Status and trends in transcranial magnetic stimulation research: a bibliometric analysis
Bibliographic record
Abstract
Background Transcranial magnetic stimulation (TMS), as a rapidly advancing non-invasive brain stimulation technique, has demonstrated reliable therapeutic efficacy and significant potential in brain function assessment and research. However, comprehensive bibliometric analyses of the overall TMS field remain limited. Therefore, this study conducts a systematic analysis of TMS-related literature from 2004 to 2023, aiming to facilitate further advancements in TMS research and development. Methods We retrieved TMS-related literature from 2004 to 2023 in the Web of Science Core Collection. Using CiteSpace and R language, we constructed network visualizations to illustrate annual publication outputs and journal distributions, demonstrate co-occurrence and collaboration patterns among authors, countries, and institutions, and establish keyword co-occurrence and reference co-citation analyses. Results Our analysis incorporated 6,278 TMS-related articles. Despite fluctuations, the publication output demonstrated an overall upward trend over the 20-year period. Daskalakis Z.J. emerged as the most prolific author, while the United States and Harvard University were identified as the leading contributing country and institution, respectively. Brain Stimulation ranked first in publication volume, whereas Clinical Neurophysiology received the highest citation count. The work by Rossi S. achieved the highest co-citation frequency. Current research hotspots include intermittent theta-burst stimulation, cognitive impairment, systematic review, and mild cognitive impairment. Conclusion Research related to TMS has been increasing annually and is a developing field. The United States leaded the way, while Harvard University was the most active institution. Daskalakis Z.J. (Canada) was the most prolific author. The most influential journals included Brain Stimulation, Clinical Neurophysiology, and Journal of Affective Disorders. Further deep collaboration among leading countries, institutions, and authors is needed. Current hotspots in TMS research involve integration with imaging techniques, clinical applications, optimization of parameters, and exploration of neurological modulation mechanisms.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.189 | 0.251 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".