def create_fsl_flame_wf(ftest=False, wf_name='groupAnalysis'):
"""
FSL `FEAT <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT>`_
BASED Group Analysis
Parameters
----------
ftest : boolean, optional(default=False)
Ftest help investigate several contrasts at the same time
for example to see whether any of them (or any combination of them) is
significantly non-zero. Also, the F-test allows you to compare the
contribution of each contrast to the model and decide on significant
and non-significant ones
wf_name : string
Workflow name
Returns
-------
grp_analysis : workflow object
Group Analysis workflow object
Notes
-----
`Source <https://github.com/openconnectome/C-PAC/blob/master/CPAC/group_analysis/group_analysis_preproc.py>`_
Workflow Inputs::
inputspec.mat_file : string (existing file)
Mat file containing matrix for design
inputspec.con_file : string (existing file)
Contrast file containing contrast vectors
inputspec.grp_file : string (existing file)
file containing matrix specifying the groups the covariance is split into
inputspec.zmap_files : string (existing nifti file)
derivative or the zmap file for which the group analysis is to be run
inputspec.z_threshold : float
Z Statistic threshold value for cluster thresholding. It is used to
determine what level of activation would be statistically significant.
Increasing this will result in higher estimates of required effect.
inputspec.p_threshold : float
Probability threshold for cluster thresholding.
inputspec.fts_file : string (existing file)
file containing matrix specifying f-contrasts
inputspec.paramerters : string (tuple)
tuple containing which MNI and FSLDIR path information
Workflow Outputs::
outputspec.merged : string (nifti file)
4D volume file after merging all the derivative
files from each specified subject.
outputspec.zstats : list (nifti files)
Z statistic image for each t contrast
outputspec.zfstats : list (nifti files)
Z statistic image for each f contrast
outputspec.fstats : list (nifti files)
F statistic for each contrast
outputspec.cluster_threshold : list (nifti files)
the thresholded Z statistic image for each t contrast
outputspec.cluster_index : list (nifti files)
image of clusters for each t contrast; the values
in the clusters are the index numbers as used
in the cluster list.
outputspec.cluster_localmax_txt : list (text files)
local maxima text file for each t contrast,
defines the coordinates of maximum value in the cluster
outputspec.overlay_threshold : list (nifti files)
3D color rendered stats overlay image for t contrast
After reloading this image, use the Statistics Color
Rendering GUI to reload the color look-up-table
outputspec.overlay_rendered_image : list (nifti files)
2D color rendered stats overlay picture for each t contrast
outputspec.cluster_threshold_zf : list (nifti files)
the thresholded Z statistic image for each f contrast
outputspec.cluster_index_zf : list (nifti files)
image of clusters for each f contrast; the values
in the clusters are the index numbers as used
in the cluster list.
outputspec.cluster_localmax_txt_zf : list (text files)
local maxima text file for each f contrast,
defines the coordinates of maximum value in the cluster
outputspec.overlay_threshold_zf : list (nifti files)
3D color rendered stats overlay image for f contrast
After reloading this image, use the Statistics Color
Rendering GUI to reload the color look-up-table
outputspec.overlay_rendered_image_zf : list (nifti files)
2D color rendered stats overlay picture for each f contrast
Order of commands:
- Merge all the Z-map 3D images into 4D image file. For details see `fslmerge <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils>`_::
fslmerge -t sub01/sca/seed1/sca_Z_FWHM_merged.nii
sub02/sca/seed1/sca_Z_FWHM.nii.gz ....
