Dynamic functional connectivity correlates of mental workload

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• High mental workload tasks engage complex cognitive functions and brain-wide information processing.
• The dynamic changes in functional brain connectivity during varying mental workload levels remain underexplored.

Purpose of the Study:

• To investigate the dynamics of brain network states during different mental workload levels.
• To identify quantifiable network features that correlate with cognitive performance and workload.
• To develop a method for decoding mental workload using electroencephalography (EEG) data.

Main Methods:

• Utilized electroencephalography (EEG) data from participants performing tasks with varying mental workload.
• Constructed gamma-band phase locking value networks to represent functional connectivity.
• Defined network states through clustering based on closeness centrality node-level metrics.
• Analyzed transitions between network states and their statistical properties.

Main Results:

• Identified non-random transitions between brain network states.
• Observed significant differences in network state statistics between low and high mental workload conditions.
• Found correlations between network state sequence features and behavioral performance.
• Achieved a 69.6% average cross-participant accuracy in decoding mental workload using a support vector machine classifier with dynamic network features.

Conclusions:

• The study presents a novel approach to analyzing EEG signal dynamics by focusing on network state transitions.
• Dynamic network features derived from EEG show potential for objective mental workload assessment.
• This methodology offers a new perspective for understanding brain function under cognitive load.