School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
J R Soc Interface. 2013 Feb 13;10(81):20130016. doi: 10.1098/rsif.2013.0016. Print 2013 Apr 6.
The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way.
研究定义在复杂网络上的动力系统为研究神经动力学的诸多特征提供了一个自然的框架，并得到了广泛的应用。然而，通常情况下，理论研究中使用的网络与他们试图复制的神经网络的空间嵌入或连接性几乎没有关系。在这里，我们利用详细的神经影像学数据来定义一个网络，其空间嵌入准确地表示了大鼠大脑皮质表面的折叠结构，并根据简单的扩展和连接规则研究了活动在该网络上的传播。通过与具有相同粗粒度统计数据的标准网络模型进行比较，我们表明皮质几何结构极大地影响了激活在网络中的传播速度。我们的结论与癫痫发作事件的理论建模高度相关，并表明那些忽略生理网络结构的研究可能会以一种潜在重要的方式简化动力学。
