# The Neuropathologic Landscape of Alzheimer's Disease in Hispanic Decedents

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2021 · $294,103

## Abstract

ABSTRACT
Citizen science, public participation in the scientific process, leverages the unique properties of human cognition
to distribute the analysis of massive data sets to the general public. Recently, the Human Computation Institute
(HCI) used this approach to crowdsource the analysis of in-vivo two-photon excitation microscopy (2PEF) data,
which has accelerated Cornell University’s Alzheimer’s disease (AD) research by a factor of five, elucidating
molecular mechanisms that underlie capillary stalling in mouse models of AD. For this administrative supplement,
we now propose to develop and validate a new online platform that crowdsources the histopathological analysis
of whole slide images (WSI) of the human brain. This would support the rapid acquisition of high-volume datasets
needed to train an automated classifier in support of our existing R01 studies investigating the neuropathological
landscape of AD within Hispanic and non-Hispanic White decedents. Deriving research-grade classification
labels from multi-source WSI entails data quality requirements that exceed today’s best automated methods.
Thus, we intend to develop a crowd-powered analytic pipeline that reduces the histopathological analysis into a
step-wise series of manageable online tasks that could be accomplished by non-expert members of the general
public, as well as “wisdom of crowd” methods for aggregating many individual answers into a single expert-like
answer. In service of these objectives, our specific aims are to develop the process, interfaces, and aggregation
methods needed to support the crowd-powered analytic pipeline and evaluate its internal validity. We will employ
a repeated cross-validation protocol to test the hypothesis that the crowd-based analysis will result in at least
95% sensitivity and 95% specificity for two major pathology categories (plaques and tangles) relative to gold
standard data provided by expert annotators. If these validation studies are successful, an online platform
incorporating this analytic pipeline will be used to execute the first ever de novo analysis of Alzheimer’s
histopathology imagery by a crowd of volunteers (geared towards those of Hispanic heritage), be used to train a
neural network model for automating the analysis, and will support the ongoing improvement of a reusable digital
pathology workflow tool that is sensitive to an expanding repertoire of pathology targets.

## Key facts

- **NIH application ID:** 10336290
- **Project number:** 3R01AG062517-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Brittany Nicole Dugger
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $294,103
- **Award type:** 3
- **Project period:** 2019-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10336290

## Citation

> US National Institutes of Health, RePORTER application 10336290, The Neuropathologic Landscape of Alzheimer's Disease in Hispanic Decedents (3R01AG062517-03S1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10336290. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*