Wearable hip-assist robot modulates cortical activation during gait in stroke patients: a functional near-infrared spectroscopy study

Su-Hyun Lee, Hwang-Jae Lee, Youngbo Shim, Won Hyuk Chang, Byung-Ok Choi, Gyu-Ha Ryu, Yun-Hee Kim, Su-Hyun Lee, Hwang-Jae Lee, Youngbo Shim, Won Hyuk Chang, Byung-Ok Choi, Gyu-Ha Ryu, Yun-Hee Kim

Abstract

Background: Gait dysfunction is common in post-stroke patients as a result of impairment in cerebral gait mechanism. Powered robotic exoskeletons are promising tools to maximize neural recovery by delivering repetitive walking practice.

Objectives: The purpose of this study was to investigate the modulating effect of the Gait Enhancing and Motivating System-Hip (GEMS-H) on cortical activation during gait in patients with chronic stroke.

Methods: Twenty chronic stroke patients performed treadmill walking at a self-selected speed either with assistance of GEMS-H (GEMS-H) or without assistance of GEMS-H (NoGEMS-H). Changes in oxygenated hemoglobin (oxyHb) concentration in the bilateral primary sensorimotor cortex (SMC), premotor cortices (PMC), supplemental motor areas (SMA), and prefrontal cortices (PFC) were recorded using functional near infrared spectroscopy.

Results: Walking with the GEMS-H promoted symmetrical SMC activation, with more activation in the affected hemisphere than in NoGEMS-H conditions. GEMS-H also decreased oxyHb concentration in the late phase over the ipsilesional SMC and bilateral SMA (P < 0.05).

Conclusions: The results of the present study reveal that the GEMS-H promoted more SMC activation and a balanced activation pattern that helped to restore gait function. Less activation in the late phase over SMC and SMA during gait with GEMS-H indicates that GEMS-H reduces the cortical participation of stroke gait by producing rhythmic hip flexion and extension movement and allows a more coordinate and efficient gait patterns. Trial registration NCT03048968. Registered 06 Feb 2017.

Keywords: Cortical activation; Functional near infrared spectroscopy; Stroke; Wearable hip-assist robot.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Gait Enhancing and Motivating System-Hip (GEMS-H)
Fig. 2
Fig. 2
a Experimental design: the block design time course, including baseline, walking, and resting periods. b Location of optodes. The fNIRS system consists of 16 light source (white) and 16 detector (black) fibers, resulting in a total of 49 channels distributed over left SMC (Channels 1, 9, 42), right SMC (Channels 3, 27, 32), left SMA (Channels 11, 12), right SMA (Channels 24, 25), left PMC (Channels 10, 13, 45), right PMC (Channels 23, 26, 36), left PFC (Channel 16), and right PFC (Channel 20). fNIRS functional near infrared spectroscopy, SMC primary sensorimotor cortex, SMA supplemental motor areas, PMC premotor cortex, PFC prefrontal cortex, GEMS-H walking with assistance of GEMS-H, NoGEMS-H walking without assistance of GEMS-H
Fig. 3
Fig. 3
The average group changes in oxyHb concentrations in each ROI under GEMS-H and NoGEMS-H conditions. *P < 0.05. SMC, primary sensorimotor cortex; SMA, supplemental motor areas; PMC, premotor cortex; PFC, prefrontal cortex; GEMS-H, walking with assistance of GEMS-H; NoGEMS-H, walking without assistance of GEMS-H

