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

成功的学习和长期记忆保留是成功社会的核心，从学校的早期教育开始，一直延伸到一生。 一百多年来的研究表明，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小时后下降，这与神经元之间单个突触水平的实验室实验类似，其强度因集中刺激而饱和。 该项目旨在了解最终导致突触在数小时的时间尺度上持续生长的突触机制。 该项目的假设是，在这段时间内，突触内部的区域会开放，为更大更强的突触腾出空间。 这项研究是帮助有学习障碍的人以及增强其他人学习的新方法的第一步。这项研究的目标是构建成像、分析和计算工具来研究突触内纳米域的结构。纳米域包含突触的新生区域和活动区域。新生区具有完全限定的突触后区域，但缺乏突触前囊泡，因此是沉默的。新的电磁断层扫描成像与新的计算分析相结合，将加深对新生区域的理解，因为新生区域会招募突触前囊泡，从而转化为活跃区域，以支持突触可塑性，而突触可塑性是间隔学习优势的基础。现有和新获得的大型数据集将通过人工智能进行大规模分析。这项研究将为数据密集型神经科学和认知科学带来变革。这些数据集和人工智能工具将通过 NSF 资助的德克萨斯高级计算中心 (TACC) 的 3Dem 门户 (3dem.org) 与神经科学界广泛共享。该项目的目标是：1) 创建计算工具，通过在诱导 LTP 或 cLTD 后的不同时间（与对照刺激相比）在海马 CA1 区域和齿状回的突触连续部分中绘制突触前囊泡对接位点来自动描绘新生区域。 2）应用基于信息论和整体突触大小的新计算分析来测量突触的存储容量，将突触重量的定义细化为包含新生区域转换期间获得的扩大的活动区域。 3) 使用 MCell 对突触纳米域 3D 结构和功能进行真实的蒙特卡罗反应扩散模拟，为新生区和活动区之间的边界提供功能估计，并确定新生区和活动区的变化如何在 LTP 和 cLTD 饱和和恢复期间改变突触功效。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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.