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

项目摘要/摘要 诊断、了解和治疗阿尔茨海默病(AD)的一个主要障碍是其 通过tau和β-淀粉样蛋白(A？)病理模式的特征，只能通过传统的 组织切片和染色方法。为了解决这一问题，最近继2018年框架之后所做的努力 由国家老龄研究所(NIA)和阿尔茨海默氏症协会(AA)提出的研究重点是 确定体内生物标记物，可替代用来表征AD，特别是沿着一个连续体。 从MRI收集的测量，如皮质厚度，构成了此类生物标记物的一类。而当 它们已被证明与AD的临床分期有关，而MRI生物标记物尚未被证明 特定于AD，因为它们不能与AD的签名模式tau/A？与Current相关联 计算工具和建模框架。该项目的目标是通过 开发和实施多模式、多尺度图像配准和分析平台，将 用于将微观病理数据与宏观MRI测量结果进行整合和统计关联 皮质厚度。约翰霍普金斯大学脑资源和AD研究中心已经准备了2D数字 内侧颞叶(MTL)组织tau(PHF-1)染色的组织学图像和相应的3D MRI 控制大脑和那些患有中晚期AD的人。单个的tau缠结被检测到 基于卷积神经网络的方法在人工标注组织学子集上的训练 样本。MRI被手动分割成MTL的区域，皮质厚度将从 从这些区域中的每个区域生成的曲面表示。该项目的总体目标将是 通过两个主要目标来实现。首先，tau缠绕和皮质厚度测量将在 Mai-Paxinos Atlas的坐标空间，通过开发使用1)的配准算法 一个多目标模型来解释组织学图像和核磁共振成像中可能的失真，2)散射 用于捕捉组织学图像中的纹理特征，以帮助预测灰色和 白质，3)区域表面表示到Mai-Paxinos地图集的非刚性转换。 其次，将使用等级来计算tau缠结和皮质厚度之间的统计相关性 捕捉数据值和相对组织面积以考虑规模差异的“多种”测量方法 (微观与宏观)和采样频率(不规则与规则)这两个数据集。应用 在这些方法中，对照和阿尔茨海默病患者的大脑样本将表征皮质厚度的相关性 测量沿AD临床连续体的tau缠绕密度以及在3D空间中的物理测量 MTL的区域，以及沿着大脑的特定轴线。这些相关性将表征 AD的皮质厚度测量，通过开源平台共享这些方法将使 这一特征将在未来用于其他MRI生物标记物。

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.