# Deep Learning with Neuroimaging Genetic Data for Alzheimer's Disease

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2021 · $667,791

## Abstract

Summary
 Alzheimer's disease (AD) affects over 44 million individuals worldwide, and the number is projected to triple
by 2050. However, currently there is no cure for AD. This project aims to develop and apply novel statistical
methods, especially deep learning, to advance neuroimaging genetics for AD. It involves novel methodological
developments in Aims 1-4, cost-effective applications to the large-scale UK Biobank neuroimaging genetic data for
AD (Aim 5), and software development (Aim 6). All four Aims for the methods development tackle emerging impor-
tant topics in deep learning with their applications to neuroimaging genetics for AD; although the other three Aims
deal with independent topics with their own other broad applications, they in turn serve for Aim 1: 1) Aim 1 applies
manually searched deep learning models for automatic feature extraction/phenotyping from neuroimages, by which
both the statistical power and biological interpretation of subsequent genome-wide association studies (GWAS)
are expected to be enhanced; 2) Aim 2 employs (automatic) neural architecture search (NAS) to more efficiently
identify better deep learning models, which are then applied to Aim 1 for enhancing feature extraction/phenotyping
and thus boosting the power of GWAS; 3) Aim 3 focuses on explainable deep learning, offering biological insights
by localizing and highlighting the most important features extracted by deep learning models that can be used for
Aim 1; 4) Aim 4 develops a novel inferential theory for deep learning, which is then applied to rigorously test for
the statistical significance of any selected/highlighted features used in Aim 1. In Aim 5, these new methods will be
applied to the UK Biobank neuroimaging and GWAS data to identify novel genetic loci and neuroimaging features
for AD. As a byproduct, we will develop and distribute software implementing the proposed methods in Aim 6.

## Key facts

- **NIH application ID:** 10267714
- **Project number:** 5R01AG069895-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Wei Pan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $667,791
- **Award type:** 5
- **Project period:** 2020-09-30 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10267714

## Citation

> US National Institutes of Health, RePORTER application 10267714, Deep Learning with Neuroimaging Genetic Data for Alzheimer's Disease (5R01AG069895-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10267714. Licensed CC0.

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