BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs

Anders Eklund,Mattias Villani,Paul Dufort,Stephen Laconte

doi:10.3389/fninf.2014.00024

Abstract

Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/).

Highlights

Functional magnetic resonance imaging has become the de facto standard methodology in contemporary efforts to image the functioning of the human brain in both health and disease
BROCCOLI was used with an Intel CPU, an Nvidia graphics processing units (GPUs) and an AMD GPU, to demonstrate that the same code can run on different types of hardware
The following software packages were compared to BROCCOLI: SPM8, FSL 5.0.4 (Smith et al, 2004) and AFNI (Cox, 1996)

Summary

Introduction

Functional magnetic resonance imaging (fMRI) has become the de facto standard methodology in contemporary efforts to image the functioning of the human brain in both health and disease. FMRI studies are trending toward larger numbers of subjects to increase their statistical power (Eklund et al, 2012a; Thyreau et al, 2012; Button et al, 2013) sometimes aided by a proliferation of data sharing initiatives (Biswal et al, 2010; Poldrack et al, 2013) 1,2 that provide open access to large amounts of data. AFNI has direct support for running some functions in parallel on several CPU cores, using the open multi-processing (OpenMP) library; FSL can take advantage of several computers or CPU cores, by installing packages like Condor or GridEngine, and has recently added graphics processing unit (GPU) support for MCMC based diffusion tensor analysis (Hernandez et al, 2013); and Huang et al (2011) recently proposed to accelerate image

Methods

Results

Conclusion