merge.nii.gz
arguments
-t : concatenate images in time
- Create mask specific for analysis. For details see `fslmaths <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils>`_::
fslmaths merged.nii.gz
-abs -Tmin -bin mean_mask.nii.gz
arguments
-Tmin : min across time
-abs : absolute value
-bin : use (current image>0) to binarise
- FSL FLAMEO to perform higher level analysis. For details see `flameo <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT>`_::
flameo --copefile = merged.nii.gz --covsplitfile = anova_with_meanFD.grp --designfile = anova_with_meanFD.mat
--fcontrastsfile = anova_with_meanFD.fts --ld=stats --maskfile = mean_mask.nii.gz --runmode=ols
--tcontrastsfile = anova_with_meanFD.con
arguments
--copefile : cope regressor data file
--designfile : design matrix file
--maskfile : mask file
--tcontrastsfile : file containing an ASCII matrix specifying the t contrasts
--fcontrastsfile : file containing an ASCII matrix specifying the f contrasts
--runmode : Interference to perform (mixed effects - OLS)
- Run FSL Easy thresh
Easy thresh is a simple script for carrying out cluster-based thresholding and colour activation overlaying::
easythresh <raw_zstat> <brain_mask> <z_thresh> <prob_thresh> <background_image> <output_root> [--mm]
A seperate workflow called easythresh is called to run easythresh steps.
.. exec::
from CPAC.group_analysis import create_fsl_flame_wf
wf = create_fsl_flame_wf()
wf.write_graph(
graph2use='orig',
dotfilename='./images/generated/group_analysis.dot'
)
High Level Workflow Graph:
.. image:: ../../images/generated/group_analysis.png
:width: 800
Detailed Workflow Graph:
.. image:: ../../images/generated/group_analysis_detailed.png
:width: 800
Examples
--------
>>> from group_analysis_preproc import create_group_analysis
>>> preproc = create_group_analysis()
>>> preproc.inputs.inputspec.mat_file = '../group_models/anova_with_meanFD/anova_with_meanFD.mat'
>>> preproc.inputs.inputspec.con_file = '../group_models/anova_with_meanFD/anova_with_meanFD.con'
>>> preproc.inputs.inputspec.grp_file = '../group_models/anova_with_meanFD/anova_with_meanFD.grp'
>>> preproc.inputs.inputspec.zmap_files = ['subjects/sub01/seeds_rest_Dickstein_DLPFC/sca_Z_FWHM.nii.gz',
'subjects/sub02/seeds_rest_Dickstein_DLPFC/sca_Z_FWHM.nii.gz']
>>> preproc.inputs.inputspec.z_threshold = 2.3
>>> preproc.inputs.inputspec.p_threshold = 0.05
>>> preproc.inputs.inputspec.parameters = ('/usr/local/fsl/', 'MNI152')
>>> preproc.run() -- SKIP doctest
"""
grp_analysis = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(fields=[
'merged_file', 'merge_mask', 'mat_file', 'con_file', 'grp_file',
'fts_file', 'z_threshold', 'p_threshold', 'parameters'
]),
name='inputspec')
outputnode = pe.Node(util.IdentityInterface(fields=[
'merged', 'zstats', 'zfstats', 'fstats', 'cluster_threshold',
'cluster_index', 'cluster_localmax_txt', 'overlay_threshold',
'rendered_image', 'cluster_localmax_txt_zf', 'cluster_threshold_zf',
'cluster_index_zf', 'overlay_threshold_zf', 'rendered_image_zf'
]),
name='outputspec')
'''
merge_to_4d = pe.Node(interface=fsl.Merge(),
name='merge_to_4d')
merge_to_4d.inputs.dimension = 't'
### create analysis specific mask
#-Tmin: min across time
# -abs: absolute value
#-bin: use (current image>0) to binarise
merge_mask = pe.Node(interface=fsl.ImageMaths(),
name='merge_mask')
merge_mask.inputs.op_string = '-abs -Tmin -bin'
'''
fsl_flameo = pe.Node(interface=fsl.FLAMEO(), name='fsl_flameo')
fsl_flameo.inputs.run_mode = 'ols'
# rename the FLAME zstat outputs after the contrast string labels for
# easier interpretation
label_zstat_imports = ["import os"]
label_zstat = pe.Node(util.Function(input_names=['zstat_list', 'con_file'],
output_names=['new_zstat_list'],
function=label_zstat_files,
imports=label_zstat_imports),
name='label_zstat')
rename_zstats = pe.MapNode(interface=util.Rename(),
name='rename_zstats',
iterfield=['in_file', 'format_string'])
