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.
Installing BrainSMASH requires:
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.
BrainSMASH is most easily installed using pip:
pip install brainsmashYou may also clone and install the source files manually:
git clone https://github.com/murraylab/brainsmash.git
cd brainsmash
python setup.py installThe BrainSMASH source code is available under the GNU General Public License v3.0.
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. Generative modeling of brain maps with spatial autocorrelation. Neuroimage, 220 (2020).
return_data keyword argument to both variogram-plotting functions in the eval module, such that users can easily customize variogram plots to their liking and/or compute numerical goodness-of-fit metrics. mapgen.Base thanks to contributions from Ross Markello. Note that generating surrogate maps before vs. after this update with the same random seed will yield different results due to a minor implementational change.mapgen.stats module.geo.volume method to compute 3D Euclidean distance matrix from an arbitrary set of voxel coordinates, per several requests.unassigned_value kwarg to cortex and subcortex.geo.subcortex to have parallel structure with cortex.geo.cortex function with Ross' new implementation, in a backwards-compatible fashion.