Investigating the Effects of Game-based Robotic Hand Training on Cortico-motor Excitability in Healthy Adults

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Abstract

Background: Recovery of hand function is essential for regaining functional independence. To our knowledge, game-based robotic hand training hasn’t been investigated using Transcranial Magnetic Stimulation (TMS). This study observes the effects of robotic hand training on cortico-motor excitability and inhibitory pathways of the extensor digitorum in a healthy population.
Methods: Participants were screened using the TMS safety checklist. The Short-Form Edinburgh Handedness Questionnaire was used to determine the non-dominant hand. Single-pulse TMS was used to assess Motor Evoked Potential peak-peak (MEPPP) amplitudes at 80, 100, 120, 140 and 160 % of Resting Motor Threshold (RMT). Paired-pulse TMS assessed Short-Latency Intracortical Inhibition at 1ms and 3ms using adaptive threshold hunting. Measures were taken at baseline and post 300 repetitions of robotic hand training.
Results: Group averages (n=9; 7 male, 2 female; aged 20-29) showed increases in MEPPP amplitude at 100, 120 and 160% RMT post training (all p = <0.03). No significant changes were detected at 80 or 140% RMT. No differences of inhibition pre and post-training at ISI 1ms or 3ms.
Conclusion: Robotic hand training could increase cortico-motor excitability in healthy adults. Further studies should investigate the effects of robotic hand training in a cross-over control trial of active and passive robotic training.

Introduction

Functional deficits are observable in neurological populations, and hand function is critical to restore in order to regain independence. The motor recovery of the hand post-stroke is challenging and has prompted neurological rehabilitative research to investigate alternate modalities. There has been increased investigation into neuroplastic changes on interventions to improve motor function (Dimyan & Cohen, 2011).

Robotic assisted therapies are part of new treatments being trialled in rehabilitative settings. The interactive component and simulated environments can be closely matched to real-world settings and functional demands (Stephenson & Stephens, 2018). Tyromotion develops technology-based therapeutic devices aimed at restoring function. Amadeo is a sensor-based interactive device that incorporates hand movement with responsive games.

Literature emphasises the importance of high repetitions of movement for clinical outcomes in motor rehabilitation (Bütefisch, Hummelsheim, Denzler, & Mauritz, 1995). Robot assisted therapies can facilitate higher repetitions than conventional therapies through active-assisted movements and the provision of visual stimulus aiding patient motivation (Dundar, Toktas, Solak, Ulasli, & Eroglu, 2014). A systematic review demonstrated that upper limb robotic therapies can significantly improve the function of the upper arm (Kwakkel, Kollen, & Krebs, 2008); however, less evidence presently explores how the aide of robot assistance is promoting improvements from a neuro-plastic point of view.

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that involves emitting pulses from a coil that can induce excitable changes under the scalp. Resting Motor Threshold (RMT) is the minimum set output from a given TMS machine that elicits a reliable Motor Evoked Potential (MEP) on 50% of occasions (Paolo M Rossini et al., 1994; Paolo Maria Rossini et al., 2015). Parameter Estimation by Sequential Testing (PEST) is an adaptive method of determining motor threshold (MT). The method involves a more accurate MT estimation with fewer trial pulses than conservative lower and higher boundary approaches. A minimum of fourteen stimuli is shown to provide a reliable MT estimation (Awiszus, 2011).

TMS can explore central and peripheral nervous system activity and the present study observes the stimulation of the corticospinal tract pathway, using single and paired-pulse TMS. Single-pulse TMS (sTMS) is used to map cortico-motor outputs, central motor conduction time and to study brain behaviour relationships (Kobayashi & Pascual-Leone, 2003; Rossi, Hallett, Rossini, Pascual-Leone, & Group, 2009). Paired-pulse TMS (ppTMS) is used to evaluate Short-Latency Intracortical Inhibition (SICI), using a sub-threshold conditioning stimulus prior to a test stimulus. It has been shown in a healthy population that inhibition occurs at an inter-stimulus interval (ISI) between 1-6ms, and facilitation between 10-15ms (Kujirai et al., 1993). The resulting MEPs can be recorded on electromyography (EMG) and are often associated with a visual muscle twitch, depending on the strength of stimulus (Paolo Maria Rossini et al., 2015).

