# Quantifying the individual contributions of comorbid tau neuropathologies using deep learning

> **NIH NIH R21** · UT SOUTHWESTERN MEDICAL CENTER · 2020 · $450,250

## Abstract

PROJECT SUMMARY/ABSTRACT
Co-occurrence of different neurodegenerative diseases is increasingly common with age and acts as a
confounding factor in the development of disease-specific biomarkers. Yet, even by the gold standard of
evaluating immunostaining for aggregated proteins in autopsy brains, pathologic complexity makes it
impossible to reliably quantify the mixture of diseases by visual inspection, especially when coexistent
disorders both feature the same aggregated protein, albeit in different disease-specific patterns. Here, we
hypothesize that recent advances in deep learning can identify the distinctive patterns of Alzheimer disease
(AD) and progressive supranuclear palsy (PSP) neuropathology, thereby allowing us to de-convolve their
individual contributions from phospho-tau immunostaining of mixed pathologies.
We will tackle this problem in three steps. First, in order to incorporate biological knowledge and enable
interpretability of our disease predictions, we will develop a set of deep learning classifiers to identify disease
relevant “features” in virtual whole slide images. These features will include different types of cells (e.g.
neurons, astrocytes), aggregates (e.g. tufted astrocytes and senile plaques that are enriched in PSP and AD,
respectively) and tissue regions (gray vs. white matter, which differ in pattern of involvement in these
diseases). Second, based on the assumption that comorbid pathologies exhibit a mixture of pure disease
features, we will build disease classifiers from pure AD and pure PSP cases. Given a local patch of tau-stained
tissue, these classifiers will return their confidence that tissue exhibited either of these diseases. We will
evaluate two approaches, one building on the “features” identified above and the other a more traditional black-
box deep learning approach working purely off of image patches. Finally, we will evaluate our pure disease
classifiers on cases with mixed pathologies based on pathologist review and concordance with antibodies to
tau isoforms whose individual histomorphologies help to distinguish between AD and PSP.
As they will identify established neuropathology features demonstrated by the widely-used AT8 phospho-tau
and 3R and 4R tau isoform immunostaining, our classifiers will be a valuable resource for future digital imaging
based studies in neuropathology. Our framework for de-convolving comorbidities from autopsy samples can be
extended to other diseases, thus enabling better integration with clinical and biomarker data, and ultimately,
improved antemortem diagnosis and therapy.

## Key facts

- **NIH application ID:** 10058010
- **Project number:** 1R21AG066012-01A1
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Satwik Rajaram
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $450,250
- **Award type:** 1
- **Project period:** 2020-09-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10058010

## Citation

> US National Institutes of Health, RePORTER application 10058010, Quantifying the individual contributions of comorbid tau neuropathologies using deep learning (1R21AG066012-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10058010. Licensed CC0.

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