Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG...

Area of Science:

• Neuroscience
• Cognitive Science
• Network Science

Background:

• Face processing is a complex spatiotemporal brain function.
• Previous research explored static network differences, but dynamic changes remain unclear.

Purpose of the Study:

• To investigate the dynamic brain network mechanisms underlying face processing.
• To characterize time-varying topological patterns during face versus non-face stimuli.

Main Methods:

• Electroencephalography (EEG) data recorded during face and non-face (ketch) tasks.
• Dynamic brain network construction using consecutive EEG segments.
• Graph theory analysis, focusing on the minimum spanning tree (MST) topology.

Main Results:

• MST topology dynamically differed between face and non-face processing.
• Face processing showed a more line-like MST (0-100 ms, alpha band) and star-like MST (100-300 ms, theta/alpha bands).
• Network efficiency for information transfer was higher during face processing, with key differences in frontal and ventral visual regions.

Conclusions:

• Dynamic MST structures reflect distinct mechanisms for face processing.
• These unique network patterns can accurately differentiate face from non-face stimuli (93.39% accuracy).
• Findings highlight the role of dynamic brain network topology in understanding face recognition.