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

> **NIH NIH F30** · JOHNS HOPKINS UNIVERSITY · 2022 · $51,752

## Abstract

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 c...

## Key facts

- **NIH application ID:** 10462257
- **Project number:** 1F30AG077736-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Kaitlin Stouffer
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $51,752
- **Award type:** 1
- **Project period:** 2023-03-01 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10462257

## Citation

> US National Institutes of Health, RePORTER application 10462257, Fluency from Flesh to Filament: Collation, Representation, and Analysis of Multi-Scale Neuroimaging data to Characterize and Diagnose Alzheimer's Disease (1F30AG077736-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10462257. Licensed CC0.

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