Neural Responses to Affective Sentences Reveal Signatures of Depression

TL;DR

This study uses EEG to identify neural signatures of depression during emotional sentence processing, revealing differences in brain activity and demonstrating potential for diagnostic applications with machine learning.

Contribution

It introduces a novel EEG-based approach to detect depression-related neural signatures during emotional language processing, with deep learning models achieving moderate classification accuracy.

Findings

Significant neural activity differences between depressed and healthy individuals.

Deep learning model achieved AUC of 0.707 in identifying depression.

Anterior electrodes are key in emotional and semantic processing.

Abstract

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.