Synapse Elimination during Development: Pruning Rates, Models, and Diseases

描述（由申请人提供）：许多神经回路是使用两阶段发育过程形成的，该过程包括旺盛突触形成的初始期，随后是活动依赖性突触消除的较长时期。虽然修剪在许多生物体和大脑区域中都有很好的记录，但很少有人注意到突触在发育过程中被消除的速度。不同的修剪率对所产生的神经回路的质量和可塑性有很大的影响，事实上，许多神经系统疾病都与关键发育期皮质中的异常突触水平有关。 我们假设，在皮层修剪率已被优化，以实现底层神经回路的连接性和鲁棒性。为了验证我们的假设，我们提出了一个联合的实验-计算研究计划，以探索突触水平在发育过程中如何变化，以及这些变化如何在网络和表型水平上表现出来。首先，我们将使用专门的电子显微镜（EM）制备，选择性地染色突触的小鼠桶皮质发育过程中的突触消除的精确速率进行量化。我们将开发一个全自动和高吞吐量的图像处理管道，使我们能够在每个时间点计数数万个突触，并获得修剪率和峰值突触密度时间的强大统计数据。其次，我们将开发突触修剪的计算模型，以评估修剪率如何影响神经回路中的信息处理。我们的计算实验将探索稀疏电路是如何出现的，能够有效和强大的编码，同时仍然足够灵活的可塑性和适应性，同时满足代谢成本。我们的模型还将帮助我们回答有关全局神经回路的问题，包括枢纽是否可能存在以及功能模块如何形成。第三，我们将检查异常发育条件下突触重新连接和重组的病例。我们将使用脆性X综合征和Rett综合征的小鼠模型重复我们的EM程序，以比较对照组的突触水平，我们将扩展我们的计算模型，以了解这些条件下的电路水平差异。 拟议的项目将产生新的计算和实验解决方案，用于分析网络的发展，并将导致生物学的见解如何本地修剪机制影响全球电路特性。

DESCRIPTION (provided by applicant): Many neural circuits are formed using a two-stage developmental process that includes an initial period of exuberant synapse formation, followed by a longer period of activity-dependent synapse elimination. While pruning has been well-documented in many organisms and brain regions, little attention has been paid to the rate at which synapses are eliminated during development. Different pruning rates have a strong effect on the quality and plasticity of the resulting neural circuits, and indeed, many neurological diseases have been linked to abnormal synapse levels in the cortex during critical developmental periods. We hypothesize that pruning rates in the cortex have been optimized to achieve both connectivity and robustness of underlying neural circuits. To test our hypothesis, we propose a joint experimental-computational research plan to explore how synapse levels change during development and how these changes manifest at the network and phenotypic levels. First, we will quantify the precise rate of synapse elimination during development of the mouse barrel cortex using a specialized electron microscopy (EM) preparation that selectively stains for synapses. We will develop a fully-automated and high-throughput image processing pipeline that will allow us to count tens of thousands of synapses per time point and gain robust statistics of pruning rates and the time of peak synapse density. Second, we will develop computational models of synaptic pruning to evaluate how pruning rates affect information processing in neural circuits. Our computational experiments will explore how sparse circuits emerge that are capable of efficient and robust encoding, while still being flexible enough for plasticity and adaptation and while meeting metabolic costs. Our models will also help us answer questions about global neural circuitry, including whether hubs are likely to exist and how functional modules are formed. Third, we will examine cases of synapse rewiring and reorganization following abnormal developmental conditions. We will repeat our EM procedure using mouse models of Fragile X syndrome and Rett syndrome to compare synapse levels with respect to control, and we will extend our computational models to understand circuit-level differences in these conditions. The proposed project will generate new computational and experimental solutions for analyzing network development and will lead to biological insights into how local pruning mechanisms affect global circuit properties.