Protocol to study brain dynamics of mammals through the hierarchy of complex correlation patterns defining a robust functional architecture.

Analyzing functional networks (FNs) of brain activity of mammals can be challenging because of their fast dynamics and non-stationary properties. Here, we present a computational protocol for extracting FNs using scaled cross-correlation (SCA) and analyzing them independently for each epoch. We outline procedures for calculating edge weight and node distance distributions, with statistical comparisons using the Cliff's delta metric. We also detail procedures for visualizing results by plotting distributions. This protocol has potential in identifying disease biomarkers. For complete details on the use and execution of this protocol, please refer to Varga et al..