(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.

Public Health Relevance

Real-time fMRI neurofeedback has been shown to be effective in treating neuropsychiatric disorders (including depression and anxiety) and holds tremendous promise for future breakthroughs, but current methods require sophisticated computer hardware and software installed at each clinical site, and the expertise to use these. This project will make RT-fMRI studies accessible to a wider set of facilities by simplifying and standardizing the software tools and allowing for cloud deployment. Cloud-computing is a key enabling technology for these treatments because it eliminates the need for on-premise technical expertise and/or high performance computing, allowing installation, configuration, and maintenance to be automated and done remotely.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Multi-Year Funded Research Project Grant (RF1)
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Special Emphasis Panel (ZMH1)
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Zhan, Ming
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Princeton University
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United States
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