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ArticleFrontiers in medicine2024

Effects of robot-assisted hand function therapy on brain functional mechanisms: a synchronized study using fNIRS and sEMG.

Changfeng Cheng, Tiantian Liu, Beibei Zhang, Xubo Wu, Zhenwang Song, Zhongzhi Zhao, Xue Ren, Minjun Zhao, Yajuan Su, Jiening Wang

Registry-linked trialAbstract read
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07019207 (The Effect of Virtual Reality-Based Dual-Task Training on Cognition, Cortical Activation, and Functional Level in Patients With Spinal Cord Injury), which is not on this map. Cited by 5 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07019207 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

The Effect of Virtual Reality-Based Dual-Task Training on Cognition, Cortical Activation, and Functional Level in Patients With Spinal Cord Injury

TypeinterventionalSponsorIstinye UniversityRan2025 to 2026Enrolled45ConditionsSpinal Cord InjuryArmsVR-Based Dual-Task Training with Motor Imagery, VR-Based Dual-Task Training without Motor Imagery, Proprioceptive Neuromuscular Facilitation
3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Changfeng Cheng *Shanghai Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Tiantian Liu *Shanghai Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Beibei ZhangThe first affiliated hospital of Anhui University of Traditional Chinese Medicine, Anhui, China.
Xubo WuShanghai Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Zhenwang SongShanghai Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Zhongzhi ZhaoShanghai Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Xue RenShanghai Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Minjun ZhaoShanghai Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yajuan SuShanghai Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Jiening WangShanghai Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Robot-assisted hand function therapy is pivotal in the rehabilitation of patients with stroke; however, its therapeutic mechanism remains elusive. Currently, research examining the impact of robot-assisted hand function therapy on brain function in patients with stroke is scarce, and there is a lack of studies investigating the correlation between muscle activity and alterations in brain function. Objective: This study aimed to investigate the correlation between forearm muscle movement and brain functional activation by employing the synchronized use of functional near-infrared spectroscopy and surface electromyography methods. Moreover, it sought to compare neural activity patterns during different rehabilitation tasks and refine the mechanism of robot-assisted hand function therapy for post-stroke hand function impairments. Methods: Stroke patients with hand dysfunction underwent three sessions of robot-assisted hand function therapy within 2 weeks to 3 months of onset. The fNIRS-sEMG synchronous technique was used to observe brain function and forearm muscle activation. Ten participants were randomly assigned to receive mirror, resistance, or passive rehabilitation training. During the intervention, cortical and muscle activation information was obtained using fNIRS and electromyographic signals. The primary outcomes included changes in oxyhemoglobin concentration and root mean square of surface electromyography. Results: Compared to the resting state, the Oxy-Hb concentration in the brain regions involved in three rehabilitation tasks with robot-assisted hand function therapy significantly increased ( Conclusion: The simultaneous sEMG-fNIRS study found a significant correlation between muscle movement and brain activity after stroke, which provides an important basis for understanding the treatment mechanism of hand function impairment.

Indexed as

brain functionfunctional near-infrared spectroscopyrobot-assisted hand function therapystrokesurface electromyography

Identifiers

PMID39544380
PMCPMC11560759

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.