The purpose of this research is to observe cortico-motor excitability pre and post game-based robotic training by measuring motor thresholds with TMS. The aim is to investigate, in a healthy population, how an active robotic therapy intervention influences cortico-motor excitability. High repetitive practice has been shown to facilitate neuroplasticity which is integral for motor recovery post-stroke (Dobkin, 2005; Lynskey, Belanger, & Jung, 2008). It is hypothesised that cortico-motor excitability will increase post robotic hand training.

Methodology

Participants

Participants were recruited from around the university via research posters, online advertisement and word of mouth. Throughout the 8 months of recruitment, 16 participants were screened for eligibility and were within the age requirements (18-45 years) with no neurological conditions or upper limb injuries. Selected participants underwent final screening for eligibility based on TMS safety guidelines (Rossi et al., 2009) and 6 participants were excluded due to neurological contraindications to TMS. One participant voluntarily dropped out. Handedness was evaluated using the Short-Form Edinburgh Handedness Questionnaire (Veale, 2014). Written informed consent was obtained by all participants and the local ethics committee approved the study in accordance with the Declaration of Helsinki.

Electromyography

Surface EMG was recorded from the Extensor Digitorum (ED) using 18mm diameter self-adhesive disposable electrodes (Ag-AgCl). Standard skin preparation was used, and electrodes placed in a belly – tendon arrangement approximately 2 cm apart over the ED. The earth electrode was placed over the olecranon process of the same arm. Participants were seated with their hand resting on a firm surface in a chosen position of comfort. Electrical noise was set at <0.01Hz.

Transcranial Magnetic Stimulation

TMS was delivered by an 8cm ‘figure of 8’ coil and a Magstim 2002 TMS machine connected to a BiStim unit (Magstim Company, Dyfed, UK). The cortical current was directed in a posterior-anterior direction and the coil held tangential to the scalp. Single-pulse TMS was used to ‘hotspot’ the representation of the Extensor Digitorum (ED) of the non-dominant hand. TMS intensity was initially set to 40% maximum stimulator output (%MSO) and increased if there were no measurable MEPs. Once the optimal site was located for eliciting MEPs in the ED, the tri-planar position of the TMS coil was taped and marked on the scalp.

The participants RMT was then evaluated using an adaptive threshold hunting software known as Motor Threshold Assessment Tool (MTAT 2.0) (Awiszus, 2003, 2011; Kallioniemi et al., 2018). The criteria for RMT was set at >0.05mv MEPPP amplitude (Carroll, Lee, Hsu, & Sayde, 2008) accompanied by a MEP with appropriate electrophysical characteristics (Groppa et al., 2012). The MTAT 2.0 software was used with 14 pulses in order to quantify MT estimate (Awiszus, 2011; Qi, Wu, & Schweighofer, 2011).

Single-pulse TMS (sTMS) was used to obtain MEPPP amplitude at 80, 100, 120, 140, and 160% of RMT. Data was collected for five sweeps at each intensity in a random order and varied across all trials. The time interval between sweeps ranged between 5-7 seconds. Sweeps were excluded when the coil operator reported themselves in the incorrect position, or physiological noise was observable.

Paired-pulse TMS (ppTMS) was used to evaluate Short-Latency Intracortical Inhibition (SICI) with a conditioned stimulus of 70% RMT (CS70) at inter-stimulus interval (ISI) 1ms and 3ms. Non-conditioned (NC) trials were assessed either side of each CS (3 x NC and 2 x CS) (Kujirai et al., 1993; McCambridge, Stinear, & Byblow, 2016). The adaptive threshold hunting procedure was used to obtain MT estimates with a MEPpp amplitude inclusion criteria >0.2mV.

The procedures for sTMS and ppTMS were recorded pre and post-training for each participant using the same initial RMT, scalp location, and TMS coil orientation.

