Create a vbm workflow that generates DARTEL-based template
>>> preproc = create_DARTEL_template()
>>> preproc.inputs.inputspec.structural_files = [os.path.abspath('s1.nii'), os.path.abspath('s3.nii')]
>>> preproc.inputs.inputspec.template_prefix = 'Template'
>>> preproc.run()
Inputs:
inputspec.structural_files : structural data to be used to create templates
inputspec.template_prefix : prefix for dartel template
Outputs:
outputspec.template_file : DARTEL template
outputspec.flow_fields : warps from input struct files to the template
Create an spm preprocessing workflow with freesurfer registration and artifact detection.
The workflow realigns and smooths and registers the functional images with the subject’s freesurfer space.
>>> preproc = create_spm_preproc()
>>> preproc.base_dir = '.'
>>> preproc.inputs.inputspec.fwhm = 6
>>> preproc.inputs.inputspec.subject_id = 's1'
>>> preproc.inputs.inputspec.subjects_dir = '.'
>>> preproc.inputs.inputspec.functionals = ['f3.nii', 'f5.nii']
>>> preproc.inputs.inputspec.norm_threshold = 1
>>> preproc.inputs.inputspec.zintensity_threshold = 3
Inputs:
inputspec.functionals : functional runs use 4d nifti
inputspec.subject_id : freesurfer subject id
inputspec.subjects_dir : freesurfer subjects dir
inputspec.fwhm : smoothing fwhm
inputspec.norm_threshold : norm threshold for outliers
inputspec.zintensity_threshold : intensity threshold in z-score
Outputs:
outputspec.realignment_parameters : realignment parameter files
outputspec.smoothed_files : smoothed functional files
outputspec.outlier_files : list of outliers
outputspec.outlier_stats : statistics of outliers
outputspec.outlier_plots : images of outliers
outputspec.mask_file : binary mask file in reference image space
outputspec.reg_file : registration file that maps reference image to
freesurfer space
outputspec.reg_cost : cost of registration (useful for detecting misalignment)
Create a vbm workflow that generates DARTEL-based warps to MNI space
Based on: http://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass10.pdf
>>> preproc = create_vbm_preproc()
>>> preproc.inputs.inputspec.fwhm = 8
>>> preproc.inputs.inputspec.structural_files = [os.path.abspath('s1.nii'), os.path.abspath('s3.nii')]
>>> preproc.inputs.inputspec.template_prefix = 'Template'
>>> preproc.run()
Inputs:
inputspec.structural_files : structural data to be used to create templates
inputspec.fwhm: single of triplet for smoothing when normalizing to MNI space
inputspec.template_prefix : prefix for dartel template
Outputs:
outputspec.normalized_files : normalized gray matter files
outputspec.template_file : DARTEL template
outputspec.icv : intracranial volume (cc - assuming dimensions in mm)