Association of Retinal Neurovascular Impairment with Disease Severity in Patients with Major Depressive Disorder: An Optical Coherence Tomography Angiography Study
Bibliographic record
Abstract
Background: Identifying the fundus objective biomarkers for the major depressive disorders (MDD) may help promote mental health. The aim of this study was to evaluate retinal neurovascular changes and further investigate their association with disease severity in MDD. Methods: This cross-sectional study conducted in the hospital enrolled patients with MDD and healthy controls.The retinal neurovascular parameters for all subjects, including vessel density (VD), thickness of ganglion cell complex (GCC) and retinal nerve fiber layer (RNFL), and optic nerve head (ONH) eg are automatically calculated by the software in optical coherence tomography angiography (OCTA). The severity of MDD including depressive symptoms, anxiety, cognition, and insomnia was assessed by Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Scale (HAMA), Montreal Cognitive Assessment (MoCA), and Insomnia Severity Index (ISI) respectively. Results: This study included 74 MDD patients (n=74 eyes) and 60 healthy controls (HCs) (n=60 eyes). MDD patients showed significantly decreased VD of superficial and deep capillary plexus, thickness of GCC and RNFL, and volume of ONH (all p < 0.05) and increased vertical cup-to-disc ratio and global loss volume (GLV) (all p < 0.05) compared to HCs. Positive associations were found between HAMD scores and cup area (r=0.30, p =0.035), cup volume (r=0.31, p =0.029), and disc area (r=0.33, p =0.020) as well as ISI scores and RNFL thickness (r=0.34, p =0.047). Conclusion: We found the retinal neurovascular impairment and its association with disease severity in MDD patients. OCTA showed promise as a potential complementary assessment tool for MDD. Keywords: major depressive disorder, retinal neurovasculature, optical coherence tomography angiography, disease severity
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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 | 0.001 | 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) | 0.001 | 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".