# Dynamic manifold-valued time series model in functional brain imaging

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2020 · $323,982

## Abstract

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.

## Key facts

- **NIH application ID:** 9860110
- **Project number:** 1R01EB028753-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** MOO K CHUNG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $323,982
- **Award type:** 1
- **Project period:** 2020-05-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9860110

## Citation

> US National Institutes of Health, RePORTER application 9860110, Dynamic manifold-valued time series model in functional brain imaging (1R01EB028753-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9860110. Licensed CC0.

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