# Developing a Univariate Neurodegeneration Imaging Biomarker with Optimal Transportation

> **NIH NIH R21** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2020 · $444,976

## Abstract

PROJECT SUMMARY / ABSTRACT
We will develop and apply a novel univariate neurodegeneration imaging biomarker to brain magnetic
resonance images (MRI) obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and
the well-characterized Arizona APOE cohort of presymptomatic individuals. Our recent work has shown that
Wasserstein distance-based brain imaging indices outperformed several other univariate brain imaging indices
in discriminating Alzheimer’s disease (AD) patients from cognitively unimpaired (CU) subjects with cross-
sectional brain MR and fluorodeoxyglucose positron emission tomography (FDG-PET) images. In the current
project, we will continue developing novel structural MRI analysis methods based on harmonic maps and the
variational principle. Specifically, we will develop 4D harmonic map algorithms to compute canonical imaging
spaces of longitudinal brain images and further compute 4D Wasserstein distance-based univariate
longitudinal neurodegeneration indices with an efficient variational framework. The proposed system will
generate simple, objective, and reliable neurodegeneration imaging biomarkers to quantify progressive
presymptomatic anatomical changes related to AD and provide concise and informative univariate outcome
measures for randomized clinical trials (RCT). To investigate the reliability and practicality of our method, we
will study brain structural MRI scans obtained from the ADNI and the Arizona APOE cohort of presymptomatic
subjects. We seek to (1) correlate the computed neurodegeneration imaging indices with longitudinal
cognitive trajectories in both ADNI and the independent Arizona APOE cohorts; (2) assess its ability to
identify early AD by distinguishing beta-amyloid-positive mild cognitive impairment (MCI)/CU subjects from
beta-amyloid-negative MCI/CU subjects in the ADNI cohort; (3) investigate its potential to predict
progression rate to the clinical stage of amnestic MCI on CU subjects of the ADNI and the younger
presymptomatic individuals of the Arizona APOE cohort; and (4) validate its potential to facilitate the
evaluation of AD treatments by reducing the required RCT sample sizes. We will conduct head-to-head
comparisons between the proposed univariate neurodegeneration biomarker and other state-of-the-art
univariate structural MRI indices with these tasks. We will also develop and freely disseminate our software
tools to the research community.

## Key facts

- **NIH application ID:** 10057855
- **Project number:** 1R21AG065942-01A1
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** Yalin Wang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $444,976
- **Award type:** 1
- **Project period:** 2020-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10057855

## Citation

> US National Institutes of Health, RePORTER application 10057855, Developing a Univariate Neurodegeneration Imaging Biomarker with Optimal Transportation (1R21AG065942-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10057855. Licensed CC0.

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