# Statistical method for neural mechanism mediating and moderating cognitive system in Alzheimer's disease and aging research.

> **NIH NIH R01** · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · 2022 · $409,683

## Abstract

Alzheimer's disease (AD), as well as normal aging, are associated with a wide range of brain
and cognitive changes. In investigating cognitive changes, it has been observed that some
people can sustain more brain changes or pathology than others, and this differential
susceptibility is related to measures such as IQ, education, vocational experiences etc. This
observation is the basis for the cognitive reserve (CR) hypothesis, where CR moderates the
effects of brain changes on cognition. Recent developments in medical imaging, particularly
multimodal neuroimaging, can provide better understanding of neural mechanisms that underlie
both cognitive changes and the role of CR. However, existing statistical methods were not
designed to accommodate large-scale multi-dimensional data, particularly for incorporating
high-dimensional moderators and mediators. To address these issues, we propose to develop,
validate, and apply software tools for the cross-sectional and longitudinal analysis of multimodal
MR brain images and cognitive data acquired from individuals with normal cognitive aging,
preclinical AD and AD from two independent studies of aging and Alzheimer's disease: the
Reference Ability Neural Networks (RANN) (Yaakov Stern, PI) and the Alzheimer's Disease
Neuroimaging Initiative (ADNI). We will demonstrate that the developed statistical methods offer
improved accuracy and robustness over current tools. First, we will develop tools for identifying
robust relationships between neurodegeneration or pathology markers and brain function
(network expression measured by task fMRI) in the presence of CR as a moderator. Second,
we will derive neural substrate of CR using resting-state functional MRI and task fMRI and then
develop statistical tools to test the moderation effect of the imaging CR proxies. Third, we will
develop the sparse moderated mediation methods for high-dimensional predictors and
mediators accounting for moderation. to test whether network expression during cognitive tasks
mediates the effect of brain changes (measured via multimodal structural MRI) on cognitive
performance, cognitive decline and dementia transition, and whether the derived neural
substrate of CR moderates the mediation.

## Key facts

- **NIH application ID:** 10320002
- **Project number:** 5R01AG062578-03
- **Recipient organization:** NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
- **Principal Investigator:** SEONJOO LEE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $409,683
- **Award type:** 5
- **Project period:** 2020-01-15 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10320002

## Citation

> US National Institutes of Health, RePORTER application 10320002, Statistical method for neural mechanism mediating and moderating cognitive system in Alzheimer's disease and aging research. (5R01AG062578-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10320002. Licensed CC0.

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