The ENIGMA TOOLBOX is an open source repository for (i) accessing 100+ ENIGMA-derived statistical maps, (ii) visualizing and manipulating cortical and subcortical surface data, and (iii) contextualizing neuroimaging findings at the microscale (using postmortem gene expression and cytoarchitecture) and macroscale (using structural and functional connectome data).
Check out our expandable online documentation at http://enigma-toolbox.readthedocs.io to learn how to:
🔌 install ENIGMA Toolbox in Python or Matlab
💯 load over 100 case-control datasets from several ENIGMA Working Groups
🥍 perform cross-disorder analyses
🚢 import your own data
🧩 map parcellated data to and from vertexwise space
🥡 export data and data results to several file formats
🧠 visualize and manipulate cortical and subcortical surface data
🔗 load preprocessed connectivity data from the Human Connectome Project (HCP)
🧬 load gene expression data from the Allen Human Brain Atlas
🎣 query pre-defined lists of disease-related genes from published GWAS
🔬 stratify neuroimaging findings according to BigBrain statistical moments and gradient
📱 stratify neuroimaging findings according to cytoarchitectural variations
🛩 build hub susceptibility models
📌 identify disease epicenters
🌪 perform spin permutation tests on parcellated data
To install the Toolbox in Python, run the following in your terminal:
git clone https://github.com/MICA-MNI/ENIGMA.git
cd ENIGMA
python setup.py installTo install the Toolbox in Matlab, run the following in your terminal:
git clone https://github.com/MICA-MNI/ENIGMA.gitAnd then simply run the following in Matlab:
addpath(genpath('/path/to/ENIGMA/matlab/'))Please acknowledge this work using the citation below:
Larivière, S., Paquola, C., Park, By. Royer, J., Wang, Y., Benkarim, O., Vos de Wael, R., Valk, S., Thomopoulos, S.I., Kirschner, M., Lewis, L.B., Evans, A.C., Sisodiya, S.M., McDonald, C.R., Thompson, P.T, Bernhardt, B.C.. The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets. Nat Methods 18, 698–700 (2021). https://doi.org/10.1038/s41592-021-01186-4
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