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.

在突触水平上绘制全脑的新兴技术很快就会产生神经解剖结构的完整地图，但活动仅与电路间接相关，在我们使用解剖图来推断活动方面留下了很大的差距。在面对整个大脑的外观尺度之前，有必要开发方法，以较小，更简单但仍然完整的模型系统从解剖结构中推断活动。神经细胞培养物（体外）是高度简化的系统，可以显示出复杂的动力学活性，可以根据空间活性进行监测，并且可以通过化学调节的参数。至关重要的是，培养物是小型，完整的网络，可以在其中映射每个物理连接并在单个细胞水平上监测活动。因此，对神经网络中的物理接线和活动如何相关的解释不会被其他实验方法所遇到的伪像或局限性混淆，而这些实验方法利用了活动物或脑组织（例如，活着的大脑切片具有成千上万的切断的连接。）。除了以后开发用于全脑的方法的底物外，神经细胞培养本身就是模型系统的感兴趣。在远离神经科学的单独发展中，对“活跃物质”的研究（自主剂的相互作用）提出了有关静态状态如何变得活跃和潜在同步的新原则。该项目的目的是使用细胞培养，神经记录和连接组学的最新工具，通过简化但与生物学相关的实验将新颖的理论观点汇总在一起。该项目的目的是产生一个明确的物理模型，其中连接神经元系统的三个关键要素：来自2D神经细胞网络的功能记录；连通性测量通过串行电子显微镜；神经系统动力学的明确理论建模。基本问题是：一个人可以从解剖学推断活动吗？这项研究的重点是包括同步在内的神经状态之间的动态过渡。癫痫病是一种同步性疾病，其中一位共同研究者的主要研究活动在小儿癫痫的临床研究中。对（时间）向癫痫发作的过渡几乎没有基本的了解，我们希望在模型系统A（参数驱动）过渡中的理解可能是有用的。模型系统在生物学和物理学中很重要。我们希望建立一个分析神经细胞培养的框架将有助于使研究正常化，否则将断开连接。 PI将与历史悠久的少数民族大学芝加哥州的电子显微镜计划合作。 CSU学生将参与数据分析，这是他们在显微镜技术方面的培训的组成部分，还是作为研究中的正式合作伙伴的培训。PIS专门询问：拥有一个可以自发发射的平衡网络而无需完全同步的意味着什么？一个人可以控制从一个通用动力学阶段到另一个通用动力学阶段的过渡吗？在这样的过渡时，是否存在紧急的空间和时间缩放？具有长距离相关性的网络之间是否存在定性差异？这项工作旨在构建一个框架，该框架可以在未来的越来越多的已发表的连接数据集衍生自不同的大脑区域和其他过时的实验平台，例如Living Brain Slices/Organoid，并为整个大脑的大规模连接分析提供了分析，这反映了NSF的法定任务和综述的范围，该奖项通过评估了范围的范围，并通过评估了基础，并具有范围。

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.