Effectiveness of Noninvasive Brain Stimulation Protocols on Drug Craving and Consumption/Relapse in Substance Use Disorders: A Systematic Review and Meta-analysis of 208 Clinical Trials and 36 Protocols
Bibliographic record
Abstract
Abstract Background Transcranial Magnetic, Electrical, and Focused-Ultrasound Stimulation (TMS/tES/tFUS) are major noninvasive brain stimulation (NIBS) techniques used to treat various psychiatric disorders, including substance use disorders (SUDs). Although NIBS with varying stimulation parameters shows promising effects on drug-related behaviors such as craving and consumption/relapse, the question of which protocol is most effective remains unresolved. Method To address this gap, we conducted a living systematic review and meta-analysis to quantify the effects of TMS/tES/tFUS on SUD. Controlled trials of TMS/tES/tFUS for all types of SUD were selected up to January 1, 2024. Findings The final systematic review included 208 trials (121 TMS, 86 tES, 1 tFUS), with 116 randomized sham-controlled trials (59 TMS, 57 tES) eligible for meta-analysis due to a low risk of bias. Data from 5,106 participants in active and 4,914 in sham groups were analyzed. TMS showed medium effects on craving (g = 0.52, 95% CI: 0.29-0.75, p < 0.001, I^2 = 89.36) and consumption (g = 0.41, CI: 0.26-0.56, p < 0.001, I^2 = 61.56). tES showed a medium effect on craving (g = 0.40, CI: 0.25-0.55, p < 0.001, I^2 = 60.69) and a small effect on consumption (g = 0.27, CI: 0.15-0.38, p = 0.013, I^2 = 22.67). Among the 36 different protocols examined, subgroup analyses identified the strongest effect for reducing both craving and consumption with high-frequency deep TMS using the H4 coil (single study) (g = 3.92 and 1.12, respectively, p < 0.001), with a maximum electric field over the frontopolar cortex. This effect was followed by high-frequency rTMS over the left DLPFC (g = 0.66 and 0.52, respectively, p < 0.05), as well as bilateral anodal-right cathodal-left (g = 0.49 and 0.42, respectively, p < 0.0001) and anodal-left cathodal-right (g = 0.38 and 0.31, respectively, p < 0.05) tES with direct current (tDCS) over the DLPFC, with maximum electric field on the frontopolar cortex. Interpretation Our results provide evidence that TMS and tES stimulation over frontopolar and DLPFC regions produce medium to large effect sizes in reducing drug craving and consumption/relapse in SUD. While requiring further replication in future studies, these findings highlight the promise of these interventions.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.004 | 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".