Decoding fMRI brain states in real-time

Abstract

This article reviews a technological advance that originates from two areas of ongoing neuroimaging innovation—(1) the use of multivariate supervised learning to decode brain states and (2) real-time functional magnetic resonance imaging (rtfMRI). The approach uses multivariate methods to train a model capable of decoding a subject's brain state from fMRI images. The decoded brain states can be used as a control signal for a brain computer interface (BCI) or to provide neurofeedback to the subject. The ability to adapt the stimulus during the fMRI experiment adds a new level of flexibility for task paradigms and has potential applications in a number of areas, including performance enhancement, rehabilitation, and therapy. Multivariate approaches to real-time fMRI are complementary to region-of-interest (ROI)-based methods and provide a principled method for dealing with distributed patterns of brain responses. Specifically, a multivariate approach is advantageous when network activity is expected, when mental strategies could vary from individual to individual, or when one or a few ROIs are not unequivocally the most appropriate for the investigation. Beyond highlighting important developments in rtfMRI and supervised learning, the article discusses important practical issues, including implementation considerations, existing resources, and future challenges and opportunities. Some possible future directions are described, calling for advances arising from increased experimental flexibility, improvements in predictive modeling, better comparisons across rtfMRI and other BCI implementations, and further investigation of the types of feedback and degree to which interface modulation is obtainable for various tasks.