GPU-based parallel group ICA for functional magnetic resonance data.

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.

authors

Jing Y,Zeng W,Wang N,Ren T,Shi Y,Yin J,Xu Q

doi

10.1016/j.cmpb.2015.02.002

subject

Has Abstract

pub_date

2015-04-01 00:00:00

pages

9-16

issue

1

eissn

0169-2607

issn

1872-7565

pii

S0169-2607(15)00025-5

journal_volume

119

pub_type

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