Fluency from Flesh to Filament: Collation, Representation, and Analysis of Multi-Scale Neuroimaging data to Characterize and Diagnose Alzheimer's Disease

从肉体到细丝的流畅性：多尺度神经影像数据的整理、表示和分析，以表征和诊断阿尔茨海默病

• 批准号: 10462257
• 负责人: Kaitlin Stouffer
• 金额: $ 5.18万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10462257
• 关键词: 3-Dimensional; Address; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease diagnosis; Alzheimer’s disease biomarker; American; Amyloid beta-Protein; Amyloidosis; Area; Atlases; Biological Markers; Brain; Categories; Clinical; Communities; Computer Models; Correlation Studies; Data; Data Analyses; Data Set; Development; Diagnosis; Dimensions; Disease; Early identification; Filament; Frequencies; Future; Glean; Goals; Histologic; Histology; Human; Image; Image Analysis; Individual; Link; Literature; MRI Scans; Magnetic Resonance Imaging; Manuals; Mathematics; Measures; Medial; Methods; Microscopic; Modeling; Molecular; Morphologic artifacts; Multimodal Imaging; Mus; National Institute on Aging; Nerve Degeneration; Neurofibrillary Tangles; PHF-1; Pathologic; Pathology; Pattern; Phase; Prevalence; Publishing; Research; Resolution; Resources; Sampling; Senile Plaques; Series; Shapes; Spatial Distribution; Specificity; Stains; Statistical Distributions; Statistical Models; Surface; Symptoms; Targeted Research; Tauopathies; Techniques; Temporal Lobe; Testing; Texture; Thick; Tissues; Training; Work; analytical tool; base; brain tissue; brain volume; clinical biomarkers; clinical practice; computational anatomy; computerized tools; convolutional neural network; data modeling; density; design; digital; disorder risk; effective intervention; entorhinal cortex; high resolution imaging; histological specimens; human old age (65+); image registration; improved; in vivo; interest; magnetic resonance imaging biomarker; mind control; multimodality; multiscale data; neuroimaging; novel; open source; specific biomarkers; statistics; stem; success; tau Proteins; tau aggregation; tool; trend; white matter

Project Summary/Abstract A major obstacle in diagnosing, understanding, and treating Alzheimer’s Disease (AD) has been its characterization by patterns of tau and beta-amyloid (Aß) pathology, only adequately seen through traditional methods of histological sectioning and staining. To address this, recent efforts following the 2018 framework put forth by the National Institute of Aging (NIA) and the Alzheimer’s Association (AA) have focused on identifying in vivo biomarkers that can be used instead to characterize AD and specifically along a continuum. Measures gleaned from MRI, such as cortical thickness, constitute one category of such biomarkers. While they have been shown to correlate with clinical stage of AD, MRI biomarkers have not been shown to be specific for AD as they have not been able to be linked to AD’s signature patterns of tau/Aß with current computational tools and modeling frameworks. The goal of this project is to address this deficiency with the development and implementation of a multi-modal, multi-scale image registration and analysis platform that will be used to integrate and statistically correlate microscopic pathology data with macroscopic MRI measures of cortical thickness. The Johns Hopkins Brain Resource and AD Research Centers have prepared 2D digital histology images stained for tau (PHF-1) and corresponding 3D MRI of medial temporal lobe (MTL) tissue from control brains and those with intermediate and advanced AD. Individual tau tangles were detected with a convolutional neural network (UNET) based approach trained on a subset of manually annotated histological samples. MRI was manually segmented into regions of the MTL, and cortical thickness will be measured from from generated surface representations of each of these regions. The project’s overall goal will be accomplished through two main aims. First, tau tangle and cortical thickness measures will be co-localized in the coordinate space of the Mai-Paxinos Atlas through the development of a registration algorithm that uses 1) a multi-target model to account for possible distortion in both histology images and MRI, 2) a “Scattering Transform” to capture textural features in histology images that help predict delineations between grey vs. white matter, 3) non-rigid transformation of regional surface representations to those of the Mai-Paxinos Atlas. Second, statistical correlations will be computed between tau tangles and cortical thickness using a hierarchy of “varifold” measures that capture both data values and relative tissue area to account for differences in scale (microscopic vs. macroscopic) and sampling frequency (irregular vs. regular) of these two datasets. Application of these methods to both control and AD brain samples will characterize the correlation of cortical thickness measures to tau tangle density along the clinical continuum of AD and physically in 3D space, within specific regions of the MTL, and along particular axes of the brain. These correlations will characterize the specificity of cortical thickness measures for AD, and the sharing of these methods via an open-source platform will enable this characterization for other MRI biomarkers in the future.

项目摘要/摘要 诊断，理解和治疗阿尔茨海默氏病（AD）的主要障碍是 通过tau和β-淀粉样蛋白（Aß）病理的模式的表征，只有传统才能充分看待 组织学切片和染色的方法。为了解决这个问题，在2018年框架之后的最新努力 国家老化研究所（NIA）和阿尔茨海默氏症协会（AA）提出的专注于 识别可用于使用AD的体内生物标志物，特别是沿连续体进行表征。 从MRI收集的措施（例如皮质厚度）构成了此类生物标志物的一类。尽管 它们已被证明与AD的临床阶段相关，MRI生物标志物尚未证明是 专门针对AD，因为它们无法与AD的标志性模式相关联 计算工具和建模框架。该项目的目的是通过 开发和实施多模式的多尺度图像注册和分析平台，该平台将 用于将微观病理学数据与宏观MRI测量进行整合和统计相关联 皮质厚度。约翰·霍普金斯大脑资源和广告研究中心已经准备了2D数字 tau（PHF-1）染色的组织学图像和介质临时叶（MTL）组织的相应3D MRI 控制大脑以及具有中间和高级广告的人。用A检测到单个Tau缠结 基于卷积神经网络（UNET）的方法，该方法是基于手动注释的一部分 样品。将MRI手动分段到MTL的区域，并将皮质厚度测量 从这些区域的每个区域的生成的表面表示。该项目的总体目标将是 通过两个主要目标完成。首先，tau缠结和皮质厚度措施将共定位在 Mai-Paxinos Atlas的坐标空间通过使用1的登记算法开发1） 一个多目标模型，以说明组织学图像和MRI中可能失真的模型，2）“散射 转换”以捕获组织学图像中的质地特征，这些特征有助于预测灰色与之间的描述。 白质，3）区域表面表示向Mai-Paxinos地图集的非刚性转化。 其次，将使用层次结构计算统计相关性 捕获数据值和相对组织面积的“ varifold”量度，以说明尺度的差异 这两个数据集的（微观与宏观）和采样频率（不规则与常规）。应用 这些方法的控制和AD脑样品将表征皮质厚度的相关性 沿着AD的临床连续体并在3D空间中进行tau缠结密度的措施， MTL的区域，沿着大脑的特定轴。这些相关性将表征 AD的皮质厚度措施，以及通过开源平台共享这些方法 未来其他MRI生物标志物的表征。