Light Promotes Neural Correlates of Fear Memory via Enhancing Brain-Derived Neurotrophic Factor (BDNF) Expression in the Prelimbic Cortex
Bibliographic record
Abstract
Exposure to light has been shown to enhance vigilance and improve working memory, possibly due to changes in prefrontal function. Ample evidence supports the critical role of prefrontal cortex (PFC) in fear memory performance. However, the effects of light on memory processing and its potential mechanisms remain unclear. Here, through rats exposure conditioned to light at different memory phases, we sought evidence for the influences by employing behavioral tests, pharmacological infusions, immunoblotting, and electrophysiological recording. Exposure to light immediately following conditioning of 30 min or longer could effectively improve consolidation of fear memory without altering short-term memory or upgrading the original fear. The absence of significant freezing during baseline and intertrial interval periods ruled out the possibility of a general induction of freezing by light. Meanwhile, rats exposed to light in homecages or conditioning chambers exhibited a similar memory phenotype, indicating that light specifically enhanced the fear stimulus rather than the contextual environment. Furthermore, light exposure elevated the training-induced brain-derived neurotrophic factor (BDNF) expression in the prelimbic, but not infralimbic, subregion of the PFC. Moreover, the BDNF-TrkB pathway, but not the BDNF-p75 NTR pathway, was involved in light-mediated fear memory. The enhancement in BDNF activity effectively facilitated firing correlates of prelimbic pyramidal neurons but not fast-spiking interneurons. Blocking the training-induced BDNF by its antibody abolished the effects of light on neural function and fear memory. Therefore, our findings indicate that light enhances training-induced BDNF expression that promotes the neural correlate of memory function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".