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通过轴突-树突重叠对啮齿动物海马结构局部回路中潜在突触连接的综合评估。

Comprehensive Estimates of Potential Synaptic Connections in Local Circuits of the Rodent Hippocampal Formation by Axonal-Dendritic Overlap.

作者信息

Tecuatl Carolina, Wheeler Diek W, Sutton Nate, Ascoli Giorgio A

机构信息

Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study; and Bioengineering Department, Volgenau School of Engineering, George Mason University, Fairfax, Virginia 22030-4444.

Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study; and Bioengineering Department, Volgenau School of Engineering, George Mason University, Fairfax, Virginia 22030-4444

出版信息

J Neurosci. 2021 Feb 24;41(8):1665-1683. doi: 10.1523/JNEUROSCI.1193-20.2020. Epub 2020 Dec 23.

Abstract

A quantitative description of the hippocampal formation synaptic architecture is essential for understanding the neural mechanisms of episodic memory. Yet the existing knowledge of connectivity statistics between different neuron types in the rodent hippocampus only captures a mere 5% of this circuitry. We present a systematic pipeline to produce first-approximation estimates for most of the missing information. Leveraging the www.Hippocampome.org knowledge base, we derive local connection parameters between distinct pairs of morphologically identified neuron types based on their axonal-dendritic overlap within every layer and subregion of the hippocampal formation. Specifically, we adapt modern image analysis technology to determine the parcel-specific neurite lengths of every neuron type from representative morphologic reconstructions obtained from either sex. We then compute the average number of synapses per neuron pair using relevant anatomic volumes from the mouse brain atlas and ultrastructurally established interaction distances. Hence, we estimate connection probabilities and number of contacts for >1900 neuron type pairs, increasing the available quantitative assessments more than 11-fold. Connectivity statistics thus remain unknown for only a minority of potential synapses in the hippocampal formation, including those involving long-range (23%) or perisomatic (6%) connections and neuron types without morphologic tracings (7%). The described approach also yields approximate measurements of synaptic distances from the soma along the dendritic and axonal paths, which may affect signal attenuation and delay. Overall, this dataset fills a substantial gap in quantitatively describing hippocampal circuits and provides useful model specifications for biologically realistic neural network simulations, until further direct experimental data become available. The hippocampal formation is a crucial functional substrate for episodic memory and spatial representation. Characterizing the complex neuron type circuit of this brain region is thus important to understand the cellular mechanisms of learning and navigation. Here we present the first numerical estimates of connection probabilities, numbers of contacts per connected pair, and synaptic distances from the soma along the axonal and dendritic paths, for more than 1900 distinct neuron type pairs throughout the dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex. This comprehensive dataset, publicly released online at www.Hippocampome.org, constitutes an unprecedented quantification of the majority of the local synaptic circuit for a prominent mammalian neural system and provides an essential foundation for data-driven, anatomically realistic neural network models.

摘要

对海马结构突触架构进行定量描述对于理解情景记忆的神经机制至关重要。然而,目前关于啮齿动物海马体中不同神经元类型之间连接统计的知识仅涵盖了该电路的5%。我们提出了一个系统流程,以对大部分缺失信息进行初步近似估计。利用www.Hippocampome.org知识库,我们根据海马结构各层和子区域内形态学上已识别的神经元类型的不同对之间的轴突 - 树突重叠,推导出局部连接参数。具体而言,我们采用现代图像分析技术,从不同性别的代表性形态重建中确定每种神经元类型的特定区域神经突长度。然后,我们使用来自小鼠脑图谱的相关解剖体积和超微结构确定的相互作用距离,计算每对神经元的平均突触数量。因此,我们估计了超过1900对神经元类型的连接概率和接触数量,使可用的定量评估增加了11倍多。海马结构中只有少数潜在突触的连接统计仍然未知,包括那些涉及长程(23%)或体细胞周围(6%)连接以及没有形态学追踪的神经元类型(7%)。所描述的方法还产生了沿树突和轴突路径从胞体到突触距离的近似测量值,这可能会影响信号衰减和延迟。总体而言,该数据集填补了定量描述海马回路的重大空白,并为生物现实神经网络模拟提供了有用的模型规范,直到有更多直接的实验数据可用。海马结构是情景记忆和空间表征的关键功能基础。因此,表征该脑区复杂的神经元类型电路对于理解学习和导航的细胞机制很重要。在这里,我们给出了整个齿状回、CA3、CA2、CA1、下托和内嗅皮质中超过1900对不同神经元类型的连接概率、每对连接的接触数量以及沿轴突和树突路径从胞体到突触距离的首次数值估计。这个全面的数据集在www.Hippocampome.org上公开发布,构成了对一个重要哺乳动物神经系统中大部分局部突触回路的前所未有的量化,并为数据驱动的、解剖学上现实的神经网络模型提供了重要基础。

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