Flexible large-scale fMRI analysis: A survey

Abstract

Functional magnetic resonance imaging (fMRI) has provided a window into the brain with wide adoption in research and even clinical settings. Data-driven methods such as those based on latent variable models and matrix/tensor factorizations are being increasingly used for fMRI data analysis. There is increasing availability of large-scale multi-subject repositories involving 1,000+ individuals. Studies with large numbers of data sets promise effective comparisons across different conditions, groups, and time points, further increasing the utility of fMRI in human brain research. In this context, there is a pressing need for innovative ideas to develop flexible analysis methods that can scale to handle large-volume fMRI data, process the data in a distributed and policy-compliant manner, and capture diverse global and local patterns leveraging the big pool of fMRI data. This paper is a survey of some of the recent research in this direction.