Urinary tetrahydrocannabinol is associated with poorer working memory performance and alterations in associated brain activity
Bibliographic record
Abstract
Worldwide, cannabis is one of the most widely used psychoactive substances and cannabis use has been implicated in poorer performance in several cognitive domains, including working memory (WM). However, the neural mechanisms underlying these WM decrements are not well understood and the current study investigated the association of cannabis involvement with WM performance and associated neural activation in the Human Connectome Project (N = 1038). Multiple indicators of cannabis involvement were examined in relation to behavioral performance and brain activity in a visual N-back task using functional magnetic resonance imaging. A positive urine drug screen for tetrahydocannabinol (THC+ status), the principal psychoactive constituent in cannabis, was associated with worse WM performance and differential brain response in areas previously linked to WM performance. Furthermore, decreases in blood-activation-level-dependent (BOLD) signal in WM task-positive brain regions and increases in task-negative regions mediated the relationship between THC+ status and WM performance. In contrast, WM performance and BOLD response during the N-back task were not associated with total lifetime cannabis use, age of first use, or other indicators of involvement, suggesting that the effects of cannabis on WM were short-term residual effects, rather than long-term persistent effects. These findings elucidate differential influences of cannabis involvement on neurocognition and have significant potential implications for occupational performance in diverse settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".