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Dynamic embedding time series models in functional brain imaging

功能性脑成像中的动态嵌入时间序列模型

基本信息

批准号：
10711521
负责人：
MOO K CHUNG
金额：
$ 36.41万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-07 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10711521
关键词：
Address Algorithms Anatomic Surface Anatomy Atrophic Behavior Brain Brain imaging Brain region Bypass Chromosome Mapping Clinical Cognition Cognitive Communities Computer software Data Data Analyses Data Set Databases Dependence Diffusion Magnetic Resonance Imaging Discrimination Functional Magnetic Resonance Imaging Future Genetic Goals Heritability Human Laplacian Magnetic Resonance Imaging Maps Methods Modeling Outcome Participant Phenotype Population Procedures Research Resolution Rest Series Shapes Signal Transduction Software Tools Statistical Methods Structure Surface Techniques Time Twin Multiple Birth Visualization Work behavior prediction cognitive task complex data connectome data structure genetic association geometric structure human subject interest morphometry network models neuroimaging novel open source tool usability user-friendly

项目摘要

Project Summary We will develop new large-scale dynamic embedding models of network data with a focus on dynamic connec- tivity matrices from non-stationary multivariate time series obtained from human functional magnetic resonance images (fMRI). We propose to model brain networks as 2D curved surfaces, where the surface geodesics give connectivity information. Our approach will bypass the use of parcellations and more accurately evaluate the 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 MRI (dMRI) and task and resting-state functional MRI (fMRI). MRI and dMRI will be used in conjunction with fMRI in building more reﬁned dynamic connectivity models. Using 243 pairs of twins in the HCP database, we will determine network phenotypes speciﬁc to behavior, cognition and their genetic associations. This study will provide the research community with the brain network heritability maps and as well as a versatile open-source toolbox of algorithms for modeling and visualizing dynamically changing large-scale brain networks.

项目摘要我们将开发新的网络数据的大规模动态嵌入模型，重点是动态连接，人体功能磁共振非平稳多变量时间序列的活性矩阵功能磁共振成像（fMRI）。我们建议将大脑网络建模为2D曲面，其中表面测地线给出连接信息。我们的方法将绕过包裹的使用，并更准确地评估功能性大脑网络在体素水平上的进化动力学。我们建议从1206名受试者的数据集构建动态变化的功能脑网络，包含T1加权磁共振图像（MRI）的人类连接组计划（HCP）数据库，弥散磁共振成像（dMRI）和任务和静息状态功能磁共振成像（fMRI）。MRI和dMRI将联合使用功能磁共振成像在构建更精细的动态连接模型。使用HCP数据库中的243对双胞胎，我们将决定特定于行为、认知及其遗传关联的网络表型。本研究将为研究界提供大脑网络遗传地图，以及一个多功能的开源用于建模和可视化动态变化的大规模大脑网络的算法工具箱。