Simplifying Cloud-Based Real-Time fMRI with the Brain Imaging Data Structure, Stephen Polcyn, UG '21 (2312124)
Real-time fMRI is a powerful technique that enables novel research and medical treatments by providing people real-time feedback (approximately every 2 seconds) about their brain activity. However, widespread deployment of real-time fMRI is hindered by existing real-time fMRI software systems, which fail to enable users to easily collaborate with colleagues on real-time fMRI experiments or sufficiently minimize the complexity of real-time fMRI's many moving parts. In my work, I created software to integrate the Brain Imaging Data Structure (BIDS), the neuroscience data standard that specifies how to organize neuroscience datasets, with RT-Cloud, a real-time fMRI software system under active development at Princeton University that uses cloud computing to run real-time fMRI's computationally intensive analysis programs. The software I designed enables researchers to easily share their real-time fMRI datasets, more quickly understand the data format our software system, RT-Cloud, uses and produces, and it connects the RT-Cloud system with BIDS-compatible software packages already familiar to researchers which can then be quickly integrated into the researchers' experiments. This reduction of complexity and increase in collaboration potential is all done while meeting real-time fMRI's strict performance requirements.