Abstract：
The high-order cognitive and executive functions are necessary for an individual to survive. The densely bidirectional innervations between the medial prefrontal cortex (mPFC) and the mediodorsal thalamus (MD) play a vital role in regulating high-order functions. Pyramidal neurons in mPFC have been classified into several subclasses according to their morphological and electrophysiological properties, but the properties of the input-specific pyramidal neurons in mPFC remain poorly understood. The present study aimed to profile the morphological and electrophysiological properties of mPFC pyramidal neurons innervated by MD. In the past, the studies for characterizing the morphological and electrophysiological properties of neurons mainly relied on the electrophysiological recording of a large number of neurons and their morphologic reconstructions. But, it is a low efficient method for characterizing the circuit-specific neurons. The present study combined the advantages of traditional morphological and electrophysiological methods with machine learning to address the shortcomings of the past method, to establish a classification model for the morphological and electrophysiological properties of mPFC pyramidal neurons, and to achieve more accurate and efficient identification of the properties from a small size sample of neurons. We labeled MD-innervated pyramidal neurons of mPFC using the trans-synaptic neural circuitry tracing method and obtained their morphological properties using whole-cell patch-clamp recording and morphologic reconstructions. The results showed that the classification model established in the present study could predict the electrophysiological properties of MD-innervated pyramidal neurons based on their morphology. MD-innervated pyramidal neurons exhibit larger basal dendritic length but lower apical dendrite complexity compared to non-MD-innervated neurons in the mPFC. The morphological characteristics of the two subtypes (ET-1 and ET-2) of mPFC pyramidal neurons innervated by MD are different, with the apical dendrites of ET-1 neurons being longer and more complex than those of ET-2 neurons. These results suggest that the electrophysiological properties of MD- innervated pyramidal neurons within mPFC correlate with their morphological properties, indicating that the different roles of these two subclasses in local circuits within PFC, as well as in PFC-cortical/subcortical brain region circuits.
摘要：
高级认知和执行功能是个体生存所必需的。内侧前额叶皮层（mPFC）和中背丘脑（MD）之间密集的双向神经支配在调节高阶功能中起着至关重要的作用。mPFC中的锥体神经元已根据其形态和电生理特性分为几个亚类，但是mPFC中输入特异性锥体神经元的特性仍然知之甚少。本研究旨在分析MD神经支配的mPFC锥体神经元的形态和电生理特性。在过去,表征神经元形态和电生理特性的研究主要依赖于大量神经元的电生理记录及其形态重建。但是,这是表征电路特定神经元的低效率方法。本研究将传统形态学和电生理学方法的优点与机器学习相结合，以解决过去方法的缺点，建立mPFC锥体神经元形态和电生理特性的分类模型,并从小样本神经元中更准确有效地识别属性。我们使用跨突触神经回路追踪方法标记了mPFC的MD神经支配的锥体神经元，并使用全细胞膜片钳记录和形态学重建获得了它们的形态学特性。结果表明，本研究建立的分类模型可以根据形态预测MD神经支配的锥体神经元的电生理特性。与mPFC中的非MD神经支配神经元相比，MD神经支配的锥体神经元表现出较大的基底树突长度，但较低的顶端树突复杂性。MD神经支配的mPFC锥体神经元的两种亚型(ET-1和ET-2)的形态特征不同,ET-1神经元的顶端树突比ET-2神经元的顶端树突更长，更复杂。这些结果表明，mPFC中MD神经支配的锥体神经元的电生理特性与其形态特性相关。表明这两个子类在PFC内的局部电路中的不同作用，以及PFC-皮质/皮质下脑区域回路。
