Network Connectivity Modeling of Heterogeneous Brain Data to Examine Ensembles of Activity Across Two Levels of Dimensionality

异构大脑数据的网络连接建模，以检查两个维度上的活动集合

• 批准号: 9170562
• 负责人: Kathleen Gates
• 金额: $ 37.14万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 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/9170562
• 关键词: Accounting; Address; Algorithms; Area; Automobile Driving; Behavior; Brain; Brain region; Categories; Classification; Cognition; Communities; Complex; Computer software; Data; Data Analyses; Development; Diagnostic; Dimensions; Ensure; Equation; Exhibits; Experimental Designs; Female; Functional Magnetic Resonance Imaging; Gender; Heterogeneity; Human; Individual; Knowledge; Maps; Methods; Modeling; Monte Carlo Method; Network-based; Neurosciences; Pattern; Performance; Persons; Process; Property; Recovery; Research; Research Personnel; Resolution; Rest; Space Models; Specificity; Staging; Statistical Algorithm; Statistical Methods; Statistical Models; Subgroup; System; Techniques; Testing; Time; Work; abstracting; attentional control; base; cognitive function; cognitive system; design; driving behavior; experience; flexibility; interest; male; meetings; network models; novel; prevent; relating to nervous system; sensory system; theories; tool

Project Summary/Abstract Network methods have emerged as some of the most useful approaches for analyzing functional MRI data. While great advancements have been made in these methods, limitations hamper the progress fMRI researchers can make in better understanding brain processes. In particular, researchers are typically limited to looking at properties within a network, such as how regions relate across time, and cannot simultaneously look at relations between known networks. However, increasingly hypotheses require understanding the brain at two scales: at the resolution of the regions of interest within a known network and at the network level. Further hampering progress is that few network methods available can reliably arrive at network models for individuals. Indeed, increasingly researchers are finding that brain processes vary greatly across individuals, and thus methods are needed that do not assume homogeneity. Brain processes in heterogeneous data can be better studied by using reliable and valid approaches that attend to individual nuances while assessing relations within and between networks. We propose to develop, test, and freely disseminate an algorithm for the network-based analysis of brain processes that attends to these problems. The theory driving our approach is that interactions within and between large neural systems and brain areas – including multiple sensory systems, cognitive functioning, and attentional control - drive behavior and subjective experiences by working in concert with each other. Towards these ends, our software will provide statistical inference frameworks for conducting network connectivity and causal-inference analyses. Importantly, the proposed algorithm uniquely would enable researchers to address data dimensionality by correlating ensembles existing at lower dimension brain activity (i.e., data reduced to network activity) as well at higher dimensionality (i.e., the full functional brain parcellated into regions) within a unified modeling framework. Following the first stage of development and testing, we will validate the algorithms on data from within a highly controlled experimental design.

项目总结/摘要 网络方法已经成为分析功能磁共振成像的最有用的方法之一 数据虽然这些方法已经取得了很大的进步，但局限性阻碍了fMRI的进展 研究人员可以更好地了解大脑过程。特别是，研究人员通常 仅限于查看网络内的属性，例如区域如何跨时间关联， 同时查看已知网络之间的关系。然而，越来越多的假设要求 在两个尺度上理解大脑：在已知网络内感兴趣区域的分辨率上 在网络层面。进一步阻碍进展的是，几乎没有可用的网络方法可以可靠地 得出个人的网络模型。事实上，越来越多的研究人员发现， 个体之间的过程差异很大，因此需要不假设同质性的方法。 使用可靠有效的方法可以更好地研究异质数据中的脑过程 在评估内部和之间关系的同时关注个人细微差别的方法 网络. 我们建议开发，测试和免费传播一种基于网络的大脑分析算法。 解决这些问题的方法。驱动我们方法的理论是， 大型神经系统和大脑区域之间的联系-包括多个感觉系统，认知系统， 功能和注意力控制-通过协同工作来驱动行为和主观体验 彼此之间 为了实现这些目标，我们的软件将提供统计推断框架， 连通性和因果推理分析。重要的是，所提出的算法独特地将使 研究人员通过关联存在于较低维度大脑中的集合来解决数据维度问题 活动（即，简化为网络活动的数据）以及在更高维度（即，全功能 大脑分成区域）。在第一阶段之后， 开发和测试，我们将验证算法的数据从一个高度控制的 实验设计