# Network level analysis of progressive brain degeneration in autosomal dominant Alzheimer disease

> **NIH NIH K99** · WASHINGTON UNIVERSITY · 2021 · $231,353

## Abstract

ADMINISTRATIVE SUPPLEMENT PROJECT SUMMARY/ABSTRACT
Alzheimer’s disease (AD) is characterized by changes including the accrual of amyloid-b (Ab) plaques and
neurofibrillary tau tangles, cortical thinning, hypometabolism, and disruptions in brain connectivity. However, the
presence of this pathology does not occur simultaneously, but propagates throughout the cortex decades before
symptoms of dementia are apparent. Researchers have noted that Ab, hypometabolism, and tau show consistent
focal disruption beginning in lateral parietal, temporal, and the posterior cingulate gyrus. We hypothesize that
this regional spread of pathology results in disrupted communication among brain networks resulting in
symptoms of cognitive decline. This proposal seeks to 1) characterize the spatiotemporal progression of brain
network degeneration and 2) determine the relationship between neuronal atrophy, brain network dysfunction,
and cognitive decline. Brain networks can be measured using resting state functional magnetic resonance
imaging to index temporal correlations in blood oxygen level dependent signal between brain regions. We will
organize brain regions into canonical functional connectivity brain networks and apply the Network Level Analysis
(NLA) analysis software, developed as part of K99 EB029343, to determine brain network associations with
neuronal atrophy (as indexed with serum neurofilament light; NfL) and symptoms of dementia (as indexed with
a global cognition composite score). NLA is an innovative approach to the analysis of connectome-wide
associations that leverages cross disciplinary biostatistical approaches and an ontological framework, allowing
for derivation of network-based brain-behavior relationships and control of false positive rate at the network level.
This administrative supplement will extend the aims of the original award, which proposed validation of NLA
using Human Connectome Project data, to include applications in AD. Specifically, this administrative
supplement will leverage a fully de-identified pre-existing dataset containing functional connectomes, NfL, and
cognitive measures in participants with autosomal dominant AD (ADAD) recruited from the Dominantly Inherited
Alzheimer Network (DIAN) study. The analysis of data from individuals with ADAD is particularly significant due
to the known timeframe and early onset of cognitive symptoms which allows for modeling of preclinical brain
network degeneration while reducing the contribution of age-related confounds. The proposed analyses of DIAN
data using NLA fulfills the National Institute of Aging Goal A to “Better understand the biology of aging and its
impact on the prevention, progression, and prognosis of disease and disability.” The research team has expertise
in Network Level Analysis (Dr. Wheelock), algorithm development (Dr. Eggebrecht), Alzheimer disease
pathophysiology (Dr. Gordon) and the resources to generate functional connectomes in the DIAN cohort for
secondary dat...

## Key facts

- **NIH application ID:** 10288428
- **Project number:** 3K99EB029343-01A1S1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Muriah D Wheelock
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $231,353
- **Award type:** 3
- **Project period:** 2020-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10288428

## Citation

> US National Institutes of Health, RePORTER application 10288428, Network level analysis of progressive brain degeneration in autosomal dominant Alzheimer disease (3K99EB029343-01A1S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10288428. Licensed CC0.

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