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:1936624
- 负责人:
- 金额:$ 21.84万
- 依托单位:
- 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:
- 关键词:
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.
在大脑与世界互动的背景下理解大脑是评估其健康和疾病状态动态的关键。功能磁共振成像(fMRI)技术能够跟踪随时间变化的血氧水平依赖信号,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,不考虑外界刺激。为了捕捉高度复杂的动态,并使理解人类认知的神经基础,该研究项目将开发新的数据分析方法来处理大脑活动,以解释涉及注意力、学习、记忆、决策和语言的关系。这一多学科的努力还将培训新一代工程师和临床医生,特别强调代表性不足的群体,他们可以在决策过程中使用新的数据分析来帮助他们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巨大影响,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,该基金支持开发一种基于模型的方法,以捕捉大脑动力学的非平稳性和分形行为。因此,它将揭示动态特征,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)。此外,本项目中基于模型的方法奠定了执行控制的框架,其中可以使用定量度量来建立有关成功执行给定任务的控制目标。与目前使用的短时记忆模型不同,该项目侧重于一个一般化的数学框架,以捕捉大脑活动的记忆程度。因此,这项研究将提供关于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。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(13)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.
- DOI:10.1073/pnas.2214634120
- 发表时间:2023-01-10
- 期刊:
- 影响因子:11.1
- 作者:Yin, Chenzhong;Imms, Phoebe;Cheng, Mingxi;Amgalan, Anar;Chowdhury, Nahian F.;Massett, Roy J.;Chaudhari, Nikhil N.;Chen, Xinghe;Thompson, Paul M.;Bogdan, Paul;Irimia, Andrei
- 通讯作者:Irimia, Andrei
Minimum Structural Sensor Placement for Switched Linear Time-Invariant Systems and Unknown Inputs
- DOI:10.1016/j.automatica.2022.110557
- 发表时间:2021-07
- 期刊:
- 影响因子:0
- 作者:Emily A. Reed;Guilherme Ramos;P. Bogdan;S. Pequito
- 通讯作者:Emily A. Reed;Guilherme Ramos;P. Bogdan;S. Pequito
Quantification of Fractional Dynamical Stability of EEG Signals as a Bio-Marker for Cognitive Motor Control
- DOI:10.3389/fcteg.2021.787747
- 发表时间:2022-02
- 期刊:
- 影响因子:0
- 作者:Emily A. Reed;Paul C. Bogdan;S. Pequito
- 通讯作者:Emily A. Reed;Paul C. Bogdan;S. Pequito
Mitigating Epilepsy by Stabilizing Linear Fractional-Order Systems
通过稳定线性分数阶系统减轻癫痫
- DOI:10.23919/acc55779.2023.10156404
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Emily A. Reed, Guilherme Ramos
- 通讯作者:Emily A. Reed, Guilherme Ramos
A Scalable Distributed Dynamical Systems Approach to Compute the Strongly Connected Components and Diameter of Networks
计算强连通分量和网络直径的可扩展分布式动力系统方法
- DOI:10.1109/tac.2022.3209446
- 发表时间:2022
- 期刊:
- 影响因子:6.8
- 作者:Reed, Emily A.;Ramos, Guilherme;Bogdan, Paul;Pequito, Sergio
- 通讯作者:Pequito, Sergio
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Paul Bogdan其他文献
Evidence of long-range dependence in power grid
电网长期依赖的证据
- DOI:
10.1109/pesgm.2016.7742029 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
L. Shalalfeh;Paul Bogdan;E. Jonckheere - 通讯作者:
E. Jonckheere
Graph Theoretical Description of Phase Transitions in Complex Multiscale Phases with Supramolecular Assemblies.
超分子组装复杂多尺度相变的图论理论描述。
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Ruochen Yang;K. Bernardino;Xiongye Xiao;Weverson R. Gomes;Davi A Mattoso;Nicholas A. Kotov;Paul Bogdan;A. F. de Moura - 通讯作者:
A. F. de Moura
Leveraging Reinforcement Learning and Large Language Models for Code Optimization
利用强化学习和大型语言模型进行代码优化
- DOI:
10.48550/arxiv.2312.05657 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Shukai Duan;Nikos Kanakaris;Xiongye Xiao;Heng Ping;Chenyu Zhou;Nesreen K. Ahmed;Guixiang Ma;M. Capotă;T. Willke;Shahin Nazarian;Paul Bogdan - 通讯作者:
Paul Bogdan
W7. NEXT-GENERATION PRECISION MEDICINE FOR SUICIDALITY PREVENTION: COMPREHENSIVE BIO-SOCIO-PSYCHOLOGICAL INTEGRATION
W7. 用于预防自杀的下一代精准医学:综合生物-社会-心理整合
- DOI:
10.1016/j.euroneuro.2024.08.216 - 发表时间:
2024-10-01 - 期刊:
- 影响因子:6.700
- 作者:
Alexander Niculescu;Helen Le-Niculescu;Rowan Bhagar;Chenzhong Yin;Kyle Roseberry;Anantha Shekhar;Sunil Kurian;Paul Bogdan - 通讯作者:
Paul Bogdan
Paul Bogdan的其他文献
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{{ truncateString('Paul Bogdan', 18)}}的其他基金
NSF Student Travel Grant for 2019 International Symposium on Networks-on-Chip (NOCS2019)
2019 年国际片上网络研讨会 (NOCS2019) NSF 学生旅费资助
- 批准号:
1939961 - 财政年份:2019
- 资助金额:
$ 21.84万 - 项目类别:
Standard Grant
Collaborative Research: MODULUS: A Novel Spatiotemporal Multifractal Analysis to Evaluate Genome Dynamics
合作研究:MODULUS:一种评估基因组动力学的新型时空多重分形分析
- 批准号:
1936775 - 财政年份:2019
- 资助金额:
$ 21.84万 - 项目类别:
Standard Grant
CPS: Small: Uncertainty-aware Framework for Specifying, Designing and Verifying Cyber-Physical Systems
CPS:小型:用于指定、设计和验证网络物理系统的不确定性感知框架
- 批准号:
1932620 - 财政年份:2019
- 资助金额:
$ 21.84万 - 项目类别:
Standard Grant
CAREER: Embracing Complexity: A Fractal Calculus Approach to the Modeling and Optimization of Medical Cyber-Physical Systems
职业:拥抱复杂性:医疗网络物理系统建模和优化的分形微积分方法
- 批准号:
1453860 - 财政年份:2015
- 资助金额:
$ 21.84万 - 项目类别:
Continuing Grant
Collaborative Research: CyberSEES: Climate-Aware Renewable Hydropower Generation and Disaster Avoidance
合作研究:CyberSEES:气候感知型可再生水力发电和防灾
- 批准号:
1331610 - 财政年份:2013
- 资助金额:
$ 21.84万 - 项目类别:
Standard Grant
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