Cannabis Use is Associated with Lower Hippocampus and Amygdala Volume: High-Resolution Segmentation of Structural Subfields in a Large Non-clinical Sample
Bibliographic record
Abstract
There is mixed evidence that individuals who use cannabis have reduced hippocampal and amygdalar gray matter volume, potentially because of small sample sizes and imprecise morphological characterization. New automated segmentation procedures have improved the measurement of these structures and allow better examination of their subfields, which have been linked to distinct aspects of memory and emotion. The current study applies this new segmentation procedure to the Human Connectome Project Young Adult dataset (N = 1081) to investigate associations of cannabis use with gray matter volume in the hippocampus and amygdala. Results revealed significant bilateral inverse associations of hippocampal volume with recent cannabis use (positive THC urine drug screen) and total lifetime consumption (ps<.001), as well as association of the left amygdala total lifetime consumption (p<.001). Hippocampal subfield analyses indicated these associations were primarily driven by the head of the hippocampus, the first section of the cornu amonis (CA1), and the subicular complex, which have been linked to verbal working memory, episodic memory, and spatial memory respectively. In the amygdala, the association was driven by subfields in the basolateral complex, which links the amygdala to the cortex and is critical in emotion regulation. No associations were detected for age of cannabis initiation or lifetime presence of cannabis use disorder. In one of the largest studies to date, these results support the hypothesis that cannabis use is linked with hippocampal and amygdalar volume and clarify both the specific subfields and the specific aspects of cannabis use that are responsible for these links.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".