The effect of robot-assisted gait training on cortical activation in stroke patients: A functional near-infrared spectroscopy study

Abstract

OBJECTIVE:

To investigate the effects of the robot–assisted gait training on cortical activation and functional outcomes in stroke patients.

METHODS:

The patients were randomly assigned: training with Morning Walk® (Morning Walk group; n = 30); conventional physiotherapy (control group; n = 30). Rehabilitation was performed five times a week for 3 weeks. The primary outcome was the cortical activation in the Morning Walk group. The secondary outcomes included gait speed, 10-Meter Walk Test (10MWT), FAC, Motricity Index–Lower (MI–Lower), Modified Barthel Index (MBI), Rivermead Mobility Index (RMI), and Berg Balance Scale (BBS).

RESULTS:

Thirty-six subjects were analyzed, 18 in the Morning Walk group and 18 in the control group. The cortical activation was lower in affected hemisphere than unaffected hemisphere at the beginning of robot rehabilitation. After training, the affected hemisphere achieved a higher increase in cortical activation than the unaffected hemisphere. Consequently, the cortical activation in affected hemisphere was significantly higher than that in unaffected hemisphere (P = 0.036). FAC, MBI, BBS, and RMI scores significantly improved in both groups. The Morning Walk group had significantly greater improvements than the control group in 10MWT (P = 0.017), gait speed (P = 0.043), BBS (P = 0.010), and MI–Lower (P = 0.047) scores.

CONCLUSION:

Robot-assisted gait training not only improved functional outcomes but also increased cortical activation in stroke patients.

Keywords

Stroke stroke rehabilitation robotics gait hospitalization functional connectivity

Get full access to this article

View all access options for this article.

References

Bergfeldt,

, Jonsson,

, Bergfeldt,

, & Julin,

(2015). Cortical activation changes and improved motor function in stroke patients after focal spasticity therapy–an interventional study applying repeated fMRI. BMC Neurology, 15, 52. https://doi.org/10.1186/s12883-015-0306-4.

Blum,

, & Korner-Bitensky,

(2008). Usefulness of the Berg Balance Scale in stroke rehabilitation: a systematic review. Physical Therapy, 88(5), 559–566. https://doi.org/10.2522/ptj.20070205.

Dawes,

(2016). Prefrontal Cortex Activation While Walking Under Dual-Task Conditions in Stroke : A Multimodal Imaging Study Prefrontal Cortex Activation While Walking UnderDual-TaskConditions in Stroke :AMultimodal Imaging Study. 30(October), 591-599. https://doi.org/10.1177/1545968315613864.Prefrontal.

Delpy,

D. T.

, Cope,

, van der Zee,

, Arridge,

, Wray,

, & Wyatt,

(1988). Estimation of optical pathlength through tissue from direct time of flight measurement. Physics in Medicine and Biology, 33(12), 1433–1442. https://doi.org/10.1088/0031-9155/33/12/008.

Demeurisse,

, Demol,

, & Robaye,

(1980). Motor evaluation in vascular hemiplegia. European Neurology, 19(6), 382–389. https://doi.org/10.1159/000115178.

Dobkin,

B. H.

(2004). Strategies for stroke rehabilitation. The Lancet. Neurology, 3(9), 528–536. https://doi.org/10.1016/S1474-4422(04)00851-8.

Gonzalez,

E. G.

, Myers,

S. J.

, Downey,

J. A.

, & Darling,

R. C.

(2001). Downey and Darling’s physiological basis of rehabilitation medicine (3rd ed.). Butterworth-Heinemann. http://www.worldcat.org/oclc/468745861.

Han,

, Jo,

S. A.

, Jo,

, Kim,

, Park,

M. H.

, & Kang,

(2008). An adaptation of the Korean mini-mental state examination (K-MMSE) in elderly Koreans: Demographic influence and population-based norms (the AGE study. Archives of Gerontology and Geriatrics, 47(3), 302–310. https://doi.org/10.1016/j.archger.2007.08.012.

Herold,

, Wiegel,

, Scholkmann,

, Thiers,

, Hamacher,

, & Schega,

(2017). Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks. Neurophotonics, 4(4), 041403. https://doi.org/10.1117/1.NPh.4.4.041403.

10.

Holtzer,

, Mahoney,

J. R.

, Izzetoglu,

, Onaral,

, & Verghese,

(2011). fNIRS study of walking and walking while talking in young and old individuals. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 66A(8), 879–887. https://doi.org/10.1093/gerona/glr068.

11.

Hwang,

I. S.

, Lee,

H. M.

, Cherng,

R. J.

, & Chen,

J. J.

(2003). Electromyographic analysis of locomotion for healthy and hemiparetic subjects—study of performance variability and rail effect on treadmill. Gait & Posture, 18(1), 1–12. https://doi.org/10.1016/s0966-6362(02)00071-1.

12.

Kim,

, Kim,

D. Y.

, Chun,

M. H.

, Kim,

S. W.

, Jeon,

H. R.

, Hwang,

C. H.

, Choi,

J. K.

, & Bae,

(2019). Effects of robot-(Morning Walk®) assisted gait training for patients after stroke: a randomized controlled trial. Clinical Rehabilitation, 33(3), 516–523. https://doi.org/10.1177/0269215518806563.

