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.

项目概要/摘要 诊断、理解和治疗阿尔茨海默病 (AD) 的一个主要障碍是其 tau 和 β-淀粉样蛋白 (Aß) 病理学模式的表征，只能通过传统方法充分观察 为了解决这个问题，最近在 2018 年框架之后做出了努力。 美国国家老龄化研究所 (NIA) 和阿尔茨海默病协会 (AA) 提出的重点是 体内生物标志物可用于表征 AD，特别是沿着连续体。 从 MRI 收集的测量数据（例如皮质厚度）构成了此类生物标志物的一类。 它们已被证明与 AD 的临床分期相关，但 MRI 生物标志物尚未被证明与 特定于 AD，因为它们无法与 AD 的 tau/Aß 签名模式与当前的 该项目的目标是通过计算工具和建模框架来解决这一缺陷。 开发和实施多模式、多尺度图像配准和分析平台 用于将显微病理数据与宏观 MRI 测量进行整合和统计关联 约翰·霍普金斯大学大脑资源和 AD 研究中心已经准备了 2D 数字化的皮质厚度。 tau (PHF-1) 染色的组织学图像以及内侧颞叶 (MTL) 组织的相应 3D MRI 对照大脑以及患有中度和晚期 AD 的大脑均被检测到。 基于卷积神经网络（UNET）的方法在手动注释的组织学子集上进行训练 MRI 样本被手动分割成 MTL 区域，并测量皮质厚度。 该项目的总体目标是 首先，tau 缠结和皮质厚度测量将共同定位于 通过开发使用 1) 的配准算法来确定 Mai-Paxinos Atlas 的坐标空间 一个多目标模型，用于解释组织学图像和 MRI 中可能出现的失真，2) “散射” Transform”捕获组织学图像中的纹理特征，有助于预测灰度与灰度之间的界限。 白质，3）区域表面表征到 Mai-Paxinos Atlas 的非刚性转换。 其次，将使用层次结构计算 tau 缠结和皮质厚度之间的统计相关性 捕获数据值和相对组织面积以解释规模差异的“varifold”测量 这两个数据集的应用（微观与宏观）和采样频率（不规则与规则）。 这些方法对对照和 AD 大脑样本的应用将表征皮质厚度的相关性 在特定的范围内，沿着 AD 的临床连续体和 3D 空间中的物理纠缠密度的措施 MTL 区域以及沿大脑特定轴的这些相关性将表征 MTL 的特异性。 AD 的皮质厚度测量，以及通过开源平台共享这些方法将使 未来其他 MRI 生物标志物的表征。