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: 10058915
• 负责人: John H Byrne
• 金额: $ 302.21万
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10058915
• 关键词: Adaptive Behaviors; Address; Affect; Anatomy; Animals; Aplysia; Arousal; Behavior; Behavior Control; Behavioral; Biological Models; Brain; Cerebrum; Choices and Control; Code; Combinatorics; Complex; Computer Models; Data; Deglutition; Electrodes; Elements; Environment; Failure; Feedback; Feeding behaviors; Food; Future; Ganglia; Hodgkin-Huxley model; Human; Implant; Interneurons; Investigation; Learning; Mechanics; Mediating; Memory; Michigan; Modeling; Monitor; Motivation; Motor; Motor Neurons; Movement; Nervous system structure; Neural Network Simulation; Neuromechanics; Neurons; Pattern; Preparation; Process; Regulation; Research; Role; Satiation; Specific qualifier value; Spinal Cord; Stimulus; Synapses; System; Techniques; Testing; Texas; Vertebrates; Work; base; behavior prediction; biomechanical model; carbon fiber; combinatorial; connectome; feeding; food surveillance; improved; insight; mathematical analysis; mechanical load; motor behavior; multidisciplinary; neural circuit; neural model; neuronal patterning; new technology; novel; novel strategies; predictive modeling; relating to nervous system; response; sensory input; sensory stimulus; success; voltage sensitive dye

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.

概括 通过动态调节功能连接组，神经元网络产生适应性行为。基于 在底层解剖连接组上，功能连接组是有效突触的配置 特定行为期间神经元活动模式基础的连接。独特的组合 神经元激活特定的功能连接组，从而产生行为（组合代码）。经过 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，以及 局部可塑性和全局动态如何调节由小型大脑系统控制的进食行为。 该研究将由由博士组成的多学科团队进行。 J. Byrne（美国德克萨斯州休斯顿），C. Chestek（美国密歇根州，安娜堡）、H. Chiel（CWRU）、E. Cropper（西奈山）、A. Susswein（巴伊兰大学）、P. Thomas (CWRU) 和 K. Weiss (西奈山)。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的计算模型相结合 电路； 2）利用大规模细胞内记录技术来分析功能连接组和 响应感官刺激而选择不同进食行为的组合控制； 3）使用 这些记录技术来分析功能性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 饱足感改变了功能连接组的偏差，从而改变了行为以及学习的方式 当动物改变行为以应对变化时，可能会添加或删除功能性连接组的元素 在环境中。结果将提供对神经组织多个层面的过程如何进行的见解 有助于规范行为。在小型大脑模型系统中进行的此类研究将提供有助于帮助 指导未来对更复杂系统（例如脊椎动物和人类）的研究。