Functional parcellation for the default mode network (DMN): a large-scale meta-analysis

This repository contains code, data, and results for Wang, Taren, Tepfer, & Smith (bioRxiv, 2019). Preprint of the manuscript: https://www.biorxiv.org/content/10.1101/225375v3

The code is based on previous work parcellating MFC and LFC (de la Vega et al., 2016; 2017):

We thank Alejandro de la Vega for sharing his code online and answering our technical questions!

Figures for the manuscript (in .eps format) available at figures/

The main results can be produced following Clustering, Coactivation and Functional preference profiles.

Python 2.7.x

For analysis:

Neurosynth tools (github.com/neurosynth/neurosynth)

Note: PyPI is acting strange so install directly from github: pip install git+https://github.com/neurosynth/neurosynth.git
Scipy/Numpy (Easiest way is using miniconda distribution)
Scikit-learn
joblib
nibabel 1.x (this is very important for the code to work!)

For visualization:

Unzip pre-generated Neurosynth dataset prior to analysis

Name		Name	Last commit message	Last commit date
Latest commit History 23 Commits
.ipynb_checkpoints		.ipynb_checkpoints
coactivation		coactivation
figures		figures
images		images
masks		masks
silhouette		silhouette
.gitignore		.gitignore
Clustering.ipynb		Clustering.ipynb
Coactivation.ipynb		Coactivation.ipynb
DMN_Figures.ipynb		DMN_Figures.ipynb
Functional preference profiles.ipynb		Functional preference profiles.ipynb
LICENSE		LICENSE
README.md		README.md
Silhouette.ipynb		Silhouette.ipynb
classification.py		classification.py
coactivation.py		coactivation.py
colors.py		colors.py
plotting.py		plotting.py
requirements.txt		requirements.txt
tools.py		tools.py
utils.py		utils.py
virtualenv-DMN.ipynb		virtualenv-DMN.ipynb

DVS-Lab/dmn-parcellation