GPU-based parallel group ICA for functional magnetic resonance data

Comput Methods Programs Biomed. 2015 Apr;119(1):9-16. doi: 10.1016/j.cmpb.2015.02.002. Epub 2015 Feb 11.

Abstract

The goal of our study is to develop a fast parallel implementation of group independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data using graphics processing units (GPU). Though ICA has become a standard method to identify brain functional connectivity of the fMRI data, it is computationally intensive, especially has a huge cost for the group data analysis. GPU with higher parallel computation power and lower cost are used for general purpose computing, which could contribute to fMRI data analysis significantly. In this study, a parallel group ICA (PGICA) on GPU, mainly consisting of GPU-based PCA using SVD and Infomax-ICA, is presented. In comparison to the serial group ICA, the proposed method demonstrated both significant speedup with 6-11 times and comparable accuracy of functional networks in our experiments. This proposed method is expected to perform the real-time post-processing for fMRI data analysis.

Keywords: GPGPU; Group ICA; Parallel computing; fMRI.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain / physiology*
  • Computational Biology
  • Electronic Data Processing
  • Functional Neuroimaging / statistics & numerical data*
  • Humans
  • Image Interpretation, Computer-Assisted
  • Magnetic Resonance Imaging / statistics & numerical data*
  • Neural Networks, Computer
  • Principal Component Analysis
  • Software