Neuromodulation to Treat Substance Use Disorders in People With Schizophrenia and Other Psychoses: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Substance use disorders (SUDs) are a common yet poorly studied comorbidity in individuals with psychotic disorders. The co-occurrence of the two complicates recovery and interferes with pharmacological and behavioral treatment response and adherence. Recently, researchers have been exploring both invasive and non-invasive neuromodulation techniques as potential treatment methods for SUDs. We review the evidence that neuromodulation may reduce substance craving and consumption in individuals with schizophrenia. METHODS: = 1,432). Of these, we identified seven studies examining the effects of repetitive transcranial magnetic stimulation (rTMS) and two studies using transcranial direct current stimulation (tDCS) on drug consumption and craving in schizophrenia or schizoaffective disorders. RESULTS: Despite the limited number of studies in this area, the evidence suggests that rTMS to the dorsolateral prefrontal cortex (DLPFC) may reduce cannabis and tobacco use in patients with schizophrenia and schizoaffective disorder. Findings with tDCS, however, were inconclusive. DISCUSSION: Our systematic review suggests that rTMS applied to DLPFC is a safe and promising therapeutic technique for the management of comorbid schizophrenia and SUDs, with the majority of the evidence in tobacco use disorder. However, there was substantial heterogeneity in study methods, underscoring the need to optimize stimulation parameters (e.g., frequency, duration, and target regions). Larger clinical trials are needed to establish the efficacy of rTMS in reducing drug consumption and craving in psychotic patients, ideally in comparison to existing pharmacological and behavioral interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.004 | 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".