A review of brain stimulation methods to treat substance use disorders
Bibliographic record
Abstract
BACKGROUND: Substance use disorders (SUDs) are a leading cause of disability worldwide. While several pharmacological and behavioral treatments for SUDs are available, these may not be effective for all patients. Recent studies using non-invasive neuromodulation techniques including Repetitive Transcranial Magnetic Stimulation (rTMS), Transcranial Direct Current Stimulation (tDCS), and Deep Brain Stimulation (DBS) have shown promise for SUD treatment. OBJECTIVE: Multiple studies were evaluated investigating the therapeutic potential of non-invasive brain stimulation techniques in treatment of SUDs. METHOD: Through literature searches (eg, PubMed, Google Scholar), 60 studies (2000-2017) were identified examining the effect of rTMS, tDCS, or DBS on cravings and consumption of SUDs, including tobacco, alcohol, cannabis, opioids, and stimulants. RESULTS: rTMS and tDCS demonstrated decreases in drug craving and consumption, while early studies with DBS suggest similar results. Results are most encouraging when stimulation is targeted to the Dorsolateral Prefrontal Cortex (DLPFC). CONCLUSIONS: Short-term treatment with rTMS and tDCS may have beneficial effects on drug craving and consumption. Future studies should focus on extending therapeutic benefits by increasing stimulation frequency and duration of treatment. SCIENTIFIC SIGNIFICANCE: The utility of these methods in SUD treatment and prevention are unclear, and warrants further study using randomized, controlled designs. (Am J Addict 2018;27:71-91).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".