Neurocognitive moderation of repetitive transcranial magnetic stimulation (rTMS) effects on cannabis use in schizophrenia: a preliminary analysis
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for cannabis use disorder in schizophrenia; however, gaps in the literature remain as to the potential role of neurocognitive functioning in treatment response. We evaluated the moderating role of select cognitive functions including baseline executive functioning, verbal memory, and sustained attention, and we explore the mediating role of changes in task performance on changes in cannabis use in both active and sham rTMS groups. Participants underwent high-frequency (20 Hz) rTMS applied to the bilateral dorsolateral prefrontal cortex 5x/week for 4 weeks. Weekly self-report of cannabis use and semi-quantitative urinary carboxy-tetrahydrocannabinol levels were recorded. A neurocognitive battery assessing verbal memory, visuospatial working memory, verbal working memory, sustained attention, delayed discounting, and complex planning was administered pre- and post-treatment. Better baseline performance on tasks assessing sustained attention, delayed discounting, and complex planning moderated the extent to which participants in the active group reduced cannabis use. There were no significant indirect pathways between treatment, changes in neuropsychological performance, and changes in cannabis use; however, active rTMS improved complex planning and sustained attention. These preliminary findings suggest that there is a moderating role of sustained attention, delayed discounting, and complex planning on the effects of rTMS on cannabis use. Further, mediation models suggest rTMS may exert direct effects on cannabis use independent of its effects on cognitive functioning in people with SCZ. Trial Registration: clinicaltrials.gov: NCT03189810.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".