Mirror neuron training improves the effectiveness of transcranial magnetic stimulation in treating vascular cognitive impairment
Bibliographic record
Abstract
Objective To explore the effect of high-frequency, repeated transcranial magnetic stimulation (rTMS) together with mirror neuron training on the cognition of persons with vascular cognitive impairment but without dementia (VCIND). Methods Thirty-three persons with VCIND were randomly divided into an rTMS+ MNS group (n=17) and an rTMS group (n=16) using a random number table. Both groups were given conventional rehabilitation training and rTMS over the left dorsolateral prefrontal cortex at 10 Hz, 2000 pulses per day at their individual motor thresholds on weekdays for 4 weeks using a CCY- I stimulator. The rTMS+ MNS group was additionally given mirror neuron training. The Montreal cognitive assessment (MoCA), the mini-mental state examination (MMSE) and the modified Barthel Index (MBI) were administered before and after the treatment. The P300 latency and amplitude of both groups were also measured. Results Before the treatment, no significant differences were found in any of the measurements. After the treatment, the average MoCA, MMSE and MBI scores had increased significantly in both groups, with those of the rTMS+ MNS group increasing significantly more than those of the rTMS group. After the treatment, the average P300 latency and amplitude of both groups were also significantly better than before the treatment. Compared with the rTMS group, the average P300 latency of the rTMS+ MNS group was significantly shorter, while the average amplitude was significantly greater. Conclusion Mirror neuron training combined with rTMS is more effective than rTMS alone in improving the cognition and ADL performance of VCIND patients. It is worth applying in clinical practice. Key words: Vascular cognitive impairment; Transcranial magnetic stimulation; Mirror neuron training
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".