# 3D Reconstruction and Analysis of Alzheimer's Patient Biopsy Samples to Map and Quantify Hallmarks of Pathogenesis and Vulnerability

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2022 · $316,000

## Abstract

PROJECT SUMMARY/ABSTRACT
This competitive supplement application requests funds to advance tools and methodology for the normalization
of large-scale, 3D EM image volumes as a means to enhance the performance, reusability, and repeatability of
high throughput artificial intelligence and machine learning (AI/ML) algorithms for automatic volume
segmentation of brain cellular and subcellular ultrastructure. This work will be conducted in the context of an
active research project that is advancing the acquisition, processing/refinement, and dissemination of large-scale
3D EM reference data derived from a remarkable collection of legacy biopsy brain samples from patients
suffering from Alzheimer’s Disease (AD) (5R01AG065549). This active project is deeply rooted in the use of
advance AI/ML technologies for delineating key ultrastructural constituents of neurons and glia exhibiting
hallmarks of the progression of AD. It is organized to comprehensively target areas associated with plaques,
tangles and brain vasculature, attending to locations where existing findings suggest cell and network
vulnerability and contain molecular interactions suspected by some to underlie the initiation and progression of
AD. Through this work, we are advancing the development and dissemination of fully trained neural-network
models for volume segmentation to simplify (and reduce the costs associated with) community efforts to extract
their own 3D geometries and associated morphometrics from this collection of AD reference data and similar
repositories of neuronal 3D EM data. With this supplemental effort, we will develop, refine and disseminate a set
of tools which allow for direct feedback and standardization of primary image quality, whereby benchmarks can
be established so as to optimize the entire process holistically, giving a more rigorously defined target for image
characteristics at time of image acquisition. The outcome of this work is to advance the use of transfer learning
methods, facilitating repeatability and reuse of trained neural network models for scalable EM image
segmentation.

## Key facts

- **NIH application ID:** 10594236
- **Project number:** 3R01AG065549-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Mark H Ellisman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $316,000
- **Award type:** 3
- **Project period:** 2020-03-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10594236

## Citation

> US National Institutes of Health, RePORTER application 10594236, 3D Reconstruction and Analysis of Alzheimer's Patient Biopsy Samples to Map and Quantify Hallmarks of Pathogenesis and Vulnerability (3R01AG065549-03S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10594236. Licensed CC0.

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