# Advanced Statistical Analytics of MRI in MS

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2021 · $574,647

## Abstract

PROJECT SUMMARY
 Quantitative radiomic analysis of MS based on MRI, performed by extracting imaging correlates of MS
pathophysiology, has been recognized as critical for more accurate and earlier diagnostics, improved precision
in clinical decision-making, and more powerful outcomes in trials for targeted MS therapeutics. Unfortunately,
the application of these approaches in MS are still in their infancy and several challenges unique to MS remain
to be solved before radiomic analyses can be translated in clinical and research practice. A major challenge for
the diagnosis and monitoring of MS is to disentangle the heterogeneity of white matter lesions, both from an
etiologic perspective and in the degree of tissue injury. The presence of confluent clusters of lesions that are
comprised of multiple lesions, particularly around the ventricular horns, poses a key challenge for dissecting this
heterogeneity in lesions: while histopathology shows great phenotypic variability both within and between
lesions, most neuroimaging studies average metrics across lesion clusters losing the valuable information about
each individual lesion. In this proposal, we propose to use advanced statistical analysis of signal intensity from
multi-parametric imaging to distinguish individual lesions and more accurately phenotype them, and thus
facilitate much greater understanding of an individual patients burden of disease and easier application to clinical
practice and research studies.
We will also create tools that will facilitate the adoption of these techniques in the
clinic. We will validate these approaches by comparison to expert neuroradiologist assessments and determine
added value of these techniques.
We further propose to develop a state-of-the-art method for the discovery of
covariate effects in diffuse processes in the normal-appearing white matter and gray matter, which will facilitate
many potential studies of MS pathology and therapeutics. We will also develop software implementations and
educational resources to disseminate the methods developed.

## Key facts

- **NIH application ID:** 10113688
- **Project number:** 5R01NS112274-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Russell Takeshi Shinohara
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $574,647
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10113688

## Citation

> US National Institutes of Health, RePORTER application 10113688, Advanced Statistical Analytics of MRI in MS (5R01NS112274-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10113688. Licensed CC0.

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