Rehabilitation nach Schlaganfall: Durch Gehirn-Computer-Schnittstelle vermittelte funktionelle Elektrostimulation

Zusammenfassung

Eine Gehirn-Computer-Schnittstelle (BCI) in der Rehabilitation von Schlaganfallpatienten ermöglicht die Steuerung einer funktionellen Elektrostimulation (FES), um eine Muskelkontraktion in der gelähmten Extremität zum Zeitpunkt der Bewegungsintention durch Erkennung entsprechender Hirnsignale auszulösen. Es wird angenommen, dass eine genaue zeitliche Kohärenz zwischen Bewegungsintention und visuellem sowie propriozeptivem Feedback, ausgelöst durch eine reale Bewegung, neuroplastische Prozesse begünstigen und eine funktionelle Verbesserung der Parese bewirken kann. In dieser systematischen Übersichtsarbeit zu randomisierten kontrollierten Studien wurden die Datenbanken Pubmed, Scopus und Web of Science durchsucht und von 516 berücksichtigten Publikationen 13 ausgewählt, die auf 7 Studienpopulationen basierten. Ein direkter Vergleich der Studien ist durch Unterschiede im Studiendesign erschwert. Fünf Studien berichten von einer verbesserten motorischen Funktion in der BCI-FES-Gruppe, davon zeigen 3 signifikante Unterschiede zwischen der BCI-FES- und der Kontrollgruppe.

Abstract

A brain-computer interface (BCI) enables delivery of functional electrical stimulation (FES), at the time point of movement intention, to induce muscle contraction in a paretic limb, using brain activity recording. It has been hypothesized that tight temporal coupling between movement intention and visual or proprioceptive feedback obtained from an actual movement can enhance neuroplasticity and thus improve limb function. We provide an overview of this approach to post-stroke rehabilitation based on a systematic review of randomised controlled trials. The PubMed, Scopus, and Web of Science databases were searched and 516 titles identified, out of which 13 papers, originating from 7 study populations that met all inclusion criteria were selected. These studies differed in the frequency, duration, and outcome measures of the therapy used. Five studies reported greater functional improvement in the BCI–FES group, with 3 studies showing a difference between the BCI-FES and control groups.

Schlüsselwörter

Brain-Computer-Interface - EEG - Schlaganfall - motorische Rehabilitation - funktionelle Elektrostimulation

Key words

Brain–computer interface - EEG - stroke - motor rehabilitation - functional electrical stimulation

Publication History

Article published online:
29 September 2020

• Literatur

• 1 Kyu HH, Abate D, Abate KH. et al. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 1859-1922
• 2 Kwakkel G, Kollen BJ, Van der Grond JV. et al. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke 2003; 34: 2181-2186
• 3 Millan JDR, Rupp R, Müller-Putz GR. et al. Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges. Front Neurosci 2010; 4: 1-15
• 4 Birbaumer N, Silvoni S, Ramos A. BMI zur Rehabilitation von Schlaganfall. Klin Neurophysiol 2013; 44: 263-267
• 5 Soekadar SR, Birbaumer N, Slutzky MW. et al. Brain-machine interfaces in neurorehabilitation of stroke. Neurobiol Dis 2015; 83: 172-179
• 6 Sailer M, Sweeney-Reed C, Lamprecht J. Robot-assisted and device-based rehabilitation of the upper extremity. Neurol Int Open 2017; 1: E242-E246
• 7 Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 2004; 134: 9-21
• 8 Eraifej J, Clark W, France B. et al. Effectiveness of upper limb functional electrical stimulation after stroke for the improvement of activities of daily living and motor function: a systematic review and meta-analysis. Syst Rev 2017; 6: 1-11
• 9 Takeuchi N, Izumi SI. Combinations of stroke neurorehabilitation to facilitate motor recovery: Perspectives on Hebbian plasticity and homeostatic metaplasticity. Front Hum Neurosci 2015; (349) 9: 1-15
• 10 Leeb R, Biasiucci A, Schmidlin T. et al. BCI controlled neuromuscular electrical stimulation enables sustained motor recovery in chronic stroke victims. Proc 6^th Int. Brain-Computer Interface Meet. Organ by BCI Soc 2016; 74: 108
• 11 Biasiucci A, Leeb R, Iturrate I. et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun 2018; 9: 1-13
• 12 Krauth R, Schwertner J, Vogt S. et al. Cortico-muscular coherence is reduced acutely post-stroke and increases bilaterally during motor recovery: a pilot study. Front Neurol 2019; 10: 1-11
• 13 Sweeney-Reed CM, Nasuto SJ. Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis. J Neurosci Methods 2009; 184: 54-70
• 14 Young B, Nigogosynn Z, Walton L. et al. Dose-response relationships using brain-computer interface technology impact stroke rehabilitation. Front Hum Neurosci 2015; 9: 1-14
• 15 Kasner SE. Clinical interpretation and use of stroke scales. Lancet Neurol 2006; 5: 603-612
• 16 Bushnell C, Bettger JP, Cockroft KM. et al. Chronic stroke outcome measures for motor function intervention trials: expert panel recommendations. Physiol Behav 2015; 8: 163-169
• 17 Li M, Liu Y, Wu Y. et al. Neurophysiological substrates of stroke patients with motor imagery-based brain-computer interface training. Int J Neurosci 2014; 124: 403-415
• 18 Jang YY, Kim TH, Lee BH. Effects of brain-computer interface-controlled functional electrical stimulation training on shoulder subluxation for patients with stroke: a randomized controlled trial. Occup Ther Int 2016; 23: 175-185
• 19 Chung E, Park S, Jang Y. et al. Effects of brain-computer interface-based functional electrical stimulation on balance and gait function in patients with stroke: preliminary results. J Phys Ther Sci 2015; 27: 513-516
• 20 Chung E, Kim J, Park S. et al. Effects of brain-computer interface-based functional electrical stimulation on brain activation in stroke patients: a pilot randomized controlled trial. J Phys Ther Sci 2015; 27: 559-562
• 21 Young BM, Nigogosyan Z, Walton LM. et al. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng 2014; 7: 1-15
• 22 Mohanty R, Sinha AM, Remsik AB. et al. Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning. Front Neurosci 2018; 12: 1-19
• 23 Remsik AB, Dodd K, Jr LW. et al. Behavioral outcomes following brain–computer interface intervention for upper extremity rehabilitation in stroke: a randomized controlled trial. Front Neurosci 2018; 12: 1-16
• 24 Remsik AB, Gjini LW, Dodd K. et al. Ipsilesional mu rhythm desynchronization and changes in motor behavior following post stroke BCI intervention for motor rehabilitation. Front Neurosci 2019; 13: 1-18
• 25 Mrachacz-Kersting N, Jiang N, Thomas Stevenson AJ. et al. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J Neurophysiol 2016; 115: 1410-1421
• 26 Kim T, Kim S, Lee B. Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial. Occup Ther Int 2016; 23: 39-47
• 27 Cervera M, Soekadar S, Ushiba J. et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann Clin Transl Neurol 2018; 5: 651-663
• 28 Carvalho R, Dias N, Cerqueira J. Brain-machine interface of upper limb recovery in stroke patients rehabilitation: a systematic review. Physiother Res Int 2019; 24 e1764 1-16