Effects of cannabinoids on resting state functional brain connectivity: A systematic review
Bibliographic record
Abstract
Cannabis products are widely used for medical and non-medical reasons worldwide and vary in content of cannabinoids such as delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD). Resting state functional connectivity offers a powerful tool to investigate the effects of cannabinoids on the human brain. We systematically reviewed functional neuroimaging evidence of connectivity during acute cannabinoid administration. A pre-registered (PROSPERO ID: CRD42020184264) systematic review of 13 studies comprising 318 participants (mean age of 25 years) was conducted and reported using the PRISMA checklist. During THC and THCv exposure vs placebo reduced connectivity with the NAcc was widely reported. Limited evidence shows that such effects are offset by co-administration of CBD. NAcc-frontal region connectivity was associated with intoxication levels. Cannabis intoxication vs placebo was associated with lower striatal-ACC connectivity. CBD and CBDv vs placebo were associated with both higher and lower connectivity between striatal-prefrontal/other regions. Overall, cannabis and cannabinoids change functional connectivity in the human brain during resting state as a function of the type of cannabinoid examined.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| 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".