A bibliometric analysis of research hotspots and trends in transcranial magnetic stimulation and Alzheimer’s disease
Bibliographic record
Abstract
Background: Research regarding Transcranial Magnetic Stimulation (TMS) and Alzheimer's disease (AD) has been increasing; however, no bibliometric analysis has yet been conducted in this domain. This study employs bibliometric methods to identify research trends and hotspots concerning AD and TMS. Methods: We conducted a search in the Web of Science Core Database for articles related to AD and TMS from January 1, 2014, to October 22, 2024. After stringent selection, we performed bibliometric analysis using Excel, VOSviewer, CiteSpace and CoreMine. Results: The number of articles pertaining to AD and TMS has increased annually, with a notable surge post-2020. The three leading countries in publication volume are China, the United States, and Italy. The top institutions contributing to this field include Harvard Medical School, the University of Toronto, and the University of Brescia. The author with the highest publication output is Giacomo Koch. The journal with the most publications is the Journal of Alzheimer's disease. The 10 most frequently occurring keywords are Alzheimer's disease, Transcranial Magnetic Stimulation, mild cognitive impairment, dementia, memory, double-blind, repetitive transcranial magnetic stimulation, noninvasive brain stimulation, cognitive impairment, and plasticity. Text mining has revealed that the anatomical structure "brain" and the gene "Amyloid Precursor Protein (APP)" are significantly related to both AD and TMS, suggesting that TMS may offer a therapeutic avenue for AD by modulating the activity of APP. Conclusion: Our article employs bibliometric methods to unveil trends in research related to AD and TMS, including collaborations among countries, regions, and authors, as well as key research hotspots. We provide objective data that serves as a reference for scientific research and clinical work concerning AD and TMS.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.195 | 0.233 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".