Synaptic signatures of perinatal cannabinoids: A systematic review of rodent hippocampal synaptic plasticity, learning, and memory
Bibliographic record
Abstract
The expanding legalization of cannabis raises significant public health concerns about its use during pregnancy, particularly due to the limited understanding of its impact on neurodevelopment. Existing research suggests that perinatal cannabis or cannabinoid exposure may impair learning and memory; however, variations in study design hinder the ability to draw generalizable conclusions. Clinical studies are limited in their observational nature and the lack of insight into neural or cellular mechanisms underlying cognitive changes, underscoring the importance of preclinical studies to explore the effects of perinatal cannabinoids in greater detail. The objective of this systematic review is to consolidate findings from existing preclinical research that investigates the effects of perinatal cannabinoid exposure on learning and memory and the putative mechanism of learning and memory, hippocampal synaptic plasticity, in rodents. This review summarizes studies on hippocampal synaptic plasticity (n = 2), spatial/visual memory (n = 13), working memory (n = 6), recognition memory (n = 12), and associative memory (n = 7). Perinatal cannabinoid-induced impairments were reported in the two synaptic plasticity studies, and in 24 out of 30 studies that examined learning and memory, with spatial memory tasks showing the most consistent deficits. While the existing evidence converges on the notion that perinatal cannabinoid exposure negatively impacts hippocampal physiology and associated memory functions, further research is needed to disentangle the influence of various methodological factors, including offspring sex and age, cannabinoid type, time of gestational exposure, and method of administration.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".