Neurophysiological prognostic factors of motor function and spasticity after stroke
DOI:
https://doi.org/10.12775/JEHS.2021.11.01.018Keywords
stroke, neural plasticity, transcranial magnetic stimulation (TMS), quantitative electroencephalography (qEEG), spasticity, motor recoveryAbstract
Purpose: Understanding the neural mechanisms of recovery of motor control and development of spasticity after stroke is paramount importance for neurorehabilitation.
Methods: For this purpose, we have analyzed several TMS and EEG variables and their association with motor recovery and development of spasticity. Forty-two subjects with stroke have taken part in the investigation. The neurophysiological examination included assessments by transcranial magnetic stimulation (TMS), intra- and inter-hemispheric EEG coherence in different frequency bands (e.g. Theta (4.0–7.99 Hz)) as determined by quantitative electroencephalography (qEEG). Motor function has been measured by Fugl-Meyer (FM), spasticity has been measured by modified Ashworth scale. Multiple univariate and multivariate linear regression analyses have been performed to identify the predictors for motor function and spasticity.
Results: Univariate analyses have shown a significant interaction of amplitude and motor threshold (MT) of injured and MT, central motor conduction time of uninjured hemisphere with motor function according to Fugle-Meyer (FM). Also significant interaction has been shown between MT of injured hemisphere and spasticity.
Multivariate analyses have shown a significant interaction of MT and beta coherence in injured, uninjured hemisphere and interhemispheric in prediction of motor function by FM. Also significant interaction of MT of injured hemisphere, delta and theta coherence between C3-C4 and spasticity has been shown.
These interaction suggests that higher beta activity in the lesioned hemisphere strengthens the association between MT and FM scores. Higher beta activity in the uninjured hemisphere strengthens the association between MT and FM scores. Higher interhemispheric beta activity between C3-C4 strengthens the association between MT and FM scores. Higher delta and theta interhemispheric activity between C3-C4 strengthens the association between MT and Ashworth scores.
Conclusions: Our results suggest that MT of both hemispheres is the strongest predictors of motor recovery after stroke. Moreover, cortical activity in the injured and uninjured hemisphere measured by qEEG provides additional information, specifying the association between MT and FM scores. MT of injured hemisphere in the association with low-frequency cortical activity are the strongest predictors of spasticity after stroke.
Thus, the combination of EEG and TMS in predicting the recovery of motor control after stroke provides additional opportunities in the study of nonlinear relationships of influencing the interhemispheric networks, uninjured hemisphere and the release of subcortical activity
References
Lloyd-Jones D, Adams R, Carnethon M, et al. Heart disease and stroke statistics-2009 update: A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2009;119(3):480–486.
Mayo NE, Wood-Dauphinee S, Ahmed S, et al. Disablement following stroke. Disabil Rehabil. 1999;21(5–6):258–268.
Hatem SM, Saussez G, Della Faille M, Prist V, Zhang X, Dispa D, Bleyenheuft Y. Rehabilitation of Motor Function after Stroke: A Multiple Systematic Review Focused on Techniques to Stimulate Upper Extremity Recovery. Front Hum Neurosci. 2016 Sep 13;10:442. doi: 10.3389/fnhum.2016.00442. PMID: 27679565; PMCID: PMC5020059.
Li S. Spasticity, Motor Recovery, and Neural Plasticity after Stroke. Front Neurol. 2017 Apr 3;8:120. doi: 10.3389/fneur.2017.00120. PMID: 28421032; PMCID: PMC5377239.
Li S, Chen YT, Francisco GE, Zhou P, Rymer WZ. A Unifying Pathophysiological Account for Post-stroke Spasticity and Disordered Motor Control. Front Neurol. 2019 May 10;10:468. doi: 10.3389/fneur.2019.00468. PMID: 31133971; PMCID: PMC6524557.
Nelles G., Jentzen W., Bockisch A., Diener H.C. Neural substrates of good and poor recovery after hemiplegic stroke: a serial PET study. J.Neurol;2011.258:2168–75.10.1007/s00415-011-6085-y
Monge-Pereira E., Molina-Rueda F., Rivas-Montero F.M., Ibáñez J., Serrano J.I., Alguacil-Diego I.M., Miangolarra-Page J.C. Electroencephalography as a post-stroke assessment method: An updated review. Neurologia;2017.32(1):40-49. doi: 10.1016/j.nrl.2014.07.002.
Kuznetsov V.V., Skachkova N.A. Non-invasive stimulation of the brain: monography. K.: Phoenix; 2016.246.
Schaul N., Gloor P., Gotman J. The EEG in deep midline lesions. Neurology;1981.31.157–167. doi: 10.1212/WNL.31.2.157.
Thibaut, A., Simis, M., Battistella, L. R., Fanciullacci, C., Bertolucci, F., Huerta-Gutierrez, R., Chisari, C., & Fregni, F. Using brain oscillations and corticospinal excitability to understand and predict post-stroke motor function. Frontiers in neurology; 2017.Vol.8. 179-187. https://doi.org/10.3389/fneur.2017.00187
Simis M., Doruk D., Imamura M., Anghinah R., Morales-Quezada L., Fregni F., Battistella L. Neurophysiologic predictors of motor function in stroke. Restor.Neurol.Neurosci; 2015.34(1):45-54. doi: 10.3233/RNN-150550. PMID: 26518670.
