Upregulating excitability of corticospinal pathways in stroke patients using TMS neurofeedback; A pilot study
W D Liang, Y Xu, J Schmidt, L X Zhang, K L Ruddy, W D Liang, Y Xu, J Schmidt, L X Zhang, K L Ruddy
Abstract
Upper limb weakness following a stroke affects 80% of survivors and is a key factor in preventing their return to independence. State-of-the art approaches to rehabilitation often require that the patient can generate some activity in the paretic limb, which is not possible for many patients in the early period following stroke. Approaches that enable more patients to engage with upper limb therapy earlier are urgently needed. Motor imagery has shown promise as a potential means to maintain activity in the brain's motor network, when the patient is incapable of generating functional movement. However, as imagery is a hidden mental process, it is impossible for individuals to gauge what impact this is having upon their neural activity. Here we used a novel brain-computer interface (BCI) approach allowing patients to gain an insight into the effect of motor imagery on their brain-muscle pathways, in real-time. Seven patients 2-26 weeks post stroke were provided with neurofeedback (NF) of their corticospinal excitability measured by the size of motor evoked potentials (MEP) in response to transcranial magnetic stimulation (TMS). The aim was to train patients to use motor imagery to increase the size of MEPs, using the BCI with a computer game displaying neurofeedback. Patients training finger muscles learned to elevate MEP amplitudes above their resting baseline values for the first dorsal interosseous (FDI) and abductor digiti minimi (ADM) muscles. By day 3 for ADM and day 4 for FDI, MEP amplitudes were sustained above baseline in all three NF blocks. Here we have described the first clinical implementation of TMS NF in a population of sub-acute stroke patients. The results show that in the context of severe upper limb paralysis, patients are capable of using neurofeedback to elevate corticospinal excitability in the affected muscles. This may provide a new training modality for early intervention following stroke.
Keywords: Brain-computer interface; Motor imagery; Neurofeedback; Stroke; TMS; Upper limb.
Conflict of interest statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.
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References
- Ang K.K., Chua K.S.G., Phua K.S., Wang C., Chin Z.Y., Kuah C.W.K. A randomized controlled trial of EEG-based motor imagery brain- computer interface robotic rehabilitation for stroke. Clin. EEG Neurosci. 2015;46:310–320.
- Boake C., Noser E., Ro T., Baraniuk S., Gaber M., Johnson R., Salmeron E.T., Tran T.M., Lai J.M., Taub E., Moye L.A., Grotta J.C., Levin H.S. Constraint-induced movement therapy during early stroke rehabilitation. Neurorehabil. Neural Repair. 2016;21(1):14–24.
- Byblow W.D., Stinear C.M., Barber P.A., Petoe M.A., Ackerley S.J. Proportional recovery after stroke depends on corticomotor integrity. Ann. Neurol. 2015;78(6):848–859. doi: 10.1002/ana.24472.
- Cicinelli P., Pasqualetti P., Zaccagnini M., Traversa R., Oliveri M., Rossini P.M. Interhemispheric Asymmetries of Motor Cortex Excitability in the Postacute Stroke Stage: A Paired-Pulse Transcranial Magnetic Stimulation Study. Stroke. J. Cerebr. Circul. 2003;34(11):2653–2658. doi: 10.1161/01.STR.0000092122.96722.72.
- Coscia M., Wessel M.J., Chaudary U., Millian J.D.R., Micera S., Guggisberg A., Vuadens P., Donoghue J., Burbaumer N., Hummel F.C. Nuerotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke. Brain. 2019;142:2182–2197.
- Cretu A., Germann M., Ruddy K., Wenderoth N. Motor resonance in primary motor cortex reflects the integration of kinematic and contextual information in accordance to a Bayesian predictive coding framework. J. Neurophysiol. 2019;121(4):1451–1464. doi: 10.1152/jn.00655.2018.
- Crow, J.L., Lincoln, N.N., Nouri, F.M., De. Weerdt, W. 2009. The effectiveness of EMG biofeedback in the treatment of arm function after stroke. Int. Disabil. Stud. 11(4), 155-160.
- Dromerick A.W., Edwardson M.A., Edwards D.F., Giannetti M.L., Barth J., Brady K.P., Chan E., Tan M.T., Tamboli I., Chia R., Orquizza M., Padilla R.M., Cheema A.K., Mapstone M.E., Fiandaca M.S., Federoff H.J., Newport E.L. Critical Periods after stroke study: translating animal stroke recovery experiments into a clinical trial. Front. Hum. Neurosci. 2015;9(231)
- Ehrsson H.H., Geyer S., Naito E. Imagery of Voluntary Movement of Fingers, Toes, and Tongue Activates Corresponding Body-Part-Specific Motor Representations. J. Neurophysiol. 2003;90(5):3304–3316. doi: 10.1152/jn.01113.2002.
- Ertelt D., Small S., Solodkin A., Dettmers C., McNamara A., Binkofski F., Buccino G. Action observation has a positive impact on rehabilitation of motor deficits after stroke. NeuroImage. 2007;36:T164–T173. doi: 10.1016/j.neuroimage.2007.03.043.
