A study on electroretinography as a biomarker for seasonal vulnerability in depression
Bibliographic record
Abstract
Depressive disorders involve disruptions in light signal processing. Seasonal Affective Disorder (SAD) has been linked to abnormalities in phototransduction, including impaired retinal responses and sleep disturbances. Similar retinal anomalies have been observed in Major Depressive Episode (MDE) patients without seasonal pattern. However, no study has directly compared light-signaling biomarkers between SAD and non-seasonal MDE using sleep assessments and electroretinography (ERG) measures. This study aims to develop a model combining clinical and ERG markers to predict seasonality in MDE patients. Patients with MDE (N = 320) were classified based on their vulnerability to seasonality using the Global Seasonality Score (GSS) from the Seasonal Pattern Assessment Questionnaire (SPAQ), with a threshold of ≥ 11 indicating seasonal vulnerability. This dimensional approach provides a more nuanced reflection of underlying pathophysiological mechanisms than the categorical DSM-5-TR classification of SAD. Subjective sleep, psychiatric, and ERG biomarkers were analyzed. Significant variables were entered into a Backward Stepwise Logistic Regression (N = 35, subset of participants who had all available data including ERG measures), and model performance was assessed using sensitivity, specificity, accuracy, AUC, ROC curve, and Youden's index. The model retained six predictors: reduced bipolar cell amplitude (rods), increased cone response amplitude at 7 cd·s⁻¹·m⁻², increased daytime sleepiness, higher depression severity, younger age, and female gender. It demonstrated good discriminative power (AUC = 0.861, sensitivity = 0.905, specificity = 0.714, Youden's index = 0.619). The model effectively distinguishes seasonal from non-seasonal MDE, explaining 30.5% of the variance. ERG is a promising tool for identifying biomarkers of Seasonal Vulnerability in depression. Enhancing predictive approaches with multimodal diagnostic and longitudinal data could further improve early detection and personalized interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".