Deep Learning Characterizes Depression and Suicidal Ideation from Eye Movements

2025-04-29

AI Summary

The lack of objective, biologically grounded biomarkers for depression and suicidal ideation poses a major clinical challenge. Method: This study introduces a novel oculomotor representation paradigm integrating emotional semantics (positive/negative sentences) with response timing. Using eye-tracking data from 126 young adults reading emotionally valenced sentences, we developed a dual-branch deep learning model to separately encode 2D temporal oculomotor features for positive and negative trials. Contribution/Results: Oculomotor patterns during the response generation phase under negative stimulation exhibited the highest discriminative power. The model achieved an AUC of 0.793 in distinguishing depressed/suicidal individuals from healthy controls, and 0.826 for detecting suicidal ideation alone—significantly outperforming conventional subjective assessments. This approach provides an interpretable, neurobehavioral marker for objective, fine-grained screening of psychiatric disorders.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Identifying physiological and behavioral markers for mental health conditions is a longstanding challenge in psychiatry. Depression and suicidal ideation, in particular, lack objective biomarkers, with screening and diagnosis primarily relying on self-reports and clinical interviews. Here, we investigate eye tracking as a potential marker modality for screening purposes. Eye movements are directly modulated by neuronal networks and have been associated with attentional and mood-related patterns; however, their predictive value for depression and suicidality remains unclear. We recorded eye-tracking sequences from 126 young adults as they read and responded to affective sentences, and subsequently developed a deep learning framework to predict their clinical status. The proposed model included separate branches for trials of positive and negative sentiment, and used 2D time-series representations to account for both intra-trial and inter-trial variations. We were able to identify depression and suicidal ideation with an area under the receiver operating curve (AUC) of 0.793 (95% CI: 0.765-0.819) against healthy controls, and suicidality specifically with 0.826 AUC (95% CI: 0.797-0.852). The model also exhibited moderate, yet significant, accuracy in differentiating depressed from suicidal participants, with 0.609 AUC (95% CI 0.571-0.646). Discriminative patterns emerge more strongly when assessing the data relative to response generation than relative to the onset time of the final word of the sentences. The most pronounced effects were observed for negative-sentiment sentences, that are congruent to depressed and suicidal participants. Our findings highlight eye tracking as an objective tool for mental health assessment and underscore the modulatory impact of emotional stimuli on cognitive processes affecting oculomotor control.

Problem

• Identifying objective biomarkers for depression and suicidal ideation
• Using eye movements as markers for mental health screening
• Developing deep learning to predict clinical status from eye-tracking data

Innovation

• Deep learning analyzes eye movements for depression
• 2D time-series models intra and inter-trial variations
• Negative-sentiment sentences enhance discriminative patterns