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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

Link to static notebook

Warp ROIs from group space to individual subject sufaces and write binary masks in functional space.

Link to static notebook

Create images with opposite phase encoding directions for topup field correction.

Link to static notebook

All of the actual analyses are contained within this notebook.

Link to static notebook

For some subjects, bbregister failed and we needed to identify bad registrations and potentially create hand-made initializations.

Link to static notebook

Main analysis code for ROI analysis for the manuscript.

Link to static notebook

Run’s FSL’s automatic ICA denoising algorithm.

Link to static notebook

Run’s FSL’s ICA decomposition on pre-processed data.

Link to static notebook

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.