Hipp Pattern Separation Code

This repository contains analysis code for the following paper:

Ian C. Ballard, Anthony D. Wager, Samuel M. McClure. Hippocampal Pattern Separation Supports Reinforcement Learning, 2019. Nature Communications.

Raw behavioral and MRI data for this study can be accessed at: https://openneuro.org/datasets/ds001590

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

Getting Started

The following software were used:

• FSL 5.0.8
• ANTS 1.9
• Lyman 0.0.10
• Freesurfer 5.3.3
• R 3.3.1

fMRI Analysis with Lyman

First, the anatomical image for each subject was processed using Freesurfer's recon-all tool to generate the cortical surface models.

Next, 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 sim.py and loc.py files included in this repository. This was performed with the following command line executions:

run_fmri.py -e sim -s subjects.txt -w preproc
run_fmri.py -e loc -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 -e sim -altmodel PE -s subjects.txt -w model reg ffx -regspace epi

For the localizer experiment, data were registered to the first run of the SIM experment by using the following command:

run_fmri.py -e loc -s subjects.txt -w model reg ffx -regspace epi -regexp sim

Behavioral Analysis Notebooks

analyze_behavioral_data.ipynb

Runs model-free analysis of behavioral data

RT_fitting.ipynb

Fits RL models to reaction time data, analyzes with leave-one-subject-out approach, and simulates values for Figure 2

RT_model_bayesian_model_comparison.ipynb

Compares RL models of reaction time data with Bayesian model comparison

all_rts.csv

Reaction time data

fMRI Analysis Notebooks

make_masks.ipynb

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

prepare_fieldmaps.ipynb

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

striatum_analysis.ipynb

Main analysis code for ROI analysis of the striatal feedback response.

run_fix.ipynb

Run’s FSL’s automatic ICA denoising algorithm.

run_melodic.ipynb

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

roi_figure.ipynb

This notebook generates the ROI mask figure in the Supplement.

compute_PSA.ipynb

Compute PSA matrices and conduct mixed-effects analysis on them

PSA_analysis.ipynb

Conduct permutation and control tests of regressions on PSA matrices

pattern_content_analysis.ipynb

Analysis for Figure 6. Uses localizer data to probe content of task representations.

Lyman software

The following scripts specify the analyses run by the Lyman ecosystem.

project.py

The lyman project file that defines general parameters for both the localizer and SIM experiments

sim.py; loc.py

The lyman experiment file for preprocessing and modeling the fMRI data.

sim-PE.py

contrasts for model of prediction errors

sim-betas.py; loc-betas.py

The lyman experiment file for first-level beta series modeling the fMRI data.

subjects.txt

The subject codes used in the processing.

License

All code is freely available under the BSD (3-clause) license.