# Statistical methods to improve reproducibility and reduce technical variability in heterogeneous multimodal neuroimaging studies of Alzheimer’s Disease

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $584,985

## Abstract

Project Summary/Abstract: Alzheimer's disease (AD) is a public health crisis with a burden of epic
proportion on the American society given its estimated cost of $277 billion in 2018 alone. Brain imaging
combined with new morphometric analytic methods has fundamentally changed our understanding of AD
progression. However, progress has been slowed because the AD brain exhibits substantial atrophy, white
matter pathology, and large deformations, which make it difficult for the most commonly used software
package to carry out the tissue segmentation on which longitudinal studies of AD patients depend heavily. We
propose to develop novel, generalizable and reproducible statistical neuroimaging pre-processing methods
tailored specifically for highly heterogeneous AD MRI/PET image populations and to subsequently assess these
methods relative to standard approaches. Specifically, we will focus on tissue class segmentation, which is often
used directly for statistical analyses or as an intermediary step for spatial or multimodal registration, as we
evaluate the performance of standard software for tissue class segmentation in a heterogeneous AD and elderly
control study population.
The primary goal of this project is to produce improved, reproducible, and open source statistical methods for
tissue class segmentation for AD patients and elderly controls. To achieve this goal we propose three main
hypotheses: 1) develop new tissue class segmentation methods for heterogeneous cross-sectional and
longitudinal studies of healthy controls, AD subjects and healthy elderly controls; 2) extend the methods to
account for different studies and experimental conditions (e.g., MRI scanner) and evaluate their reproducibility
for structural MRI and PET in young healthy controls and AD subjects and 3) develop online, freely accessible,
reproducible software tools for the assessment, validation, and reproducibility of published analytic pipelines.
The completion of this research will provide powerful tools for the analysis of neuroimaging clinical studies
from subjects with AD. This work will aid in validation, reproducibility and experimental design by improving
existing analysis techniques to accurately quantify biomarkers and treatment impact on brain pathology in AD.

## Key facts

- **NIH application ID:** 9995408
- **Project number:** 5R01AG063752-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Dana L Tudorascu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $584,985
- **Award type:** 5
- **Project period:** 2019-08-15 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9995408

## Citation

> US National Institutes of Health, RePORTER application 9995408, Statistical methods to improve reproducibility and reduce technical variability in heterogeneous multimodal neuroimaging studies of Alzheimer’s Disease (5R01AG063752-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9995408. Licensed CC0.

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