных с полушарным инфарктом мозга различной локализации. Нервные болезни. 2018;(4):41–49. https://doi.org/10.24411/2226-0757-2019-12056.

23. Nagaraja N., Forder J.R., Warach S., Merino J.G. Reversible diffusion- weighted imaging lesions in acute ischemic stroke. Neurology. 2020;94(13):571–587. https://doi.org/10.1212/wnl.0000000000009173.

24. Gale S.D., Pearson C.M. Neuroimaging predictors of stroke outcome: Implications for neurorehabilitation. NeuroRehabilitation. 2012;31(3):331–344. Available at: https://pubmed.ncbi.nlm.nih.gov/23001879.

25. Puig J., Blasco G., Alberich- Bayarri A., Schlaug G., Deco G., Biarnes C. et al. Resting-S tate Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke. Stroke. 2018;49(10):2353–2360. https://doi.org/10.1161/strokeaha.118.021319.

26. Kim B., Winstein C. Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? A Systematic Review. Neurorehabil Neural Repair. 2016;31(1):3–24. https://doi.org/10.1177/1545968316662708.

27. Kumar P., Kathuria P., Nair P., Prasad K. Prediction of Upper Limb Motor Recovery after Subacute Ischemic Stroke Using Diffusion Tensor Imaging: A Systematic Review and Meta- Analysis. J Stroke. 2016;18(1):50–59. https://doi.org/10.5853/jos.2015.01186.

28. Wu J., Srinivasan R., Burke Quinlan E., Solodkin A., Small S.L., Cramer S.C. Utility of EEG measures of brain function in patients with acute stroke. J Neurophysiol. 2016;115(5):2399–2405. https://doi.org/10.1152/jn.00978.2015.

29. Vanderschelden B., Erani F., Wu J., de Havenon A., Srinivasan R., Cramer S.C. A Measure of Neural Function Provides Unique Insights into Behavioral Deficits in Acute Stroke. Stroke. 2023;54(2):e25-e29. https://doi.org/10.1161/strokeaha.122.040841.

30. Ajčević M., Furlanis G., Naccarato M., Miladinović A., Buoite Stella A., Caruso P. et al. Hyper-acute EEG alterations predict functional and morphological outcomes in thrombolysis- treated ischemic stroke: a wireless EEG study. Med Biol Eng Comput. 2020;59(1):121–129. https://doi.org/10.1007/s11517-020-02280-z.

31. Eldeeb S., Akcakaya M., Sybeldon M., Foldes S., Santarnecchi E., Pascual- Leone A. et al. EEG-based functional connectivity to analyze motor recovery after stroke: A pilot study. Biomed Signal Process Control. 2019;49:419–426. https://doi.org/10.1016/j.bspc.2018.12.022.

32. Milani G., Antonioni A., Baroni A., Malerba P., Straudi S. Relation Between EEG Measures and Upper Limb Motor Recovery in Stroke Patients: A Scoping Review. Brain Topogr. 2022;35(5-6):651–666. https://doi.org/10.1007/s10548-022-00915-y.

33. Saes M., Zandvliet S.B., Andringa A.S., Daffertshofer A., Twisk J.W.R., Meskers C.G.M. et al. Is Resting- State EEG Longitudinally Associated With Recovery of Clinical Neurological Impairments Early Poststroke? A Prospective Cohort Study. Neurorehabil Neural Repair. 2020;34(5):389–402. https://doi.org/10.1177/1545968320905797.

34. Saes M., Meskers C.G.M., Daffertshofer A., van Wegen E.E.H., Kwakkel G. Are early measured resting-state EEG parameters predictive for upper limb motor impairment six months poststroke? Clin Neurophysiol. 2021;132(1):56–62. https://doi.org/10.1016/j.clinph.2020.09.031.

35. Sohn J., Jung I.Y., Ku Y., Kim Y. Machine-L earning-Based Rehabilitation Prognosis Prediction in Patients with Ischemic Stroke Using Brainstem Auditory Evoked Potential. Diagnostics. 2021;11(4):673. https://doi.org/10.3390/diagnostics11040673.

