# Project 4 Quantitative Methods for Brain Connectivity Network Estimation & Interference in Functional Magnetic Resonance Imaging

> **NIH NIH P20** · BROWN UNIVERSITY · 2021 · $394,586

## Abstract

PROJECT SUMMARY
Estimation of the spatial and temporal patterns of intrinsic neural activity has become a popular approach to
gaining insight into the functional organization of the brain. One method for measuring these patterns of
spontaneous activity, referred to as functional connectivity, is functional Magnetic Resonance Imaging (MRI)
measured at rest while other studies developed experimental tasks for learning about connectivity in specific
areas of the brain. Functional connectivity maps have been shown to change with age, training, levels of
consciousness, and disease status. Under certain assumptions, these functional connectivity maps
demonstrate deviations from independence between various areas of the brain, often including spatially
incongruous areas. Functional connectivity maps have been utilized to learn about differences of brain
activation patterns between disease groups via clustering voxels based on their connectivity patters. We
propose a general framework for estimating associations of brain connectivity maps with predictors of interest
after controlling for confounders using covariance regression - a statistical modeling approach that allows for
using the special structure of covariance outcomes for improved parameter estimation. The statistical
significance of the association of the predictors with the outcome maps will be assessed in this model while
correcting for the effects of other variables. The estimation of connectivity maps in a covariance regression
framework, where the map is the outcome is underdeveloped. Our proposed framework will extend the model
and incorporate evidence based realistic assumptions in the context of static functional connectivity analysis
where we assume that the connectivity is constant during the scanning session and in dynamic connectivity
analyses where time-varying patterns of changes in connectivity during the scanning session is of interest. An
important contribution of this proposal is the extension of the model to high dimensional settings as the
connectivity maps based on fMRI are often large. The proposed framework will be used to learn about
functional organization of the brain during an adaptation learning task in a functional MRI study focusing on
visual-motor connectivity changes during the task.

## Key facts

- **NIH application ID:** 10246479
- **Project number:** 5P20GM103645-09
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Ani Eloyan
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $394,586
- **Award type:** 5
- **Project period:** 2013-08-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246479

## Citation

> US National Institutes of Health, RePORTER application 10246479, Project 4 Quantitative Methods for Brain Connectivity Network Estimation & Interference in Functional Magnetic Resonance Imaging (5P20GM103645-09). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10246479. Licensed CC0.

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