rename_zstats.inputs.keep_ext = True
# create analysis specific mask
# fslmaths merged.nii.gz -abs -bin -Tmean -mul volume out.nii.gz
# -Tmean: mean across time
# create group_reg file
# this file can provide an idea of how well the subjects
# in our analysis overlay with each other and the MNI brain.
# e.g., maybe there is one subject with limited coverage.
# not attached to sink currently
merge_mean_mask = pe.Node(interface=fsl.ImageMaths(),
name='merge_mean_mask')
# function node to get the operation string for fslmaths command
get_opstring = pe.Node(util.Function(input_names=['in_file'],
output_names=['out_file'],
function=get_operation),
name='get_opstring')
# connections
'''
grp_analysis.connect(inputnode, 'zmap_files',
merge_to_4d, 'in_files')
grp_analysis.connect(merge_to_4d, 'merged_file',
merge_mask, 'in_file')
'''
grp_analysis.connect(inputnode, 'merged_file', fsl_flameo, 'cope_file')
grp_analysis.connect(inputnode, 'merge_mask', fsl_flameo, 'mask_file')
grp_analysis.connect(inputnode, 'mat_file', fsl_flameo, 'design_file')
grp_analysis.connect(inputnode, 'con_file', fsl_flameo, 't_con_file')
grp_analysis.connect(inputnode, 'grp_file', fsl_flameo, 'cov_split_file')
grp_analysis.connect(fsl_flameo, 'zstats', label_zstat, 'zstat_list')
grp_analysis.connect(inputnode, 'con_file', label_zstat, 'con_file')
grp_analysis.connect(fsl_flameo, 'zstats', rename_zstats, 'in_file')
grp_analysis.connect(label_zstat, 'new_zstat_list', rename_zstats,
'format_string')
if ftest:
grp_analysis.connect(inputnode, 'fts_file', fsl_flameo, 'f_con_file')
easy_thresh_zf = easy_thresh('easy_thresh_zf')
grp_analysis.connect(fsl_flameo, 'zfstats', easy_thresh_zf,
'inputspec.z_stats')
grp_analysis.connect(inputnode, 'merge_mask', easy_thresh_zf,
'inputspec.merge_mask')
grp_analysis.connect(inputnode, 'z_threshold', easy_thresh_zf,
'inputspec.z_threshold')
grp_analysis.connect(inputnode, 'p_threshold', easy_thresh_zf,
'inputspec.p_threshold')
grp_analysis.connect(inputnode, 'parameters', easy_thresh_zf,
'inputspec.parameters')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_threshold',
outputnode, 'cluster_threshold_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_index',
outputnode, 'cluster_index_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_localmax_txt',
outputnode, 'cluster_localmax_txt_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.overlay_threshold',
outputnode, 'overlay_threshold_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.rendered_image',
outputnode, 'rendered_image_zf')
# calling easythresh for zstats files
easy_thresh_z = easy_thresh('easy_thresh_z')
grp_analysis.connect(rename_zstats, 'out_file', easy_thresh_z,
'inputspec.z_stats')
grp_analysis.connect(inputnode, 'merge_mask', easy_thresh_z,
'inputspec.merge_mask')
grp_analysis.connect(inputnode, 'z_threshold', easy_thresh_z,
'inputspec.z_threshold')
grp_analysis.connect(inputnode, 'p_threshold', easy_thresh_z,
'inputspec.p_threshold')
grp_analysis.connect(inputnode, 'parameters', easy_thresh_z,
'inputspec.parameters')
grp_analysis.connect(inputnode, 'merged_file', get_opstring, 'in_file')
grp_analysis.connect(inputnode, 'merged_file', merge_mean_mask, 'in_file')
grp_analysis.connect(get_opstring, 'out_file', merge_mean_mask,
'op_string')
grp_analysis.connect(fsl_flameo, 'zfstats', outputnode, 'zfstats')
grp_analysis.connect(fsl_flameo, 'fstats', outputnode, 'fstats')
grp_analysis.connect(inputnode, 'merged_file', outputnode, 'merged')
grp_analysis.connect(rename_zstats, 'out_file', outputnode, 'zstats')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_threshold',
outputnode, 'cluster_threshold')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_index', outputnode,
'cluster_index')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_localmax_txt',
outputnode, 'cluster_localmax_txt')
grp_analysis.connect(easy_thresh_z, 'outputspec.overlay_threshold',
