Brain Network Connectivity and Topological Analysis During Voluntary Arm Movements

Area of Science:

• Neuroscience
• Cognitive Science
• Brain-Computer Interfaces

Background:

• Functional connectivity analysis is crucial for understanding brain dynamics.
• Electroencephalography (EEG) offers a non-invasive method to study brain activity.
• Comparing different analytical approaches is essential for robust findings.

Purpose of the Study:

• To investigate task-related changes in brain activity and functional connectivity.
• To compare the descriptive power and complementarity of event-related desynchronization (ERD), coherence, and graph-theoretical analysis.
• To evaluate the utility of network analysis in modeling motor-induced functional connectivity.

Main Methods:

• EEG recordings from 10 subjects during left/right arm movements.
• Analysis of theta, alpha, and beta frequency bands.
• Application of event-related desynchronization (ERD), coherence, and graph-theoretical analysis.

Main Results:

• Significant ERD observed in alpha and beta bands over the sensorimotor cortex during left arm movement, and in beta band during right arm movement.
• Connectivity assessment revealed stronger connections involving motor regions.
• Graph analysis indicated reduced accessibility and increased centrality in motor networks during movement.

Conclusions:

• Network analysis provides complementary information to established methods for modeling motor-induced functional connectivity.
• Combined coherence and graph analysis effectively identify functionally related cortical areas and network topology.
• These findings suggest potential clinical applications for network analysis in understanding brain function.