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<p>Objective biobehavioral markers for mental health conditions remain elusive, with diagnosis typically relying on self-reports and clinical interviews. We investigate eye tracking as a potential marker of attentional and mood biases associated with symptoms of depression and suicidal ideation from self-reported screening questionnaires. We analyze eye movements from 126 young adults during reading and responding to emotionally loaded sentences. A deep learning framework was designed to account for intra-trial and inter-trial variations in eye movements, achieving an AUC of 0.793 (95% CI: 0.766–0.819) for identifying depression/suicidality against healthy controls, and 0.826 (95% CI: 0.798–0.853) for suicidality specifically. The model also exhibited moderate accuracy in differentiating depressed from suicidal individuals (AUC: 0.609, 95% CI: 0.569–0.646). Discriminative patterns were more pronounced during response generation and for stimuli of negative sentiment. These findings suggest that eye tracking can provide objective markers of self-reported symptom severity by measuring the impact of emotional stimuli on oculomotor control.</p>

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Deep learning characterizes depression and suicidal ideation in young adults from eye movements

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月28日

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  • Kleanthis Avramidis,
  • Woojae Jeong,
  • Aditya Kommineni,
  • Sudarsana R. Kadiri,
  • Marcus Ma,
  • Colin McDaniel,
  • Myzelle Hughes,
  • Thomas McGee,
  • Elsi Kaiser,
  • Dani Byrd,
  • Assal Habibi,
  • B. Rael Cahn,
  • Idan A. Blank,
  • Kristina Lerman,
  • Takfarinas Medani,
  • Richard M. Leahy,
  • Shrikanth Narayanan

摘要

Objective biobehavioral markers for mental health conditions remain elusive, with diagnosis typically relying on self-reports and clinical interviews. We investigate eye tracking as a potential marker of attentional and mood biases associated with symptoms of depression and suicidal ideation from self-reported screening questionnaires. We analyze eye movements from 126 young adults during reading and responding to emotionally loaded sentences. A deep learning framework was designed to account for intra-trial and inter-trial variations in eye movements, achieving an AUC of 0.793 (95% CI: 0.766–0.819) for identifying depression/suicidality against healthy controls, and 0.826 (95% CI: 0.798–0.853) for suicidality specifically. The model also exhibited moderate accuracy in differentiating depressed from suicidal individuals (AUC: 0.609, 95% CI: 0.569–0.646). Discriminative patterns were more pronounced during response generation and for stimuli of negative sentiment. These findings suggest that eye tracking can provide objective markers of self-reported symptom severity by measuring the impact of emotional stimuli on oculomotor control.