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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.
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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
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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
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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
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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
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