Dynamic manifold-valued time series model in functional brain imaging

功能性脑成像中的动态流形值时间序列模型

• 批准号: 10374109
• 负责人: MOO K CHUNG
• 金额: $ 31.24万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10374109
• 关键词: Address; Age; Algorithms; Anatomy; Behavior; Behavioral Genetics; Brain; Brain imaging; Cations; Communities; Computational algorithm; Computer software; Data; Data Analytics; Data Set; Databases; Detection; Development; Diffusion Magnetic Resonance Imaging; Discrimination; Functional Magnetic Resonance Imaging; Future; Gaussian model; Genetic; Goals; Heritability; Human; Magnetic Resonance Imaging; Maps; Methods; Modeling; Participant; Pattern; Phenotype; Procedures; Property; Research; Rest; Series; Software Tools; Structure; Testing; Time; Visualization; base; behavioral outcome; cognitive task; connectome; genetic association; high dimensionality; human subject; interest; network models; neuroimaging; novel; open source; response

Abstract We will develop new large-scale dynamic models of manifold-valued data with a focus on dynamic symmetric positive definite (SPD) structures from nonstationary multivariate time series obtained from human functional magnetic resonance images (fMRI). The proposed new models and meth- ods will capture how functional brain connectivity dynamically changes over time and thus will be used to more accurately evaluate evolutionary dynamics of functional brain networks at the voxel level. We propose to build dynamically changing functional brain networks from a dataset with 1206 subjects from the Human Connectome Project (HCP) database containing T1-weighted magnetic resonance images (MRI), diffusion tensor images (DTI) and task and resting-state fMRI. MRI and DTI will be used in conjunction with fMRI in building more refined dynamic connectivity models. We will determine network phenotypes specific to behavior and their genetic associations. This study will provide the research community with the baseline brain network heritability maps as well as a versatile open-source toolbox of algorithms for modeling and visualizing dynamically changing large-scale brain networks.

摘要 我们将开发新的流形值数据的大规模动态模型，重点是动态 从非平稳多变量时间序列中获得对称正定（SPD）结构 功能性磁共振成像（fMRI）。新的模型和方法- ods将捕捉功能性大脑连接如何随时间动态变化， 用于更准确地评估功能性大脑网络的进化动力学， 体素级。我们建议从数据集构建动态变化的功能脑网络 使用来自人类连接组计划（HCP）数据库的1206名受试者， 磁共振成像（MRI）、扩散张量成像（DTI）以及任务和静息状态fMRI。 MRI和DTI将与fMRI结合使用，以建立更精细的动态连接 模型我们将确定特定于行为的网络表型及其遗传关联。 这项研究将为研究界提供基线脑网络遗传力图 以及用于动态建模和可视化的多功能开源算法工具箱 改变大规模的大脑网络。

项目成果

Discussion of 'Event history and topological data analysis'.

• DOI: 10.1093/biomet/asab023
• 发表时间: 2021-12
• 期刊: Biometrika
• 影响因子: 2.7
• 作者: Chung MK;Ombao H
• 通讯作者: Ombao H

Lattice Paths for Persistent Diagrams.

• DOI: 10.1007/978-3-030-87444-5_8
• 发表时间: 2021
• 期刊: Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data : 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, He...
• 作者: Chung MK;Ombao H
• 通讯作者: Ombao H

Hodge Laplacian of Brain Networks

• DOI: 10.1109/tmi.2022.3233876
• 发表时间: 2021-10
• 期刊: IEEE Transactions on Medical Imaging
• 影响因子: 10.6
• 作者: D. Anand;M. Chung

Topological Learning for Brain Networks

• DOI: 10.1214/22-aoas1633
• 发表时间: 2020-11
• 期刊: bioRxiv
• 作者: Tananun Songdechakraiwut;M. Chung