Measuring Brain Complexity During Neural Motor Resonance

Area of Science:

• Neuroscience
• Complexity Science
• Social Neuroscience

Background:

• EEG mu-desynchronization indexes motor resonance (MR) but doesn't fully explain nonlinear brain dynamics during MR.
• Understanding nonlinear dynamics is crucial for studying social interaction deficiencies.

Purpose of the Study:

• To investigate changes in nonlinear brain dynamics during MR.
• To test if mu frequency band complexity changes during MR and if this is frequency-specific.
• To determine if baseline complexity and complexity changes predict MR and network dynamics.

Main Methods:

• Recorded EEG from 45 healthy participants during rest and action observation (watching hands squeeze stress balls).
• Quantified complexity using multiscale entropy (MSE) in the mu rhythm.
• Performed graph theory analysis to assess brain network topology changes.

Main Results:

• Significant mu-desynchronization and increased mu band entropy during action observation; decreased entropy in other bands.
• Resting-state entropy predicted the degree of mu desynchronization.
• Observed decreased mu band clustering coefficient and decreased global alpha efficiency; MSE correlated with alpha network efficiency.

Conclusions:

• Mu wave desynchronization during MR increases local mu entropy, possibly indicating release from alpha inhibition.
• Baseline mu band MSE may mediate this release from inhibition.
• Dynamical complexity and network analysis of EEG offer novel insights into MR and nonlinearity.