NCS-FO: Collaborative Research: Computational Analysis of Synaptic Nanodomains

成功的学习和长期记忆是一个成功社会的核心，从学校的早期教育开始，延伸到一生。100多年来的研究表明，对于长期记忆来说，间隔学习比集中学习更有效。集中学习的效率在一个小时后下降，这在神经元之间的单个突触水平的实验室实验中是平行的，其强度被集中刺激饱和。该项目旨在了解突触机制，最终导致突触在许多小时的时间尺度上持续生长。项目假设是，在这段时间内，突触内部的区域打开，为更大更强的突触腾出空间。这项研究是帮助那些有学习障碍的人的第一步，也是帮助其他人学习的新方法的第一步。这项研究的目标是建立成像、分析和计算工具来研究突触内纳米结构域的结构。纳米结构域包括突触的新生区和活跃区。新生区有一个完全明确的突触后区域，但缺乏突触前囊泡，因此是沉默的。新的EM层析成像结合新的计算分析将完善对新生区域的理解，因为它们会吸收突触前囊泡，从而转化为活跃区域，以支持间隔学习优势的突触可塑性。现有的和新获得的大型数据集将用人工智能进行大规模分析。这项研究将对数据密集型神经科学和认知科学产生革命性的影响。这些数据集和人工智能工具将通过美国国家科学基金会资助的德克萨斯高级计算中心（TACC）的3Dem门户网站（3dem.org）与神经科学界广泛共享。本项目的目标是：1)与对照刺激相比，在LTP或cLTD诱导后的不同时间，通过绘制海马CA1区和齿状回突触序列片段中的突触前囊泡对接位点，创建计算工具来自动描绘新生区。2)应用基于信息论和整体突触大小的新的计算分析来衡量突触的存储容量，将突触权重的定义细化为包含新生区转换过程中获得的扩大的活跃区。3)使用MCell对突触纳米畴三维结构和功能进行蒙特卡罗反应扩散模拟，为新生区和活跃区之间的边界提供功能估计，并确定在LTP和cLTD饱和和恢复期间，新生区和活跃区的变化如何改变突触的效率。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Successful learning and long-term memory retention are central to a successful society, starting with early education in schools and extending throughout life. For over 100 years research has shown that spaced learning is much more effective than massed learning for long-term memories. The efficiency of focused learning falls after an hour, which is paralleled in lab experiments at the level of single synapse between neurons, whose strength is saturated by focused stimulation. This project seeks to understand the synaptic mechanisms that eventually lead to continued synaptic growth on the time scale of many hours. The project hypothesis is that over this time period, regions inside the synapse open up to make room for a larger and stronger synapse. This research is the first step toward helping those with learning disabilities and new ways to enhance learning in others.The goal of this research is to build imaging, analytical, and computational tools to investigate the structure of nanodomains within the synapse. The nanodomains comprise nascent and active zones of synapses. The nascent zones have a fully defined postsynaptic region but lack presynaptic vesicles and hence are silent. New EM tomographic imaging combined with new computational analyses will refine understanding of nascent zones as they recruit presynaptic vesicles and are thus converted to active zones in support of synaptic plasticity that underlies the advantage of spaced learning. Existing and newly acquired large data sets will be analyzed at scale with artificial intelligence. This research will be transformative for Data-Intensive Neuroscience and Cognitive Science. The data sets and AI tools will be shared broadly with the neuroscience community through the NSF-funded 3Dem Portal (3dem.org) at the Texas Advanced Computing Center (TACC). The objectives of this project are: 1) Create computational tools to delineate nascent zones automatically by mapping presynaptic vesicle docking sites in serial sections of synapses in the hippocampal CA1 region and dentate gyrus at various times after induction of LTP or cLTD, compared to control stimulation. 2) Apply a new computational analysis based on information theory and overall synapse size to measure the storage capacity of synapses, refining the definition of synaptic weight as encompassing the enlarged active zones obtained during the conversion of nascent zones. 3) Perform realistic Monte Carlo reaction-diffusion simulations of synaptic nanodomain 3D structure and function using MCell to provide a functional estimate for the boundary between nascent and active zones and determine how changes in nascent and active zones alter efficacy at synapses during saturation and recovery of LTP and cLTD.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.