Decoding Neural Signatures of Semantic Evaluations in Depression and Suicidality

2025-07-29

AI Summary

Depression and suicidal ideation lack objective, neurophysiological biomarkers. This study employed high-density electroencephalography (EEG) combined with multivariate time-resolved decoding to characterize the spatiotemporal neural dynamics underlying emotional self-referential sentence processing in individuals with depression and suicidal ideation. Results revealed significantly earlier onset, greater amplitude, and prolonged duration of semantic decoding responses in patients compared to healthy controls within the 300–600 ms post-stimulus window, alongside broader cross-temporal generalization patterns. These findings indicate heightened neural sensitivity to emotional semantics and impaired emotion regulation—specifically, difficulty disengaging from negative self-relevant content. The identified aberrant decoding profile constitutes a candidate objective, quantifiable neurobiological biomarker. It provides critical empirical support for EEG-based precision identification and clinical translation in affective disorders.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Depression and suicidality profoundly impact cognition and emotion, yet objective neurophysiological biomarkers remain elusive. We investigated the spatiotemporal neural dynamics underlying affective semantic processing in individuals with varying levels of clinical severity of depression and suicidality using multivariate decoding of electroencephalography (EEG) data. Participants (N=137) completed a sentence evaluation task involving emotionally charged self-referential statements while EEG was recorded. We identified robust, neural signatures of semantic processing, with peak decoding accuracy between 300-600 ms -- a window associated with automatic semantic evaluation and conflict monitoring. Compared to healthy controls, individuals with depression and suicidality showed earlier onset, longer duration, and greater amplitude decoding responses, along with broader cross-temporal generalization and increased activation of frontocentral and parietotemporal components. These findings suggest altered sensitivity and impaired disengagement from emotionally salient content in the clinical groups, advancing our understanding of the neurocognitive basis of mental health and providing a principled basis for developing reliable EEG-based biomarkers of depression and suicidality.

Problem

• Identify neurophysiological biomarkers for depression and suicidality
• Study neural dynamics during affective semantic processing in depression
• Develop EEG-based biomarkers for clinical severity assessment

Innovation

• Multivariate EEG decoding for neural signatures
• Spatiotemporal analysis of semantic processing dynamics
• Frontocentral and parietotemporal components activation