EAGER: Inferring Activity From Anatomy in Neuronal Cultures

EAGER：从神经元培养物的解剖学中推断活动

• 批准号: 2207383
• 负责人: Peter Littlewood
• 金额: $ 29.91万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207383&HistoricalAwards=false
• 关键词: EAGER; Inferring; Activity; Anatomy; Neuronal

Emerging technologies to map whole brains at the synaptic level will soon produce complete maps of neural anatomy, but activity is only indirectly related to circuits, leaving large gaps in how we use anatomical maps to infer activity. Before facing the exabyte scales of whole brains, it is necessary to develop methods to infer activity from anatomy in smaller, simpler, but still complete model systems. Neural cell cultures (in vitro) are highly simplified systems that show complex dynamical activity, can be monitored in terms of spatial activity, and can have parameters tuned by chemistry. Critically, cultures are small, complete networks where every physical connection can be mapped and activity monitored at the single cell level. Thus, interpretations on how the physical wiring and activity in neural networks are correlated will not be confounded by artifacts or limitations encountered by other experimental methods that utilize living animals or brain tissue (e.g. living brain slices have thousands of severed connections.). As well as a substrate on which to develop methods later to be applied to whole brains, neural cell cultures are of interest as model systems in their own right. In a separate development far from neuroscience, research on 'active matter' - the interactions of autonomous agents - has suggested new principles about how quiescent states become active, and potentially synchronize. This project aims to bring together the novel theoretical perspective with simplified but biologically relevant experiments, using the latest tools of cell culture, neural recording, and connectomics. The goal of the project is to produce an explicit physical model where the three key elements of neuronal systems are joined up: functional recording from a 2D neural cell network; connectivity measurement through serial electron microscopy; explicit theoretical modelling of the dynamics of the neural system. The fundamental question is: Can one infer activity from anatomy? This research focuses on dynamical transitions between neural states, including synchrony. Epilepsy is a disease of synchrony and one of the co-investigators has his principal research activity in clinical investigations of pediatric epilepsy. There is little fundamental understanding about the (temporal) transition to seizure and we hope that understanding in a model system a (parameter driven) transition could be useful. Model systems are important in biology and physics. We hope that establishing a framework to analyze neural cell cultures will help normalize investigations which would otherwise be disconnected. The PIs will work with the electron microscopy program at Chicago State, a historically minority serving university. CSU students will be engaged in data analysis both as a component of their training in microscopy techniques and as full partners in the research.The PIs specifically ask: What does it mean to have a balanced network that can spontaneously fire without complete synchrony? Can one control the transitions from one generic dynamical phase to another? Is there emergent spatial and temporal scaling at such a transition? Are there qualitative differences between networks with long- and short-range correlations? This work is intended to build a framework that can be applied in the future to the growing number of published connectomic datasets derived from different brain regions and other ex vivo experimental platforms such as living brain slices/organoids and inform the analysis of large scale connectomics in whole brains.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.

在突触水平绘制整个大脑的新兴技术将很快产生完整的神经解剖图，但活动只是与电路间接相关，这给我们如何使用解剖图来推断活动留下了很大的空白。在面对整个大脑的艾字节尺度之前，有必要开发出在更小、更简单但仍然完整的模型系统中从解剖学推断活动的方法。神经细胞培养（体外）是高度简化的系统，显示复杂的动力学活动，可以在空间活动方面进行监测，并可以通过化学调节参数。重要的是，文化是小而完整的网络，每个物理连接都可以在单个细胞水平上进行映射和活动监控。因此，对神经网络中的物理布线和活动如何相互关联的解释将不会被利用活体动物或脑组织的其他实验方法所遇到的人为因素或限制所混淆（例如，活体脑切片具有数千个切断的连接）。神经细胞培养物本身也是一种有趣的模型系统，它不仅是一种基质，在此基础上开发出以后应用于整个大脑的方法。在一个远离神经科学的独立发展中，对“活性物质”（自主代理的相互作用）的研究提出了关于静止状态如何变得活跃并可能同步的新原理。该项目旨在将新颖的理论观点与简化但生物相关的实验结合起来，使用细胞培养，神经记录和连接组学的最新工具。该项目的目标是建立一个明确的物理模型，其中神经系统的三个关键要素被连接起来：从2D神经细胞网络的功能记录;通过连续电子显微镜的连接测量;神经系统动力学的明确理论建模。根本问题是：我们能从解剖学推断出活动吗？这项研究的重点是神经状态之间的动态转换，包括同步。癫痫是一种同步性疾病，其中一名合作研究者的主要研究活动是儿童癫痫的临床研究。关于癫痫发作的（时间）过渡几乎没有基本的理解，我们希望在模型系统中理解（参数驱动的）过渡可能是有用的。模型系统在生物学和物理学中非常重要。我们希望建立一个框架来分析神经细胞培养将有助于规范研究，否则将断开。PI将与芝加哥州立大学的电子显微镜项目合作，芝加哥州立大学是一所历史上的少数民族大学。CSU的学生将参与数据分析，这既是他们显微镜技术培训的一部分，也是研究的全面合作伙伴。PI特别提出了这样一个问题：拥有一个可以在不完全同步的情况下自发发射的平衡网络意味着什么？人们能否控制从一个一般动力学阶段到另一个一般动力学阶段的过渡？在这样的过渡中，是否存在突发的空间和时间尺度？具有长距离相关性和短距离相关性的网络之间是否存在质的差异？这项工作旨在建立一个框架，该框架可以在未来应用于越来越多的已发表的来自不同大脑区域的连接组数据集和其他离体实验平台，如活脑切片/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进行评估，被认为值得支持和更广泛的影响审查标准。

项目成果

Coalescence of limit cycles in the presence of noise

存在噪声时极限环的合并

• DOI: 10.1103/physreve.109.024220
• 发表时间: 2024
• 期刊: Physical Review E
• 影响因子: 2.4
• 作者: Shmakov, Sergei;Littlewood, Peter B.
• 通讯作者: Littlewood, Peter B.