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

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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.