13.

Lee,

S. H.

, Lee,

H. J.

, Shim,

, Chang,

W. H.

, Choi,

B. O.

, Ryu,

G. H.

, & Kim,

Y. H.

(2020). Wearable hip-assist robot modulates cortical activation during gait in stroke patients: a functional near-infrared spectroscopy study. Journal of Neuroengineering and Rehabilitation, 17, 145. https://doi.org/10.1186/s12984-020-00777-0.

14.

Leff,

D. R.

, Orihuela-Espina,

, Elwell,

C. E.

, Athanasiou,

, Delpy,

D. T.

, Darzi,

A. W.

, & Yang,

G. Z.

(2011). Assessment of the cerebral cortex during motor task behaviours in adults: a systematic review of functional near infrared spectroscopy (fNIRS) studies. Neuroimage, 54(4), 2922–2936. https://doi.org/10.1016/j.neuroimage.2010.10.058.

15.

Lin,

P. Y.

, Chen,

J. J.

, & Lin,

S. I.

(2013). The cortical control of cycling exercise in stroke patients: an fNIRS study. Human Brain Mapping, 34(10), 2381–2390. https://doi.org/10.1002/hbm.22072.

16.

Lo,

, Stephenson,

, & Lockwood,

(2017). Effectiveness of robotic assisted rehabilitation for mobility and functional ability in adult stroke patients: a systematic review. JBI Database of Systematic Reviews and Implementation Reports, 15(12), 3049–3091. https://doi.org/10.11124/JBISRIR-2017-003456.

17.

Luft,

A. R.

, Macko,

R. F.

, Forrester,

L. W.

, Villagra,

, Ivey,

, Sorkin,

J. D.

, Whitall,

, McCombe-Waller,

, Katzel,

, Goldberg,

A. P.

, & Hanley,

D. F.

(2008). Treadmill exercise activates subcortical neural networks and improves walking after stroke: a randomized controlled trial. Stroke, 39(12), 3341–3350. https://doi.org/10.1161/STROKEAHA.108.527531.

18.

Maidan,

, Nieuwhof,

, Bernad-Elazari,

, Reelick,

M. F.

, Bloem,

B. R.

, Giladi,

, Deutsch,

J. E.

, Hausdorff,

J. M.

, Claassen,

J. A.

, & Mirelman,

(2016). The role of the frontal lobe in complex walking among patients with Parkinson’s disease and healthy older adults: an fNIRS study. Neurorehabilitation and Neural Repair, 30(10), 963–971. https://doi.org/10.1177/1545968316650426.

19.

Mehrholz,

, Wagner,

, Rutte,

, Meißner,

, & Pohl,

(2007). Predictive validity and responsiveness of the functional ambulation category in hemiparetic patients after stroke. Archives of Physical Medicine and Rehabilitation, 88(10), 1314–1319. https://doi.org/10.1016/j.apmr.2007.06.764.

20.

Mihara,

, Hattori,

, Hatakenaka,

, Yagura,

, Kawano,

, Hino,

, & Miyai,

(2013). Near-infrared spectroscopy-mediated neurofeedback enhances efficacy of motor imagery-based training in poststroke victims: a pilot study. Stroke, 44(4), 1091–1098. https://doi.org/10.1161/STROKEAHA.111.674507.

21.

Mihara,

, & Miyai,

(2016). Review of functional near-infrared spectroscopy in neurorehabilitation. Neurophotonics, 3(3), 031414. https://doi.org/10.1117/1.NPh.3.3.031414.

22.

Möller,

, Rusaw,

, Hagberg,

, & Ramstrand,

(2019). Reduced cortical brain activity with the use of microprocessor-controlled prosthetic knees during walking. Prosthetics and Orthotics International, 43(3), 257–265. https://doi.org/10.1177/0309364618805260.

23.

Oh,

, Song,

, & Kim,

(2018). Validating attentive locomotion training using interactive treadmill: an fNIRS study. Journal of Neuroengineering and Rehabilitation, 15, 122. https://doi.org/10.1186/s12984-018-0472-x.

24.

Okamoto,

, Dan,

, Sakamoto,

, Takeo,

, Shimizu,

, Kohno,

, Oda,

, Isobe,

, Suzuki,

, Kohyama,

, & Dan,

(2004). Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping. Neuroimage, 21(1), 99–111. https://doi.org/10.1016/j.neuroimage.2003.08.026.

25.

Shah,

, Vanclay,

, & Cooper,

(1989). Predicting discharge status at commencement of stroke rehabilitation. Stroke, 20(6), 766–769. https://doi.org/10.1161/01.str.20.6.766.

26.

Takeda,

, Gomi,

, Imai,

, Shimoda,

, Hiwatari,

, & Kato,

(2007). Shift of motor activation areas during recovery from hemiparesis after cerebral infarction: a longitudinal study with near-infrared spectroscopy. Neuroscience Research, 59(2), 136–144. https://doi.org/10.1016/j.neures.2007.06.1466.

27.

Wade,

D. T.

, & Hewer,

R. L.

(1987). Functional abilities after stroke: measurement, natural history and prognosis. Journal of Neurology, Neurosurgery and Psychiatry, 50(2), 177–182. https://doi.org/10.1136/jnnp.50.2.177.