The Influence of Recreational Substance Use in TMS Research
Bibliographic record
Abstract
(1) Background: Transcranial magnetic stimulation (TMS) approaches are widely used to study cortical and corticospinal function. However, responses to TMS are subject to significant intra-and inter-individual variability. Acute and chronic exposure to recreational substances alters the excitability of the sensorimotor system and may contribute to the variability in TMS outcome measures. The increasing prevalence of recreational substance use poses a significant challenge for executing TMS studies, but there is a lack of clarity regarding the influence of these substances on sensorimotor function. (2) Methods: The literature investigating the influence of alcohol, nicotine, caffeine and cannabis on TMS outcome measures of corticospinal, intracortical and interhemispheric excitability was reviewed. (3) Results: Both acute and chronic use of recreational substances modulates TMS measures of excitability. Despite the abundance of research in this field, we identify knowledge gaps that should be addressed in future studies to better understand the influence of these substances on TMS outcomes. (4) Conclusions: This review highlights the need for TMS studies to take into consideration the history of participant substance use and to control for acute substance use prior to testing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".