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):

https://github.com/adelavega/neurosynth-mfc

https://github.com/adelavega/neurosynth-lfc

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.

Requirements

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:

• Pandas
• nilearn
• seaborn

Unzip pre-generated Neurosynth dataset prior to analysis