MULTIREGIONAL ELECTRICAL ENCODING OF SOCIAL AGGRESSION

社会攻击的多区域电编码

基本信息

批准号：
10583574
负责人：
David E Carlson
金额：
$ 51.34万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-15 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10583574
关键词：
Action Potentials Address Aggressive behavior Agonist Animals Architecture Artificial Intelligence Atlases Behavior Behavior Control Behavioral Paradigm Biological Blood Brain Brain region Catalogs Cells Chemicals Clinic Communication Complex Cues Data Development Dopamine Electrodes Emotional Emotions Etiology Experimental Models Eye Functional Magnetic Resonance Imaging Goals Guanfacine Habenula Hour Human Hypothalamic structure Imaging Techniques Implant Individual Lateral Learning Link Machine Learning Mammals Measures Mediating Methodology Methods Modeling Molecular Monitor Motor Mus Muscle Music Neurosciences Output Oxytocin Peptides Physiological Play Prefrontal Cortex Process Research Resolution Rodent Role Sensorimotor functions Serotonin Signal Transduction Site Sleep Social Network Stream System Techniques Technology Testing Therapeutic Time Viral Vision Work brain cell cellular targeting connectome designer receptors exclusively activated by designer drugs emotional behavior experimental study free behavior human subject imaging study in vivo information processing instrument machine learning method millisecond multidisciplinary network models neural neuroregulation receptor sensorimotor system social spatial integration spatiotemporal tool

项目摘要

MULTIREGIONAL ELECTRICAL ENCODING OF SOCIAL AGGRESSION Critical developments in neuroscience have included technologies for sequencing individual brain cells, progress towards completing a full mesoscale brain cell atlas in mice, new tools for monitoring and manipulating the activity of brain cells, refined imaging techniques for developing structural and functional connectome atlases in humans, and new objective measures for characterizing behavior across species. Nevertheless, a critical gap that has yet to be addressed is the development of a model that would allow this emerging catalog of cellular information to be linked to the broad functional networks that encode emotional behavior in mammals. This gap exists in part because 1) technologies that measure, monitor, and decode mesoscale activity throughout the depth of the brain during free behavior in mammalian model species have yet to be implemented in conjunction with cellular activity can be monitored and manipulated, and 2) theoretical frameworks that link cellular activity to mesoscale network activity and that generalize across subjects on a mouse-by-mouse basis have yet to be developed. Our multi-disciplinary team has built a suite of tools for studying how brain dynamics encode complex brain states. These include advanced techniques to measure and monitor brain dynamics in vivo concurrently for months across many regions located throughout the depth of the brain at high spatiotemporal resolution in freely-behaving mice, machine-learning analytic approaches that build individual circuit activity measures into composite networks, behavioral manipulations that can be used to induce brain states related to emotions, viral methods that probe the relationship between cellular changes and the expression of brain-wide neural dynamics, and closed-loop stimulation tools that can potentially test the causality of brain network-states in mediating emotion. By integrating this suite of tools in a single framework, we intend to create a model that will catalogue how the brain generates aggression. Critically, we believe that this model framework will be broadly applicable to other emotional brain states as well.

社会攻击的多区域电编码神经科学的关键发展包括对单个脑细胞进行测序的技术，完成小鼠完整中尺度脑细胞图谱的进展，监测和监测的新工具操纵脑细胞的活动，完善成像技术以开发结构和功能人类连接组图谱，以及表征跨物种行为的新客观措施。然而，一个尚未解决的关键差距是开发一个模型，允许这种情况发生新兴的细胞信息目录将与编码情感的广泛功能网络联系起来哺乳动物的行为。存在这种差距的部分原因是 1) 测量、监控和解码技术哺乳动物模型物种在自由行为期间整个大脑深处的中尺度活动尚未与可监测和操纵的细胞活动结合实施，以及 2) 理论上将细胞活动与中尺度网络活动联系起来的框架，并在逐个鼠标的基础尚未开发。我们的多学科团队构建了一套工具来研究大脑动力学如何编码复杂的大脑州。其中包括同时测量和监测体内大脑动态的先进技术以高时空分辨率在大脑深处的许多区域进行了数月的研究自由行为的小鼠，机器学习分析方法将个体电路活动测量构建到复合网络、可用于诱导与情绪相关的大脑状态的行为操作、病毒探索细胞变化与全脑神经表达之间关系的方法动力学和闭环刺激工具，可以潜在地测试大脑网络状态的因果关系调解情绪。通过将这套工具集成到一个框架中，我们打算创建一个模型列出大脑如何产生攻击性。至关重要的是，我们相信这个模型框架将广泛也适用于其他情绪大脑状态。