Graph theory-based analysis of functional connectivity changes in brain networks underlying cognitive fatigue: An EEG...

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Cognitive fatigue impacts daily functioning and performance.
• Understanding the neurophysiological underpinnings of cognitive fatigue is crucial for developing effective interventions.
• Electroencephalography (EEG) offers a non-invasive method to study brain activity and functional connectivity.

Purpose of the Study:

• To analyze alterations in brain network functional connectivity associated with cognitive fatigue using EEG data.
• To establish a framework for enhanced detection of cognitive fatigue manifestations.
• To examine the neurophysiological substrates of cognitive fatigue.

Main Methods:

• EEG data from neurologically intact adults (aged 20-35) were collected before and after a cognitive fatigue task (Stroop task).
• Power spectral density (PSD) was analyzed across theta, alpha, and beta bands to identify the most sensitive frequency band.
• Weighted Phase Lag Index (wPLI) was used to compute functional connectivity matrices, followed by graph-theoretical analysis (global and local metrics) to characterize network topology.

Main Results:

• Significant elevations in global average PSD were observed post-fatigue across all bands, with the alpha band showing the most pronounced effect.
• Topological analysis of alpha-band wPLI networks revealed enhanced global efficiency, local efficiency, and clustering coefficient, alongside a reduction in shortest path length.
• Nodal efficiency was preferentially enhanced in central and anterior cortical regions post-fatigue.

Conclusions:

• Alpha-band activity is highly sensitive to cognitive fatigue induced by sustained tasks.
• Cognitive fatigue involves compensatory mechanisms that improve local and global neural information processing efficiency.
• The findings elucidate novel neurophysiological mechanisms of cognitive fatigue, highlighting a balance between adaptive network reconfiguration and system efficiency.