Antonacci Yuri, Bara Chiara, Sparacino Laura, Pirovano Ileana, Mastropietro Alfonso, Rizzo Giovanna, Faes Luca
IEEE Trans Biomed Eng. 2025 May;72(5):1655-1664. doi: 10.1109/TBME.2024.3516943. Epub 2025 Apr 22.
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.
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.
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.
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.
了解运动任务期间的脑动力学是神经科学中的一项重大挑战，通常局限于研究成对的相互作用。本研究对手握任务执行期间人类大脑运动网络内特定节点、成对和高阶相互作用进行了全面的分层表征。
从执行运动任务的10名健康受试者的头皮脑电图信号中重建脑源活动，识别对侧和同侧运动网络内的五个脑区。以频谱熵率为基础，对α和β频段的动态信息进行分解，我们评估了每个脑区内单个节律的可预测性、区域对活动之间共享的信息以及来自两个以上区域的信号组之间的高阶相互作用。
在运动任务执行期间，观察到运动网络内不同阶次的分层相互作用总体下降。除了单源动力学可预测性增加和成对相互作用强度降低外，还发现脑源之间的冗余在统计学上显著减少。这些变化主要影响由著名的感觉运动μ节律驱动的α频段的动力学。
这项工作强调了使用统一框架在频域中检查分层组织的脑源相互作用的重要性，该框架能够充分捕捉运动网络的复杂动力学。
