Analyses for More is meaningful: The magnitude effect in intertemporal choice depends on self-control
This repository contains analysis code for the following paper:
Ian C. Ballard, Bokyung Kim, Anthony Liatsis, Jonathan D. Cohen, Samuel M. McClure. More is meaningful: The magnitude effect in intertemporal choice depends on self-control .
The code is contained within several IPython notebooks that performed the analyses and generated all figures used in the manuscript.
First, the anatomical image for each subject was processed using Freesurfer to generate the cortical surface models. The code for this can be found in recon-all.ipynb.
Next, for the Princeton subjects we prepare fieldmap images from two images taken with opposite phase encoding directions. Details can be found here: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup. The code for this analysis can be found in prepare_fieldmaps.ipynb.
Next, the functional data were preproccesed with FSL, Freesurfer, and
Nipype using lyman. The
processing used the experiment parameters in the mag.py file included in
this repository. This must be run separately for ASU and Princeton subjects due to two differences described in mag.py. This was performed with the following command line.
run_fmri.py -s subjects.txt -w preproc
Next, we conducted an ICA decomposition (run_melodic.ipynb) and automatically classified and removed noise components (run_fix.ipynb). Details can be found at http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX/UserGuide.
Next, first-level models were fit to each run, registered to the space of the first functional run, and a fixed effects analysis was conducted.
run_fmri.py -s subjects.txt -w model reg ffx -regspace epi
Once those commands have been executed, every analysis can be generated using these notebooks.
All behavioral analysis for the Hunger and Justify experiments can be found in behavioral/analyze_behavioral_studies.ipynb. Raw data for both datasets are also stored here as csv files.
###fMRI Analysis Notebooks
Warp ROIs from group space to individual subject sufaces and write binary masks in functional space.
Create images with opposite phase encoding directions for topup field correction.
All of the actual analyses are contained within this notebook.
For some subjects, bbregister failed and we needed to identify bad registrations and potentially create hand-made initializations.
Main analysis code for ROI analysis for the manuscript.
Run’s FSL’s automatic ICA denoising algorithm.
Run’s FSL’s ICA decomposition on pre-processed data.
This notebook generates the ROI figure in the manuscript.
- Freesurfer: 5.3
- FSL: 5.0
- R: 3.3.1
v0.0.10
The lyman project file that was used to process the data.
The lyman experiment file for preprocessing the fMRI data.
The lyman experiment file for first-level modeling the fMRI data.
The subject codes used in the processing.