The aim of this paper is to present a dynamic causal modeling (DCM) framework for hippocampal activity measured via voltage-sensitive dye imaging (VSDI). We propose a DCM model of the hippocampus that summarizes interactions between the hilus, CA3 and CA1 regions. The activity of each region is governed via a neuronal mass model with two inhibitory and one/two excitatory neuronal populations, which can be linked to measurement VSDI by scaling neuronal activity. To optimize the model structure for the hippocampus, we propose two Bayesian schemes: Bayesian hyperparameter optimization to estimate the unknown electrophysiological properties necessary for constructing a mesoscopic hippocampus model; and Bayesian model reduction to determine the parameterization of neural properties, and to test and include potential connections (morphologically inferred without direct evidence yet) in the model by evaluating group-level model evidence. The proposed method was applied to model spatiotemporal patterns of accumulative responses to consecutive stimuli in separate groups of wild-type mice and epileptic aristaless-related homeobox gene (Arx) conditional knock-out mutant mice (Arx-/+;Dlx5/6CRE-IRES-GFP) in order to identify group differences in the effective connectivity within the hippocampus. The causal role of each group-differing connectivity in generating mutant-like responses was further tested. The group-level analysis identified altered intra- and inter-regional effective connectivity, some of which are crucial for explaining mutant-like responses. The modelling results for the hippocampal activity suggest the plausibility of the proposed mesoscopic hippocampus model and the usefulness of utilizing the Bayesian framework for model construction in the mesoscale modeling of neural interactions using DCM.

中文翻译：

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通过介观电压敏感染料成像测量的海马活动的动态因果模型

本文的目的是提出一个动态因果建模 (DCM) 框架，用于通过电压敏感染料成像 (VSDI) 测量的海马活动。我们提出了海马的 DCM 模型，该模型总结了门、CA3 和 CA1 区域之间的相互作用。每个区域的活动通过具有两个抑制性和一个/两个兴奋性神经元群的神经元质量模型进行控制，可以通过缩放神经元活动将其与测量 VSDI 联系起来。为了优化海马的模型结构，我们提出了两种贝叶斯方案：贝叶斯超参数优化来估计构建介观海马模型所需的未知电生理特性；和贝叶斯模型缩减以确定神经属性的参数化，并通过评估组级模型证据来测试和包含模型中的潜在联系（在没有直接证据的情况下进行形态学推断）。所提出的方法应用于模拟不同组的野生型小鼠和癫痫 aristaless 相关同源框基因 (Arx) 条件敲除突变小鼠 (Arx-/+;Dlx5/6CRE-IRES) 对连续刺激的累积反应的时空模式-GFP) 以确定海马体内有效连接的组差异。进一步测试了每个组不同连接在产生突变样反应中的因果作用。组级分析确定了区域内和区域间有效连接的改变，其中一些对于解释突变样反应至关重要。