Integration of Virtual Reality into Transcranial Magnetic Stimulation Improves Cognitive Function in Patients with Parkinson’s Disease with Cognitive Impairment: A Proof-of-Concept Study
Bibliographic record
Abstract
BACKGROUND: Emerging evidence has indicated the positive effects of repetitive transcranial magnetic stimulation (rTMS) on patients with Parkinson's disease (PD) for the treatment of mild cognitive impairment (MCI). OBJECTIVE: Investigating whether combining virtual reality (VR) training with rTMS can further enhance cognitive improvement induced by rTMS treatment. METHODS: We randomly assigned 40 patients with PD and MCI into three groups, namely the rTMS-VR group (n = 13), rTMS group (n = 11), and sham rTMS group (n = 16). rTMS was administered as 10 consecutive sessions of intermittent theta burst stimulation (iTBS) over the left dorsolateral prefrontal cortex. In the rTMS-VR group, VR training was administered immediately after each rTMS session. Cognitive function was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and Montreal Cognitive Assessment (MoCA) at baseline, immediately after intervention, and at 3-month follow-up. RESULTS: Compared with the rTMS group, the rTMS-VR group exhibited significantly more improvements in total and delayed memory scores of the RBANS and the visuospatial/executive function score of the MoCA after intervention (p = 0.000∼0.046) and the delayed memory score of the RBANS at 3-month follow-up (p = 0.028). CONCLUSION: The integrated rTMS-VR protocol achieved a superior outcome in global cognitive function, more effectively enhancing working memory and visuospatial executive function than did the rTMS protocol alone. The combination of VR and rTMS can be an effective regimen for improving the cognitive function of patients with PD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".