抑郁症和自杀意念患者面部情绪识别的神经认知加工:眼动追踪和脑电图研究。

IF 3.5 Q3 PSYCHIATRY
Alpha psychiatry Pub Date : 2026-02-25 eCollection Date: 2026-02-01 DOI:10.31083/AP44992
Qianlan Yin, Huijing Xu, Ying Zhu, Meng Liang, Qian Jiang, Bin Zhao, Taosheng Liu
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引用次数: 0

摘要

背景:自杀意念(SI)是一个重要的问题,了解其神经认知基础对改进风险评估至关重要。本研究利用眼动追踪、脑电图(EEG)和反卷积建模相结合的多模态方法,研究抑郁症和SI患者在面部识别过程中神经认知加工的改变。方法:记录抑郁症患者在面部识别任务中的眼动和脑电图数据。我们分析了视觉注意模式(注视时间、跳动速度)和对情绪刺激的事件相关电位。反卷积分析分离了微眼跳相关活动(如基于回归的事件相关电位(rERP)和基于回归的注视相关电位(rFRP))。结果:SI个体表现出对情绪面孔的注意偏倚,其特征是第一次注视时间较短,跳眼速度较快。在悲伤的情况下,rFRP的振幅也会降低,表明神经反应发生了改变。结合眼动和脑电数据(曲线下面积AUC = 0.771)比单独眼动数据(AUC = 0.643)提高了SI检测的准确性。结论:这些发现为抑郁症和SI患者情绪面孔的神经认知加工改变提供了新的证据。这种多模式方法强调了将眼动追踪和脑电图测量相结合作为识别高危个体的生物标志物的潜力。未来的研究应该集中在更大、更多样化的样本和纵向设计上,以验证这些发现并将其转化为临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neurocognitive Processing of Facial Emotion Recognition in Individuals With Depression and Suicidal Ideation: An Eye-Tracking and EEG Study.

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.

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