# Secondary analysis of functional MRI and resting state connectivity in white matter

> **NIH NIH RF1** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2021 · $1,320,938

## Abstract

Abstract / Summary
This proposal aims to perform novel, secondary analyses on large archives of publicly-available fMRI
studies in order to quantify the functional characteristics of white matter (WM) and their changes during
normal aging and in the progression to Alzheimer’s Disease (AD). Blood oxygenation level dependent
(BOLD) effects have been used to detect neural activity in grey matter (GM) for many years, but BOLD
signals in WM have traditionally been ignored so they have not been considered in previous analyses.
However, WM BOLD signals have been evaluated in relatively small numbers of healthy brains, and these
studies have shown that WM shows robust, tract-specific BOLD changes in response to stimuli, WM
exhibits inter-regional correlations in a resting state similar to those used to infer functional connectivity in
cortex, and signal fluctuations within WM tracts in a resting state show strong correlations to specific GM
cortical volumes engaged together in functional networks. We therefore propose to adapt the tools
developed for analyzing GM connectivity and for diffusion imaging of WM to analyze the functional changes
in WM with age in >7,900 imaging studies available publicly. In Aim 1, we will detect and characterize
changes in WM functional networks with normal aging by analyzing subjects from the Baltimore Longitudinal
Study of Aging (BLSA), the Open Access Series of Imaging Studies (OASIS-3) and the Alzheimer’s Disease
Neuroimaging Initiative (ADNI). Neuroimaging studies of AD suggest that WM abnormalities exist at a
preclinical stage of the disease, so detecting and quantifying altered WM function may be an important
metric of functional changes in this disorder. In Aim 2 therefore we will measure alterations in WM functional
networks in subjects enrolled in the ADNI, BLSA, and OASIS-3 databases. In both Aims we will also
measure co-variations of WM connectivities with behavioral, clinical and genetic assessments to establish
how WM functional metrics reflect behavior and cognition and change with increasing cognitive impairment.
In Aim 3 we will extend the creation of atlases and machine learning from current studies of diffusion MRI
to quantifying WM functional MRI by creating age-adjusted atlases of WM functional MRI properties to
enable normative comparisons and provide canonical templates for both functional and structural
connectivity network analyses. We will also apply data-driven deep learning to identify individual signatures
of impairment on a whole-brain basis. A failure of white matter functional integrity is clearly implicated in
aging and neurodegeneration. This proposal will develop new understandings of the functional changes in
WM across the lifespan, identify pathological changes in WM function with AD, and create new data-driven
tools for interpretation of WM fMRI.

## Key facts

- **NIH application ID:** 10190338
- **Project number:** 1RF1MH123201-01A1
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** John C Gore
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,320,938
- **Award type:** 1
- **Project period:** 2021-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10190338

## Citation

> US National Institutes of Health, RePORTER application 10190338, Secondary analysis of functional MRI and resting state connectivity in white matter (1RF1MH123201-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10190338. Licensed CC0.

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