# Pooled analysis of multiple sclerosis findings on multi-site 7 Tesla MRI

> **NIH NIH R01** · UNIVERSITY OF MARYLAND BALTIMORE · 2022 · $483,995

## Abstract

Project Summary/Abstract
Background: Although current MRI performs well as a tool to measure white matter (WM) inflammation in MS,
its ability to quantify gray matter (GM) pathology, meningeal inflammation, and chronic inflammatory changes
are limited. For this reason, researchers have looked to 7-tesla (7T) MRI for more robust in vivo measures of
pathology. Although the increased sensitivity of 7T MRI for these aspects of MS have stimulated much
interest, the impact of previous studies have been limited due to small sample sizes, varying methodologies,
cumbersome analysis methods, and conflicting results – all of which have limited the generalizability of findings
and more widespread dissemination of the use of this tool.
Objective: In this study, we aim to advance the use of 7T MRI as a tool for diagnosis, prognosis, and
treatment effect monitoring by performing multi-site, large sample size evaluation of imaging correlates of
cortical and deep GM lesions, chronic active WM lesions (WM lesions with paramagnetic rims on MRI), and
meningeal inflammation (leptomeningeal inflammation (LME) on MRI) and validation of their clinical relevance.
We also will look develop automated tools for identification and quantification of these findings on 7T MRI.
Study Design: Imaging and clinical data will be derived from previous and ongoing studies of the use of 7T
MRI in MS performed at multiple collaborative sites under the umbrella of the North American Imaging in MS
(NAIMS) Collaborative, including the University of Maryland, Baltimore, Harvard-Brigham and Women’s
Hospital, the National Institutes of Neurological Disorders and Stroke, the University of Pennsylvania, and the
Montreal Neurological Institute at McGill University. This includes data on up to 814 individual persons with
MS, 1232 individual study visits, and a follow up period of up to 5 years. GM lesions, WM lesions with
paramagnetic rims, and LME will be identified on 7T, with critical evaluation of methods and their relationship
to clinical data. Further, the manual analysis results will be used as training sets for deep learning algorithms
for identification of these abnormalities on future 7T MRI scans.
Impact: This study would provide the data on measurement variability and generalizable clinical importance of
the imaging findings being investigated, along with providing automated tools for future, high-throughput
studies. In advancing these precise measures of aspects of MS pathology only poorly measured on lower field
MRI, this study will lead to more accurate MS diagnoses, improvements in the ability to monitor changes in MS
pathology over time, and perhaps lead to the screening of new pharmaceutical interventions to target GM
lesions, WM lesions with paramagnetic rims, and LME.

## Key facts

- **NIH application ID:** 10430261
- **Project number:** 5R01NS122980-02
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** Daniel M Harrison
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $483,995
- **Award type:** 5
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10430261

## Citation

> US National Institutes of Health, RePORTER application 10430261, Pooled analysis of multiple sclerosis findings on multi-site 7 Tesla MRI (5R01NS122980-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10430261. Licensed CC0.

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