Fan Denggui, Wang Qingyun, Su Jianzhong, Xi Hongguang
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China.
Department of Dynamics and Control, Beihang University, Beijing, 100191, China.
J Comput Neurosci. 2017 Dec;43(3):203-225. doi: 10.1007/s10827-017-0658-4. Epub 2017 Sep 22.
It is believed that thalamic reticular nucleus (TRN) controls spindles and spike-wave discharges (SWD) in seizure or sleeping processes. The dynamical mechanisms of spatiotemporal evolutions between these two types of activity, however, are not well understood. In light of this, we first use a single-compartment thalamocortical neural field model to investigate the effects of TRN on occurrence of SWD and its transition. Results show that the increasing inhibition from TRN to specific relay nuclei (SRN) can lead to the transition of system from SWD to slow-wave oscillation. Specially, it is shown that stimulations applied in the cortical neuronal populations can also initiate the SWD and slow-wave oscillation from the resting states under the typical inhibitory intensity from TRN to SRN. Then, we expand into a 3-compartment coupled thalamocortical model network in linear and circular structures, respectively, to explore the spatiotemporal evolutions of wave states in different compartments. The main results are: (i) for the open-ended model network, SWD induced by stimulus in the first compartment can be transformed into sleep-like slow UP-DOWN and spindle states as it propagates into the downstream compartments; (ii) for the close-ended model network, weak stimulations performed in the first compartment can result in the consistent experimentally observed spindle oscillations in all three compartments; in contrast, stronger periodic single-pulse stimulations applied in the first compartment can induce periodic transitions between SWD and spindle oscillations. Detailed investigations reveal that multi-attractor coexistence mechanism composed of SWD, spindles and background state underlies these state evolutions. What's more, in order to demonstrate the state evolution stability with respect to the topological structures of neural network, we further expand the 3-compartment coupled network into 10-compartment coupled one, with linear and circular structures, and nearest-neighbor (NN) coupled network as well as its realization of small-world (SW) topology via random rewiring, respectively. Interestingly, for the cases of linear and circular connetivities, qualitatively similar results were obtained in addition to the more irregularity of firings. However, SWD can be eventually transformed into the consistent low-amplitude oscillations for both NN and SW networks. In particular, SWD evolves into the slow spindling oscillations and background tonic oscillations within the NN and SW network, respectively. Our modeling and simulation studies highlight the effect of network topology in the evolutions of SWD and spindling oscillations, which provides new insights into the mechanisms of cortical seizures development.
人们认为，丘脑网状核（TRN）在癫痫发作或睡眠过程中控制纺锤波和棘慢波放电（SWD）。然而，这两种活动之间时空演化的动力学机制尚未得到充分理解。鉴于此，我们首先使用单室丘脑皮质神经场模型来研究TRN对SWD发生及其转变的影响。结果表明，TRN对特定中继核（SRN）抑制作用的增强可导致系统从SWD转变为慢波振荡。特别地，研究表明，在典型的TRN对SRN抑制强度下，对皮质神经元群体施加刺激也可使系统从静息状态引发SWD和慢波振荡。然后，我们分别将其扩展为线性和环形结构的三室耦合丘脑皮质模型网络，以探索不同室中波状态的时空演化。主要结果如下：（i）对于开放式模型网络，第一个室中刺激诱发的SWD在传播到下游室时可转变为类似睡眠的慢上升 - 下降和纺锤波状态；（ii）对于封闭式模型网络，在第一个室中进行的弱刺激可导致在所有三个室中出现与实验观察一致的纺锤波振荡；相反，在第一个室中施加更强的周期性单脉冲刺激可诱导SWD和纺锤波振荡之间的周期性转变。详细研究表明，由SWD、纺锤波和背景状态组成的多吸引子共存机制是这些状态演化的基础。此外，为了证明状态演化相对于神经网络拓扑结构的稳定性，我们进一步将三室耦合网络扩展为十室耦合网络，分别具有线性和环形结构、最近邻（NN）耦合网络以及通过随机重连实现的小世界（SW）拓扑结构。有趣的是，对于线性和环形连接的情况，除了放电更不规则外，还获得了定性相似的结果。然而，对于NN和SW网络，SWD最终都可转变为一致的低幅振荡。特别是，在NN和SW网络中，SWD分别演变为慢纺锤波振荡和背景强直振荡。我们的建模和模拟研究突出了网络拓扑结构在SWD和纺锤波振荡演化中的作用，这为皮质癫痫发作发展机制提供了新的见解。
