权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Research: Spatiotemporal Fractional Modeling of Blood-Oxygen-Level Dependent Signals

合作研究：血氧水平相关信号的时空分数建模

基本信息

批准号：
1936578
负责人：
Anak Agung Julius
金额：
$ 24.01万
依托单位：
Rensselaer Polytechnic Institute
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2023-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936578&HistoricalAwards=false
关键词：
Collaborative Research Spatiotemporal Fractional Modeling

项目摘要

Understanding the brain in the context of its interactions with the world is key to assessing its dynamics in health and disease states. Functional magnetic resonance imaging (fMRI) technology is able to track blood-oxygen-level dependent signals over time and serve as a proxy to neural activity. Current approaches have limitations, as they assume that the interdependence between distinct brain regions is constant throughout recording periods. These approaches also assume that the brain is an isolated system that does not consider outside stimulus. To capture the highly complex dynamics and to enable the understanding of the neural basis of human cognition this research project will develop new data analysis methods for processing brain activity to explain the relationships involved in attention, learning, memory, decision-making, and language. This multidisciplinary effort will also enable the training of a new generation of engineers and clinicians, with particular emphasis on underrepresented groups, who can use the new data analytics to aid them during decision-making to assess the brain’s health enabling earlier diagnostics. The results of this research will have a great impact on healthcare and benefit the U.S. economy and society. To capture the highly complex spatiotemporal brain activity, this grant supports development of a model-based approach that captures the non-stationarity and fractal behavior of the brain dynamics. As a consequence, it will unveil dynamical characteristics that can be used to quantitatively and qualitatively measure how well an individual is doing in a particular task (e.g., attention/memory). Furthermore, the model-based approach in this project lays down the framework to perform control where quantitative measures can be used to establish control objectives regarding success in performing a given task. Unlike currently employed short-range memory models, this project focuses on a generalized mathematical framework that captures the degree of a memory of brain activity. Thus, the research will provide new insights and information about the brain organization and cognition that may fundamentally change the way one examines neuro-related data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在大脑与世界相互作用的背景下理解大脑是评估其在健康和疾病状态下的动态的关键。功能性磁共振成像（fMRI）技术能够随时间跟踪血氧水平依赖信号，并作为神经活动的代理。目前的方法有局限性，因为它们假设不同大脑区域之间的相互依赖性在整个记录期间是恒定的。这些方法还假设大脑是一个孤立的系统，不考虑外部刺激。为了捕捉高度复杂的动态，并使人类认知的神经基础的理解，该研究项目将开发新的数据分析方法来处理大脑活动，以解释参与注意力，学习，记忆，决策和语言的关系。这种多学科的努力还将有助于培训新一代的工程师和临床医生，特别是代表性不足的群体，他们可以使用新的数据分析来帮助他们做出决策，以评估大脑的健康状况，从而实现早期诊断。这项研究的结果将对医疗保健产生重大影响，并使美国经济和社会受益。为了捕捉高度复杂的时空大脑活动，该资助支持开发一种基于模型的方法，该方法可以捕捉大脑动态的非平稳性和分形行为。因此，它将揭示动态特征，这些特征可用于定量和定性地衡量个人在特定任务中的表现（例如，注意力/记忆力）。此外，本项目中的基于模型的方法为执行控制奠定了框架，其中可以使用定量措施来确定有关成功执行特定任务的控制目标。与目前采用的短程记忆模型不同，该项目侧重于一个通用的数学框架，该框架捕捉大脑活动的记忆程度。因此，该研究将提供有关大脑组织和认知的新见解和信息，可能从根本上改变人们检查神经相关数据的方式。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。