Neural Responses to Affective Sentences Reveal Signatures of Depression

2025-06-06

AI Summary

Major depressive disorder (MDD) involves aberrant temporal dynamics in integrating emotional and self-referential information, yet robust, stimulus-driven neurophysiological markers remain elusive. Method: We employed high-temporal-resolution scalp electroencephalography (EEG) to capture dynamic neural responses while participants read emotionally valenced self-referential statements. A deep learning classifier integrated with spatial ablation analysis was applied to identify discriminative spatiotemporal features. Contribution/Results: We identified a stable, stimulus-locked, depression-specific neural signature localized to anterior electrodes—particularly prefrontal regions—implicated in affective semantic processing. This signature demonstrated functional specificity in differentiating MDD subgroups with and without suicidal ideation. The model achieved an AUC of 0.707 for MDD vs. healthy controls and 0.624 for subgroup classification. Critically, the marker is both interpretable—anchored in well-defined neurocognitive processes—and generalizable across heterogeneous clinical samples. This work advances precision psychiatry by providing a temporally resolved, mechanistically grounded neurobiomarker for MDD stratification and pathophysiological insight.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Multimodal Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• How depression affects emotional and self-referential processing
• Identifying neural signatures to distinguish depressed from healthy individuals
• Developing diagnostic tools using EEG-based deep learning models

Innovation

• EEG measures neural responses to affective sentences
• Deep learning model distinguishes depression with AUC 0.707
• Anterior electrodes key for semantic and affective processing