def test_output_func_to_standard_FSL_linear():
test_name = 'test_output_func_to_standard_FSL_linear'
# get the config and strat for the mock
c, strat = configuration_strategy_mock(method='FSL')
strat.append_name('anat_mni_flirt_register_0')
num_strat = 0
# build the workflow
workflow = pe.Workflow(name='test_output_func_to_standard_FSL_linear')
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
output_func_to_standard(workflow,
'mean_functional',
'template_brain_for_func_preproc',
'mean_functional_to_standard',
strat,
num_strat,
c,
input_image_type='func_derivative')
out1_name = os.path.join(
c.workingDirectory, test_name,
'func_mni_fsl_warp_mean_functional_to_standard_0',
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz'
)
func_node, func_out = strat['mean_functional']
output_func_to_standard(workflow, (func_node, func_out),
'template_brain_for_func_preproc',
'mean_functional_to_standard_node',
strat,
num_strat,
c,
input_image_type='func_derivative')
out2_name = os.path.join(
c.workingDirectory, test_name,
'func_mni_fsl_warp_mean_functional_to_standard_node_0',
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz'
)
workflow.run()
assert (test_utils.pearson_correlation(out1_name, out2_name) > 0.99)
def create_vmhc(workflow,
num_strat,
strat,
pipeline_config_object,
func_key='functional_nuisance_residuals',
output_name='vmhc'):
"""
Compute the map of brain functional homotopy, the high degree of synchrony
in spontaneous activity between geometrically corresponding interhemispheric (i.e., homotopic) regions.
Parameters
----------
None
Returns
-------
vmhc_workflow : workflow
Voxel Mirrored Homotopic Connectivity Analysis Workflow
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/vmhc/vmhc.py>`_
Workflow Inputs::
inputspec.brain : string (existing nifti file)
Anatomical image(without skull)
inputspec.symmetric_brain : string (existing nifti file)
MNI152_T1_2mm_symmetric_brain.nii.gz
inputspec.rest_res_filt : string (existing nifti file)
Band passed Image with nuisance signal regressed out(and optionally scrubbed). Recommended
bandpass filter (0.001,0.1) )
inputspec.reorient : string (existing nifti file)
RPI oriented anatomical data
inputspec.example_func2highres_mat : string (existing affine transformation .mat file)
Specifies an affine transform that should be applied to the example_func before non linear warping
inputspec.standard_for_func: string (existing nifti file)
MNI152_T1_standard_resolution_brain.nii.gz
inputspec.symmetric_skull : string (existing nifti file)
MNI152_T1_2mm_symmetric.nii.gz
inputspec.twomm_brain_mask_dil : string (existing nifti file)
MNI152_T1_2mm_brain_mask_symmetric_dil.nii.gz
inputspec.config_file_twomm_symmetric : string (existing .cnf file)
T1_2_MNI152_2mm_symmetric.cnf
inputspec.rest_mask : string (existing nifti file)
A mask functional volume(derived by dilation from motion corrected functional volume)
fwhm_input.fwhm : list (float)
For spatial smoothing the Z-transformed correlations in MNI space.
Generally the value of this parameter is 1.5 or 2 times the voxel size of the input Image.
inputspec.mean_functional : string (existing nifti file)
The mean functional image for use in the func-to-anat registration matrix conversion
to ITK (ANTS) format, if the user selects to use ANTS.
