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.

总结 适应性行为通过动态调节功能性连接体从神经元网络中产生。基于 在一个潜在的解剖学连接体上，一个功能性连接体是有效突触的结构。 在特定行为中，神经元活动模式的基础连接。的独特组合 神经元激活特定的功能连接体，从而产生行为（组合代码）。通过 结合神经网络和生物力学建模，细胞内记录，以及新开发的大规模 记录技术，我们将分析功能性连接体及其对行为的组合控制， 局部可塑性和全局动力学如何调节进食行为，这是由一个小的大脑系统控制的。 这项研究将由一个多学科的团队进行，该团队由J·伯恩博士（U。Texas，Houston），C. Chestek（U. Michigan，安阿伯），H. Chiel（CWRU），E.克罗珀山（Mt. Sinai），A. Susswein（Bar Ilan U.），P. 托马斯（CWRU）和K.韦斯山Sinai）。该项目将：1）开发一个预测神经力学模型， 将进食肌肉组织的生物力学模型与进食神经系统的计算模型相结合， 电路; 2）使用大规模和细胞内记录技术来分析功能性连接体， 组合控制，用于响应于感官刺激而选择不同的进食行为;以及3）使用 这些记录技术来分析功能连接体及其组合控制的方式， 被调节因素、动机和学习重新配置。我们还将研究唤醒 和饱足改变了功能性连接体的偏向，从而改变了行为， 当动物改变行为以应对变化时， 在环境中。这些结果将为深入了解神经组织的多个层次的过程提供帮助。 有助于行为规范。在小型大脑模型系统中进行的此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。