# Brain Connectivity Signatures of Behavior and Cognition in Duchenne Muscular Dystrophy

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $429,000

## Abstract

PROJECT SUMMARY
Therapies (drugs and genetics-based) in Duchenne Muscular Dystrophy (DMD) are targeted towards improving
muscle function and arresting the progression of disease. As a result, the skeletal and even the cardiac
musculature are well researched. However, studies focusing on brain structure have been sparse even though
varying cognitive deficits have been reported across the spectrum. With advancing treatments, such as exon
skipping, as well as steroid alternatives such as VBP15 moving to human trials, there is renewed optimism about
improving the life expectancy and quality of patients with DMD. Consequently, a better understanding of the
brain structure and its putative link with cognitive function becomes important. Our plan is to collect pilot brain
imaging data in 36 children (24 children with DMD and 12 healthy controls, both 8~21 years) using cutting-edge
imaging protocols from the Human Connectome Project. Our overarching goal is to identify a brain connectivity
biomarker of cognition in DMD by investigating the potential of structural neuroimaging signatures and functional
connectomic motifs and relating them to gene expression profiles in the brain.

## Key facts

- **NIH application ID:** 10355348
- **Project number:** 1R21NS125184-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Shantanu H. Joshi
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $429,000
- **Award type:** 1
- **Project period:** 2021-09-30 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10355348

## Citation

> US National Institutes of Health, RePORTER application 10355348, Brain Connectivity Signatures of Behavior and Cognition in Duchenne Muscular Dystrophy (1R21NS125184-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10355348. Licensed CC0.

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