# Cloud-based Software Framework to Simplify and Standardize Real-Time fMRI

> **NIH NIH RF1** · PRINCETON UNIVERSITY · 2020 · $1,164,992

## Abstract

Project Summary: (30 lines of text max)
 We propose to create an open-source cloud-based software system for real-time fMRI neurofeedback
experiments. Our goal is to make real-time fMRI neurofeedback broadly accessible for both the scientific and
clinical communities, in order to accelerate both basic research and the development and deployment of
clinical treatments.
 Real-time fMRI neurofeedback (RT-fMRI) is an increasingly important area of research. RT-fMRI is
growing in prevalence, with the number of articles including “real-time fMRI neurofeedback” increasing from 64
in 2005 to 1,520 in 2020 (Google Scholar). Several hundred people attended the most recent rtFIN (Real-time
Functional Imaging and Neurofeedback) conference. Furthermore, a sizable fraction of RT-fMRI experiments
(18% of the studies included in Thibault et al., 2018) focused specifically on clinical populations, demonstrating
its relevance to diagnosis and treatment of neuropsychiatric disorders. We anticipate future clinical applications
for RT-fMRI will significantly broaden the community of users into the thousands. However, the potential for
continued and even faster growth is currently limited by the need for customized software and hardware, as
well as the expertise needed for their use. We aim to eliminate both of these barriers and thereby allow any
investigator and/or clinician to execute real-time neurofeedback experiments at facilities that are not
specialized for this purpose.
 This project will support and foster Open Science in neuroimaging by adhering to BRAIN Initiative
standards, such as the Brain Imaging Data Structure (BIDS) data format and the OpenNeuro data repository.
This will further promote use of these tools by diverse researchers with different skill sets for the
implementation of RT-fMRI studies.
 This proposed project will build on our experience in developing open source tools for neuroimaging
(BrainIAK), and from conducting an off-site RT-fMRI clinical study for depression (Mennen et al,. in prep). After
developing real-time software at Princeton, we created a real-time cloud framework that was accessible to the
Penn Medicine imaging facility where the depression study was conducted. Our initial version of RT-fMRI
software was the first cloud-based application deployed by the Penn Medicine IT group, thus breaking ground
on the administrative as well as technical requirements for a clinical trial of this type. This effort won a Fierce
Innovation Award (Penn Medicine News Release, 2018).
 In total, this project will: 1) deliver an easy-to-use software framework for building RT-fMRI experiments
to run locally or in the cloud; 2) incorporate BRAIN Initiative standards to RT-fMRI including compatibility with
BIDS data formats, OpenNeuro repositories, and creation of BIDS apps; 3) create a sample-set of
pre-packaged RT-fMRI experiments for immediate use by researchers.

## Key facts

- **NIH application ID:** 10123207
- **Project number:** 1RF1MH125318-01
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** JONATHAN D COHEN
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,164,992
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10123207

## Citation

> US National Institutes of Health, RePORTER application 10123207, Cloud-based Software Framework to Simplify and Standardize Real-Time fMRI (1RF1MH125318-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10123207. Licensed CC0.

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