Calcium imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures

Pathophysiological explanations of epilepsy typically focus on either the micro/mesoscale (e.g. excitation-inhibition imbalance), or on the macroscale (e.g. network architecture). Linking abnormalities across spatial scales remains difficult, partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved. Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole. Spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models, allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics. This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale. Fluctuations of both synaptic connection strength and their temporal dynamics are required to explain observed seizure patterns. These findings highlight distinct changes in local (intrinsic) and long-range (extrinsic) synaptic transmission dynamics as a possible seizure pathomechanism and illustrate how our Bayesian model inversion approach can be used to link existing neural mass models of seizure activity and novel experimental methods.癫痫的病理生理学阐释通常聚焦于两类空间尺度：微观/介观尺度（例如兴奋-抑制失衡），或是宏观尺度（例如网络架构）。实现不同空间尺度间异常的跨尺度关联仍颇具挑战，部分原因在于当前难以在涉及的各类尺度下同步采集神经元特征信号的技术局限。本研究采用戊四氮（pentylenetetrazole）诱导急性癫痫发作期间的幼斑马鱼脑部光片成像（light sheet imaging）技术。随后，我们借助神经群模型（neural mass models）对发作期间自发神经元活动的频谱变化进行建模，以此实现针对有效网络连接及其潜在突触动力学变化的贝叶斯推断（Bayesian inference）。此项针对斑马鱼脑部癫痫发作的动态因果建模（dynamic causal modelling）研究揭示了宏观与介观尺度下突触耦合的同步变化。若要解释观测到的癫痫发作模式，需同时纳入突触连接强度及其时间动力学的波动特性。本研究结果凸显了局部（固有）与长程（外在）突触传递动力学的特异性变化，其或为癫痫发作的潜在病理机制；同时也展示了我们的贝叶斯模型反演（Bayesian model inversion）方法如何用于关联现有癫痫活动神经群模型与新兴实验技术。