Analyses for Waskom et al. (2014) J Neurosci

This repository contains analysis code for the following paper:

Waskom, M.L., Kumaran, D., Gordon, A.M., Rissman, J., Wagner, A.D. (2014) Frontoparietal representations of task context support the flexible control of goal-directed cognition. Journal of Neuroscience. 34(32): 10743-10755.

The paper itself is availible on the Journal of Neuroscience website.

The code is contained within several IPython notebooks that performed the analyses and generated all figures used in the manuscript.

Preprocessing

Two processing steps were performed outside the scope of the notebooks. First, the anatomical image for each subject was processed using Freesurfer to generate the cortical surface models. Specifically, the following command line was used for each subject:

recon-all -s $subject -all -3T

Additionally, the functional data were preproccesed with FSL, Freesurfer, and Nipype using lyman. The processing used the experiment parameters in the dksort.py file included in this repository and was performed with the following command line:

run_fmri.py -s subjects.txt -w preproc reg -t -reg epi -unsmoothed

Once those commands have been executed, every analysis can be generated using these notebooks.

###Analysis Notebooks

To reproduce the analyses in the manuscript, the notebooks should be executed in the following order.

Mask_Generation.ipynb

Link to static notebook

Using the label files in roi/, warp the ROIs from group space to individual subject sufaces and write binary masks in functional space.

Event_Info_Generation.ipynb

Link to static notebook

Read the .mat files generated by PsychToolBox and create

• a master .csv file with behavioral and design information and

• specific event files for the decoding analyses.

Behavioral_and_Decoding_Analyses.ipynb

Link to static notebook

All of the actual analyses are contained within this notebook.

Searchlight_Analysis.ipynb

Link to static notebook

The searchlight analysis is performed separately within this notebook and the process_searchlight.py script.

Design_Figure.ipynb

Link to static notebook

This notebook generates the experimental design figure in the manuscript.

ROI_Figure.ipynb

Link to static notebook

This notebook generates the ROI figure in the manuscript. Unlike the other notebooks, it requires PySurfer.

Software Versions

The versions of all Python packages that were installed when these analyses were performed are contained in the dependencies.txt file.

To facilitate reproduction of these results, the minimum set of required packages are contained in the conda_requirements.txt and pip_requirements.txt files. These can be used to set up an analysis environment using conda and pip respectively. Please note that an effort was made for accuracy, but these files were created after the bulk of the analyses had been run and cached.

Other software versions are recorded here:

MRI Processing

• Freesurfer: 5.3
• FSL: 5.0

R Packages

• R: 3.0.2
• lme4: 0.999999-2

Lyman

Two different git snapshots of lyman correspond most directly to the analyses.

• Preprocessing using run_fmri.py
• Decoding analyses using lyman.mvpa

Other Data

rois/

Freesurfer .label files defining the ROIs in common surface space. These labels were created from files distributed by Freesurfer.

figures/

Figures that are output by these notebooks.

project.py

The lyman project file that was used to process the data.

dksort.py

The lyman experiment file for preprocessing the fMRI data.

subjects.txt

The subject codes used in the processing.

dependencies.txt

A pip dependencies file that can be used to install a virtual environment with software versions that were used for these analyses.

License

The code in this notebook is under a BSD (3-clause) license.