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.. _ahelp_neuro_deconvolve.py:

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neuro_deconvolve.py
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.. contents:: :local:


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.. code-block:: none

    
    ===========================================================================
    neuro_deconvolve.py:
    
    Generate a script that would apply 3dTfitter to deconvolve an MRI signal
    (BOLD response curve) into a neuro response curve.
    
    Required parameters include an input dataset, a script name and an output
    prefix.
    
    ----------------------------------------------------------------------
    examples:
    
        1. deconvolve 3 seed time series
    
            The errts time series might be applied to the model, while the
            all_runs and fitts and for evaluation, along with the re-convolved
            time series generated by the script.
    
            Temporal partitioning is on the todo list.
    
               neuro_deconvolve.py                                      \
                  -infiles seed.all_runs.1D seed.errts.1D seed.fitts.1D \
                  -tr 2.0 -tr_nup 20 -kernel BLOCK                      \
                  -script script.neuro.txt
    
    old examples:
    
        old 1. 3d+time example
    
               neuro_deconvolve.py         \
                  -input run1+orig         \
                  -script script.neuro     \
                  -mask_dset automask+orig \
                  -prefix neuro_resp
    
        old 2. 1D example
    
               neuro_deconvolve.py        \
                  -input epi_data.1D      \
                  -tr 2.0                 \
                  -script script.1d       \
                  -prefix neuro.1D
    
    
    ----------------------------------------------------------------------
    informational arguments:
    
        -help                       : display this help
        -hist                       : display the modification history
        -show_valid_opts            : display all valid options (short format)
        -ver                        : display the version number
    
    ----------------------------------------
    required arguments:
    
        -input INPUT_DATASET        : set the data to deconvolve
    
            e.g. -input epi_data.1D
    
        -prefix PREFIX              : set the prefix for output filenames
    
            e.g. -prefix neuro_resp
    
                    --> might create: neuro_resp+orig.HEAD/.BRIK
    
        -script SCRIPT              : specify the name of the output script
    
            e.g. -script neuro.script
    
    ----------------------------------------
    optional arguments:
    
    
        -kernel KERNEL              : set the response kernel
    
            default: -kernel GAM
    
        -kernel_file FILENAME       : set the filename to store the kernel in
    
            default: -kernel_file resp_kernel.1D
    
          * This data should be at the upsampled TR.
    
            See -tr_nup.
    
        -mask_dset DSET             : set a mask dataset for 3dTfitter to use
    
            e.g. -mask_dset automask+orig
    
        -old                        : make old-style script
    
            Make pre-2015.02.24 script for 1D case.
    
        -tr TR                      : set the scanner TR
    
            e.g. -tr 2.0
    
            The TR is needed for 1D formatted input files.  It is not needed
            for AFNI 3d+time datasets, since the TR is in the file.
    
        -tr_nup NUP                 : upsample factor for TR
    
            e.g. -tr_nup 25
    
            Deconvolution is generally done on an upsampled TR, which allows
            for sub-TR events and more accurate deconvolution.  NUP should be
            the number of pieces each original TR is divided into.  For example,
            to upsample a TR of 2.0 to one of 0.1, use NUP = 20.
    
            TR must be an integral multiple of TR_UP.
    
        -verb LEVEL                 : set the verbose level
    
            e.g. -verb 2
    
    
    - R Reynolds  June 12, 2008
    ===========================================================================