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DOI 10.1002/cns.71198

Медицинская библиотека · CNS neuroscience & therapeutics · 2026

Eye-Tracking Phenotypes Across the Depressive Spectrum: Joint Modeling of Diagnostic Group and Symptom Severity.

Chen Xiangting, Huang Min

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Аннотация

AIMS: To characterize eye-tracking alterations across the depressive spectrum and evaluate their value for diagnostic stratification and symptom severity estimation.

METHODS

In this cross-sectional study, 183 participants completed a multi-paradigm eye-tracking battery. Group differences and associations with 17-item Hamilton Depression Rating Scale (HAMD-17) scores were examined with multiple-comparison correction and covariate adjustment. Classification, regression, and multi-task models used repeated nested resampling with fold-specific preprocessing.

RESULTS

False discovery rate (FDR)-significant between-group differences were observed for two first-fixation latency measures from lateral-gaze orienting and for antisaccade accuracy. Among 35 candidate features, 33 had covariate-adjusted group associations at p < 0.05. Across 539 retained features, covariate-adjusted generalized additive models identified eight FDR-significant associations with HAMD-17. In multi-task analysis, Route B had numerically higher mean internal performance than Route A, although paired between-route differences were imprecisely estimated (accuracy, 0.929 vs. 0.864; macro F1, 0.928 vs. 0.859; R2, 0.834 vs. 0.596). Regression-only performance was similar to Route B (R2, 0.847).

CONCLUSION

Eye-tracking alterations spanned the depressive spectrum and were associated with symptom severity after covariate adjustment. Between-route differences were imprecisely estimated, and joint-task training showed no clear improvement in severity estimation. External and temporal validation are required before clinical use.

Для цитирования: Chen Xiangting, Huang Min. Eye-Tracking Phenotypes Across the Depressive Spectrum: Joint Modeling of Diagnostic Group and Symptom Severity.. CNS neuroscience & therapeutics. 2026. DOI: 10.1002/cns.71198.

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