Tyromotion

Participants were seated at the Tyromotion Amadeo machine with the non-dominant hand secured in a comfortable position, the elbow flexed at 90 degrees and the wrist and shoulders neutral. Finger magnets were attached to the distal four fingers and connected to the magnets of the robotic finger levers.  Active range of motion (AROM) was measured and recorded for finger flexion and extension and calibrated for each participant. Two robotic training games (“Shooting Cans” and “Rubbish Pickup”) were administered using 75% of participants predetermined AROM and the Amadeo mid-range resistance setting. Participants completed 2-minutes of levels 6 to 10 consecutively for each game. Repetitions were recorded using a digital counter where one cycle of flexion and extension equated to one repetition. Training was terminated at the completion of 300 repetitions (Carroll et al., 2008).

Statistical analysis

IBM SPSS software was used to analyse all raw data. Statistical significance was evaluated, and reported as an F-statistic as: [F(Dftime, Dferror) = F-value, p = p-value] and effect sizes from ANOVA were determined as partial ε-squared (ηp2). A Shapiro-Wilk’s test was used to test for normality and Mauchly’s test of sphericity was used to determine non-spherical data. When sphericity was violated a Greenhouse-Geisser correction was applied.

A two-way rmANOVA with factors TIME (pre and post) and INTENSITY (80, 100, 120, 140 and 160% RMT) used to assess changes in cortical excitability. Paired-sample t-tests were used to determine if post-training values were significantly different from pre-training at each given intensity.

SICI was calculated using the formula, % threshold change = (C – NC/NC) × 100, to assess inhibition at 1ms and 3ms ISI. The abbreviation C and NC correspond to conditioned and non-conditioned MT (%MSO). A similar two-way rmANOVA was performed for analyses of inhibition with factors TIME (pre and post) and ISI (1ms and 3ms) to determine change in post-training cortical excitability.

Statistical significance was set at p < 0.05. Post hoc tests were conducted using multiple pairwise comparisons with a modified Bonferroni procedure (Rom, 1990) and paired-sample t-tests. Baseline descriptive statistics Age and RMT are presented in text as mean ± standard deviation (SD). All other values are presented in text as mean ± standard error (SE).

 

Results

Nine neurologically healthy, right handed participants (7 male, 2 female, aged 20-29, mean age = 23.67 ± 2.74 years) completed the study. At baseline, RMT was 40.76 ± 5.03% of maximum stimulator output (%MSO). None of the participants experienced adverse events with any of the procedures.

MEPPP amplitude was measured for each participant pre and post-training. Analysis of MEPs included only trials where EMG noise was < 2 standard deviations from the participants mean. The main result is shown in Figure 1. Paired-sample t-tests of the MEPPP amplitudes were greater at Post100 (-0.11 ± 0.04; t8 = -2.88, p = 0.02), Post120 (-0.34 ± 0.13; t8 = -2.72, p = 0.03) and Post160 (-0.43 ± 0.14; t8 = -3.09, p = 0.02) but not Post80 (0.0001 ± 0.003; t8 = 0.39, p = 0.97) and Post140 (-0.23 ± 0.18; t8 = -1.28, p = 0.24). A rmANOVA with a Greenhouse-Geisser correction indicated a main effect of TIME (F1, 8 = 11.70, p = 0.01, ηp2 = 0.59) and INTENSITY (F2, 18 = 22.38, p = < 0.0005, ηp2 = 0.74) and no interaction (F3, 20 = 2.50, p = 0.10, ηp2 = 0.24). Mean MEPpp amplitudes were plotted against percentage of RMT for pre and post-training measures to give a stimulus-response curve (Fig. 1).

Figure 1.Group averages (n=9) of MEPPP amplitude for ED at each given percentage of RMT (80, 100, 120, 140 and 160). Error bars represent ± SE. The stimulus-response curves represent single-pulse TMS measures pre-training (grey) and post-training (black). Paired-sample t-tests revealed post-training MEPpp amplitude increase at Post100, Post120, and Post160 (* p = < 0.03).