36. Su Y.Y., Xiao S.Y., Haupt W.F., Zhang Y., Zhao H., Pang Y. et al. Parameters and Grading of Evoked Potentials: Prediction of Unfavorable Outcome in Patients with Severe Stroke. J Clin Neurophysiol. 2010;27(1):25–29. https://doi.org/10.1097/wnp.0b013e3181cb4282.

37. Zhang J.J., Sánchez Vidaña D.I., Chan J.N.M., Hui E.S.K., Lau K.K., Wang X. et al. Biomarkers for prognostic functional recovery poststroke: A narrative review. Front Cell Dev Biol. 2023;10:1062807. https://doi.org/10.3389/fcell.2022.1062807.

38. Barker A.T., Jalinous R., Freeston I.L. Non-invasive magnetic stimulation of human motor cortex. Lancet. 1985;325(8437):1106–1107. https://doi.org/10.1016/s0140-6736(85)92413-4.

39. Siebner H.R., Funke K., Aberra A.S., Antal A., Bestmann S., Chen R. et al. Transcranial magnetic stimulation of the brain: What is stimulated? – A consensus and critical position paper. Clin Neurophysiol. 2022;140:59–97. https://doi.org/10.1016/j.clinph.2022.04.022.

40. Di Lazzaro V., Oliviero A., Profice P., Ferrara L., Saturno E., Pilato F. et al. The diagnostic value of motor evoked potentials. Clin Neurophysiol. 1999;110(7):1297–1307. https://doi.org/10.1016/s1388-2457(99)00060-7.

41. Jo J.Y., Lee A., Kim M.S., Park E., Chang W.H., Shin Y.I. et al. Prediction of Motor Recovery Using Quantitative Parameters of Motor Evoked Potential in Patients with Stroke. Ann Rehabil Med. 2016;40(5):806. https://doi.org/10.5535/arm.2016.40.5.806.

42. Song Z., Dang L., Zhou Y., Dong Y., Liang H., Zhu Z., Pan S. Why do stroke patients with negative motor evoked potential show poor limb motor function recovery? Neural Regen Res. 2013;8(29):2713–2724. https://doi.org/10.3969/j.issn.1673-5374.2013.29.003.

43. Sawaki L., Butler A.J., Xiaoyan L., Wassenaar P.A., Mohammad Y.M., Blanton S. et al. Constraint- Induced Movement Therapy Results in Increased Motor Map Area in Subjects 3 to 9 Months After Stroke. Neurorehabil Neural Repair. 2008;22(5):505–513. https://doi.org/10.1177/1545968308317531.

44. Rogasch N.C., Thomson R.H., Farzan F., Fitzgibbon B.M., Bailey N.W., Hernandez- Pavon J.C. et al. Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties. NeuroImage. 2014;101:425–439. https://doi.org/10.1016/j.neuroimage.2014.07.037.

45. Ahn S., Fröhlich F. Pinging the brain with transcranial magnetic stimulation reveals cortical reactivity in time and space. Brain Stimul. 2021;14(2):304–315. https://doi.org/10.1016/j.brs.2021.01.018.

46. Chung S.W., Rogasch N.C., Hoy K.E., Fitzgerald P.B. Measuring Brain Stimulation Induced Changes in Cortical Properties Using TMS-EEG. Brain Stimul. 2015;8(6):1010–1020. https://doi.org/10.1016/j.brs.2015.07.029.

47. Keser Z., Buchl S.C., Seven N.A., Markota M., Clark H.M., Jones D.T. et al. Electroencephalogram (EEG) With or Without Transcranial Magnetic Stimulation (TMS) as Biomarkers for Post-stroke Recovery: A Narrative Review. Front Neurol. 2022;13:827866. https://doi.org/10.3389/fneur.2022.827866.

