# Virtual Histology for Assessing MS Pathologies

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2024 · $444,632

## Abstract

PROJECT SUMMARY
Multiple sclerosis (MS) is an inflammatory demyelinating disease with, ultimately, irreversible axonal injury
leading to permanent neurological disabilities. Preventing disease progression or treating progressive MS
remains a major unmet clinical need. We have previously developed a novel data-driven model-selection
diffusion basis spectrum imaging (DBSI) to accurately image inflammation, demyelination, and axonal injury,
as well as quantifying axonal loss in the presence of vasogenic edema in experimental autoimmune
encephalomyelitis (EAE) and spinal cord injury mice, and brain WM pathologies in MS.
MRI does not distinguish inter- from intra-axonal water signals, reflecting a weighted-average of signals
between the two compartments. However, our recent observation that DBSI derived axial diffusivity (DBSI-λǁ)
was slightly elevated in normal appearing white matter (NAWM) in people with MS (pwMS). This elevated
DBSI-λǁ added uncertainty in assessing whether axonal injury (against the notion that ↓DBSI-λǁ ≈ axonal injury)
is present in NAWM of these pwMS. In this proposed study, we will refine DBSI to further improve its sensitivity
and specificity to axonal injury/loss, demyelination, and inflammation for accurately assessing disease
progression and therapeutic efficacy in pwMS.
Since MRI does not distinguish inter- from intra-axonal water signals, it reflects a weighted-average between
inter- and intra-axonal signals. In the presence of inflammation-associated edema or minor axonal loss in
pwMS, the longer diffusion time for human scanners coupled with the increased inter-axonal space will lead to
increased DBSI-λǁ masking the detectability of axonal injury. Thus, through separating inter- and intra-axonal
water compartment signals, the sensitivity and specificity to axonal injury of DBSI-derived intra-axonal λ||
(DBSI-IA-λ||) may be improved. This new model will still preserve the isotropic diffusion specificity to
inflammation and tissue loss.
We propose three specific aims to prove or disprove this hypothesis: Aim 1. To perform DBSI and DBSI-IA
analyses on autopsy specimens from pwMS followed by conventional histology and immunohistochemical
staining. Aim 2. To perform DBSI and DBSI-IA modeling on perfused frog sciatic nerve with and without
contrast agent to separate inter-/intra-axonal space water signal. Aim 3a. To develop a Diffusion Histology
Imaging (DHI) approach combining DBSI/DBSI-IA metrics and machine/deep learning algorithms to
recapitulate histology specificity to MS pathology. Aim 3b. To translate DBSI-IA model to analyze existing DWI
data from the cohort of pwMS previously imaged in an expired program project.

## Key facts

- **NIH application ID:** 10754513
- **Project number:** 5R01NS116091-04
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** SHENG-KWEI SONG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $444,632
- **Award type:** 5
- **Project period:** 2020-12-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10754513

## Citation

> US National Institutes of Health, RePORTER application 10754513, Virtual Histology for Assessing MS Pathologies (5R01NS116091-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10754513. Licensed CC0.

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