Evidence map›Paper›PMID 41901923›Full record

SynthesisSensors (Basel, Switzerland)2026

Neuroplasticity Mechanism of Stroke Rehabilitation Training System Based on Virtual Reality: A Review.

Runzi Cheng, Hui Xu, Xing Wang

Abstract readSystematic Review
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Runzi ChengDepartment of Biomedical Engineering, Chongqing University, Chongqing 400044, China.
Hui XuDepartment of Biomedical Engineering, Chongqing University, Chongqing 400044, China.
Xing WangDepartment of Biomedical Engineering, The Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The paper systematically reviews the application status of virtual reality technology in the rehabilitation of upper-limb movement, lower-limb gait balance, and cognitive function of stroke patients. Based on electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS), the correlation mechanism of virtual reality promoting brain functional reorganization and neural remodeling is analyzed from the perspective of task-oriented training, reinforcement learning, and neural regulation. The virtual reality rehabilitation scheme can accurately match the actual needs of clinical rehabilitation, and exploring the internal mechanism of its intervention in the dynamic process of rehabilitation is helpful to promote the deep integration of virtual reality technology and rehabilitation medicine. This study integrates high temporal resolution EEG activity data, magnetic resonance imaging spatial positioning information, cerebral hemodynamic data, and virtual reality system behavior data, realizing the systematic quantitative output of rehabilitation effect in the "human-computer" interactive closed loop. Finally, the future development direction is projected from the aspects of system optimization, standard setting, and multi-technology integration to provide a reference for promoting the clinical application and development of virtual reality technology in stroke rehabilitation.

Indexed as

NeuroimagingNeuronal PlasticityStrokeStroke RehabilitationVirtual RealityHumansMultimodal ImagingTreatment Outcomeneuroplasticityrehabilitation trainingstrokevirtual reality

Identifiers

PMID41901923
PMCPMC13030400

What OpenQuestion holds

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Registered trials

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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.