Code accompanying the paper "Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields" by Kupers, Kim, & Grill-Spector (2024) published in Nature Communications.
The goal of this project is to operationalize and elucidate how simultaneous and sequential visual stimuli are processed within population receptive fields, in space and time, and generate a lower response for simultaneous over sequential stimulus presentations of otherwise similar colorful peripheral square stimuli.
Kupers, E.R., Kim, I. & Grill-Spector, K. Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields. Nat Commun 15, 6885 (2024). https://doi.org/10.1038/s41467-024-51243-7
Data are stored on the Open Science Foundation URL: https://osf.io/rpuhs (main paper) and https://osf.io/e83az/ (supplemental data).
SpatiotemporalPRF toolbox: https://github.com/VPNL/spatiotemporalPRFs
VistaSoft toolbox: https://github.com/vistalab/vistasoft
NOTE: We are in the process of integrating the spatiotemporal pRF simulation code (stRet toolbox by Insub Kim) into the main vistasoft toolbox. We recommend using Insub Kim's forked repository (https://github.com/KimInsub/vistasoft) in the meantime.
- simseqRootPath.m
- downloadDataFromOSF.m
- s_simseqAnalysis_overview
- Preprocess nifti data to mrVista gray
- Prepare data for modelfits (make ROIs, concatenate condition parfiles, detrend, average, combine, and chop measured time series).
- Make model predictions given pRFs
- Fit model predictions to data
- Find best fitting R^2 for CST pRF exponent (grid fit)
- General visualization
- s_makeAllManuscriptFigures.m : Recreate manuscript figures 2,3,4,6,7,8 and supplementary figures with data and simulations
- stimulus/
- folder with code for stimulus generation and running MRI experiment (stim_mri)
- external/
- folder with borrowed functions from other toolboxes
- analysis/
- folder with analysis scripts and subfunctions, and figure plotting functions
NB. Participants S1 through S10 in the paper correspond to participants with different naming convention ("subjXX") from a larger dataset shared between multiple projects: [S1: subj01, S2: subj02, S3: subj03, S4: subj07, S5: subj08, S6: subj09, S7: subj10, S8: subj11, S9: subj12, S10: subj13]