Literature DB >> 33788815

Effects of passive and active training modes of upper-limb rehabilitation robot on cortical activation: a functional near-infrared spectroscopy study.

Jinyu Zheng1,2, Ping Shi1,2,3, Mengxue Fan1,2, Sailan Liang1,2, Sujiao Li1,2, Hongliu Yu1,2,3.   

Abstract

OBJECTIVE: The purpose of this study is to investigate the cortical activation during passive and active training modes under different speeds of upper extremity rehabilitation robots.
METHODS: Twelve healthy subjects completed the active and passive training modes at various speeds (0.12, 0.18, and 0.24 m/s) for the right upper limb. The functional near-infrared spectroscopy (fNIRS) was used to measure the neural activities of the sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and prefrontal cortex (PFC).
RESULTS: Both the active and passive training modes can activate SMC, PMC, SMA, and PFC. The activation level of active training is higher than that of passive training. At the speed of 0.12 m/s, there is no significant difference in the intensity of the two modes. However, at the speed of 0.24 m/s, there are significant differences between the two modes in activation levels of each region of interest (ROI) (P < 0.05) (SMC: F = 8.90, P = 0.003; PMC: F = 8.26, P = 0.005; SMA: F = 5.53, P = 0.023; PFC: F = 9.160, P = 0.003).
CONCLUSION: This study mainly studied on the neural mechanisms of active and passive training modes at different speeds based on the end-effector upper-limb rehabilitation robot. Slow, active training better facilitated the cortical activation associated with cognition and motor control.See Video Abstract, http://links.lww.com/WNR/A621.
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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Year:  2021        PMID: 33788815     DOI: 10.1097/WNR.0000000000001615

Source DB:  PubMed          Journal:  Neuroreport        ISSN: 0959-4965            Impact factor:   1.837


  2 in total

1.  Vibrotactile enhancement in hand rehabilitation has a reinforcing effect on sensorimotor brain activities.

Authors:  Qiang Du; Jingjing Luo; Qiying Cheng; Youhao Wang; Shijie Guo
Journal:  Front Neurosci       Date:  2022-10-04       Impact factor: 5.152

2.  A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity.

Authors:  Neelesh Kumar; Konstantinos P Michmizos
Journal:  Sci Rep       Date:  2022-01-20       Impact factor: 4.379

  2 in total

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