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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".