# Characterization of relationship between tau pathology and neurodegeneration in Alzheimer's disease using multimodal imaging

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $796,113

## Abstract

Project Summary
The pathophysiology of Alzheimer’s disease (AD) is characterized by the accumulation of Amyloid (Aβ) plaques
and tau neurofibrillary tangles (NFT). While the presence of both plaques (A+) and tangles (T+) are essential to
the biological definition of AD as recently codified in the ATN research classification framework, tau (T) is thought
to be the primary driver of downstream neurodegeneration (N) and the resulting cognitive impairment. However,
there is substantial variability in the T-N relationship – manifested in higher or lower atrophy than expected for
the level of tau in a given brain region, even in carefully curated research cohorts. What does this variability
represent? In this study, we explore the idea that a quantitative measure of the variability in the canonical
relationship between T and N is itself a “mismatch metric” that can help characterize different underlying
phenotypes and modulatory factors. We will examine this by modeling region-wise measures of T vs. N obtained
from in-vivo imaging in a cohort A+ symptomatic individuals. SUVR from tau-PET imaging and cortical thickness
from structural MRI will serve as regional measures of T and N respectively. We will then use data-driven
clustering for phenotype discovery based on the model residuals. Region-wise model residuals capture spatial
variation in the T-N relationship, conceptually extending the ATN framework from the dichotomous T/N +/-
designations to a richer description that may reflect differing spatial topography of underlying co-pathologies.
 The concept of the T-N mismatch metric and its ability to identify underlying phenotypes will be evaluated
in multiple publicly available and institutional datasets, each of which will provide a diverse collection of
phenotypes. We will also perform evaluation in a dataset of ex-vivo specimens of A+ individuals. We will obtain
quantitative measures of N from ex-vivo MRI as a semi-automated cortical thickness estimate, and of T using
digital histopathology techniques, in multiple brain regions. Gold standard histopathology measures (e.g. TDP-
43, alpha-synuclein, non-AD tau, vascular disease) obtained in these samples will help evaluate whether T-N
mismatch metric can help identify phenotypes with non-AD co-pathology. Finally, we will evaluate if the T-N
mismatch metric is predictive of future cognitive decline as well as rates of longitudinal neurodegenerative
changes in the brain.

## Key facts

- **NIH application ID:** 10775806
- **Project number:** 5R01AG072796-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Sandhitsu Das
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $796,113
- **Award type:** 5
- **Project period:** 2022-04-15 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10775806

## Citation

> US National Institutes of Health, RePORTER application 10775806, Characterization of relationship between tau pathology and neurodegeneration in Alzheimer's disease using multimodal imaging (5R01AG072796-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10775806. Licensed CC0.

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