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

大脑网络中的有效连接：发现潜在结构、网络复杂性和循环。

• 批准号: 9170388
• 负责人: Stephen José Hanson
• 金额: $ 40.28万
• 依托单位: RUTGERS THE STATE UNIV OF NJ NEWARK
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-27 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9170388
• 关键词: Advanced Development; Area; Attention; Beds; Biological Markers; Biological Neural Networks; Brain; Brain imaging; Cognitive; Common Core; Communication; Coupling; Data; Detection; Development; Diagnostic tests; Etiology; Feedback; Functional Magnetic Resonance Imaging; Functional disorder; Graph; Human; Individual; Information Networks; Intention; Length; Measurement; Measures; Memory; Mental Processes; Mental disorders; Methods; Modality; Modeling; Motivation; Nature; Network Infrastructure; Neurosciences; Neurosciences Research; Play; Prefrontal Cortex; Principal Investigator; Process; Property; Recurrence; Research; Research Personnel; Role; Schizophrenia; Series; Short-Term Memory; Signal Transduction; Social Functioning; Source; Structure; Testing; Thinking; Time; Validation; Visual Pathways; abstracting; affective neuroscience; base; brain pathway; causal model; cognitive function; cognitive neuroscience; cognitive task; computer based statistical methods; design; executive function; feeding; flexibility; functional outcomes; language perception; language processing; mathematical analysis; network models; neuroimaging; novel; novel diagnostics; novel therapeutics; programs; relating to nervous system; signal processing; simulation; social; social neuroscience; statistics; success; theories; tool

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.

首席调查员/项目主任(最后、第一、中间)：Hanson，Stephen，JoséRFA-EB-15-006 项目摘要/摘要 从最早的神经科学研究开始，核心方法就专注于匹配特定的功能 对局部大脑结构和神经活动的影响。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。尽管表面上 这个程序成功地识别了与记忆、注意力、执行控制、行动相关的大脑区域- 感知、语言等。这是许多其他领域在基本认知/感知过程中参与的典型情况 任务，通常被认为是“背景”的、“次要的”或通常是无关紧要的，因此 已被忽略。鉴于大脑连接的基本性质，认知神经科学的理论将非常 可能涉及关于影响的假设--有时被称为“有效连通性”(Friston等人，1994， 斯波恩斯，2011)-在基本的心理过程中，大脑的一个区域可能对另一个区域产生影响。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 进程可能包括区域网络，这些区域交互操作以将分布式定义为 作为一种局部计算。假设网络、电路或 大脑区域集群在实施各种潜在的社会或 社会感知功能。许多这种假想的网络被认为是围绕着“中枢”组织起来的 它与其他区域同步，但对于给定的功能既不是排他性的，也不是必要和充分的。零件 大脑网络的这种明显的灵活性可以归因于对组件或 给定网络的特定功能。例如，许多与社交网络相关的大脑网络 功能，包括相似的功能、相似的区域和重叠的网络。作为社交/情感和 认知神经科学继续发展，理清这些网络将变得越来越关键 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，混乱的网络及其功能非但没有减少，反而增加了。 社会和认知神经科学领域已经发展到有原则的方法来识别 网络连接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式的新框架的开发 贝叶斯搜索称为图像(Ramsey等人，2010)，使用模拟和实验测试。我们也 目的开发新的认知神经科学战术和策略，以专门测试图形模型在 最后，我们还将开发两个新的方向，包括估计递归(反馈)网络 信息流和潜在结构支持大脑网络内的复杂性和交流。