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.
Alzheimer's disease (AD) is a public health crisis with a burden of epic proportions on the American society given its estimated cost of $277 billion in 2018 alone. Neuroimaging methods implemented in standard software have played a critical role in characterizing AD pathology and treatment response, but the full potential of these methods has not been reached because the AD brain exhibits substantial atrophy, white matter pathology, and large deformations, all of which are difficult for current neuroimaging methods to handle. Our primary goal is to produce improved, reproducible, open source methodological approaches for tissue class segmentation that will remove unwanted technical variability and are specifically customized for heterogeneous multimodal neuroimaging studies of AD patients. The completion of this application will provide a crucial set of tools for the analysis of neuroimaging clinical studies from subjects with AD. This work will aid in validation and reproducibility by improving existing analysis techniques to accurately quantify biomarkers and treatment impact on brain pathology in AD.