Cannabis use‐related working memory deficit mediated by lower left hippocampal volume
Bibliographic record
Abstract
The association between cannabis exposure and working memory impairment and its neural substrates remain unclear. In this cross-sectional observational study, we investigated this by examining the relationship between frequency of exposure to cannabis, working memory performance and regional brain volumes and tested whether lower volumes of cortical and subcortical structures mediate the association between cannabis exposure and working memory deficit using the Human Connectome Project data from 234 individuals with self-reported cannabis exposure and 174 individuals unexposed to cannabis. We tested the relationship between self-reported frequency of cannabis exposure and list-sorting working memory task performance (total number of correct responses), between T1 weighted MRI-derived regional grey-matter volumes and working memory task performance as well as between frequency of cannabis exposure and brain volumes after controlling for potential confounders. Finally, mediation analysis was carried out to test whether deficit in working memory performance associated with cannabis use was mediated by its association with lower grey-matter volume. Participants who reported higher frequency of cannabis use tended to have lower number of correct responses in the list-sorting working memory task and lower bilateral hippocampal volumes. Association between severity of cannabis exposure as indexed by frequency of cannabis use and impairment in working memory was mediated by lower left hippocampal volume in cannabis users. We report evidence in support of the left hippocampus volume-mediated working memory impairment associated with recreational cannabis exposure. Future studies employing prospective longitudinal design are necessary to examine the cause-effect relationships of cannabis exposure on working memory and brain volumes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".