References

    1. Belda-Lois J-M, Mena-del Horno S, Bermejo-Bosch I, Moreno JC, Pons JL, Farina D, Iosa M, Molinari M, Tamburella F, Ramos A. Rehabilitation of gait after stroke: a review towards a top-down approach. J Neuroeng Rehabil. 2011;8:66. doi: 10.1186/1743-0003-8-66.
    1. Sheffler LR, Chae J. Hemiparetic Gait. Phys Med Rehabil Clin N Am. 2015;26:611–623. doi: 10.1016/j.pmr.2015.06.006.
    1. Moore SA, Hickey A, Lord S, Del Din S, Godfrey A, Rochester L. Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study. J Neuroeng Rehabil. 2017;14:130. doi: 10.1186/s12984-017-0341-z.
    1. Norouzi-Gheidari N, Archambault PS, Fung J. Effects of robot-assisted therapy on stroke rehabilitation in upper limbs: systematic review and meta-analysis of the literature. J Rehabil Res Dev. 2012;49:479–496. doi: 10.1682/JRRD.2010.10.0210.
    1. Sivan M, O'Connor RJ, Makower S, Levesley M, Bhakta B. Systematic review of outcome measures used in the evaluation of robot-assisted upper limb exercise in stroke. J Rehabil Med. 2011;43:181–189. doi: 10.2340/16501977-0674.
    1. Jayaraman A, O'Brien MK, Madhavan S, Mummidisetty CK, Roth HR, Hohl K, Tapp A, Brennan K, Kocherginsky M, Williams KJ, et al. Stride management assist exoskeleton vs functional gait training in stroke: A randomized trial. Neurology. 2019;92:e263–e273. doi: 10.1212/WNL.0000000000006782.
    1. Lee HJ, Lee S, Chang WH, Seo K, Shim Y, Choi BO, Ryu GH, Kim YH. A wearable hip assist robot can improve gait function and cardiopulmonary metabolic efficiency in elderly adults. IEEE Trans Neural Syst Rehabil Eng. 2017;25:1549–1557.
    1. Kim DS, Lee HJ, Lee SH, Chang WH, Jang J, Choi BO, Ryu GH, Kim YH. A wearable hip-assist robot reduces the cardiopulmonary metabolic energy expenditure during stair ascent in elderly adults: a pilot cross-sectional study. BMC Geriatr. 2018;18:230. doi: 10.1186/s12877-018-0921-1.
    1. Lee SH, Lee HJ, Chang WH, Choi BO, Lee J, Kim J, Ryu GH, Kim YH. Gait performance and foot pressure distribution during wearable robot-assisted gait in elderly adults. J Neuroeng Rehabil. 2017;14:123. doi: 10.1186/s12984-017-0333-z.
    1. Lee H-J, Lee S-H, Seo K, Lee M, Chang WH, Choi B-O, Ryu G-H, Kim Y-H. Training for walking efficiency with a wearable hip-assist robot in patients with stroke: a pilot randomized controlled trial. Stroke. 2019;50:3545–3552. doi: 10.1161/STROKEAHA.119.025950.
    1. Fukuyama H, Ouchi Y, Matsuzaki S, Nagahama Y, Yamauchi H, Ogawa M, Kimura J, Shibasaki H. Brain functional activity during gait in normal subjects: a SPECT study. Neurosci Lett. 1997;228:183–186. doi: 10.1016/S0304-3940(97)00381-9.
    1. Dobkin BH. Strategies for stroke rehabilitation. Lancet Neurol. 2004;3:528–536. doi: 10.1016/S1474-4422(04)00851-8.
    1. Herold F, Wiegel P, Scholkmann F, Thiers A, Hamacher D, Schega L. Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks. Neurophotonics. 2017;4:041403. doi: 10.1117/1.NPh.4.4.041403.
    1. Quaresima V, Bisconti S, Ferrari M. A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults. Brain Lang. 2012;121:79–89. doi: 10.1016/j.bandl.2011.03.009.
    1. Bunce SC, Izzetoglu M, Izzetoglu K, Onaral B, Pourrezaei K. Functional near-infrared spectroscopy. IEEE Eng Med Biol Mag. 2006;25:54–62. doi: 10.1109/MEMB.2006.1657788.
    1. Khan RA, Naseer N, Qureshi NK, Noori FM, Nazeer H, Khan MU. fNIRS-based neurorobotic Interface for gait rehabilitation. J Neuroeng Rehabil. 2018;15:7. doi: 10.1186/s12984-018-0346-2.
    1. Luft AR, Macko RF, Forrester LW, Villagra F, Ivey F, Sorkin JD, Whitall J, McCombe-Waller S, Katzel L, Goldberg AP, Hanley DF. Treadmill exercise activates subcortical neural networks and improves walking after stroke: a randomized controlled trial. Stroke. 2008;39:3341–3350. doi: 10.1161/STROKEAHA.108.527531.
    1. Miyai I, Yagura H, Oda I, Konishi I, Eda H, Suzuki T, Kubota K. Premotor cortex is involved in restoration of gait in stroke. Ann Neurol. 2002;52:188–194. doi: 10.1002/ana.10274.
    1. Miyai I, Yagura H, Hatakenaka M, Oda I, Konishi I, Kubota K. Longitudinal optical imaging study for locomotor recovery after stroke. Stroke. 2003;34:2866–2870. doi: 10.1161/01.STR.0000100166.81077.8A.
