The neurobiological mechanisms of photoperiod impact on brain functions: a comprehensive review
Bibliographic record
Abstract
Variations in day length, or photoperiodism, whether natural or artificial light, significantly impact biological, physiological, and behavioral processes within the brain. Both natural and artificial light sources are environmental factors that significantly influence brain functions and mental well-being. Photoperiodism is a phenomenon, occurring either over a 24 h cycle or seasonally and denotes all biological responses of humans and animals to these fluctuations in day and night length. Conversely, artificial light occurrence refers to the presence of light during nighttime hours and/or its absence during the daytime (unnaturally long and short days, respectively). Light at night, which is a form of light pollution, is prevalent in many societies, especially common in certain emergency occupations. Moreover, individuals with certain mental disorders, such as depression, often exhibit a preference for darkness over daytime light. Nevertheless, disturbances in light patterns can have negative consequences, impacting brain performance through similar mechanisms albeit with varying degrees of severity. Furthermore, changes in day length lead to alterations in the activity of receptors, proteins, ion channels, and molecular signaling pathways, all of which can impact brain health. This review aims to summarize the mechanisms by which day length influences brain functions through neural circuits, hormonal systems, neurochemical processes, cellular activity, and even molecular signaling pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".