EFFECTIVE CONNECTIVITY IN BRAIN NETWORKS: Discovering Latent Structure, Network Complexity and Recurrence.

主要研究者/项目负责人（最后，第一，中间）：Hanson，Stephen，José RFA-EB-15-006 项目总结/摘要 从神经科学研究的最早期开始，核心方法就集中在匹配特定的功能上 局部大脑结构和神经活动。大脑结构和功能之间的关系一直是 开发和应用新方法和新发现的关键动机。尽管表面 这个项目在识别与记忆、注意力、执行控制、行动- 感知、语言等等。在基本的认知/感知过程中， 任务，通常被认为是“背景”，“次要”或往往只是无关紧要的领域， 忽视鉴于大脑连接的基本性质，认知神经科学理论将非常 可能涉及关于影响的假设-有时称为“有效连接”（Friston等人，1994， Sporns，2011）--在基本的心理过程中，一个大脑区域可能会影响到另一个大脑区域。是否 我们认为语言处理，工作记忆或简单的检测任务，认知和知觉， 过程可能包括区域网络，这些区域网络交互地操作以定义分布式的 也是一种本地计算。网络、电路或 大脑区域的集群在执行各种潜在的社会或 社会知觉功能许多这些假设的网络被认为是围绕“枢纽”组织的 这些区域与其他区域同步，但对于给定功能来说既不是排他性的，也不是必要的和充分的。部分 大脑网络的这种明显的灵活性可以归因于对组成部分的持续模糊， 特定网络的特定功能。例如，许多与社交相关的大脑网络 功能相似、区域相似、网络重叠。作为社会/情感和 随着认知神经科学的不断发展，解开这些网络的纠缠将变得越来越重要， 以确定个人网络在各种社会，感知和认知功能中所扮演的角色。 不幸的是，近年来，网络及其功能的混乱程度非但没有减少，反而增加了。 社会和认知神经科学领域已经发展到一个地步， 网络连通性以及实现这一点的工具很可能是变革性的，但肯定是紧迫的。在这 我们的目标是推进基于有效连接模型的新框架的发展 和贝叶斯搜索称为IMAGES（Ramsey et al 2010）使用模拟和实验测试。我们也 旨在开发新的认知神经科学战术和策略，以专门测试图形模型， 最后，我们还将开发两个新的方向，包括递归（反馈）网络的估计 信息流和潜在结构支持大脑网络内的复杂性和通信。

Principal investigator/Program Director (Last, first, middle): Hanson, Stephen, José RFA-EB-15-006 Project Summary/Abstract Since the earliest days of neuroscience research, core methods have focused on matching specific functions to local brain structure and neural activity. The relationship between brain structure and function has been a key motivation for the development and application of novel methods and discovery. Despite the apparent success of this program in identifying brain areas associated with memory, attention, executive control, action- perception, language, etc.. it is typical for many other areas to be engaged during basic cognitive/perceptual tasks, areas that are often considered “background,” “secondary” or often just irrelevant and are consequently ignored. Given the fundamental nature of the connectivity in brain, theories of cognitive neuroscience will very likely involve hypotheses about the influence—sometimes called “effective connectivity” (Friston et al, 1994, Sporns, 2011)--- that one brain area may have upon another in the course of basic mental processes. Whether we consider language processing, working memory or simple detection tasks, cognitive and perceptual processes are likely to include networks of regions that operate interactively to define, both, a distributed as well as a kind of local computation. It has become increasingly common to posit that networks, circuits, or clusters of brain areas communicate with one another in the implementation of various potential social or social-perceptual functions. Many of these hypothesized networks are thought to be organized around "hubs" that synchronize other areas but are neither exclusive, nor necessary and sufficient, for a given function. Part of this apparent flexibility of brain networks can be attributed to continued ambiguity about the components or particular function of a given network. For example, many of the brain networks associated with social functioning, include similar function, similar areas, and overlapping networks. As social/affective and cognitive neuroscience continues to evolve it will be more and more critical to disentangle these networks in order to identify the role that individual networks play in various social, perceptual and cognitive function. Unfortunately, the muddle of networks and their functions has increased rather than decreased in recent years. The field of social and cognitive neuroscience has evolved to a point where principled methods for identifying network connectivity, and the tools to do so, could well be trans-formative but certainly are urgent. In this proposal we aim to advance the development of a novel framework based on a model of effective connectivity and Bayesian search called IMaGES (Ramsey et al 2010) using simulation and experimental tests. We also aim to develop novel Cognitive Neuroscience tactics and strategies to specifically test graphical models in the brain and finally we will also develop two new directions including estimation of Recurrent (feedback) network information flow and the Latent structure supporting the complexity and communication within brain networks.