A novel approach to analyzing functional connectomics and combinatorial control in a tractable small-brain closed-loop system

一种在易处理的小脑闭环系统中分析功能连接组学和组合控制的新方法

• 批准号: 10700737
• 负责人: John H Byrne
• 金额: $ 89.26万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10700737
• 关键词: novel; approach; analyzing; functional; connectomics

SUMMARY Adaptive behaviors emerge from neuronal networks by dynamically regulating functional connectomes. Based on an underlying anatomical connectome, a functional connectome is the configuration of effective synaptic connections that underlies a pattern of neuronal activity during a specific behavior. Unique combinations of neurons activate specific functional connectomes, thereby generating a behavior (a combinatoric code). By combining neural network and biomechanical modeling, intracellular recording, and newly developed large-scale recording techniques, we will analyze functional connectomes and their combinatoric control of behavior, and how local plasticity and global dynamics mediate feeding behavior, which is controlled by a small brain system. The research will be performed by a multidisciplinary team consisting of Drs. J. Byrne (U. Texas, Houston), C. Chestek (U. Michigan, Ann Arbor), H. Chiel (CWRU), E. Cropper (Mt. Sinai), A. Susswein (Bar Ilan U.), P. Thomas (CWRU) and K. Weiss (Mt. Sinai). The project will: 1) develop a predictive neuromechanical model that incorporates a biomechanical model of the feeding musculature with a computational model of the feeding neural circuitry; 2) use large-scale and intracellular recording techniques to analyze the functional connectome and combinatoric control for choices among different feeding behaviors in response to sensory stimuli; and 3) use these recording techniques to analyze the ways in which the functional connectome and its combinatoric control are reconfigured by modulatory factors, motivation, and learning. We also will examine the ways in which arousal and satiation change the bias of the functional connectome and thus alter behavior, and the ways in which learning may add or remove elements of the functional connectome as an animal modifies behavior to respond to changes in the environment. The results will provide insights into how processes at multiple levels of neural organization contribute to regulation of behavior. Such studies in a small brain model system will provide insights that will help guide future investigations in more complex systems, such as vertebrates and humans.

摘要 神经元网络通过动态调节功能连接产生适应性行为。基座 在潜在的解剖连接体上，功能连接体是有效突触的构型 在特定行为中构成神经元活动模式的连接。独特的组合 神经元激活特定的功能连接，从而产生一种行为(一种组合密码)。通过 结合神经网络和生物力学建模、细胞内记录和新开发的大规模 记录技术，我们将分析功能连接及其对行为的组合控制，以及 局部可塑性和全球动力学如何调节摄食行为，这是由一个小的大脑系统控制的。 这项研究将由一个由J.Byrne博士(德克萨斯大学、休斯顿大学)、C. 切斯特克(密歇根大学，安娜堡)，H.齐尔(CWRU)，E.Cropper(Mt.Sinai)、A.SusSwein(Bar Ilan U)、P. Thomas(CWRU)和K.Weiss(Mt.西奈半岛)。该项目将：1)开发一种预测性神经力学模型 将摄食肌肉的生物力学模型与摄食神经的计算模型结合在一起 电路；2)使用大规模和细胞内记录技术分析功能连接体和 对不同摄食行为的组合控制对感觉刺激的反应；3)使用 这些记录技术分析了功能连接体及其组合控制的方式 通过调节因素、动机和学习进行重新配置。我们还将研究唤醒的方式 而饱腹感改变了功能性连接体的偏向，从而改变了行为和学习的方式 可以在动物改变行为以对变化作出反应时添加或移除功能连接体的元件 在环境中。这些结果将提供对多个水平的神经组织的过程的洞察 有助于规范行为。在小型大脑模型系统中进行的这类研究将提供有助于 指导未来对更复杂系统的研究，如脊椎动物和人类。