The Simultaneous Application of Transcranial Magnetic Stimulation and Virtual Reality to Treat Cognitive Deficits Among Stroke Patients: A Randomized Controlled Trial
Bibliographic record
Abstract
Background and objective Integrating virtual reality (VR) and transcranial magnetic stimulation (TMS) offers a promising strategy for stroke rehabilitation, as it specifically focuses on reorganizing neural connections and activating brain activity in the cortex. The main goal is to create equitable connections between the brain's two hemispheres to enhance the execution of voluntary movements by stimulating the central executive network (CEN) to strengthen both motor and cognitive abilities. This study aims to propose a therapeutic approach that can improve cognitive recovery and overall quality of life in patients after a stroke. Methods A total of 69 participants were enrolled in the study based on certain inclusion and exclusion criteria. The patients underwent pre-assessment and were randomly allocated into three groups: Group 1 received simultaneous repetitive TMS (rTMS) and virtual reality treatment (VRT), Group 2 received rTMS combined with sham VRT, and Group 3 received sham stimulation with VRT, in a 1:1:1 ratio using opaque, sealed, and stapled envelopes (SNOSE). Post-assessment was carried out using the same measures: the National Institutes of Health Stroke Scale (NIHSS), Addenbrooke's Cognitive Test (ACE III), and Montreal Cognitive Assessment (MOCA). Statistical analysis was conducted to determine the specific outcomes. Data analysis was carried out using IBM SPSS Statistics version 29 (IBM Corp., Armonk, NY), employing student's t-test for within-group comparisons and repeated measures ANOVA for between-group comparisons. The significance level was set at 5%. Results The results demonstrated statistical significance in NIHSS scores across all treatment groups (p<0.001). Regarding cognitive outcomes, improvements were observed in memory, language, and overall cognitive performance (ACE III) within all groups (p<0.05), with significant between-group outcomes (p = 0.009, p = 0.01, p = 0.004, respectively), suggesting variations in treatment effects across cognitive domains. However, no significant differences between groups were found in terms of fluency and visuospatial skills (p = 0.49, p = 0.13), indicating no treatment effects in these domains. Conclusions Based on our findings, the combined intervention involving rTMS and VRT, compared to sham treatments, demonstrates promising outcomes in alleviating stroke severity and improving specific cognitive functions such as memory, language, and overall cognitive performance. Additionally, the combined administration offers a more effective therapy than when they are administered separately.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".