outputnode, 'overlay_threshold')
grp_analysis.connect(easy_thresh_z, 'outputspec.rendered_image',
outputnode, 'rendered_image')
return grp_analysis
def create_group_analysis(ftest=False, wf_name='groupAnalysis'):
"""
FSL `FEAT <http://www.fmrib.ox.ac.uk/fsl/feat5/index.html>`_
BASED Group Analysis
Parameters
----------
ftest : boolean, optional(default=False)
Ftest help investigate several contrasts at the same time
for example to see whether any of them (or any combination of them) is
significantly non-zero. Also, the F-test allows you to compare the
contribution of each contrast to the model and decide on significant
and non-significant ones
wf_name : string
Workflow name
Returns
-------
grp_analysis : workflow object
Group Analysis workflow object
Notes
-----
`Source <https://github.com/openconnectome/C-PAC/blob/master/CPAC/group_analysis/group_analysis_preproc.py>`_
Workflow Inputs::
inputspec.mat_file : string (existing file)
Mat file containing matrix for design
inputspec.con_file : string (existing file)
Contrast file containing contrast vectors
inputspec.grp_file : string (existing file)
file containing matrix specifying the groups the covariance is split into
inputspec.zmap_files : string (existing nifti file)
derivative or the zmap file for which the group analysis is to be run
inputspec.z_threshold : float
Z Statistic threshold value for cluster thresholding. It is used to
determine what level of activation would be statistically significant.
Increasing this will result in higher estimates of required effect.
inputspec.p_threshold : float
Probability threshold for cluster thresholding.
inputspec.fts_file : string (existing file)
file containing matrix specifying f-contrasts
inputspec.paramerters : string (tuple)
tuple containing which MNI and FSLDIR path information
Workflow Outputs::
outputspec.merged : string (nifti file)
4D volume file after merging all the derivative
files from each specified subject.
outputspec.zstats : list (nifti files)
Z statistic image for each t contrast
outputspec.zfstats : list (nifti files)
Z statistic image for each f contrast
outputspec.fstats : list (nifti files)
F statistic for each contrast
outputspec.cluster_threshold : list (nifti files)
the thresholded Z statistic image for each t contrast
outputspec.cluster_index : list (nifti files)
image of clusters for each t contrast; the values
in the clusters are the index numbers as used
in the cluster list.
outputspec.cluster_localmax_txt : list (text files)
local maxima text file for each t contrast,
defines the coordinates of maximum value in the cluster
outputspec.overlay_threshold : list (nifti files)
3D color rendered stats overlay image for t contrast
After reloading this image, use the Statistics Color
Rendering GUI to reload the color look-up-table
outputspec.overlay_rendered_image : list (nifti files)
2D color rendered stats overlay picture for each t contrast
outputspec.cluster_threshold_zf : list (nifti files)
the thresholded Z statistic image for each f contrast
outputspec.cluster_index_zf : list (nifti files)
image of clusters for each f contrast; the values
in the clusters are the index numbers as used
in the cluster list.
outputspec.cluster_localmax_txt_zf : list (text files)
local maxima text file for each f contrast,
defines the coordinates of maximum value in the cluster
outputspec.overlay_threshold_zf : list (nifti files)
3D color rendered stats overlay image for f contrast
After reloading this image, use the Statistics Color
Rendering GUI to reload the color look-up-table
outputspec.overlay_rendered_image_zf : list (nifti files)
2D color rendered stats overlay picture for each f contrast
Order of commands:
- Merge all the Z-map 3D images into 4D image file. For details see `fslmerge <http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html>`_::
fslmerge -t sub01/sca/seed1/sca_Z_FWHM_merged.nii
sub02/sca/seed1/sca_Z_FWHM.nii.gz ....
