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Investigating Computation and Communication in Multi-Scale Brain Networks

研究多尺度脑网络中的计算和通信

基本信息

批准号：
8337289
负责人：
Ryan Thomas Canolty
金额：
$ 8.48万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-30 至 2013-08-31
项目状态：
已结题

项目摘要

PROJECT SUMMARY This proposal seeks to elucidate cross-level coupling (CLC) in multi-scale brain networks - that is, characterizing interactions between different spatial and temporal levels of brain organization, with a particular focus on the connection between spiking in single neurons and the distributed activity of the large-scale, dynamic functional networks in which they are embedded. This project will clarify the nature of multi-scale coupling and investigate causal mechanisms by which these interactions can be controlled. A key motivation for this research is that multi-scale CLC may provide a novel route for rehabilitative therapies and the design of new devices for clinical intervention following traumatic brain injury or stroke. Importantly, this project employs the real-time, chronic, multi-site, high-density microelectrode array recording techniques used in brain-machine interface (BMI) research combined with novel modeling and analysis methods. This approach was recently used to demonstrate that oscillatory phase coupling between multiple brain areas coordinates the spiking of single neurons as well as anatomically-dispersed functional cell assemblies (Canolty, et al., 2010, PNAS). These results, showing that dynamic patterns of large-scale network activity may serve to regulate neuronal ensembles, provide a striking example of functional interactions in multi-scale brain networks. This proposal builds on these results with the aim of further characterizing the functional role of interactions in multi-scale brain networks, their dependence on task conditions and various other experimental manipulations, and investigates the potential for external control of CLC via causal intervention using electrical stimulation. The first specific aim, targeting characterization of CLC, investigates how CLC in multi-scale brain networks changes across different behavioral tasks, between distinct neuronal groups, over extended periods of learning, and under pressure from operant conditioning. The second aim, targeting control of CLC, investigates the feasibility of using electrical stimulation to directly map network connectivity and causally influence neuronal activity and behavior by entraining different patterns of oscillatory network coupling. Together, these two aims provide the focus for a vigorous new research agenda targeting the exciting but poorly understood phenomenon of CLC in ways that may prove immediately useful in the development of effective clinical applications.

项目总结这一建议试图阐明多尺度脑网络中的跨层耦合(CLC)-即，表征不同空间和时间层次的大脑组织之间的相互作用，具有特定的关注单个神经元的尖峰与大范围的分布活动之间的联系，它们所嵌入的动态功能网络。本项目将澄清多尺度的性质耦合并研究可以控制这些相互作用的因果机制。一个关键的动机对于本研究来说，多尺度的CLC可能为康复治疗和设计康复治疗提供了一条新的途径创伤性脑损伤或中风后临床干预的新设备。重要的是，本项目采用了实时、慢性、多点、高密度的微电极阵列记录脑机接口(BMI)研究中使用的技术与新的建模和分析相结合方法：研究方法。这一方法最近被用来证明多个脑区协调单个神经元和解剖分散的功能细胞的尖峰放电组件(Canolty等人，2010年，PNAS)。这些结果表明，大规模网络的动态模式活动可能有助于调节神经元集合，提供了一个显著的例子，功能相互作用多尺度大脑网络。这项建议建立在这些结果的基础上，目的是进一步表征相互作用在多尺度大脑网络，它们对任务条件和各种其他实验操作的依赖，并研究了通过使用电刺激的因果干预对CLC进行外部控制的可能性。第一个具体目标，CLC的靶向特征，研究CLC如何在多尺度脑网络中在不同的行为任务中，在不同的神经元组之间，在延长的时间内学习，以及在操作条件反射的压力下。第二个目标，《中图法》的目标控制利用电刺激直接绘制网络连通性和因果影响图的可行性通过引入不同模式的振荡网络耦合，神经元的活动和行为。加在一起，这些两个目标为充满活力的新研究议程提供了重点，目标是令人兴奋但鲜为人知的 CLC现象在有效的临床开发中可能立即被证明是有用的申请。