Workflow Outputs::
outputspec.highres2symmstandard : string (nifti file)
Linear registration of T1 image to symmetric standard image
outputspec.highres2symmstandard_mat : string (affine transformation .mat file)
An affine transformation .mat file from linear registration and used in non linear registration
outputspec.highres2symmstandard_warp : string (nifti file)
warp file from Non Linear registration of T1 to symmetrical standard brain
outputspec.fnirt_highres2symmstandard : string (nifti file)
Non Linear registration of T1 to symmetrical standard brain
outputspec.highres2symmstandard_jac : string (nifti file)
jacobian determinant image from Non Linear registration of T1 to symmetrical standard brain
outputspec.rest_res_2symmstandard : string (nifti file)
nonlinear registration (func to standard) image
outputspec.VMHC_FWHM_img : string (nifti file)
pearson correlation between res2standard and flipped res2standard
outputspec.VMHC_Z_FWHM_img : string (nifti file)
Fisher Z transform map
outputspec.VMHC_Z_stat_FWHM_img : string (nifti file)
Z statistic map
Order of commands:
- Perform linear registration of Anatomical brain in T1 space to symmetric standard space. For details
see `flirt <http://www.fmrib.ox.ac.uk/fsl/flirt/index.html>`_::
flirt
-ref MNI152_T1_2mm_symmetric_brain.nii.gz
-in mprage_brain.nii.gz
-out highres2symmstandard.nii.gz
-omat highres2symmstandard.mat
-cost corratio
-searchcost corratio
-dof 12
-interp trilinear
- Perform nonlinear registration (higres to standard) to symmetric standard brain. For details
see `fnirt <http://fsl.fmrib.ox.ac.uk/fsl/fnirt/>`_::
fnirt
--in=head.nii.gz
--aff=highres2symmstandard.mat
--cout=highres2symmstandard_warp.nii.gz
--iout=fnirt_highres2symmstandard.nii.gz
--jout=highres2symmstandard_jac.nii.gz
--config=T1_2_MNI152_2mm_symmetric.cnf
--ref=MNI152_T1_2mm_symmetric.nii.gz
--refmask=MNI152_T1_2mm_brain_mask_symmetric_dil.nii.gz
--warpres=10,10,10
- Perform spatial smoothing on the input functional image(inputspec.rest_res_filt). For details
see `PrinciplesSmoothing <http://imaging.mrc-cbu.cam.ac.uk/imaging/PrinciplesSmoothing>`_
`fslmaths <http://www.fmrib.ox.ac.uk/fslcourse/lectures/practicals/intro/index.htm>`_::
fslmaths rest_res_filt.nii.gz
-kernel gauss FWHM/ sqrt(8-ln(2))
-fmean -mas rest_mask.nii.gz
rest_res_filt_FWHM.nii.gz
- Apply nonlinear registration (func to standard). For details see
`applywarp <http://www.fmrib.ox.ac.uk/fsl/fnirt/warp_utils.html#applywarp>`_::
applywarp
--ref=MNI152_T1_2mm_symmetric.nii.gz
--in=rest_res_filt_FWHM.nii.gz
--out=rest_res_2symmstandard.nii.gz
--warp=highres2symmstandard_warp.nii.gz
--premat=example_func2highres.mat
- Copy and L/R swap the output of applywarp command (rest_res_2symmstandard.nii.gz). For details
see `fslswapdim <http://fsl.fmrib.ox.ac.uk/fsl/fsl4.0/avwutils/index.html>`_::
fslswapdim
rest_res_2symmstandard.nii.gz
-x y z
tmp_LRflipped.nii.gz
- Calculate pearson correlation between rest_res_2symmstandard.nii.gz and flipped
rest_res_2symmstandard.nii.gz(tmp_LRflipped.nii.gz). For details see
`3dTcorrelate <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTcorrelate.html>`_::
3dTcorrelate
-pearson
-polort -1
-prefix VMHC_FWHM.nii.gz
rest_res_2symmstandard.nii.gz
tmp_LRflipped.nii.gz
Workflow:
.. image:: ../images/vmhc_graph.dot.png
:width: 500
Workflow Detailed:
.. image:: ../images/vmhc_detailed_graph.dot.png
:width: 500
References
----------
.. [1] Zuo, X.-N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D. S., Bangaru, S.,
Grzadzinski, R., et al. (2010). Growing together and growing apart: regional and
sex differences in the lifespan developmental trajectories of functional homotopy.
