Title: Distributed rank-1 dictionary learning: Towards fast and scalable solutions for fMRI big data analytics,
Abstract: The use of functional brain imaging for research and diagnosis has benefitted
greatly from the recent advancements in neuroimaging technologies, as well as
the explosive growth in size and availability of fMRI data. While it has been
shown in literature that using multiple and large scale fMRI datasets can
improve reproducibility and lead to new discoveries, the computational and
informatics systems supporting the analysis and visualization of such fMRI big
data are extremely limited and largely under-discussed. We propose to address
these shortcomings in this work, based on previous success in using dictionary
learning method for functional network decomposition studies on fMRI data. We
presented a distributed dictionary learning framework based on rank-1 matrix
decomposition with sparseness constraint (D-r1DL framework). The framework was
implemented using the Spark distributed computing engine and deployed on three
different processing units: an in-house server, in-house high performance
clusters, and the Amazon Elastic Compute Cloud (EC2) service. The whole
analysis pipeline was integrated with our neuroinformatics system for data
management, user input/output, and real-time visualization. Performance and
accuracy of D-r1DL on both individual and group-wise fMRI Human Connectome
Project (HCP) dataset shows that the proposed framework is highly scalable. The
resulting group-wise functional network decompositions are highly accurate, and
the fast processing time confirm this claim. In addition, D-r1DL can provide
real-time user feedback and results visualization which are vital for
large-scale data analysis.