Metadata-Version: 1.0
Name: hrf_opt
Version: 1.0.3
Summary: Optimize hemodynamic response function parameters.
Home-page: https://github.com/MSchnei/hrf_opt
Author: Marian Schneider
Author-email: marian.schneider@maastrichtuniversity.nl
License: GNU General Public License Version 3
Description: hrf_opt
        =======
        
        Optimize hemodynamic response function parameters.
        
        A free & open source package for finding best-fitting hemodynamic
        response function (HRF) parameters for fMRI data. Optimization takes
        place within the framework of population receptive field (pRF)
        parameters.
        
        The fitting process requires, for every voxel of fMRI data, optimized
        pRF parameters. These can be obtained using
        `pyprf_feature <https://github.com/MSchnei/pyprf_feature>`__.
        
        Installation
        ------------
        
        For installation, follow these steps:
        
        0. (Optional) Create conda environment
        
        .. code:: bash
        
            conda create -n env_hrf_opt python=2.7
            source activate env_hrf_opt
            conda install pip
        
        1. Clone repository
        
        .. code:: bash
        
            git clone https://github.com/MSchnei/hrf_opt.git
        
        2. Install hrf_opt with pip
        
        .. code:: bash
        
            pip install /path/to/cloned/hrf_opt
        
        How to use
        ----------
        
        1. Run pyprf_feature to obtain an initial guess of the pRF parameters
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        See `here <https://github.com/MSchnei/pyprf_feature>`__ for more
        information on how to use pyprf_feature. In brief, open a terminal and
        run:
        
        ::
        
            pyprf_feature -config path/to/custom_pRF_config.csv
        
        2. Obtain model responses for every voxel for best-fitting pRF model
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        When pyprf_feature is done, run it again with -save_tc and -mdl_rsp
        flag. This will save the fitted pRF model time courses and corresponding
        neural responses to disk:
        
        ::
        
            pyprf_feature -config path/to/custom_pRF_config.csv -save_tc -mdl_rsp
        
        3. Adjust the csv file for hrf_opt
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Adjust the information in the config_default.csv file in the hrf_opt
        folder, such that the provided information is correct. It is recommended
        to make a specific copy of the csv file for every subject.
        
        4. Run hrf_opt
        ~~~~~~~~~~~~~~
        
        Open a terminal and run:
        
        ::
        
            hrf_opt -config path/to/custom_hrf_opt_config.csv
        
        References
        ----------
        
        This application is based on the following work:
        
        -  Dumoulin, S. O., & Wandell, B. A. (2008). Population receptive field
           estimates in human visual cortex. NeuroImage, 39(2), 647–660.
           https://doi.org/10.1016/j.neuroimage.2007.09.034
        
        -  Harvey, B. M., & Dumoulin, S. O. (2011). The Relationship between
           Cortical Magnification Factor and Population Receptive Field Size in
           Human Visual Cortex: Constancies in Cortical Architecture. Journal of
           Neuroscience, 31(38), 13604–13612.
           https://doi.org/10.1523/JNEUROSCI.2572-11.2011
        
        License
        -------
        
        The project is licensed under `GNU General Public License Version
        3 <http://www.gnu.org/licenses/gpl.html>`__.
        
Keywords: pRF,fMRI,retinotopy
Platform: UNKNOWN
