# Advanced machine learning algorithms that integrate multi-modal neuroimaging to quantify the heterogeneity in Alzheimer's Disease

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2022 · $535,629

## Abstract

Abstract
Alzheimer's Disease (AD) affects over 5 million Americans posing a significant burden to the community
and health care system. Machine learning (ML) methods have been crucial in detecting the disease and
characterizing its progression. Due to the lack of an in vivo “ground truth” diagnosis, ML approaches
have typically relied on clinically derived labels and a case-control design in their search for a single
imaging pattern that optimally distinguishes between the two groups in the case-control design.
However, heterogeneity within clinical labels may degrade performance and interpretability. The goal of
this project is to address this limitation and accurately characterize heterogeneity in preclinical and
symptomatic AD. Given that age is a major risk factor for developing dementia, we will characterize
healthy aging using multimodal neuroimaging data and ML in Aim 1. To this end, we propose to develop
a novel unsupervised multi-view machine learning tool that can integrate information from multiple
imaging modalities (i.e., structural Magnetic Resonance Imaging, and amyloid and tau sensitive Positron
Emission Tomography) in a principled way. This will enable us to define the normal trajectory of age-
related changes across all modalities, providing the necessary context to understand AD pathology. We
will characterize AD pathology using multimodal neuroimaging data and ML in Aim 2. To this end, we
propose to develop a novel semi-supervised ML framework that integrates multimodal information and
derives data-driven disease dimensions. This is achieved by identifying and quantifying at the individual
level imaging patterns that capture neuroanatomical and neuropathological alterations. Our approach
builds on our extensive prior work on using an advanced, unsupervised multivariate pattern analysis
technique, termed orthonormal projective non-negative matrix factorization, for analyzing neuroimaging
data. Importantly, our project leverages two large multimodal datasets, the Knight AD Research Center
(ADRC) cohort and AD Neuroimaging Initiative (ADNI), which sample participants across the continuum
of AD making them ideal for investigating heterogeneity of AD pathology using advanced ML techniques.
If successful, our approaches could be used for studying any brain disorder and could be readily
integrated into personalized medicine strategies in the future when rich, multimodal imaging data
collection will become a routine diagnostic procedure in hospitals.

## Key facts

- **NIH application ID:** 10323673
- **Project number:** 5R01AG067103-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Aristeidis Sotiras
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $535,629
- **Award type:** 5
- **Project period:** 2021-01-15 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10323673

## Citation

> US National Institutes of Health, RePORTER application 10323673, Advanced machine learning algorithms that integrate multi-modal neuroimaging to quantify the heterogeneity in Alzheimer's Disease (5R01AG067103-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10323673. Licensed CC0.

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