# User-friendly Analysis Platform for Decentralized Multi-site Diffusion MRI Studies

> **NIH NIH R03** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $89,500

## Abstract

Project Summary
Diffusion MRI (dMRI) is a leading in-vivo imaging methodology for investigating subtle microstructural changes
in the brain’s white matter, which often requires multi-site collaborations for data collection. Decentralized
processing approaches are becoming the preferred approach for multi-site data collection because they support
improved personal data protection and are scalable. Using a decentralized approach, The Enhancing Neuro
Imaging Genetics through Meta Analysis (ENIGMA) studies produced seminal findings across different
disorders. However, the dMRI pipeline established for decentralized ENIGMA dMRI studies is not automatic, not
user friendly, and requires each site to have strong programming and dMRI expertise, which is not the case in
many clinical sites. The goal of this project is to help alleviate the site-level technical burdens, which would
promote decentralized studies by enabling the addition of more sites.
Decentralized multi-site studies are needed for rigorous and statistically robust identification of subtle
neurological changes, but the technical difficulties which make decentralized dMRI analysis highly resource-
intensive, are impeding research sites from participating. The technical difficulties include the installation of
multiple software tools and their software dependencies, analysis instructions that include multiple scripting steps
that are hard to follow for novice users, and quality control (QC) steps that require expertise in dMRI.
The central goal of this application is to develop a user-friendly dMRI analysis platform for decentralized studies
which will facilitate the processing and QC by pursuing two specific aims: 1) develop an automated dMRI
processing pipeline with extensive data QC steps; and 2) provide dashboard function and containerize the
pipeline. Under the first aim, various neuroimaging and image processing utilities will be linked together to
automatize the dMRI processing pipeline while estimating extensive QC measures, which will be curated for
non-dMRI experts to help intuitively understand the data quality. For the second aim, a web-server will be
developed to provide a dashboard that is used to interact with the pipeline and visualize the outputs. Also, all the
components will be containerized as a single package for easier dissemination and deployment using docker.
The innovative approaches proposed in this application will simplify the dMRI analysis, enable optimal user
experience requiring minimal manual user-interventions in the installation and operation of the containerized
pipeline, and will facilitate a dashboard for intuitive user interaction. Our new and substantively different approach
is significant because it is expected to resolve much of the technical burden impeding the collaborative
investigation of brain changes in a decentralized approach, encouraging more sites to participate in multi-site
dMRI studies.

## Key facts

- **NIH application ID:** 10883763
- **Project number:** 5R03EB034775-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Kang Ik Cho
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $89,500
- **Award type:** 5
- **Project period:** 2023-07-06 → 2025-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10883763

## Citation

> US National Institutes of Health, RePORTER application 10883763, User-friendly Analysis Platform for Decentralized Multi-site Diffusion MRI Studies (5R03EB034775-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10883763. Licensed CC0.

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