# Systems Modeling of White Matter Microstructural Abnormalities in Alzheimer's Disease

> **NIH NIH F32** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $70,617

## Abstract

Project Summary
Alzheimer’s disease (AD) is the most common form of dementia in the United States.
Microstructural abnormalities in white matter (WM) are often reported in AD and are associated
with neurodegeneration. Even though neuroimaging studies identified disruption of WM integrity
in AD several key questions remain unanswered including which brain regions have the
strongest WM changes in early stage of AD and what biological processes underlie WM
abnormality during disease progression. To address these critical questions, we will
systematically interrogate diffusion tensor imaging (DTI), genetic, gene expression and clinical
information using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data. Our goal in this
proposal is to perform network and comprehensive genetic analysis using neuroimaging
parameters and genomics data to better understand basic mechanisms and biological pathways
underlying WM changes during disease progression. We hypothesize that key genetic drivers
play significant role in loss of WM integrity and connectivity in AD-related brain regions. In our
first aim we will identify significant DTI-features in regions relevant to AD-pathology related
endophenotypes using cross-sectional and longitudinal data. Through correlating regional DTI
features with clinical and cognitive traits such as CSF tau/p-tau and abeta levels, episodic
memory scores and amyloidosis (PET), we will quantify regional white matter abnormality and
identified most vulnerable brain regions. In the second aim we will perform co-diffusion-
expression network analysis of the combined DTI and gene expression data. To accomplish this
aim multiscale gene co-expression network analysis will be performed on the gene expression
data to identify co-expressed gene modules that were further correlated with DTI features.
Lastly, we will perform deep biological mining on the co-diffusion-expression networks to identify
potential upstream regulators and downstream indicators for white matter microstructure and
connectivity abnormality in AD. We will integrate DTI and multi–omics data to understand the
underlying biological mechanisms through identification of common/rare and structural variants,
epigenetic changes or any level of blood metabolite changes related to WM structure during
disease progression. In summary, integrative framework in this proposal will allow us the
identification of genetic risk factors and key biological pathways underlying disruption of WM
changes in early stage of AD. The proposed research in this fellowship will provide me with
advance training in computational genetics, complex data processing, and neuroimaging. The
training plan will build on my understanding of systems biology of WM changes in AD.

## Key facts

- **NIH application ID:** 10074127
- **Project number:** 5F32AG064904-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Emrin horgusluoglu-moloch
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $70,617
- **Award type:** 5
- **Project period:** 2019-11-01 → 2021-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10074127

## Citation

> US National Institutes of Health, RePORTER application 10074127, Systems Modeling of White Matter Microstructural Abnormalities in Alzheimer's Disease (5F32AG064904-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10074127. Licensed CC0.

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