# Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRI

> **NIH NIH R01** · VANDERBILT UNIVERSITY · 2021 · $665,123

## Abstract

PROJECT SUMMARY
Alzheimer’s Disease and related dementia are a growing public health crisis affecting 5.8 million Americans, yet
there are only four FDA-approved medications for Alzheimer’s Disease, none of which are disease-modifying.
Hence, early detection and diagnosis are key to successful patient management and biomarkers are needed for
evaluating new therapies in clinical trials. White matter changes are increasingly implicated in early Alzheimer’s
Disease progression, and diffusion weighted magnetic resonance imaging (DW-MRI) has been included in many
national-scale studies. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due
to variation in acquisition protocols, sites, and scanners. DW-MRI enables quantification of brain microstructure
and facilitates structural connectivity mapping. Substantial recent progress has been made with calibration and
harmonization to reduce inter-subject variance and improve interpretability of computed measures. Yet, the
fundamental challenge remains that clinical application of DW-MRI (as currently implemented) is
confounded by inter-scanner and inter-site effects.
To improve understanding of structural changes in Alzheimer’s Disease, we will construct and evaluate three
separate analysis strategies to characterize, calibrate, and optimize DW-MRI for single-subject biomarker
development for Alzheimer’s Disease. We will integrate and optimize our strategies using large retrospective
multi-site studies and validate the approaches on two distinct prospective cohorts. Specifically, we aim to:
Aim 1: Optimize data-driven techniques for stability across sessions, scanners/sites, and field strengths
Impact: Harmonized DW-MRI methods will increase sensitivity to Alzheimer’s Disease and its prodromal stages.
Aim 2: Translate innovations in microstructural harmonization to structural connectivity (tractography)
Impact: Harmonizing structural connectivity will improve understanding of white matter in Alzheimer’s Disease.
Aim 3: Advance statistical tools for single-subject inference through normative database construction
Impact: Data-driven resources for uncertainty estimation will enable robust single-single subject inference.
Relevance and Impact on Healthcare: The proposed research will advance understanding of Alzheimer’s
Disease through (1) quantitative harmonization of DW-MRI biomarkers, (2) protocols for harmonization of
retrospective and prospective DW-MRI studies, and (3) new tools for single subject inference targeting older
cohorts. We will organize workshops/challenges to maximize the translational impact on clinical science. The
long-term goal of our research is to (1) provide a well-validated strategy to quantitatively evaluate DW-MRI data
across sites, (2) enhance DW-MRI biomarkers for Alzheimer’s Disease, and (3) advance patient care. Our
research strategy will transform the manner in which DW-MRI data are interpreted and enable single-subject
machine learning to int...

## Key facts

- **NIH application ID:** 10316671
- **Project number:** 2R01EB017230-05A1
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Bennett A. Landman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $665,123
- **Award type:** 2
- **Project period:** 2015-09-20 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10316671

## Citation

> US National Institutes of Health, RePORTER application 10316671, Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRI (2R01EB017230-05A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10316671. Licensed CC0.

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