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# 'default': apply func-to-anat and anat-to-template transforms on motion corrected functional image.
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# 'abcd': apply motion correction, func-to-anat and anat-to-template transforms on each of raw functional volume using FSL applywarp based on ABCD-HCP pipeline.
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# 'single_step_resampling': apply motion correction, func-to-anat and anat-to-template transforms on each of raw functional volume using ANTs antsApplyTransform based on fMRIPrep pipeline.
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using: 'single_step_resampling'
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# 'single_step_resampling_from_stc': apply motion correction, func-to-anat and anat-to-template transforms on each of raw functional volume using ANTs antsApplyTransform based on fMRIPrep pipeline.
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using: 'single_step_resampling_from_stc'
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functional_preproc:
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motion_estimates_and_correction:
@@ -339,6 +339,9 @@ nuisance_corrections:
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# run: [On, Off] - this will run both and fork the pipeline
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run: [Off]
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# run nuisance regression in BOLD or template space
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space: template
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# switch to Off if nuisance regression is off and you don't want to write out the regressors
# 'default': apply func-to-anat and anat-to-template transforms on motion corrected functional image.
327
327
# 'abcd': apply motion correction, func-to-anat and anat-to-template transforms on each of raw functional volume using FSL applywarp based on ABCD-HCP pipeline.
328
-
# 'single_step_resampling': apply motion correction, func-to-anat and anat-to-template transforms on each of raw functional volume using ANTs antsApplyTransform based on fMRIPrep pipeline.
328
+
# 'single_step_resampling_from_stc': apply motion correction, func-to-anat and anat-to-template transforms on each of raw functional volume using ANTs antsApplyTransform based on fMRIPrep pipeline.
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