# Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease

> **NIH NIH U01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2024 · $1,146,699

## Abstract

Alzheimer's disease (AD) is characterized by the progressive impairment of cognitive and
memory functions and is the most common form of dementia in the elderly. It affects 5.6 million
Americans over the age of 65 and exacts tremendous and increasing demands on patients,
caregivers, and healthcare resources, making this condition among the most significant public
health problems of our time. Despite extensive studies, our understanding of the biology and
pathophysiology of AD is still limited, hindering advances in the development of therapeutic and
preventive strategies. Genetic studies of AD have successfully identified 40 novel loci but these
explain only a fraction of the overall disease risk, suggesting opportunities for additional
discoveries. Advanced neuroimaging is an essential part of current AD clinical and research
investigations, which generally focus on relatively few imaging phenotypes developed by neuro-
radiologists. However, there is a growing interest in exploiting the high-content information in
large-scale, high dimensional multimodal neuroimaging data to identify novel AD biomarkers.
Deep learning (DL) methods, an emerging area of machine learning research, uses raw images
to derive optimal vector representations of imaging contents, which can be used as informative
AD endophenotypes. To overcome the low interpretability traditionally attributed to DL, whole
genome sequence data provide an opportunity to identify novel genes underlying the DL-
derived imaging endophenotypes and test their association with AD and AD-related traits in
large cohort samples. The proposed project will leverage existing neuroimaging and genetic
data resources from the UK Biobank, the Alzheimer's Disease Sequencing Project (ADSP), the
Alzheimer's Disease Neuroimaging Initiative (ADNI), and the Cohorts for Heart and Aging
Research in Genomic Epidemiology (CHARGE) consortium, and will be conducted by a
multidisciplinary team of investigators. We will derive AD endophenotypes from neuroimaging
data in the UK Biobank using deep learning (DL). We will identify novel genetic loci associated
with DL-derived imaging endophenotypes and optimize the co-heritability of these
endophenotypes with AD-related phenotypes using UK Biobank genetic data. We will leverage
resources and collaborations with AD Consortia and the power of DL-derived neuroimaging
endophenotypes to identify novel genes for Alzheimer's Disease and AD-related traits. Also, we
will develop DL-based neuroimaging harmonization and imputation methods and distribute
implementation software to the research community. We expect to discover new genes relevant
to AD which may leads to understanding of molecular basis of AD and potential new treatment.

## Key facts

- **NIH application ID:** 10885136
- **Project number:** 5U01AG070112-04
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** MYRIAM FORNAGE
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,146,699
- **Award type:** 5
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10885136

## Citation

> US National Institutes of Health, RePORTER application 10885136, Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (5U01AG070112-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10885136. Licensed CC0.

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