Neuromodulation for Cannabis Use: A Scoping Review
Bibliographic record
Abstract
This scoping review explores the use of neuromodulation techniques in individuals with cannabis use. Our goal was to determine whether cannabis use alters cortical excitation and inhibition in the context of neuromodulation and to determine whether neuromodulation affects craving and cannabis use patterns. A systematic search was conducted using PubMed, OVID Medline, and PsycINFO from inception to 20 December 2022. Our review identified ten relevant studies, eight of which used Transcranial Magnetic Stimulation (TMS), while two employed Transcranial Direct Current Stimulation (tDCS). Findings from TMS studies suggest that cannabis users exhibit altered cortical inhibition, with decreased short interval intracortical inhibition (SICI) compared to non-users. Single sessions of rTMS did not have any impact on cannabis craving. By contrast, two studies found that multiple sessions of rTMS reduced cannabis use, but these changes did not meet the threshold for statistical significance and both studies were limited by small sample sizes. The two included tDCS studies found contradictory results, with one showing reduced cannabis craving with active treatment and another showing no effect of active treatment on craving compared to sham. Future studies should further explore the effects of multiple treatment sessions and different neuromodulation modalities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".