# Advanced machine learning algorithms that integrate genomewide, longitudinal MRI and demographic data to predict future cognitive decline toward dementia

> **NIH NIH R01** · CORNELL UNIVERSITY · 2021 · $410,000

## Abstract

ABSTRACT
The “preclinical” phase of Alzheimer’s disease (AD) is characterized by abnormal levels of brain
amyloid accumulation in the absence of major symptoms, can last decades, and potentially holds the
key to successful therapeutic strategies. Today there is an urgent need for quantitative biomarkers
and genetic tests that can predict clinical progression at the individual level. This project will develop
cutting edge machine learning algorithms that will mine high dimensional, multi-modal, and
longitudinal data to derive models that yield individual-level clinical predictions in the context of
dementia. The developed prognostic models will specifically utilize ubiquitous and affordable data
types: structural brain MRI scans, saliva or blood-derived genome-wide sequence data, and
demographic variables (age, education, and sex). Prior research has demonstrated that all these
variables are strongly associated with clinical decline to dementia, however to date we have no model
that can harvest all the predictive information embedded in these high dimensional data.
Machine learning (ML) algorithms are increasingly used to compute clinical predictions from high-
dimensional biomedical data such as clinical scans. Yet, most prior ML methods were developed for
applications where the ``prediction’’ task was about concurrent condition (e.g., discriminate cases and
controls); and established risk factors (e.g., age), multiple modalities (e.g., genotype and images) and
longitudinal data were not fully exploited. This application’s core innovation will be to develop
rigorous, flexible, and practical ML methods that can fully exploit multi-modal, longitudinal, and high-
dimensional biomedical data to compute prognostic clinical predictions.
The proposed project will build on the PI’s strong background in computational modeling and analysis
of large-scale biomedical data. We will employ an innovative Bayesian ML framework that offers the
flexibility to handle and exploit real-life longitudinal and multi-modal data. We hypothesize that the
developed models will be more useful than alternative benchmarks for identifying preclinical
individuals who are at heightened risk of imminent clinical decline. We will use a statistically rigorous
approach for discovery, cross-validation, and benchmarking the developed tools. This project will
yield freely distributed, documented, and validated software and models for predicting future clinical
progression based on whole-genome, longitudinal structural MRI and demographic data. We believe
the algorithms and software we develop will yield invaluable tools for stratifying preclinical AD
subjects in drug trials, optimizing future therapies, and minimizing the risk of adverse effects.

## Key facts

- **NIH application ID:** 10188360
- **Project number:** 5R01AG053949-05
- **Recipient organization:** CORNELL UNIVERSITY
- **Principal Investigator:** Mert Rory Sabuncu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $410,000
- **Award type:** 5
- **Project period:** 2017-07-01 → 2021-07-02

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10188360

## Citation

> US National Institutes of Health, RePORTER application 10188360, Advanced machine learning algorithms that integrate genomewide, longitudinal MRI and demographic data to predict future cognitive decline toward dementia (5R01AG053949-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10188360. Licensed CC0.

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