Developmental regulation of fear learning and anxiety behavior by endocannabinoids
Bibliographic record
Abstract
The developing brain undergoes substantial maturation into adulthood and the development of specific neural structures occurs on differing timelines. Transient imbalances between developmental trajectories of corticolimbic structures, which are known to contribute to regulation over fear learning and anxiety, can leave an individual susceptible to mental illness, particularly anxiety disorders. There is a substantial body of literature indicating that the endocannabinoid (eCB) system critically regulates stress responsivity and emotional behavior throughout the life span, making this system a novel therapeutic target for stress- and anxiety-related disorders. During early life and adolescence, corticolimbic eCB signaling changes dynamically and coincides with different sensitive periods of fear learning, suggesting that eCB signaling underlies age-specific fear learning responses. Moreover, perturbations to these normative fluctuations in corticolimbic eCB signaling, such as stress or cannabinoid exposure, could serve as a neural substrate contributing to alterations to the normative developmental trajectory of neural structures governing emotional behavior and fear learning. In this review, we first introduce the components of the eCB system and discuss clinical and rodent models showing eCB regulation of fear learning and anxiety in adulthood. Next, we highlight distinct fear learning and regulation profiles throughout development and discuss the ontogeny of the eCB system in the central nervous system, and models of pharmacological augmentation of eCB signaling during development in the context of fear learning and anxiety.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".