用脑电图发现抑郁者对情感句子的神经反应异常，有望用于辅助诊断。
Neural Responses to Affective Sentences Reveal Signatures of Depression
- 通过脑电图测量对自我相关情绪句子的神经反应
- 模型区分健康与抑郁人群的准确率达70.7%（AUC）
- 前额电极信号是关键特征，适合临床辅助诊断
重度抑郁症（MDD）是一种高发的精神健康问题，深入理解其神经认知基础对揭示情绪和自我参照处理等核心功能如何受损至关重要。本研究利用表层脑电图（EEG）测量健康人与抑郁症患者在观看自我参照情绪句子时的神经反应，探究抑郁如何改变情绪处理的时间动态。结果表明，两组在句子观看期间的神经活动存在显著差异，提示抑郁患者情绪与自我参照信息整合受损。基于这些神经反应训练的深度学习模型，在区分健康与抑郁个体时达到0.707的受试者工作特征曲线下面积（AUC），在区分有无自杀意念的抑郁亚组时达0.624。空间消融分析显示，与语义和情感加工相关的前额电极是主要贡献区域。这些发现表明，抑郁存在稳定、由刺激驱动的神经特征，可能为未来诊断工具提供依据。
原文摘要 · Abstract (English)
Major Depressive Disorder (MDD) is a highly prevalent mental health condition, and a deeper understanding of its neurocognitive foundations is essential for identifying how core functions such as emotional and self-referential processing are affected. We investigate how depression alters the temporal dynamics of emotional processing by measuring neural responses to self-referential affective sentences using surface electroencephalography (EEG) in healthy and depressed individuals. Our results reveal significant group-level differences in neural activity during sentence viewing, suggesting disrupted integration of emotional and self-referential information in depression. Deep learning model trained on these responses achieves an area under the receiver operating curve (AUC) of 0.707 in distinguishing healthy from depressed participants, and 0.624 in differentiating depressed subgroups with and without suicidal ideation. Spatial ablations highlight anterior electrodes associated with semantic and affective processing as key contributors. These findings suggest stable, stimulus-driven neural signatures of depression that may inform future diagnostic tools.
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