# CRCNS: Repurposing Transformers to Characterize Gut Microbiome & Aging Brain Axis

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $369,886

## Abstract

For debilitating neurodegenerative diseases such as Alzheimer’s disease (AD), some drugs have been 
approved but only offer a modest attenuation of cognitive decline. There is a need to better understand the 
role of modifiable risk factors which could decrease the risk of AD and other types of dementia. The human 
gut microbiome–comprising of microbes and their associated genes–shows differences in composition 
between healthy individuals and those with AD dementia, and based on recent studies appears to be a 
promising modifiable risk factor. Despite encouraging findings, large gaps remain regarding our knowledge 
of the gut microbiome’s relationship with the aging brain and dementia pathology measured via 
neuroimaging, fluid biomarkers, and cognitive assessments. Fortunately, datasets that can enable this 
analysis are growing in terms of sample sizes, making a comprehensive machine-learning based 
investigation of these associations timely and potentially highly rewarding. To this end, our team proposes 
to make use of and extend contemporary developments in Transformer-based models that power 
advancements in large language model (LLM) technologies, and repurpose them into specialized models 
for these analyses for multi-modal datasets. The key components of this project include the following aims. 
Aim 1: We will design special Transformer-based models that will be trained using microbiome data via 
attaching novel trainable modules. These repurposed models will enable analysis of associations between 
gut microbiome composition and brain health in dementia. Aim 2: Our models will be extended to handle 
3D neuroimaging data with an eye on computational efficiency. The models will be rigorously evaluated on 
large imaging datasets as well as on multi-modal datasets. Their performance/generalization behavior will 
be profiled. Aim 3: The models will be used to study the relationship between the gut microbiome and the 
brain, anchored by eight specific scientific hypotheses. Pretrained models and software tools will be 
disseminated to the community. Significance: This project will yield sophisticated methods for multimodal 
analysis of microbiome, brain imaging and clinical/cognitive data for aging and Alzheimer’s disease. These 
tools will be disseminated and can be used to investigate a variety of scientific hypotheses to determine 
novel relationships between the microbiome and the brain. This in turn is expected to open new avenues 
for preventing and treating neurodegenerative disease and improve the health and well-being of aging 
Americans.

## Key facts

- **NIH application ID:** 11083259
- **Project number:** 1R01AG092220-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Barbara Brigitta Bendlin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $369,886
- **Award type:** 1
- **Project period:** 2024-09-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11083259

## Citation

> US National Institutes of Health, RePORTER application 11083259, CRCNS: Repurposing Transformers to Characterize Gut Microbiome & Aging Brain Axis (1R01AG092220-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11083259. Licensed CC0.

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