Neural Responses to Affective Sentences Reveal Signatures of Depression

Area of Science:

• Neuroscience
• Psychiatry
• Cognitive Science

Background:

• Major Depressive Disorder (MDD) is a common mental health condition.
• Understanding its neurocognitive basis is crucial for effective treatment.
• Emotional and self-referential processing deficits are key features of MDD.

Purpose of the Study:

• To investigate how depression symptoms alter the temporal dynamics of emotional processing.
• To measure neural responses to self-referential affective sentences using EEG.
• To identify potential neural markers for depression and suicidal ideation.

Main Methods:

• Surface electroencephalography (EEG) was used to record neural responses.
• Participants included healthy individuals and those with MDD.
• A deep learning model was trained to classify participants based on neural data.

Main Results:

• Significant group-level differences in neural activity were observed between healthy and depressed individuals.
• The deep learning model achieved an AUC of 0.72 for distinguishing healthy from depressed participants.
• The model also differentiated depressed subgroups with and without suicidal ideation (AUC=0.65).
• Anterior electrode sites were identified as crucial for semantic and affective processing.

Conclusions:

• Depression is associated with disrupted integration of emotional and self-referential information.
• Stable, stimulus-driven neural signatures of depression symptoms were identified.
• These neural signatures show potential for informing future depression screening tools.