48. Gray W.A., Palmer J.A., Wolf S.L., Borich M.R. Abnormal EEG Responses to TMS During the Cortical Silent Period Are Associated with Hand Function in Chronic Stroke. Neurorehabil Neural Repair. 2017;31(7):666–676. https://doi.org/10.1177/1545968317712470.

49. Watson P.A., Gignac G.E., Weinborn M., Green S., Pestell C. A Meta- Analysis of Neuropsychological Predictors of Outcome Following Stroke and Other Non- Traumatic Acquired Brain Injuries in Adults. Neuropsychol Rev. 2020;30(2):194–223. https://doi.org/10.1007/s11065-020-09433-9.

50. Jampathong N., Laopaiboon M., Rattanakanokchai S., Pattanittum P. Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis. BMC Neurol. 2018;18(1):26. https://doi.org/10.1186/s12883-018-1032-5.

51. Campagnini S., Arienti C., Patrini M., Liuzzi P., Mannini A., Carrozza M.C. Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review. J Neuroeng Rehabil. 2022;19(1):54. https://doi.org/10.1186/s12984-022-01032-4.

52. Edwards J.D., Kapoor A., Linkewich E., Swartz R.H. Return to work after young stroke: A systematic review. Int J Stroke. 2017;13(3):243–256. https://doi.org/10.1177/1747493017743059.

53. Ali M., VandenBerg K., Williams L.J., Williams L.R., Abo M., Becker F. et al. Predictors of Poststroke Aphasia Recovery. Stroke. 2021;52(5):1778–1787. https://doi.org/10.1161/strokeaha.120.031162.

54. Buch E.R., Rizk S., Nicolo P., Cohen L.G., Schnider A., Guggisberg A.G. Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging. Neurology. 2016;86(20):1924–1925. https://doi.org/10.1212/wnl.0000000000002675.

55. Stinear C.M. Prediction of recovery of motor function after stroke. The Lancet Neurology. 2010;9(12):1228–1232. https://doi.org/10.1016/s1474-4422(10)70247-7.

56. Stinear C.M., Barber P.A., Petoe M., Anwar S., Byblow W.D. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain. 2012;135(8):2527–2535. https://doi.org/10.1093/brain/aws146.

57. Stinear C.M., Byblow W.D., Ackerley S.J., Smith M.C., Borges V.M., Barber P.A. PREP2: A biomarker- based algorithm for predicting upper limb function after stroke. Ann Clin Transl Neurol. 2017;4(11):811–820. https://doi.org/10.1002/acn3.488.

58. Connell L.A., Chesworth B., Ackerley S., Smith M.C., Stinear C.M. Implementing the PREP2 Algorithm to Predict Upper Limb Recovery Potential After Stroke in Clinical Practice: A Qualitative Study. Phys Ther. 2021;101(5):pzab040. https://doi.org/10.1093/ptj/pzab040.

59. Бушкова Ю.В., Ковражкина Е.А., Стаховская Л.В., Иванова Г.Е. Опыт практического применения оценки предикторов восстановления функции верхней конечности после инсульта. Фарматека. 2019;26(3):77–82. https://doi.org/10.18565/pharmateca.2019.3.77-82.

60. Stinear C.M., Byblow W.D., Ackerley S.J., Barber P.A., Smith M.C. Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency. Stroke. 2017;48(4):1011–1019. https://doi.org/10.1161/strokeaha.116.015790.

61. Smith M.C., Ackerley S.J., Barber P.A., Byblow W.D., Stinear C.M. PREP2 Algorithm Predictions Are Correct at 2 Years Poststroke for Most Patients. Neurorehabil Neural Repair. 2019;33(8):635–642. https://doi.org/10.1177/1545968319860481.

62. Smith M.C., Barber P.A., Stinear C.M. The TWIST Algorithm Predicts Time to Walking Independently After Stroke. Neurorehabil Neural Repair. 2017;31(10-11):955–964. https://doi.org/10.1177/1545968317736820.