Brain Surrogate Maps with Autocorrelated Spatial Heterogeneity.
Project description
BrainSMASH
BrainSMASH (Brain Surrogate Maps with Autocorrelated Spatial Heterogeneity) is a Python-based computational platform for statistical testing of spatially autocorrelated brain maps. At the heart of BrainSMASH is the ability to simulate surrogate brain maps with spatial autocorrelation that is matched to spatial autocorrelation in a target brain map. Additional utilities are provided for users using Connectome Workbench style surface-based neuroimaging files.
Exhaustive documentation can be found here.
Dependencies
Installing BrainSMASH requires:
- Python 3+
- numpy
- scipy
- pandas
- nibabel
- matplotlib
- scikit-learn
If you wish to use the additional utilities provided for Connectome Workbench users, you must have
Connectome Workbench installed with the wb_command
executable locatable in your
system PATH environment variable.
Installation
BrainSMASH is most easily installed using pip:
pip install brainsmash
You may also clone and install the source files manually:
git clone https://github.com/murraylab/brainsmash.git
cd brainsmash
python setup.py install
License
The BrainSMASH source code is available under the GNU General Public License v3.0.
Reference
Please cite the following paper if you use BrainSMASH in your research:
Burt, J.B., Helmer, M., Shinn, M.W., Anticevic, A., Murray, J.D. (2020). Generative modeling of brain maps with spatial autocorrelation. Neuroimage (In Press).
Core development team
- Joshua B Burt, Murray Lab - Yale University
- John D Murray, Murray Lab - Yale University
Contributors
Ross Markello - Montreal Neurological Institute
Change Log
- 0.6.0 Added
unassigned_value
kwarg tocortex
andsubcortex
. - 0.5.2 Introduced a bug during the last bug fix.
- 0.5.1 Fixed bug which caused distances to be written to file one-dimensionally.
- 0.5.0 Updated
geo.subcortex
to have parallel structure withcortex
. - 0.4.0 Replaced
geo.cortex
function with Ross' new implementation, in a backwards-compatible fashion. - 0.3.0 Added ability to set seed/random state in Base and Sampled classes.
- 0.2.0 Added Ross Markello's implementation of Dijkstra's algorithm for efficiently computing surface-based distances.
- 0.1.1 Fixed bug in NaN handling.
- 0.1.0 Added goodness-of-fit metrics to stats module.
- 0.0.9 Fixed bug in Sampled.sampled.permute_map().
- 0.0.8 Relaxed nibabel version dependency.
- 0.0.7 Removed console print statements.
- 0.0.6 Fixed masked dense array handling.
- 0.0.1 Initial beta release.
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