A systematic review and meta-analysis of neuromodulation therapies for substance use disorders
Bibliographic record
Abstract
While pharmacological, behavioral and psychosocial treatments are available for substance use disorders (SUDs), they are not always effective or well-tolerated. Neuromodulation (NM) methods, including repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS) and deep brain stimulation (DBS) may address SUDs by targeting addiction neurocircuitry. We evaluated the efficacy of NM to improve behavioral outcomes in SUDs. A systematic literature search was performed on MEDLINE, PsychINFO, and PubMed databases and a list of search terms for four key concepts (SUD, rTMS, tDCS, DBS) was applied. Ninety-four studies were identified that examined the effects of rTMS, tDCS, and DBS on substance use outcomes (e.g., craving, consumption, and relapse) amongst individuals with SUDs including alcohol, tobacco, cannabis, stimulants, and opioids. Meta-analyses were performed for alcohol and tobacco studies using rTMS and tDCS. We found that rTMS reduced substance use and craving, as indicated by medium to large effect sizes (Hedge's g > 0.5). Results were most encouraging when multiple stimulation sessions were applied, and the left dorsolateral prefrontal cortex (DLPFC) was targeted. tDCS also produced medium effect sizes for drug use and craving, though they were highly variable and less robust than rTMS; right anodal DLPFC stimulation appeared to be most efficacious. DBS studies were typically small, uncontrolled studies, but showed promise in reducing misuse of multiple substances. NM may be promising for the treatment of SUDs. Future studies should determine underlying neural mechanisms of NM, and further evaluate extended treatment durations, accelerated administration protocols and long-term outcomes with biochemical verification of substance use.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".