Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine the mathematical modelling with structural information from non-invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along the example of diffusion-weighted magnetic resonance imaging (MRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e. the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

中文翻译：

---

特定于患者的网络连接性与下一代神经质量模型相结合，可测试癫痫发作的临床假设

癫痫发作潜在的动力学在空间和时间上跨越多个尺度，因此，了解癫痫发作机制需要确定这些尺度内和跨这些尺度的癫痫发作成分之间的关​​系，并分析其动态库。根据这种观点，已经开发了从单神经元到神经种群的数学模型。在这项研究中，我们考虑了一个神经质量模型，该模型能够精确地重现异构尖峰神经网络的动力学。我们将数学建模与来自非侵入性大脑成像的结构信息相结合，从而构建大规模的大脑网络模型以探索紧急情况的动力学并检验临床假设。我们提供了关于外部驱动器对表现出多重稳定性的神经元网络的影响的综合研究，为了研究神经解剖学连通性矩阵在形成紧急动力学中的作用。特别是，我们系统地研究了网络显示出从低活动状态到高活动状态的过渡的条件，我们将其识别为癫痫样事件。这种方法使我们能够研究生物物理参数和变量，从而导致网络一级的多个募集事件。我们进一步利用拓扑网络度量来解释受试者及其大脑区域之间的差异和类比，以显示不同参数值下的募集事件。我们以20名健康受试者和15名癫痫患者的扩散加权磁共振成像（MRI）连接器为例，与基于网络的脑部疾病所观察到的结构连接性的个体变化与数学动力学模型相关联时，具有解释脑部动力学时空组织变化的能力。尤其是对于癫痫患者，通过整合癫痫发生区（EZ）（即高度同步的癫痫发作起源的本地网络）上的临床假设，我们已经确定了募集事件的顺序并讨论了它们与拓扑特性的联系特定的连接组。基于已实施的一组精确平均场方程所做的预测与在招募的二级网络上进行的临床术前评估相吻合。如在基于网络的脑部疾病中观察到的那样，具有解释脑动力学时空组织变化的能力。尤其是对于癫痫患者，通过整合癫痫发生区（EZ）（即高度同步的癫痫发作起源的本地网络）上的临床假设，我们已经确定了募集事件的顺序并讨论了它们与拓扑特性的联系特定的连接组。基于已实施的一组精确平均场方程所做的预测与在招募的二级网络上进行的临床术前评估相吻合。如在基于网络的脑部疾病中观察到的那样，具有解释脑动力学时空组织变化的能力。尤其是对于癫痫患者，通过整合癫痫发生区（EZ）（即高度同步的癫痫发作起源的本地网络）上的临床假设，我们已经确定了募集事件的顺序并讨论了它们与拓扑特性的联系特定的连接组。基于已实施的一组精确平均场方程所做的预测与在招募的二级网络上进行的临床术前评估相吻合。通过在癫痫发生区（EZ）（即高度同步的癫痫发作起源的本地网络）上整合临床假设，我们已经确定了募集事件的序列，并讨论了它们与特定连接体拓扑特性的联系。基于已实施的一组精确平均场方程所做的预测与在招募的二级网络上进行的临床术前评估相吻合。通过在癫痫发生区（EZ）（即高度同步的癫痫发作起源的本地网络）上整合临床假设，我们已经确定了募集事件的序列，并讨论了它们与特定连接体拓扑特性的联系。基于已实施的一组精确平均场方程所做的预测与在招募的二级网络上进行的临床术前评估相吻合。