# Using connectomics to characterize risk for Alzheimer's Disease

> **NIH NIH R01** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2021 · $732,130

## Abstract

PROJECT DESCRIPTION
The prevalence of Alzheimer’s Disease (AD) is expected to increase significantly in the next 30 years. While
research efforts continue to focus on the causes of AD and to develop effective medical treatments, there is
also a pressing need to characterize risk for AD before the disease is diagnosed. There may be subtle
changes in brain and cognitive function that are detectable before major symptoms emerge. One approach for
characterizing these vulnerabilities is the use of functional and structural neuroimaging to identify risk profiles
for AD. The present study proposes and tests a model of AD pathology using neuroimaging network analysis
and machine learning approaches to provide insight on widespread changes in information processing in the
AD brain. The proposed model hypothesizes that some aspects of network information processing reflect
neurodegeneration and cognitive decline associated with AD pathology (e.g., hub connectivity and global
connectivity) whereas other network properties reflect attempts to compensate for compromised information
processing (e.g., diffusion of information). In addition, this proposal compares the efficacy of models with
respect to discriminating diagnostic categories (e.g., machine learning classification of AD and clinically normal
subjects) versus isolating underlying dimensions of AD cognitive decline (e.g., machine learning prediction of
memory and language scores from network features). Finally, this study will determine whether features of AD
pathology are present in an at-risk sample of subjects; namely, individuals diagnosed with amnestic mild
cognitive impairment (aMCI). This will be examined by transferring the AD network models to aMCI subjects
and testing whether the model can discriminate aMCI from clinically normal matched controls and whether the
model can predict scores on cognitive tests. The analytic approach will using resting state fMRI data as a
primary assay of network integrity, but diffusion imaging and task fMRI data will also be examined in an
exploratory aim. The general approach will recruit individuals with AD, aMCI and clinically normal matched
controls for each diagnostic group. The groups that are compared directly will be matched for amyloid status,
as indicated by florbetapir PET imaging. The novel contributions of this project include (a) testing a network
model of AD pathology that unifies various measures of network functioning, (b) comparing efficacy of
modeling with respect to delineating diagnostic categories versus capturing underlying cognitive dimensions of
AD, and (c) transferring the AD model to an at-risk group to assess disease vulnerability. This latter innovation
can be applied in future studies to any group of subjects that is defined at risk, such as those with genetic
vulnerability or positive family history. The present study also sets the stage for a subsequent longitudinal
follow-up study to validate whether individuals identified at ri...

## Key facts

- **NIH application ID:** 10189467
- **Project number:** 5R01AG055132-05
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Jane E Joseph
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $732,130
- **Award type:** 5
- **Project period:** 2017-09-30 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189467

## Citation

> US National Institutes of Health, RePORTER application 10189467, Using connectomics to characterize risk for Alzheimer's Disease (5R01AG055132-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10189467. Licensed CC0.

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