Intended and Non-Volitional Knee Joint Movements Elicit Distinct Functional Brain Networks.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biomedical Engineering

Background:

• Electroencephalography (EEG) signal dynamics are vital for understanding brain function in rehabilitation, plasticity, and brain-computer interfaces (BCI).
• Traditional EEG analysis focuses on power spectra, but non-linear indices and network analysis offer deeper insights into brain dynamics and neural communication.

Purpose of the Study:

• To investigate the structural properties of functional brain networks during motor execution under varying physical demand conditions.
• To compare network characteristics during active (voluntary) and passive (assisted) knee joint flexion tasks.

Main Methods:

• Functional brain networks were constructed using EEG data from 16 electrodes across μ, β, and γ frequency bands.
• Key network metrics, including node degree centrality, clustering coefficient, and betweenness centrality, were estimated.
• Analysis compared network properties during voluntary, assisted, and resting states.

Main Results:

• Motor execution decreased overall network connectivity while enhancing communication efficiency.
• The γ and μ frequency bands were more involved in voluntary movements, while the β band dominated assisted movements.
• Distinct spatial distributions of electrode contributions were observed between voluntary and assisted conditions.

Conclusions:

• Motor execution significantly reshapes EEG functional network dynamics, impacting connectivity and communication efficiency.
• Specific frequency bands (γ, μ, β) exhibit differential involvement in voluntary versus assisted motor control.
• The findings suggest distinct neural mechanisms underlying voluntary and non-volitional movements, beyond simple connectivity modulation.