Exploring neural reorganization and modifiable connectivity patterns in sadness: a multi-band EEG study

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Psychiatry

Background:

• Negative emotions impact cognitive and emotional functioning, potentially contributing to mental disorders.
• Neural mechanisms of emotion induction and regulation are not fully understood.
• Prior research has identified broad neural correlates of emotion but lacks specific mechanistic insights.

Purpose of the Study:

• To investigate sadness-related alterations in neural activity using electroencephalography (EEG) based network analysis.
• To assess spontaneous emotion regulation following sadness induction.
• To identify discriminative functional connectivity (FC) features associated with emotional transitions.

Main Methods:

• Induced sadness using audiovisual stimuli and assessed spontaneous recovery.
• Constructed functional connectivity (FC) networks from multi-band EEG signals across baseline, sadness, and regulation states.
• Applied a machine learning framework with feature selection and classification to identify discriminative FC features.

Main Results:

• Graph-theoretical analysis revealed significant emotion-related alterations in functional networks across α, β, and θ frequency bands.
• Machine learning identified modifiable FC features, primarily in occipitotemporal-prefrontal networks within the α band.
• Demonstrated frequency-specific network dynamics during sadness induction and regulation.

Conclusions:

• Provided quantitative evidence of network dynamics underlying sadness processing and regulation.
• Identified specific brain network alterations associated with emotional states.
• Suggests potential for EEG-based biomarkers for assessing and regulating emotional dysfunction in clinical settings.