# Building predictive algorithms to identify resilience and resistance to Alzheimer's disease

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $1,032,210

## Abstract

PROJECT SUMMARY
There are two observed phenomena that defy the traditional Alzheimer’s disease (AD) trajectory; those who
resist the accumulation of AD pathology (amyloid and/or tau) despite evidence of risk factors, and those who
present with AD pathology but remain resilient to cognitive decline. Classifying these individuals who will likely
manifest resistance or resilience to AD over their lifetime is critical for informing clinical practice and transforming
clinical trial recruitment. It remains unclear how combinations of risk factors, whether demographic, vascular or
neuroimaging, may help to increase accuracy for predicting an individuals’ likelihood of manifesting resistance
or resilience to AD. Further, very little is understood about how sex, race and their interaction influence these
phenomena. Relatively limited sample sizes and low racial diversity have so far hampered studies. The overall
goal of this proposal is to develop and validate robust predictive algorithms of resistance and resilience to AD by
harmonizing data from 13 well characterized and racially diverse cohorts of clinically normal older adults
(n=~15,000). This innovative proposal could transform approaches for both clinical decision making and clinical
trials. Based on a simple set of easily accessible medical information, such as demographics, vascular risk,
APOEe4 status, and brain volumetric data when available, our validated models will provide interpretable patient-
level predictions of resistance and resilience with 10-year risk estimates of AD pathological burden and cognitive
decline given a patient’s profile. Similarly, our predictive algorithms will provide a predictive framework of who
should be invited for initial screening and serve to predict those most likely to accumulate Ab/tau or exhibit short
term decline within the course of a clinical trial. We propose to harmonize data from 13 cohorts of ~15,000
clinically normal individuals, to accomplish the following aims: (1) build predictive algorithms to classify those
who are resistant to either amyloid or tau and validate these models to demonstrate their utility in clinical practice
and AD prevention trials, (2) build and validate predictive algorithms to classify those who are cognitively resilient
in the face of abnormal levels of amyloid or tau, and (3) examine how intersections between sex and race can
produce more refined individualized risk profiles that are reflective of these two critical population strata that are
known risk factors for AD. Our strong interdisciplinary team spans the breadth of cognitive neuroscience, PET
and MR neuroimaging, biostatistics, behavioral neurology, and epidemiology. Our multi-PI team reflect four
critical areas of expertise that are essential to this proposal: (1) data harmonization, (2) neuroimaging, (3)
machine learning, and (4) cognitive resilience. We have published a range of data harmonization approaches
for both cognitive and PET neuroimaging data, which ca...

## Key facts

- **NIH application ID:** 10843862
- **Project number:** 5R01AG079142-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Rachel Frances Buckley
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,032,210
- **Award type:** 5
- **Project period:** 2023-06-01 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10843862

## Citation

> US National Institutes of Health, RePORTER application 10843862, Building predictive algorithms to identify resilience and resistance to Alzheimer's disease (5R01AG079142-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10843862. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*