Pathophysiolpgical modelling of brain systems from microscale to macroscale remains difficult in group comparisons partly because of the infeasibility of modelling the interactions of thousands of neurons at the scales involved. Here, to address the challenge, we present a novel approach to construct differential causal networks directly from electroencephalogram (EEG) data. The proposed network is based on conditionally coupled neuronal circuits which describe the average behaviour of interacting neuron populations that contribute to observed EEG data. In the network, each node represents a parameterised local neural system while directed edges stand for node-wise connections with transmission parameters. The network is hierarchically structured in the sense that node and edge parameters are varying in subjects but follow a mixed-effects model. A novel evolutionary optimisation algorithm for parameter inference in the proposed method is developed using a loss function derived from Chen-Fliess expansions of stochastic differential equations. The method is demonstrated by application to the fitting of coupled Jansen-Rit local models. The performance of the proposed method is evaluated on both synthetic and real EEG data. In the real EEG data analysis, we track changes in the parameters that characterise dynamic causality within brains that demonstrate epileptic activity. We show evidence of network functional disruptions, due to imbalance of excitatory-inhibitory interneurons and altered epileptic brain connectivity, before and during seizure periods.

翻译：在群体比较中，从微观尺度到宏观尺度对脑系统进行病理生理学建模仍然存在困难，部分原因在于在相关尺度上对数千个神经元相互作用进行建模的不可行性。为解决这一挑战，本文提出一种直接从脑电图数据构建差分因果网络的新方法。所提出的网络基于条件耦合的神经元回路，这些回路描述了贡献于观测脑电图数据的相互作用神经元群体的平均行为。在该网络中，每个节点代表一个参数化的局部神经系统，而有向边则表示具有传输参数的节点间连接。该网络具有层次化结构，即节点和边参数在个体间变化但遵循混合效应模型。通过从随机微分方程的Chen-Fliess展开推导损失函数，我们开发了一种用于所提方法中参数推断的新型进化优化算法。该方法通过应用于耦合Jansen-Rit局部模型的拟合得到验证。所提方法的性能在合成和真实脑电图数据上均进行了评估。在真实脑电图数据分析中，我们追踪了表征癫痫活动大脑内动态因果性的参数变化。我们提供了癫痫发作期前后因兴奋-抑制性中间神经元失衡及癫痫脑连接改变导致网络功能紊乱的证据。