Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, England, United Kingdom.
Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea.
PLoS One. 2020 Feb 6;15(2):e0221380. doi: 10.1371/journal.pone.0221380. eCollection 2020.
Computational studies of the influence of different network parameters on the dynamic and topological network effects of brain stimulation can enhance our understanding of different outcomes between individuals. In this study, a brain stimulation session along with the subsequent post-stimulation brain activity is simulated for a period of one day using a network of modified Wilson-Cowan oscillators coupled according to diffusion imaging based structural connectivity. We use this computational model to examine how differences in the inter-region connectivity and the excitability of stimulated regions at the time of stimulation can affect post-stimulation behaviours. Our findings indicate that the initial inter-region connectivity can heavily affect the changes that stimulation induces in the connectivity of the network. Moreover, differences in the excitability of the stimulated regions seem to lead to different post-stimulation connectivity changes across the model network, including on the internal connectivity of non-stimulated regions.
计算研究不同网络参数对脑刺激的动态和拓扑网络效应的影响，可以增强我们对个体间不同结果的理解。在这项研究中，我们使用基于扩散成像的结构连接来模拟修改后的 Wilson-Cowan 振荡器网络，对一天的脑刺激过程及其后续的脑活动进行模拟。我们使用这个计算模型来研究刺激时不同区域间的连接和刺激区域的兴奋性差异如何影响刺激后的行为。我们的研究结果表明，初始区域间连接会严重影响刺激引起的网络连接变化。此外，刺激区域的兴奋性差异似乎会导致模型网络中不同的刺激后连接变化，包括非刺激区域的内部连接。
