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<p>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 symptoms 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 0.72 in distinguishing healthy from depressed participants, and 0.65 in differentiating depressed subgroups with and without suicidal ideation symptoms. Spatial ablations highlight anterior electrodes associated with semantic and affective processing as key contributors. These findings suggest stable, stimulus-driven neural signatures of depression symptoms that may inform future screening tools.</p>

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Neural Responses to Affective Sentences Reveal Signatures of Depression

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月15日

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

摘要

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 symptoms 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 0.72 in distinguishing healthy from depressed participants, and 0.65 in differentiating depressed subgroups with and without suicidal ideation symptoms. Spatial ablations highlight anterior electrodes associated with semantic and affective processing as key contributors. These findings suggest stable, stimulus-driven neural signatures of depression symptoms that may inform future screening tools.