eigenstrapping 0.0.1.10

For generating surrogate brain maps with spatial autocorrelation using geometric eigenmodes.

The eigenstrapping toolbox is designed to help researchers generate statistically-rigorous models for null hypothesis testing between brain maps using non-local spectral shape descriptors - or geometric eigenmodes. Documentation can be found here.

Features

• A growing library of eigenmodes of standard surfaces and surface densities (fsaverage, fsLR)

• Cortical and subcortical null models for assessing statistical correspondence between brain maps

• Generation of geometric eigenmodes on user-derived surfaces

Installation Guide

Eigenstrapping is available in Python 3.7+. MATLAB version coming soon!

Dependencies

To install eigenstrapping, the following Python packages are required:

• numpy

• scipy

• scikit-learn

• lapy

• matplotlib

• nibabel

• nilearn

• pandas

• brainspace

• netneurotools

nibabel and nilearn are required for surfaces and volumes. matplotlib is only required for fitting plots in fit.py and some of the surface plotting functions. Future improvements will reduce the number of dependencies needed.

Additional dependencies

• Connectome Workbench

• FreeSurfer 6.0.0+

Installation

eigenstrapping can be installed using pip:

pip install eigenstrapping

Alternatively, you can install the package from the Github repository:

git clone https://github.com/SNG-newy/eigenstrapping.git
cd eigenstrapping
python setup.py install

Citing

When using eigenstrapping, please cite the following manuscript:

• null

And please also cite the papers for the method that we use to calculate eigenmodes on the surface:

• Laplace-Beltrami spectra as ‘Shape-DNA’ of surfaces and solids. Reuter M, Wolter F-E, Peinecke N. Computer-Aided Design. 2006;38(4):342-366. http://dx.doi.org/10.1016/j.cad.2005.10.011

• BrainPrint: a discriminative characterization of brain morphology. Wachinger C, Golland P, Kremen W, Fischl B, Reuter M. Neuroimage. 2015;109:232-48. http://dx.doi.org/10.1016/j.neuroimage.2015.01.032 http://www.ncbi.nlm.nih.gov/pubmed/25613439

License information

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License cc-by-nc-sa. The full license can be found in the LICENSE file in the eigenstrapping distribution.