# Advanced neuroimaging visualization for cloud computing ecosystems

> **NIH NIH P50** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2021 · $223,500

## Abstract

Project Summary/Abstract
The Center for the Study of Aphasia Recovery (C-STAR, P50-DC014664) explores recovery
from language impairments following stroke, bringing together a diverse team of specialists from
communication sciences, neurology, psychology, statistics and neuroimaging. This project
acquires a broad range of magnetic resonance imaging (MRI) modalities (structural, diffusion,
arterial spin labelling, functional, resting state) from stroke survivors to understand the brain
areas critical for language, improve prognosis, and identify the optimal treatment or
compensation strategy for each individual. The neuroimaging core for C-STAR has developed
unique methods to visualize these different modalities, in support of the research projects.
These methods have dramatically extended our popular MRIcroGL and Surfice tools to better
leverage modern hardware and provide unique visualizations. However, these tools are
currently available only as native standalone desktop applications for Windows, Linux and
MacOS.
The primary aim of this supplement will be to translate our native applications to cloud-based
solutions. Specifically, we will use modular JavaScript and Vue.js components that can be
embedded into web pages. This will allow users to visually inspect and disseminate results
regardless of operating system or device. Our novel techniques are compatible with any web
browser that supports the WebGL 2 standard, thereby supporting computers, tablets and
phones. Already, the developers of the popular FSL pipeline plan to use our modules to create
interactive images to replace the static web pages generated by their current FEAT tool. While
other solutions exist for this niche, none provide the unique visualization properties we have
developed in our current applications. Indeed, the open source web-based tool we will develop
will showcase these features and allow other developers to use our code to improve their own
tools. Because we use GitHub and the NeuroImaging Tools & Resources Collaboratory (NITRC,
www.nitrc.org), users will be able to post issues, request new features, and submit
improvements. Our automated tests allow Continuous Integration (CI) and validated
performance. Translation of our current suite of visualization tools to cloud-based programs will
make data more findable, accessible, interoperable, and reusable (FAIR).

## Key facts

- **NIH application ID:** 10404887
- **Project number:** 3P50DC014664-06A1S2
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** JULIUS FRIDRIKSSON
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $223,500
- **Award type:** 3
- **Project period:** 2016-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10404887

## Citation

> US National Institutes of Health, RePORTER application 10404887, Advanced neuroimaging visualization for cloud computing ecosystems (3P50DC014664-06A1S2). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10404887. Licensed CC0.

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