The First “Hit” to the Endocannabinoid System? Associations Between Prenatal Cannabis Exposure and Frontolimbic White Matter Pathways in Children
Bibliographic record
Abstract
Background: -tetrahydrocannabinol and its bioactive metabolites, readily cross the placenta and accumulate in the fetal brain, disrupting neurodevelopment. Recent research using the Adolescent Brain Cognitive Development (ABCD) Study cohort has linked prenatal cannabis exposure (PCE) to greater neurobehavioral problems and lower total gray and white matter volume in children. Here, we examined the impact of PCE on frontolimbic white matter pathways that are critical for cognitive- and emotion-related functioning, show a high density of cannabinoid receptors, and are susceptible to cannabis exposure during other periods of rapid neurodevelopment (e.g., adolescence). Methods: This study included 11,530 children (mean ± SD age = 118.99 ± 7.49 months; 47% female) from the ABCD Study cohort. Linear mixed-effects models were used to examine the effects of caregiver-reported PCE on fractional anisotropy of 10 frontolimbic pathways (5 per hemisphere). Results: = .007) fornix, and these results remained significant after adjusting for a variety of covariates, multiple comparisons, fractional anisotropy of all fibers, and using a quality-control cohort only. Conclusions: In sum, we demonstrated small, yet reliable, effects of PCE on white matter integrity during childhood, particularly in the fornix, which plays a crucial role in emotion- and memory-related processes. Future studies are needed to understand the impacts of small changes in brain structure or function on neurodevelopment and risk of neurobehavioral problems.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".