# Functional Connectome of Brain White Matter

> **NIH NIH R01** · VANDERBILT UNIVERSITY · 2024 · $446,618

## Abstract

ABSTRACT / SUMMARY
 The goals of this research are to extend our discoveries of how blood oxygenation level dependent (BOLD)
signals in white matter (WM), detected using functional magnetic resonance imaging (fMRI), are related to neural
activity in gray matter (GM), and to implement new analyses that properly incorporate WM signals into models
of brain function derived from imaging data. For the past three decades, nearly all analyses of brain fMRI data
have ignored WM signals and usually have removed them as nuisance regressors. However, that view has
changed in light of more recent evidence that WM BOLD signals represent potentially important and heretofore
overlooked indicators of neural activity that are intimately related to how cortical regions communicate, and so
should be incorporated into complete assessments of functional connectivity. We have recently shown that
BOLD signals are robustly detectable in WM when appropriate analyses are used, that the hemodynamic
response function in WM is different from GM, and that WM tracts show reproducible patterns of apparent
connectivity which may be summarized in Functional Connectivity Matrices (FCMs), obtained by analyzing
resting state correlations between segmented WM and GM parcellations. Furthermore, distinct, reproducible
networks of WM emerge from data-driven analyses in similar manner to cortical circuits. In this proposal we aim
to develop new analyses and apply them to large numbers of publicly available data. We aim (1) to quantify the
functional relationships between WM fibers and GM circuits at a finer scale and in greater detail. We will extend
the concept of FCMs to three dimensions to derive those WM tracts that show synchronous time courses with
pairs of GM regions that themselves are identified from a matrix of GM-GM connectivity; (2) to use data-driven,
model-free independent component analyses to identify WM and GM functional networks and quantify the
correlations between them; and (3) to construct a suite of detailed and quantitative atlases characterizing
functional connectivity and network topology in WM, and establish their relationships with behavioral and
cognitive measures. Templates and digital atlases provide a way to spatially normalize data to common spaces,
and measure normal and abnormal variations quantitatively. Extending and applying the methodology from
structural and diffusion MRI fields to create atlases of WM functional data will enable reproducible quantification,
normalization, and interpretation of our results. Each analysis will also examine the influence of gender and age
on WM functional metrics.
Impact: BOLD signals in WM reflect neural activity that is related to cortical brain function, so analyses of the
functional engagement of WM are essential to properly model brain networks. This research would demonstrate
how WM and GM activities are related, and how to integrate them to obtain a more complete model of brain
organization. The results will la...

## Key facts

- **NIH application ID:** 10843184
- **Project number:** 5R01NS129855-02
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** ZHAOHUA DING
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $446,618
- **Award type:** 5
- **Project period:** 2023-06-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10843184

## Citation

> US National Institutes of Health, RePORTER application 10843184, Functional Connectome of Brain White Matter (5R01NS129855-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10843184. Licensed CC0.

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