A highly parallelized framework for computationally intensive MR data analysis
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The goal of this study was to develop a comprehensive magnetic resonance (MR) data analysis framework for handling very large datasets with user-friendly tools for parallelization and to provide an example implementation.
Materials and methods
Commonly used software packages (AFNI, FSL, SPM) were connected via a framework based on the free software environment R, with the possibility of using Nvidia CUDA GPU processing integrated for high-speed linear algebra operations in R. Three hundred single-subject datasets from the 1,000 Functional Connectomes project were used to demonstrate the capabilities of the framework.
A framework for easy implementation of processing pipelines was developed and an R package for the example implementation of Fully Exploratory Network ICA was compiled. Test runs on data from 300 subjects demonstrated the computational advantages of a processing pipeline developed using the framework compared to non-parallelized processing, reducing computation time by a factor of 15.
The feasibility of computationally intensive exploratory analyses allows broader access to the tools for discovery science.
KeywordsMagnetic resonance imaging fMRI High performance computing Statistical computing
This study was funded by the Institute for the Study of Affective Neuroscience (ISAN), by the Oesterreichische Nationalbank (OeNB P13890, OeNB P13903, OeNB P12982) and by the Austrian Science Fund (FWF) as part of the Special Research Program 35 (SFB-35). The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC).
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