Analyses from the Shared States project.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
ANALYSES
MATERIALS_PROCEDURES_DESIGN
RESULTS/Voxel_tables
.gitignore
Dockerfile
README.md
patternanalysis_methods_example.pdf
sharedstates_fullarticle_draft.pdf

README.md

SharedStates

This repository contains the scripts and notebooks of the SharedStates project, corresponding to the submitted article: Oosterwijk, Snoek, Rotteveel, Barrett, & Scholte. (in prep). Shared states: Using MVPA to test neural overlap between self-focused emotion imagery and other-focused emotion understanding.


Dependencies

Most of the analyses documented in the repository depend on the scikit-learn package (http://scikit-learn.org/) and the skbold package (https://github.com/lukassnoek/skbold). The latter package is being developed alongside my PhD-project and includes some tools to make the construction of machine learning pipelines easier. Since submitting the paper for the first time, much of the code in the skbold package has changed. Therefore, the main (original) analyses depend on and older version of skbold, which has been branched off under the name 'SharedStates'. To install this 'version' of the package, you can use pip:

$ pip install git+https://github.com/lukassnoek/skbold.git@SharedStates

In response to several reviews, we have included additional analyses which depend on a more recent version of the skbold package, which can be installed as follows:

$ pip install git+https://github.com/lukassnoek/skbold.git@master

Repo structure/contents:

  • ANALYSES. This folder contains all scripts for the project.
  • MATERIALS_PROCEDURES_DESIGN. This folder contains the Presentation (Neurobs) scripts used for stimulus presentation (Self-focused task and Other-focused task) and the stimulus materials.

Docker image

To make the analyses from this study fully reproducible, I'm working on a Dockerfile to create a Linux environment with the appropriate software (FSL, Python packages, etc.), data (fMRI-data & metadata), and analysis-scripts (from the current repository). The image can be pulled as follows (warning: about 4 GB in size!):

$ docker pull lukassnoek/nibase

^ NOT YET FUNCTIONAL!