# Individual functional brain mapping for biomarker discovery in Alzheimer's

> **NIH NIH R01** · TRUSTEES OF INDIANA UNIVERSITY · 2024 · $764,642

## Abstract

PROJECT SUMMARY: Alzheimer’s disease (AD) affects over 6 million Americans, and its incidence is projected
to double by 2050 due to an aging population. In the fight against AD, there is a pressing need for novel
biomarkers to 1) identify clinical trial participants at risk of decline and 2) identify and track patients eligible for
emerging treatments. Gold standard AD biomarkers require positron emission tomography (PET) imaging or
cerebral spinal fluid (CSF) collected via lumbar puncture. These procedures are expensive and/or invasive,
presenting a barrier to widespread adoption. Blood-based biomarkers are under development but are not yet
validated and may benefit from combination with other biomarkers. A principal goal of the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) 3 is to promote development of novel AD biomarkers, including the use of
functional magnetic resonance imaging (fMRI). FMRI is used to study the functional connectivity (FC) and
organization of the brain, and fMRI studies have revealed functional brain changes associated with AD. FMRI-
based biomarkers of AD could complement existing biomarkers by providing a non-invasive, first-line screening
method before PET and CSF are collected. FMRI could also be combined with blood biomarkers and established
structural MRI markers—all of which can be routinely collected clinically—to construct powerful and widely
available multimodal biomarkers. Despite all of this, no fMRI-based biomarker for AD exists to date. This is in
part due to the high noise levels of fMRI and the common use of naive statistical methods, which together lead
to noisy estimates of FC and other functional brain features. Two conventional workarounds—averaging many
subjects or collecting hours of data on individual subjects—are not viable clinically. This project will address this
gap by developing computationally efficient Bayesian techniques with high accuracy and deploying those
methods for fMRI-based biomarker discovery in AD. Our models leverage information across subjects via
population-derived priors or “templates”, which are previously estimated, to extract nuanced and precise
functional brain features in individuals. These models avoid the need for burdensome prolonged scans. They
can be fit to data from a single subject at a time, making them clinically viable and computationally advantageous.
To maximize the benefits of hierarchical modeling, we utilize grayordinates data, a recent technological advance
in image processing that improves inter-subject anatomical alignment. To deploy these techniques effectively in
multi-site datasets like the ADNI, image harmonization is necessary to avoid confounding site effects. Existing
harmonization methods can be applied to fMRI summary measures, but are not applicable to fMRI time series,
which are a complex mixture of latent features. To address this critical gap, we will develop a novel harmonization
method for fMRI time series data, with high potential impa...

## Key facts

- **NIH application ID:** 10985050
- **Project number:** 1R01AG083919-01A1
- **Recipient organization:** TRUSTEES OF INDIANA UNIVERSITY
- **Principal Investigator:** Amanda F Mejia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $764,642
- **Award type:** 1
- **Project period:** 2024-08-15 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10985050

## Citation

> US National Institutes of Health, RePORTER application 10985050, Individual functional brain mapping for biomarker discovery in Alzheimer's (1R01AG083919-01A1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10985050. Licensed CC0.

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