There was no difference in RMT suppression at SICI of 1ms post-training (5.93 ± 4.56; t8 = 1.30, p = 0.23) and 3ms post-training (-0.32 ± 4.50; t8 = -0.07, p = 0.95) shown in Figure 2. ANOVA with a Greenhouse-Geisser correction showed a main effect of ISI (F1, 8 = 6.32, p = 0.04, ηp2 = 0.44), but no main effect of TIME (F1, 8 = 0.60, p = 0.46, ηp2 = 0.70) and no ISI × TIME interaction (F1, 8 = 1.31, p = 0.29, ηp2 = 0.14). Post hoc analyses using the Bonferroni correction revealed RMT suppression was different between ISI 1ms and 3ms (11.83 ± 4.71; p = 0.04). Paired-sample t-tests confirmed SICI 1ms was different to SICI 3ms pre-training (14.95 ± 4.74; t8 = 3.16, p = 0.01) and no different post-training (8.70 ± 6.06; t8 = 1.44, p = 0.19).

The average NC MT was 46.20 ± 1.81%MSO pre-training and 43.98 ± 2.20%MSO post-training. As expected, NC MT was suppressed post-training (2.23 ± 0.83; t8 = 2.67, p = 0.03) (Fig.3).

Figure 2. Representation of group averages (n=9) for calculated percentage of Inhibition (threshold change %) at CS70 for ISI 1ms (black) and 3ms (grey) pre and post-training. Error bars represent ± SE.

Figure 3. Non-conditioned group averages (n=9) pre and post-training. Error bars represent ± SE. There was a difference between pre and post-training for NC MT (p = 0.03).

Discussion 

To date, this is the first study that evaluates the effects of game-based robotic hand training on cortico-motor excitability and the inhibitory pathways. Exploring this potential could expand the understanding of neuroplasticity in rehabilitation of paretic limb populations such as stroke affected patients (Johansson, 2011). Whilst novel, this study provides some promising results for high repetition robotic hand training and its effects on acute neuroplasticity of the contralateral hemisphere (Carroll et al., 2008; Koeneke, Lutz, Herwig, Ziemann, & Jäncke, 2006).

The main finding in this study was that mean MEPPP amplitudes post-training increased significantly at intensities of 100 (p = 0.02), 120 (p = 0.03) and 160 (p = 0.02) % RMT. This suggests that 300 repetitions of robotic hand training can induce positive changes in acute cortico-motor excitability of healthy adults. Intensities of 80 and 140% RMT did not increase significantly; however, there was still an observable increase in the mean data of 140% RMT post-training (0.23 ± 0.18) which fits the trend of the research results (Fig. 1).

No differences were observed from baseline SICI to post-training at ISI 1ms (p = 0.23) or 3ms (p = 0.95). There was a notable decrease in MT for SICI 1ms post-training (5.93 ± 4.56) suggesting some suppression of the neural inhibitory mechanisms; however, this data is inconclusive and further research would need to look to validate these effects. The main difference discovered in the SICI data was shown at pre-training between ISI 1ms and 3ms (p = 0.03). Interestingly, the population mean showed inhibition at 1ms ISI (12.98 ± 4.28) and facilitation at 3ms ISI (-1.97 ± 3.64) (Fig. 2). Contrary to these findings, current literature states that inhibition should be observed between ISI of 1-5ms and facilitation between ISI 8-15ms (Kujirai et al., 1993; Ridding, Rothwell, & Inzelberg, 1995; Shimizu et al., 1999; Ziemann, Rothwell, & Ridding, 1996). This present data could be falsely represented due to the large variability between participants and would need to be further quantified to draw conclusions.

Analysis of the SICI NC stimulus revealed a difference in MT between pre and post-training (p = 0.03), which suggests that cortico-motor excitability is observed after a period of game-based robotic hand training. The SICI NC stimulus represents that of baseline and post RMT trials in previous studies and the results observed here are consistent with the current knowledge surrounding neuroplasticity after hand training (Sunderland & Tuke, 2005).

In conclusion, the present results indicate that robotic hand training has increased effects on cortico-motor excitability in a healthy population.

One limitation highlighted in the present study is the small sample size in comparison to other TMS studies investigating cortico-motor excitability for motor recovery (Richards, Stewart, Woodbury, Senesac, & Cauraugh, 2008). A control group was also not implemented which challenges the efficacy of robotic hand training against conventional hand therapy. The game-based robotic hand training device can also be considered not challenging enough for a healthy population.