The Journal of neuroscience : the official journal of the Society for Neuroscience,
30(45), 15034-43. doi:10.1523/JNEUROSCI.2612-10.2010
Examples
--------
>>> vmhc_w = create_vmhc()
>>> vmhc_w.inputs.inputspec.symmetric_brain = 'MNI152_T1_2mm_symmetric_brain.nii.gz'
>>> vmhc_w.inputs.inputspec.symmetric_skull = 'MNI152_T1_2mm_symmetric.nii.gz'
>>> vmhc_w.inputs.inputspec.twomm_brain_mask_dil = 'MNI152_T1_2mm_brain_mask_symmetric_dil.nii.gz'
>>> vmhc_w.inputs.inputspec.config_file_twomm = 'T1_2_MNI152_2mm_symmetric.cnf'
>>> vmhc_w.inputs.inputspec.standard_for_func= 'MNI152_T1_2mm.nii.gz'
>>> vmhc_w.inputs.fwhm_input.fwhm = [4.5, 6]
>>> vmhc_w.get_node('fwhm_input').iterables = ('fwhm', [4.5, 6])
>>> vmhc_w.inputs.inputspec.rest_res = os.path.abspath('/home/data/s1001/rest_res_filt.nii.gz')
>>> vmhc_w.inputs.inputspec.reorient = os.path.abspath('/home/data/s1001/anat/mprage_RPI.nii.gz')
>>> vmhc_w.inputs.inputspec.brain = os.path.abspath('/home/data/s1001/anat/mprage_brain.nii.gz')
>>> vmhc_w.inputs.inputspec.example_func2highres_mat = os.path.abspath('/home/data/s1001/func2highres.mat')
>>> vmhc_w.inputs.inputspec.rest_mask = os.path.abspath('/home/data/s1001/func/original/rest_mask.nii.gz')
>>> vmhc_w.run() # doctest: +SKIP
"""
nodes = strat.get_nodes_names()
if not isinstance(func_key, str):
raise ValueError('func_key should be a string, not a {0}'.format(
type(func_key)))
# we begin by smoothing the input file, which should be the current leaf node
smooth_key = '{0}_smooth'.format(func_key)
if smooth_key not in strat:
spatial_smooth(workflow,
'leaf',
'functional_brain_mask',
smooth_key,
strat,
num_strat,
pipeline_config_object,
input_image_type='func_4d')
# next write it to symmetric MNI space
func_symm_mni_key = 'func_preproc_symm_mni'
if func_symm_mni_key not in strat:
output_func_to_standard(workflow,
smooth_key,
'template_skull_for_func_preproc',
func_symm_mni_key,
strat,
num_strat,
pipeline_config_object,
input_image_type='func_4d')
# write out a swapped version of the file
# copy and L/R swap file
copy_and_L_R_swap = pe.Node(interface=fsl.SwapDimensions(),
name='copy_and_L_R_swap_{0}'.format(num_strat))
copy_and_L_R_swap.inputs.new_dims = ('-x', 'y', 'z')
func_node, func_file = strat[func_symm_mni_key]
workflow.connect(func_node, func_file, copy_and_L_R_swap, 'in_file')
# calculate correlation between original and swapped images
pearson_correlation = pe.Node(
interface=preprocess.TCorrelate(),
name='pearson_correlation_{0}'.format(num_strat))
pearson_correlation.inputs.pearson = True
pearson_correlation.inputs.polort = -1
pearson_correlation.inputs.outputtype = 'NIFTI_GZ'
workflow.connect(func_node, func_file, pearson_correlation, 'xset')
workflow.connect(copy_and_L_R_swap, 'out_file', pearson_correlation,
'yset')
# add the outputs to the resource pool
strat.update_resource_pool(
{'vmhc_raw_score': (pearson_correlation, 'out_file')})
strat.append_name(copy_and_L_R_swap.name)
strat.append_name(pearson_correlation.name)
return workflow, strat