Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.
Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.
Hum Brain Mapp. 2019 Apr 1;40(5):1445-1457. doi: 10.1002/hbm.24458. Epub 2018 Nov 15.
Intrinsic brain activity is organized in spatial-temporal patterns, called resting-state networks (RSNs), exhibiting specific structural-functional architecture. These networks presumably reflect complex neurophysiological processes and have a central role in distinct perceptual and cognitive functions. In this work, we propose an innovative approach for characterizing RSNs according to their underlying neural oscillations. We investigated specific electrophysiological properties, including spectral features, fractal dimension, and entropy, associated with eight core RSNs derived from high-density electroencephalography (EEG) source-reconstructed signals. Specifically, we found higher synchronization of the gamma-band activity and higher fractal dimension values in perceptual (PNs) compared with higher cognitive (HCNs) networks. The inspection of this underlying rapid activity becomes of utmost importance for assessing possible alterations related to specific brain disorders. The disruption of the coordinated activity of RSNs may result in altered behavioral and perceptual states. Thus, this approach could potentially be used for the early detection and treatment of neurological disorders.
大脑的内在活动以被称为静息态网络（RSN）的时空模式组织，呈现出特定的结构-功能架构。这些网络推测反映了复杂的神经生理过程，并在不同的感知和认知功能中起核心作用。在这项工作中，我们提出了一种根据其潜在的神经振荡来描述 RSN 的创新方法。我们研究了与从高密度脑电图（EEG）源重建信号中得出的八个核心 RSN 相关的特定电生理特性，包括频谱特征、分形维数和熵。具体来说，我们发现与高认知（HCNs）网络相比，感知（PNs）网络中的伽马波段活动具有更高的同步性和更高的分形维数值。检查这种潜在的快速活动对于评估与特定脑疾病相关的可能改变变得至关重要。RSN 的协调活动的中断可能导致行为和感知状态的改变。因此，这种方法可能可用于神经疾病的早期检测和治疗。