merge.nii.gz
arguments
-t : concatenate images in time
- Create mask specific for analysis. For details see `fslmaths <http://www.fmrib.ox.ac.uk/fslcourse/lectures/practicals/intro/index.htm#fslutils>`_::
fslmaths merged.nii.gz
-abs -Tmin -bin mean_mask.nii.gz
arguments
-Tmin : min across time
-abs : absolute value
-bin : use (current image>0) to binarise
- FSL FLAMEO to perform higher level analysis. For details see `flameo <http://www.fmrib.ox.ac.uk/fsl/feat5/index.html>`_::
flameo --copefile = merged.nii.gz --covsplitfile = anova_with_meanFD.grp --designfile = anova_with_meanFD.mat
--fcontrastsfile = anova_with_meanFD.fts --ld=stats --maskfile = mean_mask.nii.gz --runmode=ols
--tcontrastsfile = anova_with_meanFD.con
arguments
--copefile : cope regressor data file
--designfile : design matrix file
--maskfile : mask file
--tcontrastsfile : file containing an ASCII matrix specifying the t contrasts
--fcontrastsfile : file containing an ASCII matrix specifying the f contrasts
--runmode : Interference to perform (mixed effects - OLS)
- Run FSL Easy thresh
Easy thresh is a simple script for carrying out cluster-based thresholding and colour activation overlaying::
easythresh <raw_zstat> <brain_mask> <z_thresh> <prob_thresh> <background_image> <output_root> [--mm]
A seperate workflow called easythresh is called to run easythresh steps.
High Level Workflow Graph:
.. image:: ../images/group_analysis.dot.png
:width: 800
Detailed Workflow Graph:
.. image:: ../images/group_analysis_detailed.dot.png
:width: 800
Examples
--------
>>> from group_analysis_preproc import create_group_analysis
>>> preproc = create_group_analysis()
>>> preproc.inputs.inputspec.mat_file = '../group_models/anova_with_meanFD/anova_with_meanFD.mat'
>>> preproc.inputs.inputspec.con_file = '../group_models/anova_with_meanFD/anova_with_meanFD.con'
>>> preproc.inputs.inputspec.grp_file = '../group_models/anova_with_meanFD/anova_with_meanFD.grp'
>>> preproc.inputs.inputspec.zmap_files = ['subjects/sub01/seeds_rest_Dickstein_DLPFC/sca_Z_FWHM.nii.gz',
'subjects/sub02/seeds_rest_Dickstein_DLPFC/sca_Z_FWHM.nii.gz']
>>> preproc.inputs.inputspec.z_threshold = 2.3
>>> preproc.inputs.inputspec.p_threshold = 0.05
>>> preproc.inputs.inputspec.parameters = ('/usr/local/fsl/', 'MNI152')
>>> preproc.run() -- SKIP doctest
"""
grp_analysis = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(fields=['merged_file',
'merge_mask',
'mat_file',
'con_file',
'grp_file',
'fts_file',
'z_threshold',
'p_threshold',
'parameters']),
name='inputspec')
outputnode = pe.Node(util.IdentityInterface(fields=['merged',
'zstats',
'zfstats',
'fstats',
'cluster_threshold',
'cluster_index',
'cluster_localmax_txt',
'overlay_threshold',
'rendered_image',
'cluster_localmax_txt_zf',
'cluster_threshold_zf',
'cluster_index_zf',
'overlay_threshold_zf',
'rendered_image_zf']),
name='outputspec')
'''
merge_to_4d = pe.Node(interface=fsl.Merge(),
name='merge_to_4d')
merge_to_4d.inputs.dimension = 't'
### create analysis specific mask
#-Tmin: min across time
# -abs: absolute value
#-bin: use (current image>0) to binarise
merge_mask = pe.Node(interface=fsl.ImageMaths(),
name='merge_mask')
merge_mask.inputs.op_string = '-abs -Tmin -bin'
'''
fsl_flameo = pe.Node(interface=fsl.FLAMEO(),
name='fsl_flameo')
fsl_flameo.inputs.run_mode = 'ols'
