# Investigating Cellular Neighborhoods and Tissue Architecture in White Matter Hyperintensities

> **NIH NIH F31** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $48,974

## Abstract

PROJECT SUMMARY/ABSTRACT
White matter hyperintensities (WMH) are radiologically defined regions of myelin rarefaction. WMH are found in
over 90% of individuals over the age of 65, and increase the risk of other adverse outcomes, such as late-life
depression, stroke, and Alzheimer’s Disease (AD). Incidence of WMH increase Alzheimer’s Disease likelihood
two-fold and WMH burden correlates with AD onset, progression, and severity. Despite the broad and impactful
implications of these pathologies, WMH histopathology is incompletely known due to the difficulty locating these
lesions in postmortem coronal tissue. We have overcome this challenge by creating an ex vivo 7T MRI pipeline
and alignment system to locate WMH in postmortem coronal slabs. Using this pipeline, we will investigate
histopathologic signatures in WMH and normally appearing white matter (NAWM) to investigate theories of WMH
composition previously explored in the neuroimaging literature. Neuroimaging studies demonstrate a clear
cerebrovascular underpinning, but the underlying mechanism remains unknown. This project will investigate the
relationship between cerebrovascular pathologies (i.e., arteriolosclerosis, perivascular space dilation, and blood
brain barrier leakage) with WMH histopathologic signatures subtypes and patient outcomes. The objective of
this proposed project is to conduct rigorous and robust spatial analyses of cellular and tissue architecture within
WMH and NAWM tissue and investigate the correlation between these features with dementia progression and
co-morbidities in Alzheimer’s Disease. Specifically, this project will evaluate pathologic correlates of various
cerebrovascular abnormalities and altered cell (oligodendrocytes, microglia, macrophages, endothelial cells,
pericytes), myelin, and axon distribution. Random forest regression algorithms will reveal histopathologic
features with high predictive value towards WMH vs. NAWM designation. Hierarchical clustering algorithms (i.e,
agglomerative nesting) will uncover clinically relevant subtypes of WMH. A weakly-supervised attention-based
deep learning model will reveal regions of hematoxylin and eosin (H&E) images that are predictive of WMH vs.
NAWM. We will next determine regions of WMH H&E images predictive of clinical co-morbidities (i.e.,
hypertension) and clinical outcomes (i.e., dementia progression) in AD patients. Together, these methods will
comprehensively characterize distinct spatial relationships within WMH pathologies that correlate with patient
characteristics and outcomes. These insights will inform personalized medicine approaches targeting WMH in
AD and stand as a basis for future studies exploring WMH mechanisms.

## Key facts

- **NIH application ID:** 10998547
- **Project number:** 1F31AG090079-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Dana Julian
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $48,974
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10998547

## Citation

> US National Institutes of Health, RePORTER application 10998547, Investigating Cellular Neighborhoods and Tissue Architecture in White Matter Hyperintensities (1F31AG090079-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10998547. Licensed CC0.

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