Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals

Area of Science:

• Neuroscience
• Cognitive Psychology
• Biomedical Engineering

Background:

• Attention bias is known to influence behavioral indicators in depression.
• The specific impact of attention bias on brain network topology in mild depression (MD) remains unclear.

Purpose of the Study:

• To investigate the abnormal brain network topology in mild depression (MD) patients using EEG signals.
• To explore how attention bias affects brain network topology during a visual search task in MD patients.

Main Methods:

• Employed functional brain network analysis and hierarchical clustering on EEG signals.
• Utilized a visual search paradigm to assess behavioral and network differences between MD and control groups.
• Analyzed network properties including local efficiency, clustering coefficient, and community structure.

Main Results:

• MD patients exhibited significantly longer reaction times compared to the normal group.
• Functional brain networks showed significant differences, with more inter-hemispheric long-distance connections than intra-hemispheric short-distance connections.
• MD patients displayed reduced local efficiency, destroyed community structures in frontal and parietal-occipital lobes, and frontal asymmetry, particularly in the beta band.

Conclusions:

• Mild depression is associated with altered brain network topology, characterized by a shift towards long-distance connections at the expense of local connectivity.
• These topological changes, especially frontal asymmetry and altered long-range connections, correlate with depressive symptom severity.
• The findings provide novel insights into the cognitive processing mechanisms underlying mild depression.