Exploratory and Confirmatory Neurocognitive Modeling with Latent Variables

具有潜在变量的探索性和验证性神经认知模型

• 批准号: 2051186
• 负责人: Joachim Vandekerckhove
• 金额: $ 34.96万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051186&HistoricalAwards=false
• 关键词: Exploratory; Confirmatory; Neurocognitive; Modeling; Latent

This research project will improve methods for analyzing brain and behavior measurements. Existing methods for combining multiple sources of brain data are limited. They cannot take advantage of the joint measurement of different kinds of brain activity to predict behavior. This project will combine multiple recent developments in mathematical psychology and cognitive neuroscience to arrive at a powerful new data analysis method that can process many sources of data at once. The methods to be developed will identify the shared and unique contributions of brain regions to behavior and can be used to discover new functions of brain regions. Broader use of these methods also will facilitate theory-building in cognitive (neuro)science. The project will involve early-career scientists who will receive training in neuroscience or cognitive science. The investigators will document their modeling techniques as part of a series of publicly available video lectures. The project also will develop a large code base and user-friendly software, thus contributing to the global research and education infrastructure.This project will combine recent developments in mathematical psychology (Bayesian latent variable modeling with cognitive models) and cognitive neuroscience (modeling of neurocognitive relationships) to develop new methods for analyzing brain data. Current approaches to brain-data analysis emphasize looking for separate relationships between individual physiological measures and behavior. Direct analysis of the relationship between different types of physiological measures usually does not involve behavioral data. Identification of the processes that give rise to patterns in different measures is carried out without direct links to behavior. However, multiple neuroimaging techniques can provide common underlying information that informs cognitive models of human behavior as well as the physical location of the ongoing processes. For instance, both fMRI and EEG are driven, at least in part, by the same underlying neural processes. At the same time, each technique also contains unique information, so that some cognitive processes underlying human behavior can potentially only be informed by specific techniques or combinations thereof. This project will make it possible to translate such theories about these complex relationships into principled statistical models whose predictions and assumptions can be put to the empirical test. The new methods will be applied to archival data sets, thus contributing to generalizable knowledge in a way that is cost-effective and ethical.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.

这项研究项目将改进分析大脑和行为测量的方法。现有的组合多源大脑数据的方法是有限的。他们不能利用不同类型的大脑活动的联合测量来预测行为。该项目将结合数学心理学和认知神经科学的多种最新发展，得出一种强大的新数据分析方法，可以同时处理多种来源的数据。即将开发的方法将识别大脑区域对行为的共同和独特贡献，并可用于发现大脑区域的新功能。这些方法的广泛使用也将促进认知(神经)科学的理论建设。该项目将包括职业生涯早期的科学家，他们将接受神经科学或认知科学方面的培训。研究人员将记录他们的建模技术，作为一系列公开视频讲座的一部分。该项目还将开发大型代码库和用户友好的软件，从而为全球研究和教育基础设施做出贡献。该项目将结合数学心理学(贝叶斯潜变量建模与认知模型)和认知神经科学(神经认知关系建模)的最新发展，开发分析大脑数据的新方法。目前的大脑数据分析方法强调寻找个体生理测量和行为之间的独立关系。直接分析不同类型的生理测量之间的关系通常不涉及行为数据。识别在不同衡量标准中产生模式的过程是在没有与行为直接联系的情况下进行的。然而，多种神经成像技术可以提供共同的潜在信息，为人类行为的认知模型以及正在进行的过程的物理位置提供信息。例如，fMRI和EEG至少在一定程度上都是由相同的潜在神经过程驱动的。同时，每种技术也包含独特的信息，因此人类行为背后的一些认知过程可能只能通过特定的技术或其组合来获得信息。这个项目将有可能将这些关于这些复杂关系的理论转化为有原则的统计模型，这些模型的预测和假设可以进行实证检验。新方法将应用于档案数据集，从而以一种具有成本效益和伦理的方式促进概括性知识。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Parsing memory and nonmemory contributions to age-related declines in mnemonic discrimination performance: a hierarchical Bayesian diffusion decision modeling approach.

解析记忆和非记忆对助记辨别性能与年龄相关的下降的贡献：分层贝叶斯扩散决策建模方法。

• DOI: 10.1101/lm.053838.123
• 发表时间: 2023
• 期刊: Learning & memory (Cold Spring Harbor, N.Y.)
• 作者: Chwiesko,Caroline;Janecek,John;Doering,Stephanie;Hollearn,Martina;McMillan,Liv;Vandekerckhove,Joachim;Lee,MichaelD;Ratcliff,Roger;Yassa,MichaelA
• 通讯作者: Yassa,MichaelA

Cortico-Brainstem Mechanisms of Biased Perceptual Decision-Making in the Context of Pain

• DOI: 10.1016/j.jpain.2021.11.006
• 发表时间: 2022-04-02
• 期刊: JOURNAL OF PAIN
• 影响因子: 4
• 作者: Wiech，Katja;Eippert，Falk;Tracey，Irene
• 通讯作者: Tracey，Irene

Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals

• DOI: 10.1109/ijcnn55064.2022.9892272
• 发表时间: 2022-07
• 期刊: 2022 International Joint Conference on Neural Networks (IJCNN)
• 作者: Qi Sun;Khuong Vo;K. Lui;Michael D. Nunez;J. Vandekerckhove;R. Srinivasan

Piecewise Linear and Stochastic Models for the Analysis of Cyber Resilience

用于网络弹性分析的分段线性和随机模型

• DOI: 10.1109/ciss56502.2023.10089725
• 发表时间: 2023
• 期刊: 2023 57th Annual Conference on Information Sciences and Systems (CISS
• 作者: Weisman, Michael J.;Kott, Alexander;Vandekerckhove, Joachim
• 通讯作者: Vandekerckhove, Joachim

Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling

使用生成对抗网络构建图形模型进行脑电图信号建模

• DOI: 10.1109/icassp43922.2022.9747783
• 发表时间: 2022
• 期刊: IEEE ICASSP
• 作者: Vo, Khuong;Vishwanath, Manoj;Srinivasan, Ramesh;Dutt, Nikil;Cao, Hung
• 通讯作者: Cao, Hung