Kozelkin A.A. Differentiated approaches to predicting the clinical and social outcome of the acute period of cerebral ischemic supratentorial stroke using spectral analysis of the electroencephalographic pattern in the onset of the disease. Zaporizhzhya medical journal. 2013. 5(80). 36-39.
Hari, R., Baillet, S., Barnes, G., Burgess, R., Forss, N., Gross, J., Hämäläinen, M., Jensen, O., Kakigi, R., Mauguière, F., Nakasato, N., Puce, A., Romani, G. L., Schnitzler, A., & Taulu, S. IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG). Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 2018. 129(8), 1720–1747. https://doi.org/10.1016/j.clinph.2018.03.042
Schaul N. The fundamental neural mechanisms of electroencephalography. Electroencephalogr. Clin.Neurophysiol;1998.106.101–107. doi:10.1016/S0013-4694(97)00 111-9.
Wu J., Srinivasan R., Quinlan E. B., Solodkin A., Small S. L., Cramer S. C. Utility of EEG measures of brain function in patients with acute stroke. J.Neurophysiol;2016.115.2399–2405. 10.1152/jn.00978.2015
Assenza, Giovanni, and Vincenzo Di Lazzaro. A useful electroencephalography marker of brain plasticity: delta waves. Neural regeneration research;2015.10.1216-7. doi:10.4103/1673-5374.162698
Cassidy J.M., Wodeyar, Wu J., Kaur K., Masuda A.K., Srinivasan R., Cramer S.C. Low-Frequency Oscillations Are a Biomarker of Injury and Recovery After Stroke. Stroke. 2020;51:1442-1450. DOI: 10.1161/STROKEAHA.120.028932
Finnigan S., van Putten M.J. EEG in ischaemic stroke: Quantitative EEG can uniquely inform (sub-)acute prognoses and clinical management. Clin. Neurophysiol;2013.124.10–19.doi: 10.1016/j.clinph.2012.07.003.
Graziadio S., Tomasevic L., Assenza G., Tecchio F., Eyre J.A. The myth of the 'unaffected' side after unilateral stroke: is reorganisation of the non-infarcted corticospinal system to re-establish balance the price for recovery? Exp.Neurol.;2012 Dec;238(2).168-75. doi: 10.1016/j.expneurol.2012.08.031.
Feige B., Scheffler K., Esposito F., di Salle F., Hennig J., Seifritz E. Cortical and subcortical correlates of electroencephalographic α rhythm modulation. J. Neurophysiol; 2005.93.2864–2872. doi: 10.1152/jn.00721.2004
Gilbertson T, Lalo E, Doyle L, Di Lazzaro V, Cioni B, Brown P. Existing motor state is favored at the expense of new movement during 13-35 Hz oscillatory synchrony in the human corticospinal system. J. Neurosci.;2005. 25.7771–9. Doi:10.1523/JNEUROSCI.1762-05.2005
Kühn A.A, Williams D., Kupsch A., Limousin P., Hariz M., Schneider G.H. Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance. Brain; 2004. 127. 735–46. Doi:10.1093/brain/awh106
Cherkasov S. Development of pathological synergies and spasticity in patients with different degrees of impairment of corticospinal innervation due to hemisphere ischemic stroke. Journal of transport medicine. 2018;4(81):37-44.
Stinear C. Prediction of recovery of motor function after stroke. Lancet Neurol. 2010 Dec;9(12):1228-1232. doi: 10.1016/S1474-4422(10)70247-7. Epub 2010 Oct 27. PMID: 21035399.
Schulz R, Braass H, Liuzzi G, Hoerniss V, Lechner P, Gerloff C, Hummel FC. White matter integrity of premotor-motor connections is associated with motor output in chronic stroke patients. Neuroimage Clin. 2014 Nov 18;7:82-6. doi: 10.1016/j.nicl.2014.11.006. PMID: 25610769; PMCID: PMC4299958.
Boyd LA, Hayward KS, Ward NS, Stinear CM, Rosso C, Fisher RJ, Carter AR, Leff AP, Copland DA, Carey LM, Cohen LG, Basso DM, Maguire JM, Cramer SC. Biomarkers of stroke recovery: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable. Int J Stroke. 2017 Jul;12(5):480-493. doi: 10.1177/1747493017714176. PMID: 28697711; PMCID: PMC6791523.a
McDonnell MN, Stinear CM. TMS measures of motor cortex function after stroke: A meta-analysis. Brain Stimul. 2017 Jul-Aug;10(4):721-734. doi: 10.1016/j.brs.2017.03.008. Epub 2017 Mar 23. PMID: 28385535.
Pfurtscheller G, Stancák A Jr, Neuper C. Post-movement beta synchronization. A correlate of an idling motor area? Electroencephalogr Clin Neurophysiol. 1996 Apr;98(4):281-93. doi: 10.1016/0013-4694(95)00258-8. PMID: 8641150.
Gerloff C, Bushara K, Sailer A, Wassermann EM, Chen R, Matsuoka T, Waldvogel D, Wittenberg GF, Ishii K, Cohen LG, Hallett M. Multimodal imaging of brain reorganization in motor areas of the contralesional hemisphere of well recovered patients after capsular stroke. Brain. 2006 Mar;129(Pt 3):791-808. doi: 10.1093/brain/awh713. Epub 2005 Dec 19. PMID: 16364955.
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