### create analysis specific mask
# fslmaths merged.nii.gz -abs -bin -Tmean -mul volume out.nii.gz
#-Tmean: mean across time
# create group_reg file
# this file can provide an idea of how well the subjects
# in our analysis overlay with each other and the MNI brain.
# e.g., maybe there is one subject with limited coverage.
# not attached to sink currently
merge_mean_mask = pe.Node(interface=fsl.ImageMaths(),
name='merge_mean_mask')
#function node to get the operation string for fslmaths command
get_opstring = pe.Node(util.Function(input_names=['in_file'],
output_names=['out_file'],
function=get_operation),
name='get_opstring')
#connections
'''
grp_analysis.connect(inputnode, 'zmap_files',
merge_to_4d, 'in_files')
grp_analysis.connect(merge_to_4d, 'merged_file',
merge_mask, 'in_file')
'''
grp_analysis.connect(inputnode, 'merged_file',
fsl_flameo, 'cope_file')
grp_analysis.connect(inputnode, 'merge_mask',
fsl_flameo, 'mask_file')
grp_analysis.connect(inputnode, 'mat_file',
fsl_flameo, 'design_file')
grp_analysis.connect(inputnode, 'con_file',
fsl_flameo, 't_con_file')
grp_analysis.connect(inputnode, 'grp_file',
fsl_flameo, 'cov_split_file')
if ftest:
#calling easythresh for zfstats file
grp_analysis.connect(inputnode, 'fts_file',
fsl_flameo, 'f_con_file')
easy_thresh_zf = easy_thresh('easy_thresh_zf')
grp_analysis.connect(fsl_flameo, 'zfstats',
easy_thresh_zf, 'inputspec.z_stats')
grp_analysis.connect(inputnode, 'merge_mask',
easy_thresh_zf, 'inputspec.merge_mask')
grp_analysis.connect(inputnode, 'z_threshold',
easy_thresh_zf, 'inputspec.z_threshold')
grp_analysis.connect(inputnode, 'p_threshold',
easy_thresh_zf, 'inputspec.p_threshold')
grp_analysis.connect(inputnode, 'parameters',
easy_thresh_zf, 'inputspec.parameters')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_threshold',
outputnode, 'cluster_threshold_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_index',
outputnode, 'cluster_index_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_localmax_txt',
outputnode, 'cluster_localmax_txt_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.overlay_threshold',
outputnode, 'overlay_threshold_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.rendered_image',
outputnode, 'rendered_image_zf')
#calling easythresh for zstats files
easy_thresh_z = easy_thresh('easy_thresh_z')
grp_analysis.connect(fsl_flameo, 'zstats',
easy_thresh_z, 'inputspec.z_stats')
grp_analysis.connect(inputnode, 'merge_mask',
easy_thresh_z, 'inputspec.merge_mask')
grp_analysis.connect(inputnode, 'z_threshold',
easy_thresh_z, 'inputspec.z_threshold')
grp_analysis.connect(inputnode, 'p_threshold',
easy_thresh_z, 'inputspec.p_threshold')
grp_analysis.connect(inputnode, 'parameters',
easy_thresh_z, 'inputspec.parameters')
grp_analysis.connect(inputnode, 'merged_file',
get_opstring, 'in_file')
grp_analysis.connect(inputnode, 'merged_file',
merge_mean_mask, 'in_file')
grp_analysis.connect(get_opstring, 'out_file',
merge_mean_mask, 'op_string')
grp_analysis.connect(fsl_flameo, 'zfstats',
outputnode, 'zfstats')
grp_analysis.connect(fsl_flameo, 'fstats',
outputnode, 'fstats')
grp_analysis.connect(inputnode, 'merged_file',
outputnode, 'merged')
grp_analysis.connect(fsl_flameo, 'zstats',
outputnode, 'zstats')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_threshold',
outputnode, 'cluster_threshold')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_index',
outputnode, 'cluster_index')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_localmax_txt',
outputnode, 'cluster_localmax_txt')
grp_analysis.connect(easy_thresh_z, 'outputspec.overlay_threshold',
outputnode, 'overlay_threshold')
grp_analysis.connect(easy_thresh_z, 'outputspec.rendered_image',
outputnode, 'rendered_image')
return grp_analysis