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

提高阿尔茨海默病异质多模态神经影像研究的可重复性和减少技术变异性的统计方法

• 批准号: 10390304
• 负责人: Dana L Tudorascu
• 金额: $ 59.3万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10390304
• 关键词: Address; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease pathology; Alzheimer' s disease patient; American; Assessment tool; Atrophic; Benchmarking; Biological Markers; Brain; Brain Pathology; Brain imaging; Clinical; Clinical Research; Clinical Trials; Computer software; Cross-Sectional Studies; Custom; Data; Disease Progression; Elderly; Exhibits; Experimental Designs; Goals; Gold; Human; Image; Individual; Lead; Longitudinal Studies; MRI Scans; Magnetic Resonance Imaging; Measurement; Methodology; Methods; Pathology; Performance; Play; Population; Positron-Emission Tomography; Public Health; Publishing; Reader; Reproducibility; Research; Role; Sample Size; Societies; Software Tools; Statistical Data Interpretation; Statistical Methods; Study Subject; Techniques; Testing; Tissues; Validation; Work; analytical method; base; brain tissue; brain volume; cerebral atrophy; connectome; cost; cost estimate; design; heterogenous data; image processing; imaging software; imaging study; improved; innovation; interest; longitudinal database; longitudinal dataset; multimodal neuroimaging; multimodality; neuroimaging; novel; open source; radiologist; study population; tool; treatment response; white matter

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.

项目概要/摘要：阿尔茨海默病（AD）是一种公共卫生危机， 考虑到仅2018年的估计成本为2770亿美元，这对美国社会的影响很大。脑成像 结合新的形态学分析方法，从根本上改变了我们对AD的认识 进展然而，由于AD脑表现出显著的萎缩，白色， 物质病理学和大变形，这使得最常用的软件 软件包进行组织分割，AD患者的纵向研究在很大程度上依赖于。我们 建议开发新的、可推广的和可重复的统计神经成像预处理方法 专门针对高度异质性的AD MRI/PET图像人群定制，并随后评估这些 相对于标准方法。具体来说，我们将重点关注组织类分割，这通常是 直接用于统计分析或作为空间或多模态配准的中间步骤，因为我们 评价标准软件在异质性AD和老年人中用于组织分类分割的性能 对照研究人群。 这个项目的主要目标是产生改进的、可重复的和开源的统计方法， AD患者和老年对照的组织类别分割。为了实现这一目标，我们提出了三个主要目标。 假设：1）开发新的组织分类分割方法， 健康对照、AD受试者和健康老年对照的纵向研究; 2）将方法扩展到 考虑到不同的研究和实验条件（例如，MRI扫描仪），并评价其重现性 用于年轻健康对照组和AD受试者的结构MRI和PET，以及3）在线开发，免费访问， 可复制的软件工具，用于评估，验证和已发布的分析管道的再现性。 本研究的完成将为神经影像学临床研究的分析提供有力的工具 从AD患者身上这项工作将有助于验证，再现性和实验设计，通过改进 现有的分析技术，以准确地量化生物标志物和治疗对AD脑病理学的影响。