Brain network dynamics in the alpha band during a complex postural control task.

Objective 
To decipher brain network dynamic remodeling from
electroencephalography (EEG) during a complex postural control task combining
virtual reality and a moving platform. 
Approach 
EEG (64 electrodes) data from
158 healthy subjects were acquired. The experiment is divided into several phases,
and visual and motor stimulation is applied progressively. We combined advanced
source-space EEG networks with clustering algorithms to decipher the brain networks
states (BNS) that occurred during the task. 
Main results
The results show that BNS distribution describes the different phases of the experiment with specific transitions
between visual, motor, salience, and default mode networks coherently. We also showed
that age is a key factor that affects the dynamic transition of BNSs in a healthy
cohort. 
Significance 
This study validates an innovative approach, based on a robust methodology and a consequent cohort, to quantify the brain networks dynamics in the BioVRSea paradigm. This work is an important step towards a quantitative evaluation
of brain activities during PC and could lay the foundation for developing brain-based
biomarkers of PC-related disorders.