    1. Yang M, Yang Z, Yuan T, Feng W, Wang P. A systemic review of functional near-infrared spectroscopy for stroke: current application and future directions. Front Neurol. 2019;10:58. doi: 10.3389/fneur.2019.00058.
    1. Lu CF, Liu YC, Yang YR, Wu YT, Wang RY. Maintaining Gait Performance by cortical activation during dual-task interference: a functional near-infrared spectroscopy study. PLoS ONE. 2015;10:e0129390. doi: 10.1371/journal.pone.0129390.
    1. Suzuki M, Miyai I, Ono T, Oda I, Konishi I, Kochiyama T, Kubota K. Prefrontal and premotor cortices are involved in adapting walking and running speed on the treadmill: an optical imaging study. Neuroimage. 2004;23:1020–1026. doi: 10.1016/j.neuroimage.2004.07.002.
    1. Strangman G, Culver JP, Thompson JH, Boas DA. A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. Neuroimage. 2002;17:719–731. doi: 10.1006/nimg.2002.1227.
    1. Naseer N, Hong K-S. fNIRS-based brain-computer interfaces: a review. Front Human Neurosci. 2015;9:3.
    1. Boas DA, Gaudette T, Strangman G, Cheng X, Marota JJ, Mandeville JB. The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics. Neuroimage. 2001;13:76–90. doi: 10.1006/nimg.2000.0674.
    1. Koenraadt KL, Roelofsen EG, Duysens J, Keijsers NL. Cortical control of normal gait and precision stepping: an fNIRS study. Neuroimage. 2014;85(Pt 1):415–422. doi: 10.1016/j.neuroimage.2013.04.070.
    1. Picard N, Strick PL. Imaging the premotor areas. Curr Opin Neurobiol. 2001;11:663–672. doi: 10.1016/S0959-4388(01)00266-5.
    1. Miyai I, Tanabe HC, Sase I, Eda H, Oda I, Konishi I, Tsunazawa Y, Suzuki T, Yanagida T, Kubota K. Cortical mapping of gait in humans: a near-infrared spectroscopic topography study. Neuroimage. 2001;14:1186–1192. doi: 10.1006/nimg.2001.0905.
    1. Atsumori H, Kiguchi M, Katura T, Funane T, Obata A, Sato H, Manaka T, Iwamoto M, Maki A, Koizumi H. Noninvasive imaging of prefrontal activation during attention-demanding tasks performed while walking using a wearable optical topography system. J Biomed Opt. 2010;15:046002. doi: 10.1117/1.3462996.
    1. Piper SK, Krueger A, Koch SP, Mehnert J, Habermehl C, Steinbrink J, Obrig H, Schmitz CH. A wearable multi-channel fNIRS system for brain imaging in freely moving subjects. Neuroimage. 2014;85:64–71. doi: 10.1016/j.neuroimage.2013.06.062.
    1. Wylie GR, Graber HL, Voelbel GT, Kohl AD, DeLuca J, Pei Y, Xu Y, Barbour RL. Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study. Neuroimage. 2009;47:473–481. doi: 10.1016/j.neuroimage.2009.04.056.
    1. Hausdorff JM, Yogev G, Springer S, Simon ES, Giladi N. Walking is more like catching than tapping: gait in the elderly as a complex cognitive task. Exp Brain Res. 2005;164:541–548. doi: 10.1007/s00221-005-2280-3.
    1. Marder E, Bucher D. Central pattern generators and the control of rhythmic movements. Curr Biol. 2001;11:R986–R996. doi: 10.1016/S0960-9822(01)00581-4.
    1. Iosa M, Gizzi L, Tamburella F, Dominici N. Neuro-motor control and feed-forward models of locomotion in humans. Front Hum Neurosci. 2015;9:306. doi: 10.3389/fnhum.2015.00306.
    1. Scheeren T, Schober P, Schwarte L. Monitoring tissue oxygenation by near infrared spectroscopy (NIRS): background and current applications. J Clin Monit Comput. 2012;26:279–287. doi: 10.1007/s10877-012-9348-y.
    1. Moriarty TA, Mermier C, Kravitz L, Gibson A, Beltz N, Zuhl M. Acute aerobic exercise based cognitive and motor priming: practical applications and mechanisms. Front Psychol. 2019;10:2790. doi: 10.3389/fpsyg.2019.02790.
    1. Ono Y, Noah JA, Zhang X, Nomoto Y, Suzuki T, Shimada S, Tachibana A, Bronner S, Hirsch J. Motor learning and modulation of prefrontal cortex: an fNIRS assessment. J Neural Eng. 2015;12:066004. doi: 10.1088/1741-2560/12/6/066004.
    1. Jueptner M, Stephan KM, Frith CD, Brooks DJ, Frackowiak RS, Passingham RE. Anatomy of motor learning. I. Frontal cortex and attention to action. J Neurophysiol. 1997;77:1313–1324. doi: 10.1152/jn.1997.77.3.1313.

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