Impact of Marijuana on Response Inhibition: an fMRI Study in Young Adults
Bibliographic record
Abstract
Rationale: Marijuana use in adolescence is prevalent and increasing. Understanding the neural correlates of the impact of this use is critical for policy making and for youth awareness. Objectives The effects of marijuana use on response inhibition were investigated in 19–21-year-olds using functional magnetic resonance imaging (fMRI). Methods: Participants were members of the Ottawa Prenatal Prospective Study, a longitudinal study that collected a unique body of information on participants from infancy to young adulthood including: prenatal drug history, detailed cognitive/behavioral performance, and current and past drug use. This information allowed for the control of an unparalleled number of potentially confounding variables including: prenatal marijuana, nicotine, alcohol, and caffeine exposure and offspring alcohol, marijuana, and nicotine use. Ten marijuana users and 14 nonusers that served as controls performed a Go/No-Go task while fMRI blood oxygen level-dependent response was examined. Results: Despite similar task performance, there was a positive relationship between amount of marijuana smoked and activation in right thalamus, premotor cortex and middle frontal gyrus. These regions form part of the neural network responsible for inhibition control. There was also a positive dose dependent relationship with marijuana and activation in inferior parietal lobe and precuneus, also parts of response inhibition pathways. Conclusions: These results suggest a dose dependent alteration in neural functioning during response inhibition after controlling for other prenatal and current drug use. These alterations may be necessary in order to compensate for neural changes in response inhibition circuits caused by long term marijuana use that began during adolescence/young adulthood.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".