A Statistical Method of Identifying Interactions in Neuron–Glia Systems Based on Functional Multicell Ca2+ Imaging

Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron–glia network. We attempted to identify neuron–glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron–glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron–glia systems.神经元与胶质细胞之间的串扰或许是大脑信息处理过程中的重要组成部分。本研究提出一种全新的统计方法，用于辨识神经元-胶质细胞网络中的相互作用。我们通过构建神经元-胶质细胞网络的广义线性模型（generalized linear model, GLM），采用基于最大后验估计（maximum-a-posteriori, MAP）的参数估计手段，旨在从神经元与胶质细胞的活动数据中识别二者的交互关系。本研究关注的相互作用涵盖功能连接与响应函数两类。为验证特定神经元向胶质细胞、或胶质细胞向神经元的连接是否真实存在，我们对添加或移除连接后得到的广义线性模型的交叉验证似然值进行了评估；仅当修改操作提升了交叉验证似然值时，才认可该连接的存在。我们将该方法应用于一组采集自大鼠海马CA3区的高通量多细胞体外钙离子成像（Ca2+ imaging）数据集，并通过基于替代数据法的统计检验，对连接估计结果的可靠性进行了评估。基于估计得到的连接模式所得到的研究结论，与当前已有的生理学认知高度契合，这表明本方法可用于揭示神经元-胶质细胞系统中尚未被发现的功能。