# Project 10: Statistical Models for Group Comparison of Functional MRI Data

> **NIH NIH P20** · UNIVERSITY OF NEVADA RENO · 2020 · $319

## Abstract

PROJECT SUMMARY/ABSTRACT 
Statistical Models for Group Comparison of Functional MRI Data 
Recently, many large-scale neuroimaging datasets have been collected and analyzed in an attempt to 
elucidate brain activities including but not limited to the pathology of psychiatric disorders and cognitive brain 
functions. However, only a few approaches have been developed for simultaneously analyzing multi-subject 
neuroimaging data. In this project, we will propose statistical models for integrating functional connectivity 
pattern across subjects. We will consider two types of data collected in different ways: 1) multi-subject 
functional MRI data obtained from one or more populations, and 2) multi-subject repeated-measures fMRI data 
obtained from one or more populations. For the first type of data, we will develop a dimension reduction 
method which considers spatial and temporal dependency. The spatial maps extracted will be used to detect 
group differences and further for image classification. While taking spatial and temporal correlations into 
account, we will study statistical procedures involved in the sparse estimation algorithm, including the choices 
of the weight function, bandwidth, and tuning parameter. In addition, we will examine how to measure spatial 
and temporal similarity between two brain voxels in order to impose a weight on the neighboring voxels. 
Furthermore, we will study the functional connectivity patterns for different groups by estimating penalized 
correlation functions. The proposed connectivity method will be verified by using simulation studies and 
acquired fMRI datasets. For the second type of data, we will consider three special cases: 1) repeated- 
measures fMRI data that do not depend on age or time, 2) longitudinal fMRI data, and 3) longitudinal fMRI data 
with different spatial resolutions. In a further aim, we will develop statistical models to address these special 
cases which can be generalized into a unified model framework. Then, we will establish testing procedures to 
detect group differences for each case. In summary, we will study statistical models to analyze multi-subject 
fMRI data collected in various ways, and also consider spatial-temporal correlations as well as high- 
dimensionality of the data for proposing new statistical procedures such as model selection criteria. The 
proposed research is important because it addresses the essential steps for analyzing highly correlated fMRI 
data for multi-subject and multi-group conditions. By applying the proposed models, we will be able to detect 
group differences with increased power. Moreover, the statistical models we will develop will help us to 
address research questions effectively in multi-subject fMRI studies.

## Key facts

- **NIH application ID:** 9984415
- **Project number:** 5P20GM103650-09
- **Recipient organization:** UNIVERSITY OF NEVADA RENO
- **Principal Investigator:** Mihye Ahn
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $319
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9984415

## Citation

> US National Institutes of Health, RePORTER application 9984415, Project 10: Statistical Models for Group Comparison of Functional MRI Data (5P20GM103650-09). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9984415. Licensed CC0.

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