# Modeling Resilience to Alzheimer's Disease Pathology in Cognitively Healthy Older Adults

> **NIH NIH R03** · UNIVERSITY OF CALIFORNIA BERKELEY · 2021 · $160,377

## Abstract

Project Summary
Cognitive decline in aging is common but not universal. In fact, some older adults have normal cognitive
performance despite evidence of Alzheimer’s disease (AD) pathology in their brains. Resilience, or successfully
coping with pathology, is a new concept that provides a framework for studying variability in the cognitive
consequences of pathological changes in the aging brain. The critical idea is that individuals with high resilience
will only experience cognitive decline as a result of pathology when the burden is high. In contrast, less pathology
may cause changes in cognition in individuals with low resilience. This proposal addresses a major challenge to
the study of resilience: the development of an accurate, quantitative measurement of the phenomenon. Using
neuroimaging techniques for in vivo measurement of pathology combined with novel quantitative biological
measurements and statistical approaches, the research described in this proposal represents a cutting-edge
combination of concepts and tools. Specifically, spatial and temporal coupling measures will be calculated
relating tau positron emission tomography (PET) imaging to neurodegeneration or atrophy at the individual level.
The coupling of these steps in the AD pathological cascade may help to better define resilience, which is likely
related to the dynamics of that cascade. Thus, another crucial novel aspect of this approach is the
conceptualization of resilience as a biological phenomenon reflecting relationships between different variables
on the causal pathway to cognitive decline. Spatial coupling will be measured using voxelwise spatial correlations
while latent difference score models will be used to estimate the extent to which change in tau-PET predicts
atrophy. Next, linear models will be used to predict baseline cognition in four domains: episodic memory, working
memory, executive functioning and processing speed. Spatial and temporal coupling metrics will be included in
these linear models. Residuals from linear models predicting cognition, which are a measure of cognitive
resilience, will be extracted and used in the creation of the general resilience factor. Briefly, a partial least squares
path model will be used to define three latent factors: cognitive resilience, cognitive reserve and brain reserve.
A general resilience factor will be determined from these three measurement model latent factors. As a validation
step, general resilience factor scores will be extracted and used to predict changes in cognition and measures
of cognitive engagement. The expectation is that an accurate, quantitative resilience measure will be a useful
predictor of future cognitive outcomes. A separate dataset from ADNI will be used as a replication sample to
ensure reproducibility of this approach and its applicability to a larger multi-site cohort. Ultimately, a delay in the
onset of AD, even just by several years, would greatly decrease the overall prevalence of the d...

## Key facts

- **NIH application ID:** 10217667
- **Project number:** 1R03AG067033-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Theresa M. Harrison
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $160,377
- **Award type:** 1
- **Project period:** 2021-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10217667

## Citation

> US National Institutes of Health, RePORTER application 10217667, Modeling Resilience to Alzheimer's Disease Pathology in Cognitively Healthy Older Adults (1R03AG067033-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10217667. Licensed CC0.

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