Spectral Information Dynamics of Cortical Signals Uncover the Hierarchical Organization of the Human Brain's Motor Network

Objective: Understanding brain dynamics during motor tasks is a significant challenge in neuroscience, often limited to studying pairwise interactions. This study provides a comprehensive hierarchical characterization of node-specific, pairwise and higher-order interactions within the human brain's motor network during handgrip task execution. Methods: The brain source activity was reconstructed from scalp EEG signals of ten healthy subjects performing a motor task, identifying five brain regions within the contralateral and ipsilateral motor networks. Using the spectral entropy rate as the basis for the decomposition of dynamic information in the alpha and beta frequency bands, we assessed the predictability of the individual rhythms within each brain region, the information shared between the activity of pairs of regions, and the higher-order interactions among groups of signals from more than two regions. Results: An overall decrease in hierarchical interactions at different orders within the motor network was observed during motor task execution. In addition to an increase in the predictability of single-source dynamics and a decrease in the strength of pairwise interactions, a statistically significant reduction in redundancy between brain sources was found. These changes primarily affected the dynamics of the alpha frequency band, driven by the well-known sensorimotor mu rhythm. Conclusions and Significance: This work emphasizes the importance of examining hierarchically-organized brain source interactions in the frequency domain using a unified framework which fully captures the complex dynamics of the motor network.