- Feigin V.L., Forouzanfar M.H., Krishnamurthi R., Mensah G.A., Connor M., Bennett D.A. Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010. Lancet. 2010;2014(383):245–255.
- Hanakawa T., Immisch I., Toma K., Dimyan M.A., van Gelderen P., Hallett M. Functional Properties of Brain Areas Associated With Motor Execution and Imagery. J. Neurophysiol. 2003;89(2):989–1002. doi: 10.1152/jn.00132.2002.
- Jackson P.L., Lafleur M.F., Malouin F., Richards C., Doyon J. Potential role of mental practice using motor imagery in neurologic rehabilitation. Arch. Phys. Med. Rehabil. 2001;82(8):1133–1141. doi: 10.1053/apmr.2001.24286.
- Johnson C.O., Nguyen M., Roth G.A., Nichols E., Alam T., Abate D., Adebayo O.M. Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(5):439–458.
- Laffont I., Bakhti K., Coroian F., van Dokkum L., Mottet D., Schweighofer N. Innovative technologies applied to sensori- motor rehabilitation after stroke. Ann. Phys. Rehabil. Med. 2014;57:543–551.
- Lawrence E.S., Coshall C., Dundas R., Stewart J., Rudd A.G., Howard R. Estimates of the prevalence of acute stroke impairments and disability in a multiethnic population. Stroke. 2001;32:1279–1284.
- Lotze M., Montoya P., Erb M., Hülsmann E., Flor H., Klose U. Activation of Cortical and Cerebellar Motor Areas during Executed and Imagined Hand Movements: An fMRI Study. J. Cogn. Neurosci. 2006;11(5):491–501. doi: 10.1162/089892999563553.
- Miltner W.H.R., Bauder H., Sommer M., Dettmers C., Taubb R. Effects of constraint induced movement therapy on patients with chronic motor defecits after stroke; A replication. Stroke. 1999;30(3):586–592.
- Murphy T.H., Corbett D. Plasticity during stroke recovery: from synapse to behaviour. Nat. Rev. Neurosci. 2009;10:861–872.
- National Clinical Programme for Rehabilitation Medicine (NCPRM). 2017. Rehabilitation Medicine. Model of Care for the provision of specialist rehabilitation services in Ireland. .
- Ruddy, K., Balsters, J., Mantini, D., Liu, Q., Kassraian-Fard, P., Enz, N., Mihelj, E., Chander, B., Soekadar, S., Wenderoth, N. 2018. Neural activity related to volitional regulation of cortical excitability. eLife, 7:e40843 DOI: 10.7554/eLife.40843.
- Shah R., Wilkins E., Nichols M., Kelly P., El-Sadi F., Wright L., Townsend N. Epidemiology report: trends in sex-specific cerebrovascular disease mortality in Europe based on WHO mortality data. Eur. Heart J. 2019;40(9):755–764.
- Soekadar, S.R., Witkowski, M., Gomez, C., Opisso, E., Medina, J., Cortese, M., Cempini, M., Carozza, M.C., Cohen, L.G., Birbaumer, N., Vitiello. 2016. Sci. Robot. 1. Eaag3296.
- Stinear C.M., Byblow W.D., Steyvers M., Levin O., Swinnen S.P. Kinesthetic, but not visual, motor imagery modulates corticomotor excitability. Exp. Brain Res. 2005;168(1–2):157–164. doi: 10.1007/s00221-005-0078-y.
- Stinear C., Barber A., Petoe M., Anwar S., Byblow W. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain. 2012;136(8):2527–2535.
- Stroke Association. 2017 State of the Nation; Stroke Statistics. .
- Taub E., Crago J.E., Uswatte G. Constraint-induced movement therapy: a new approach to treatment in physical rehabilitation. Rehabil. Psychol. 1998;43:152–170.
- Taube W., Lorch M., Zeiter S., Keller M. Non-physical practice improves task performance in an unstable, perturbed environment: motor imagery and observational balance training. Front. Hum. Neurosci. 2014;8(522):1048. doi: 10.3389/fnhum.2014.00972.
- Taube, W., Mouthon, M., Leukel, C., Cortex, H. H., 2015. Brain activity during observation and motor imagery of different balance tasks: an fMRI study. Cortex 64, 102-114.http://.
- Thickbroom G.W., Byrnes M.L., Archer S.A., Mastaglia F.L. Motor outcome after subcortical stroke correlates with the degree of cortical reorganization. Clin. Neurophysiol. 2004;115(9):2144–2150. doi: 10.1016/j.clinph.2004.04.001.
- Ushiba J., Soekadar S.R. Brain-machine interfaces for rehabilitation of poststroke hemiplegia. Prog. Brain Res. 2016;228:163–183.
- Van Dokkum L.E.H., Ward T., Laffont I. Brain computer interfaces for neurorehabilitation—its current status as a rehabilitation strategy post-stroke. Ann. Phys. Rehabil. Med. 2015;58:3–8.
- Wade D.T. Measurement in neurological rehabiltation. Curr. Opin. Neurol. Neurosurg. 1992;5:682–686.
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