Metadata-Version: 1.0
Name: pyprf_motion
Version: 1.0.4
Summary: Population receptive field analysis for motion-sensitive                     early- and mid-level visual cortex.
Home-page: https://github.com/MSchnei/pyprf_motion
Author: Marian Schneider, Ingo Marquardt
Author-email: marian.schneider@maastrichtuniversity.nl
License: GNU General Public License Version 3
Description: pyprf_motion
        ============
        
        Population receptive field analysis for motion-sensitive early- and
        mid-level visual cortex.
        
        This is an extension of the `pyprf
        package <https://github.com/ingo-m/pypRF>`__. Compared to pyprf,
        pyprf_motion offers stimuli that were specifically optimized to elicit
        responses from motion-sensitive areas. On the analysis side,
        pyprf_motion offers some additional features made necessary by the
        different stimulation type (model positions defined in polar
        coordinates, sub-TR temporal resolution for model creation,
        cross-validation for model fitting) at the cost of some speed and
        flexibility. There is currently no support for GPU.
        
        Installation
        ------------
        
        For installation, follow these steps:
        
        0. (Optional) Create conda environment
        
        .. code:: bash
        
           conda create -n env_pyprf_motion python=2.7
           source activate env_pyprf_motion
           conda install pip
        
        1. Clone repository
        
        .. code:: bash
        
           git clone https://github.com/MSchnei/pyprf_motion.git
        
        2. Install numpy, e.g. by running:
        
        .. code:: bash
        
           pip install numpy
        
        3. Install pyprf_motion with pip
        
        .. code:: bash
        
           pip install /path/to/cloned/pyprf_motion
        
        Dependencies
        ------------
        
        `Python 2.7 <https://www.python.org/download/releases/2.7/>`__
        
        +----------------------------------------------+----------------+
        | Package                                      | Tested version |
        +==============================================+================+
        | `NumPy <http://www.numpy.org/>`__            | 1.14.0         |
        +----------------------------------------------+----------------+
        | `SciPy <http://www.scipy.org/>`__            | 1.0.0          |
        +----------------------------------------------+----------------+
        | `NiBabel <http://nipy.org/nibabel/>`__       | 2.2.1          |
        +----------------------------------------------+----------------+
        | `cython <http://cython.org/>`__              | 0.27.1         |
        +----------------------------------------------+----------------+
        | `tensorflow <https://www.tensorflow.org/>`__ | 1.4.0          |
        +----------------------------------------------+----------------+
        | `scikit-learn <scikit-learn.org/>`__         | 0.19.1         |
        +----------------------------------------------+----------------+
        
        How to use
        ----------
        
        1. Present stimuli and record fMRI data
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The PsychoPy scripts in the stimulus_presentation folder can be used to
        map motion-sensitive visual areas (especially area hMT+) using the pRF
        framework.
        
        1. Specify your desired parameters in the config file.
        
        2. Run the createTexMasks.py file to generate relevant masks and
           textures. Masks and textures will be saved as numpy arrays in .npz
           format in the parent folder called MaskTextures.
        
        3. Run the createCond.py file to generate the condition order. Condition
           and target presentation orders will be saved as numpy arrays in .npz
           format in the parent folder called Conditions.
        
        4. Run the stimulus presentation file motLoc.py in PsychoPy. The
           stimulus setup should look like the following screen-shot:
        
        2. Prepare spatial and temporal information for experiment as arrays
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        1. Run prepro_get_spat_info.py in the prepro folder to obtain an array
           with the spatial information of the experiment.
        
        2. Run prepro_get_temp_info.py in the prepro folder to obtain an array
           with the temporal information of the experiment.
        
        3. Prepare the input data
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The input data should be motion-corrected, high-pass filtered and
        (optionally) distortion-corrected. If desired, spatial as well as
        temporal smoothing can be applied. The PrePro folder contains some
        auxiliary scripts to perform some of these functions.
        
        4. Adjust the csv file
        ~~~~~~~~~~~~~~~~~~~~~~
        
        Adjust the information in the config_default.csv file in the Analysis
        folder, such that the provided information is correct. It is recommended
        to make a specific copy of the csv file for every subject.
        
        5. Run pyprf_motion
        ~~~~~~~~~~~~~~~~~~~
        
        Open a terminal and run
        
        ::
        
           pyprf_motion -config path/to/custom_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
        
        -  Amano, K., Wandell, B. A., & Dumoulin, S. O. (2009). Visual field
           maps, population receptive field sizes, and visual field coverage in
           the human MT+ complex. Journal of Neurophysiology, 102(5), 2704–18.
           https://doi.org/10.1152/jn.00102.2009
        
        -  van Dijk, J. A., de Haas, B., Moutsiana, C., & Schwarzkopf, D. S.
           (2016). Intersession reliability of population receptive field
           estimates. NeuroImage, 143, 293–303.
           https://doi.org/10.1016/j.neuroimage.2016.09.013
        
        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