Further studies should investigate a cross-over controlled trial of active and passive robotic hand training, including evaluation of the prolonged effects of cortico-motor excitability. Research should also look to investigate neuroplastic effects of robotic hand training on neurological populations with functional deficits, such as stroke. A greater understanding of dose-response relationships between robotic training and cortico-motor excitability could help aid the development of future robot based rehabilitative protocol.

Disclosures

The authors declare there were no financial or professional conflicts of interests.

References

Awiszus, F. (2003). TMS and threshold hunting Supplements to Clinical neurophysiology (Vol. 56, pp. 13-23). doi: https://doi.org/10.1016/S1567-424X(09)70205-3

Awiszus, F. (2011). Fast estimation of transcranial magnetic stimulation motor threshold: is it safe? Brain stimulation, 4(1), 58-59; discussion 60-53. doi:https://doi.org/10.1016/j.brs.2010.09.004

Bütefisch, C., Hummelsheim, H., Denzler, P., & Mauritz, K.-H. (1995). Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. Journal of the neurological sciences, 130(1), 59-68. doi:https://doi.org/10.1016/0022-510X(95)00003-K

Carroll, T. J., Lee, M., Hsu, M., & Sayde, J. (2008). Unilateral practice of a ballistic movement causes bilateral increases in performance and corticospinal excitability. Journal of applied physiology, 104(6), 1656-1664. doi:https://doi.org/10.1152/japplphysiol.01351.2007

Dimyan, M. A., & Cohen, L. G. (2011). Neuroplasticity in the context of motor rehabilitation after stroke. Nature Reviews Neurology, 7(2), 76. doi:https://doi.org/10.1038/nrneurol.2010.200

Dobkin, B. H. (2005). Rehabilitation after stroke. New England Journal of Medicine, 352(16), 1677-1684. doi:http://doi.org/10.1056/NEJMcp043511

Dundar, U., Toktas, H., Solak, O., Ulasli, A., & Eroglu, S. (2014). A comparative study of conventional physiotherapy versus robotic training combined with physiotherapy in patients with stroke. Topics in stroke rehabilitation, 21(6), 453-461. doi:https://doi.org/10.1310/tsr2106-453

Groppa, S., Oliviero, A., Eisen, A., Quartarone, A., Cohen, L., Mall, V., . . . Thickbroom, G. (2012). A practical guide to diagnostic transcranial magnetic stimulation: report of an IFCN committee. Clinical neurophysiology, 123(5), 858-882. doi:https://doi.org/10.1016/j.clinph.2012.01.010

Johansson, B. (2011). Current trends in stroke rehabilitation. A review with focus on brain plasticity. Acta Neurologica Scandinavica, 123(3), 147-159. doi:https://doi.org/10.1111/j.1600-0404.2010.01417.x

Kallioniemi, E., Savolainen, P., Järnefelt, G., Koskenkorva, P., Karhu, J., & Julkunen, P. (2018). Transcranial magnetic stimulation modulation of corticospinal excitability by targeting cortical I-waves with biphasic paired-pulses. Brain stimulation, 11(2), 322-326. doi:https://doi.org/10.1016/j.brs.2017.10.014

Kobayashi, M., & Pascual-Leone, A. (2003). Transcranial magnetic stimulation in neurology. The lancet neurology, 2(3), 145-156. doi:https://doi.org/10.1016/S1474-4422(03)00321-1

Koeneke, S., Lutz, K., Herwig, U., Ziemann, U., & Jäncke, L. (2006). Extensive training of elementary finger tapping movements changes the pattern of motor cortex excitability. Experimental brain research, 174(2), 199-209. doi:https://doi.org/10.1007/s00221-006-0440-8

Kujirai, T., Caramia, M., Rothwell, J. C., Day, B., Thompson, P., Ferbert, A., . . . Marsden, C. D. (1993). Corticocortical inhibition in human motor cortex. The Journal of physiology, 471(1), 501-519. doi:https://doi.org/10.1113/jphysiol.1993.sp019912

