spatiotemporalPRFs

Code repository to reconstruct, fit, and use spatiotemporal pRF models.

This code is used in the following two papers:

1. Characterizing Spatiotemporal Population Receptive Fields in Human Visual Cortex with fMRI by Insub Kim, Eline R. Kupers, Garikoitz Lerma-Usabiaga and Kalanit Grill-Spector (2024) Journal of Neuroscience, 44(2) e0803232023; https://doi.org/10.1523/JNEUROSCI.0803-23.2023

2. Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields by Eline R. Kupers, Insub Kim, and Kalanit Grill-Spector (2024) Nature Communications, 15, 6885. https://doi.org/10.1038/s41467-024-51243-7

Overview

• stPRFsRootPath.m : function to define root path
• stPredictBOLDFromStim.m : main function to reconstruct spatiotemporal pRFs and generate predicted time series (neural and BOLD) given the stimulus sequence

  • INPUTS:

    • Stim
    • Params struct with modeling options: what pRF model to use, what HRF, etc.

  • STEPS:

    1. get3DSpatiotemporalpRFs(params) --> create pRF filters (linear step)
    2. getPRFStimResponse(stim, PRF filters, params) --> stim * pRF = pRF response (linear step)
    3. applyReLU(pRFResponse(pRF response, params) --> relu(pRF response) (nonlinear step)
    4. applyNonlinearity(relu pRF Response, params) --> predicted neural response (nonlinear step)
    5. (Optional) Combine neural channels (e.g., transient on and transient off)
    6. (Optional) Normalize height of neural channels to max = 1.
    7. getPredictedBOLDresponse(predicted neural response, HRF, params) --> predicted neural response * HRF = predited BOLD response (linear step)

  • OUTPUTS:

    • Predicted time series: linear filtered pRF response, (final) neural response, and BOLD response
    • pRFs
    • HRF
    • params

Folder organization

• functions
  • external
  • utils
  • wrappers
  • example

Example:

• run example/s_simpleTutorial.m!