Denoise Benchmark (DNB) is an architecture for testing and comparing denoising methods for task-based fMRI. The performance metric is cross-validation accuracy, whereby a denoising method is evaluated based on how accurately its estimate of task-related responses predict held-out data. DNB is written in MATLAB and consists of three main components: (1) fMRI data (21 datasets available) (2) Code framework for automatic evaluation of denoising methods (3) Implementations of several denoising methods DNB is developed by Kendrick Kay (kendrick@post.harvard.edu).
To get started with DNB, add it to your MATLAB path: addpath(genpath('DNB')); Then, change to the DNB directory and try running the example scripts.
For additional information, please visit: http://kendrickkay.net/DNB/
Terms of use: This content is licensed under a Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/3.0/us/). You are free to share and adapt the content as you please, under the condition that you cite the appropriate manuscript (see below).
If you use the DNB in your research, please cite the following paper: Kay KN, Rokem A, Winawer J, Dougherty RF and Wandell BA (2013) GLMdenoise: a fast, automated technique for denoising task-based fMRI data. Front. Neurosci. 7:247. doi: 10.3389/fnins.2013.00247
History of major code changes:
- 2013/07/03 - Version 1.0.
These functions download various files not included in the DNB repository:
- DNBdownloaddata.m - Download datasets to the DNBdata directory
- DNBdownloadresults.m - Download results to the DNBresults directory
These are top-level functions:
- DNBrun.m - Evaluate a specific denoising method on a specific dataset
- DNBloaddata.m - Load in a dataset (requires the data to have been downloaded)
- DNBevaluatemethod.m - A helper function called by DNBrun.m
These are implementations of various denoising methods:
- DNBmethod_GLMdenoise.m - GLMdenoise
- DNBmethod_GLMdenoiseblind.m - GLMdenoise with no exclusion of task-related voxels
- DNBmethod_GLMdenoisescramble.m - GLMdenoise but with phase-scrambled noise regressors
- DNBmethod_GLMglobalsignal.m - GLMstandard with a global signal regressor
- DNBmethod_GLMmelodic.m - ICA-based denoising using FSL's MELODIC utility
- DNBmethod_GLMmelodicconservative.m - GLMmelodic using a conservative threshold for identification of noise components
- DNBmethod_GLMmelodicliberal.m - GLMmelodic using a liberal (aggressive) threshold for identification of noise components
- DNBmethod_GLMmotion.m - GLMstandard with motion parameters as regressors
- DNBmethod_GLMomnibus.m - A combination of RETROICOR+RVHRCOR, the use of a global signal regressor, and the use of motion regressors
- DNBmethod_GLMretro1.m - RETROICOR followed by GLMstandard
- DNBmethod_GLMretro2.m - RETROICOR+RVHRCOR followed by GLMstandard
- DNBmethod_GLMstandard.m - GLMdenoise with no noise regressors
These directories contain various utilities:
- external - A directory containing external MATLAB toolboxes
- utilities - A directory containing various utility functions
These are additional files:
- example1.m - An example script showing how to run a denoising method on a dataset
- example2.m - An example script that loads the results of the DNB and generates figures illustrating these results
- README - The file you are reading
To maintain compactness, the DNB repository omits large files. These large files can be automatically downloaded using DNBdownloaddata.m and DNBdownloadresults.m, or can be manually downloaded at http://www.cmrr.umn.edu/~kendrick/DNBdata/ http://www.cmrr.umn.edu/~kendrick/DNBresults/
Here is a summary of the downloadable files:
- DNBdata - This directory contains the fMRI data
- datasetNN.mat - Data for dataset NN
- datasetNN_physio.tar - Physiological data for dataset NN (available for some datasets)
- DNBresults - This directory contains the DNB results
- results.mat - All benchmarking results
- figures.tar - Figures illustrating the results
DNB has several code dependencies besides what is provided in the DNB repository. These dependencies are as follows: (1) GLMdenoise (http://github.com/kendrickkay/GLMdenoise/) (2) FSL (http://fsl.fmrib.ox.ac.uk/fsl/) Only the 'melodic' and 'fsl_regfilt' utilities from FSL are used. It is assumed that a command-line call to 'melodic' and 'fsl_regfilt' will invoke the appropriate executables.
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Copyright (c) 2013, Kendrick Kay All rights reserved.
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