Kwakkel, G., Kollen, B. J., & Krebs, H. I. (2008). Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabilitation and neural repair, 22(2), 111-121. doi:https://doi.org/10.1177/1545968307305457

Lynskey, J. V., Belanger, A., & Jung, R. (2008). Activity-dependent plasticity in spinal cord injury. Journal of rehabilitation research and development, 45(2), 229. doi:https://dx.doi.org/10.1682%2FJRRD.2007.03.0047

McCambridge, A. B., Stinear, J. W., & Byblow, W. D. (2016). Are ipsilateral motor evoked potentials subject to intracortical inhibition? Journal of neurophysiology, 115(3), 1735-1739. doi:https://doi.org/10.1152/jn.01139.2015

Qi, F., Wu, A. D., & Schweighofer, N. (2011). Fast estimation of transcranial magnetic stimulation motor threshold. Brain stimulation, 4(1), 50-57. doi:https://doi.org/10.1016/j.brs.2010.06.002

Richards, L. G., Stewart, K. C., Woodbury, M. L., Senesac, C., & Cauraugh, J. H. (2008). Movement-dependent stroke recovery: a systematic review and meta-analysis of TMS and fMRI evidence. Neuropsychologia, 46(1), 3-11. doi:https://doi.org/10.1016/j.neuropsychologia.2007.08.013

Ridding, M., Rothwell, J., & Inzelberg, R. (1995). Changes in excitability of motor cortical circuitry in patients with Parkinson’s disease. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 37(2), 181-188. doi:https://doi.org/10.1002/ana.410370208

Rom, D. M. (1990). A sequentially rejective test procedure based on a modified Bonferroni inequality. Biometrika, 77(3), 663-665. doi:https://doi.org/10.1093/biomet/77.3.663

Rossi, S., Hallett, M., Rossini, P. M., Pascual-Leone, A., & Group, S. o. T. C. (2009). Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clinical neurophysiology, 120(12), 2008-2039. doi:https://doi.org/10.1016/j.clinph.2009.08.016

Rossini, P. M., Barker, A., Berardelli, A., Caramia, M., Caruso, G., Cracco, R., . . . Lücking, C. (1994). Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee. Electroencephalography and clinical neurophysiology, 91(2), 79-92. doi:https://doi.org/10.1016/0013-4694(94)90029-9

Rossini, P. M., Burke, D., Chen, R., Cohen, L., Daskalakis, Z., Di Iorio, R., . . . George, M. (2015). Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: basic principles and procedures for routine clinical and research application. An updated report from an IFCN Committee. Clinical neurophysiology, 126(6), 1071-1107. doi:https://doi.org/10.1016/j.clinph.2015.02.001

Shimizu, T., Filippi, M., Palmieri, M., Oliveri, M., Vernieri, F., Pasqualetti, P., & Rossini, P. (1999). Modulation of intracortical excitability for different muscles in the upper extremity: paired magnetic stimulation study with focal versus non-focal coils. Clinical neurophysiology, 110(3), 575-581. doi:https://doi.org/10.1016/S1388-2457(98)00081-9

Stephenson, A., & Stephens, J. (2018). An exploration of physiotherapists’ experiences of robotic therapy in upper limb rehabilitation within a stroke rehabilitation centre. Disability and Rehabilitation: Assistive Technology, 13(3), 245-252. doi:https://doi.org/10.1080/17483107.2017.1306593

Sunderland, A., & Tuke, A. (2005). Neuroplasticity, learning and recovery after stroke: a critical evaluation of constraint-induced therapy. Neuropsychological rehabilitation, 15(2), 81-96. doi:https://doi.org/10.1080/09602010443000047

Veale, J. F. (2014). Edinburgh handedness inventory–short form: a revised version based on confirmatory factor analysis. Laterality: Asymmetries of Body, Brain and Cognition, 19(2), 164-177. doi:https://doi.org/10.1080/1357650X.2013.783045

Ziemann, U., Rothwell, J. C., & Ridding, M. C. (1996). Interaction between intracortical inhibition and facilitation in human motor cortex. The Journal of physiology, 496(3), 873-881. doi:https://doi.org/10.1113/jphysiol.1996.sp021734

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