↓ Skip to main content

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

Overview of attention for article published in Frontiers in Neuroinformatics, March 2014
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

twitter
13 X users
googleplus
1 Google+ user
video
1 YouTube creator

Readers on

mendeley
135 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs
Published in
Frontiers in Neuroinformatics, March 2014
DOI 10.3389/fninf.2014.00024
Pubmed ID
Authors

Anders Eklund, Paul Dufort, Mattias Villani, Stephen LaConte

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 mm(3) 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/).

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 135 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 2%
United Kingdom 3 2%
United States 2 1%
Austria 1 <1%
Czechia 1 <1%
Italy 1 <1%
Canada 1 <1%
Malaysia 1 <1%
China 1 <1%
Other 1 <1%
Unknown 120 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 29%
Student > Ph. D. Student 29 21%
Professor > Associate Professor 13 10%
Student > Master 13 10%
Student > Bachelor 7 5%
Other 22 16%
Unknown 12 9%
Readers by discipline Count As %
Psychology 34 25%
Neuroscience 20 15%
Computer Science 14 10%
Medicine and Dentistry 12 9%
Engineering 10 7%
Other 21 16%
Unknown 24 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 May 2021.
All research outputs
#3,265,874
of 22,745,803 outputs
Outputs from Frontiers in Neuroinformatics
#194
of 743 outputs
Outputs of similar age
#33,941
of 220,988 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#5
of 21 outputs
Altmetric has tracked 22,745,803 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 743 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 220,988 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.