# fMRI physiological signatures of aging and Alzheimer's Disease

> **NIH NIH RF1** · VANDERBILT UNIVERSITY · 2021 · $1,055,589

## Abstract

PROJECT SUMMARY/ABSTRACT
The growing availability of large functional magnetic resonance imaging (fMRI) datasets has enabled new
investigations into functional systems of the human brain. A challenge – but also opportunity – of fMRI arises
from the fact that BOLD signal stems from multiple intertwined neural and physiological sources. One major
contributor to fMRI signals arises from slow (<0.15 Hz) fluctuations in respiration volume (RV) and heart rate
(HR); these systemic physiological fluctuations can account for a substantial proportion of fMRI signals across
gray matter, and exhibit spatial patterns that overlap with functional networks. While often treated as a
confound, the components of fMRI data linked with systemic physiology may itself present useful information
about brain function and physiology, enabling novel investigation of brain vasculature, autonomic function, and
brain-body interactions. However, many existing fMRI datasets lack concurrent physiological recordings, and
current data-driven techniques do not unambiguously resolve low-frequency physiological signal sources
without peripheral cardiac and respiratory recordings for reference. This proposal conducts novel analyses to
establish associations between fMRI physiological responses, brain networks, and neurocognitive function.
Further, new techniques are proposed for extracting RV and HR time series directly from fMRI data, thereby
enriching existing fMRI datasets with missing physiological information. Through analysis of large, public
datasets, we will: 1) optimize and validate a deep learning technique for reconstructing physiological time
series from resting-state fMRI data alone, which generalizes to participants across the adult lifespan; and 2)
relate brain-wide fMRI physiological features to age and phenotypic variation; and 3) probe the value of fMRI
physiological responses as early markers of Alzheimer's Disease. We will make all of the resulting signals,
models, and code readily available to the community, so that researchers can apply and extend our methods to
enhance the value of many existing datasets. Through approaches for resolving neural and physiological
sources underlying fMRI signal dynamics, this project also has implications for increasing the precision of fMRI
for mapping brain circuits at the level of the individual.

## Key facts

- **NIH application ID:** 10361105
- **Project number:** 1RF1MH125931-01A1
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Catherine Elizabeth Chang
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,055,589
- **Award type:** 1
- **Project period:** 2021-09-15 → 2025-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10361105

## Citation

> US National Institutes of Health, RePORTER application 10361105, fMRI physiological signatures of aging and Alzheimer's Disease (1RF1MH125931-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10361105. Licensed CC0.

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