Pupillary response to blue light as a biomarker of seasonal pattern in Major Depressive Episode: A clinical study using pupillometry
Bibliographic record
Abstract
Depressive disorders are characterized by disturbances in light signal processing. More specifically, an alteration of the melanopsin response is suggested. The post-illumination pupillary response (PIPR) to blue light (post-blue PIPR) is increasingly used as a marker of the activity of intrinsically photosensitive melanopsin ganglion cells (ipRGCs). We hypothesized that individuals with Major Depressive Episode (MDE) who exhibited a higher vulnerability to season patterns showed a decreased ability to transmit light signals to the brain. We explored the correlation between the post-blue PIPR and the Global Seasonality Score (GSS) in 21 patients with MDE. The GSS was assessed using the Seasonal Pattern Assessment Questionnaire (SPAQ). The results revealed that decreased relative and absolute post-blue PIPR, suggesting a melanopsinergic hyposensitivity, were associated independently and significantly with higher seasonality in the psychological factor including a greater seasonal variation in sleep duration, mood, energy level and social activity, but were not associated with higher seasonality in the dietary factor (including weight and appetite seasonal variations) or with the severity of anxiety, depression, or sleep disturbances. Interestingly, mediation analyses highlight independent bidirectional effects of high vulnerability to season of psychological factors and decreased ipRGC sensitivity. Post-blue PIPR could be an objective marker of seasonal changes in daylight exposure in patients with MDE. Further research could explore post-blue PIPR as a state or trait biomarker for depressive disorders and the seasonal pattern, and its potential role in predicting therapeutic response to light therapy.
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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 | 0.000 |
| 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".