Bayesian mapping of the striatal microcircuit reveals robust asymmetries in the probabilities and distances of connections
Bayesian mapping of the striatal microcircuit reveals robust asymmetries in the probabilities and distances of connections
复制标题
纹状体微电路的贝叶斯映射揭示了连接概率和距离的鲁棒不对称性
DOI：
10.1101/2021.06.08.447507
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Cinotti F
中科院分区：
文献类型：
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作者：
Cinotti F
纹状体9S复合体微电路是由其表达D1和D2受体的投射神经元和至少5种中间神经元之间的连接组成的。对这个回路的准确了解可能是理解纹状体9的功能作用及其在广泛的运动和认知障碍中的功能障碍所必需的。我们在这里介绍了一种使用细胞内记录数据绘制神经元连接性图的贝叶斯方法，它允许我们同时评估神经元类型之间联系的概率、证据的强度以及它对距离的依赖。用它来合成小鼠纹状体的完整图谱，我们发现了两种不对称的有力证据：投射神经元连接的选择性不对称，D2神经元与其他投射神经元的连接密度是D1神经元的两倍，但两个亚型都不优先与另一种投射神经元连接；以及长度-尺度不对称，在投射神经元连接距离的两倍以上，神经元间连接的概率仍然是不可忽略的。我们进一步表明，我们的贝叶斯方法可以评估连接变化的证据，使用来自发育中的纹状体和亨廷顿9S病小鼠模型的数据。通过量化我们对微电路知识的不确定性，我们的方法揭示了与当前数据一致的广泛的潜在纹状体接线图。意义统计要正确理解神经元电路的功能，重要的是要准确地了解单个神经元之间的连接率，以及这种比率如何随着神经元对的距离而变化。我们提出了一种从实验数据中提取这种信息的贝叶斯方法，并将其应用于小鼠纹状体，这是一种参与学习和决策的皮质下结构，由各种不同的投射神经元和中间神经元组成。我们得到的统计图不仅揭示了对神经元类型之间联系概率的最可靠估计，而且还揭示了它们的证据强度以及它们对距离的依赖。
The striatum9s complex microcircuit is made by connections within and between its D1- and D2-receptor expressing projection neurons and at least five species of interneuron. Precise knowledge of this circuit is likely essential to understanding striatum9s functional roles and its dysfunction in a wide range of movement and cognitive disorders. We introduce here a Bayesian approach to mapping neuron connectivity using intracellular recording data, which lets us simultaneously evaluate the probability of connection between neuron types, the strength of evidence for it, and its dependence on distance. Using it to synthesize a complete map of the mouse striatum, we find strong evidence for two asymmetries: a selective asymmetry of projection neuron connections, with D2 neurons connecting twice as densely to other projection neurons than do D1 neurons, but neither subtype preferentially connecting to another; and a length-scale asymmetry, with interneuron connection probabilities remaining non-negligible at more than twice the distance of projection neuron connections. We further show that our Bayesian approach can evaluate evidence for wiring changes, using data from the developing striatum and a mouse model of Huntington9s disease. By quantifying the uncertainty in our knowledge of the microcircuit, our approach reveals a wide range of potential striatal wiring diagrams consistent with current data.SIGNIFICANCE STATEMENTTo properly understand a neuronal circuit9s function, it is important to have an accurate picture of the rate of connection between individual neurons and how this rate changes with the distance separating pairs of neurons. We present a Bayesian method for extracting this information from experimental data and apply it to the mouse striatum, a subcortical structure involved in learning and decision-making, which is made up of a variety of different projection neurons and interneurons. Our resulting statistical map reveals not just the most robust estimates of the probability of connection between neuron types, but also the strength of evidence for them, and their dependence on distance.