Copyright 2018 - 2019 Sebastiaan Mathôt (https://www.cogsci.nl/smathot)
This repository contains code to perform population-receptive-field (PRF) mapping based on the retinotopic-mapping data from the StudyForrest project. PRF mapping is a technique to estimate the spatial coordinates and standard deviation of receptive fields of voxels in fMRI data.
This code has been developed on Ubuntu 18.04 with FSL 5.0 installed through NeuroDebian. However, all code should run with minor adjustments on any system. The following packages are required:
- Python libraries for numeric computing and neuroimaging:
- nilearn
- sklearn
- nibabel
- nipy
- datamatrix
- FSL 5.0
datalad get inputs/studyforrest-data-aligned/sub-*/in_bold3Tp2/sub-*_task-retmap*_run-*_bold.nii.gz
datalad get inputs/studyforrest-data-aligned/sub-*/in_bold3Tp2/sub-*_task-avmovie_run-*_bold_mcparams.txt
datalad get inputs/studyforrest-data-templatetransforms/templates/grpbold3Tp2/brain.nii.gz
datalad get inputs/studyforrest-data-templatetransforms/sub-*/bold3Tp2/in_grpbold3Tp2/subj2tmpl_warp.nii.gz
datalad get inputs/studyforrest-data-phase2/stimuli/retinotopic_mapping/*.mkv
datalad get inputs/studyforrest-data-phase2/sub-*/ses-movie/func/sub-*_ses-movie_task-movie_run-*_recording-eyegaze_physio.tsv.gz
The folder inputs/videos should contain the video fragments of forrest gump as used during recording. These are available upon request from the maintainers of the Study Forrest project, but are not included in the repository due to copyright issues.
The data from StudyForrest is in a participant-specific space. The scripts below convert this data to MNI space, which is what we will use for the rest of the scripts.
./code/warp-retmap.sh
./code/warp-movie.sh
The following scripts need to be executed in order. The will generate various intermediate files, which are stored in the outputs folder, and which are used by the analysis notebook.
python3 code/create-prf-maps.py # -> outputs/prf-matrix.pkl
python3 code/create-luminance-traces.py # -> inputs/luminance-traces/sub-*/run-*.csv
python3 code/create-pupil-traces.py # -> inputs/pupil-traces/sub-*/run-*.csv
python3 code/create-correlation-matrices.py # -> outputs/outputs/correlation-matrix.csv
# outputs/correlation-matrix.pkl
# outputs/longish-correlation-matrix.csv
Open code\main-analysis.ipynb in JupyterLab/ Jupyter Notebook.
This code is distributed under the terms of the GNU General Public License 3. The full license should be included in the file COPYING, or can be obtained from: