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Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
Published on: July 31, 2016
Neurocognitive Processing of Facial Emotion Recognition in Individuals With Depression and Suicidal Ideation: An
Qianlan Yin1,2, Huijing Xu1, Ying Zhu1
1Department of Psychiatry, Faculty of Psychology and Mental Health, Naval Medical University, 200000 Shanghai, China.
Background:
Suicide ideation (SI) is a critical concern, and understanding its neurocognitive underpinnings is essential for improved risk assessment. This study investigates altered neurocognitive processing during face recognition in individuals with depression and SI, utilizing a multimodal approach combining eye-tracking, electroencephalography (EEG), and deconvolution modeling.
Methods:
Eye-tracking and EEG data were recorded during face recognition tasks in individuals with depression, with and without SI. We analyzed visual attention patterns (fixation durations, saccadic velocities) and event-related potentials to emotional stimuli. Deconvolution analysis separated microsaccade-related activities (like regression-based event-related potential (rERP) and regression-based fixation-related potential (rFRP)).
Results:
Individuals with SI exhibited attentional biases toward emotional faces, characterized by shorter first fixation durations and faster saccadic velocities. Reduced rERP amplitudes in response to surprise and decreased rFRP amplitudes during sad conditions were also observed, suggesting altered neural responses. Integrating eye-tracking and EEG data (the area under the curve (AUC) = 0.771) improved the accuracy of detecting SI compared to eye-tracking alone (AUC = 0.643).
Conclusions:
These findings provide novel evidence for altered neurocognitive processing of emotional faces in individuals with depression and SI. The multimodal approach highlights the potential of combining eye-tracking and EEG measures as biomarkers for identifying individuals at risk. Future research should focus on larger, more diverse samples and longitudinal designs to validate these findings and translate them into clinical applications.
