BrainStat : A toolbox for statistical analysis of neuroimaging data¶
Welcome to BrainStat’s documentation!
BrainStat is a toolbox for the statistical analysis and context decoding of neuroimaging data. It implements both univariate and multivariate linear models and interfaces with the BigBrain Atlas, Allen Human Brain Atlas and Nimare databases. BrainStat flexibly handles common surface, volume, and parcel level data formats, and provides a series of interactive visualization functions. The toolbox has been implemented in both Python and MATLAB, the two most widely adopted programming languages in the neuroimaging and neuroinformatics communities. It is openly available, and documented here.
Developers¶
Sara Lariviere - MICA Lab, Montreal Neurological Institute
Şeyma Bayrak - Max Planck Institute for Human Cognitive and Brain Sciences
Reinder Vos de Wael - MICA Lab, Montreal Neurological Institute
Oualid Benkarim - MICA Lab, Montreal Neurological Institute
Raul Cruces - MICA Lab, Montreal Neurological Institute
Jessica Royer - MICA Lab, Montreal Neurological Institute
Peer Herholz - Montreal Neurological Institute
Seok-Jun Hong - Sungkyunkwan University
Sofie Valk - Max Planck Institute for Human Cognitive and Brain Sciences
Boris Bernhardt - Montreal Neurological Institute
License¶
The BrainStat source code is available under the BSD (3-Clause) license.
Support¶
If you have problems installing the software or questions about usage and documentation, or something else related to BrainStat, you can post to the Issues section of our repository.
Installation Guide¶
BrainStat is available in Python and MATLAB.
Python installation¶
BrainStat requires Python 3.7+. Assuming you have the correct version of Python installed and aliased to python, you can install BrainStat by running the following
python -m pip install brainstat
Python Dependencies¶
If you want to use the meta analysis module, you’ll also have to download and install the package pyembree. This package is only available through conda-forge:
conda install -c conda-forge pyembree
MATLAB installation¶
This toolbox is compatible with MATLAB versions R2019b and newer.
We recommend installing the toolbox through the Mathworks FileExchange. Simply download the file as a toolbox and open the .mltbx file in MATLAB. Alternatively, you can install the same .mltbx file from our GitHub Releases.
If you don’t want to install BrainStat as a MATLAB Toolbox, you can also simply download the repository and run the following in MATLAB:
addpath(genpath('/path/to/BrainStat/brainstat_matlab/'))
If you want to load BrainStat every time you start MATLAB, type edit
startup and append the above line to the end of this file.
MATLAB Dependencies¶
BrainStat relies on functionality included in the BrainSpace toolbox. Please see the BrainSpace installation guide for installation instructions.
If you wish to open gifti files (required for data loader function) you will also need to install the gifti library.
Python Index¶
Python Tutorials¶
API¶
Below are links to descriptions of the important MATLAB functions used in BrainStat.
The API for the surface visualization function plot_hemispheres can be found
here.
Neuroimaging statistics toolbox.
Modules
BrainStat’s context decoding module. |
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Data included with BrainStat. |
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Module for the handling of meshes and mesh data. |
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The statistics tools of BrainStat |
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Unit tests and their data generation. |
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Functions required for the BrainStat Tutorials |
MATLAB Index¶
MATLAB Tutorials¶
For MATLAB tutorials, we recommend viewing these either through the Examples tab
on our FileExchange page, or by opening the files in MATLAB
(PACKAGE_DIRECTORY/tutorials). Alternatively, we’ve also included copies
of these live scripts in ReadTheDocs:
Funding¶
Our research is kindly supported by:
Canadian Institutes of Health Research (CIHR)
National Science and Engineering Research Council of Canada (NSERC)
Azrieli Center for Autism Research
The Montreal Neurological Institute]
Canada Research Chairs Program
BrainCanada
SickKids Foundation
Helmholtz Foundation
We would also like to thank these funders for training/salary support
Savoy Foundation for Epilepsy (to RV)
Richard and Ann Sievers Award (to RV)
Healthy Brain and Healthy Lives (to OB)
Fonds de la Recherche du Quebec - Sante (to BB)
Credits¶
Some references that are incorporated into BrainStat
SurfStat references¶
Worsley KJ et al. (2009) A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory. NeuroImage, Volume 47, Supplement 1, July 2009, Pages S39-S41. https://doi.org/10.1016/S1053-8119(09)70882-1
Chung MK et al. (2010) General Multivariate Linear Modeling of Surface Shapes Using SurfStat Neuroimage. 53(2):491-505. doi: 10.1016/j.neuroimage.2010.06.032
Random field theory references¶
Adler RJ and Taylor JE (2007). Random fields and geometry. Springer. Hagler DJ Saygin AP and Sereno MI (2006). Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data. NeuroImage, 33:1093-1103.
Hayasaka S, Phan KL, Liberzon I, Worsley KJ and Nichols TE (2004). Non-Stationary cluster size inference with random field and permutation methods. NeuroImage, 22:676-687.
Taylor JE and Adler RJ (2003), Euler characteristics for Gaussian fields on manifolds. Annals of Probability, 31:533-563.
Taylor JE and Worsley KJ (2007). Detecting sparse signal in random fields, with an application to brain mapping. Journal of the American Statistical Association, 102:913-928.
Worsley KJ, Andermann M, Koulis T, MacDonald D, and Evans AC (1999). Detecting changes in non-isotropic images. Human Brain Mapping, 8:98-101.