# collect all the con images for each contrast.
contrast_ids = list(range(1, len(contrasts) + 1))
l2source = pe.Node(nio.DataGrabber(infields=['fwhm', 'con']), name="l2source")
# we use .*i* to capture both .img (SPM8) and .nii (SPM12)
l2source.inputs.template = os.path.abspath(
'spm_dartel_tutorial/l1output/*/con*/*/_fwhm_%d/con_%04d.*i*')
# iterate over all contrast images
l2source.iterables = [('fwhm', fwhmlist), ('con', contrast_ids)]
l2source.inputs.sort_filelist = True
"""Use :class:`nipype.interfaces.spm.OneSampleTTestDesign` to perform a
simple statistical analysis of the contrasts from the group of
subjects (n=2 in this example).
"""
# setup a 1-sample t-test node
onesamplettestdes = pe.Node(spm.OneSampleTTestDesign(), name="onesampttestdes")
l2estimate = pe.Node(spm.EstimateModel(), name="level2estimate")
l2estimate.inputs.estimation_method = {'Classical': 1}
l2conestimate = pe.Node(spm.EstimateContrast(), name="level2conestimate")
cont1 = ('Group', 'T', ['mean'], [1])
l2conestimate.inputs.contrasts = [cont1]
l2conestimate.inputs.group_contrast = True
"""As before, we setup a pipeline to connect these two nodes (l2source
-> onesamplettest).
"""
l2pipeline = pe.Workflow(name="level2")
l2pipeline.base_dir = os.path.abspath('spm_dartel_tutorial/l2output')
l2pipeline.connect([
(l2source, onesamplettestdes, [('outfiles', 'in_files')]),
(onesamplettestdes, l2estimate, [('spm_mat_file', 'spm_mat_file')]),
# collect all the con images for each contrast.
contrast_ids = range(1, len(contrasts) + 1)
l2source = pe.Node(nio.DataGrabber(infields=['fwhm', 'con']), name="l2source")
# we use .*i* to capture both .img (SPM8) and .nii (SPM12)
l2source.inputs.template = os.path.abspath(
'spm_tutorial2/l1output/*/con*/*/_fwhm_%d/con_%04d.*i*')
# iterate over all contrast images
l2source.iterables = [('fwhm', fwhmlist), ('con', contrast_ids)]
l2source.inputs.sort_filelist = True
"""Use :class:`nipype.interfaces.spm.OneSampleTTestDesign` to perform a
simple statistical analysis of the contrasts from the group of
subjects (n=2 in this example).
"""
# setup a 1-sample t-test node
onesamplettestdes = pe.Node(interface=spm.OneSampleTTestDesign(),
name="onesampttestdes")
l2estimate = pe.Node(interface=spm.EstimateModel(), name="level2estimate")
l2estimate.inputs.estimation_method = {'Classical': 1}
l2conestimate = pe.Node(interface=spm.EstimateContrast(),
name="level2conestimate")
cont1 = ('Group', 'T', ['mean'], [1])
l2conestimate.inputs.contrasts = [cont1]
l2conestimate.inputs.group_contrast = True
"""As before, we setup a pipeline to connect these two nodes (l2source
-> onesamplettest).
"""
l2pipeline = pe.Workflow(name="level2")
l2pipeline.base_dir = os.path.abspath('spm_tutorial2/l2output')
l2pipeline.connect([
Grab the data
"""
#Node: DataGrabber - to collect all the con images for each contrast
l2volSource = pe.Node(nio.DataGrabber(infields=['con']), name="l2volSource")
path2normcons = experiment_dir + '/results/level1_output/subject*/normcons/ants_con_%04d.nii'
l2volSource.inputs.template = path2normcons
l2volSource.iterables = [('con',contrast_ids)] # iterate over all contrast images
"""
Define nodes
"""
#Node: OneSampleTTest - to perform an one sample t-test analysis on the volume
oneSampleTTestVolDes = pe.Node(interface=spm.OneSampleTTestDesign(),
name="oneSampleTTestVolDes")
#Node: EstimateModel - to estimate the model
l2estimate = pe.Node(interface=spm.EstimateModel(), name="l2estimate")
l2estimate.inputs.estimation_method = {'Classical' : 1}
#Node: EstimateContrast - to estimate the contrast (in this example just one)
l2conestimate = pe.Node(interface = spm.EstimateContrast(), name="l2conestimate")
cont1 = ('Group','T', ['mean'],[1])
l2conestimate.inputs.contrasts = [cont1]
l2conestimate.inputs.group_contrast = True
#Node: Threshold - to threshold the estimated contrast
l2threshold = pe.Node(interface = spm.Threshold(), name="l2threshold")
l2threshold.inputs.contrast_index = 1
tImage = glob.glob('/media/Data/work/KPE_SPM/Sink/1stLevel/_subject_id_1263/spmT_00*.nii')
for con_image in tImage:
nilearn.plotting.plot_glass_brain(con_image,
display_mode='lyrz', colorbar=True, plot_abs=False, threshold=2)
conImage = glob.glob('/media/Data/work/KPE_SPM/Sink/1stLevel/_subject_id_1263/con_00*.nii')
for con_image in tImage:
nilearn.plotting.plot_stat_map(nilearn.image.smooth_img(con_image, 6), display_mode='x',
threshold=2.3, bg_img=anatimg, dim=1) #, cut_coords=(-5, 0, 5, 10, 15), dim=1)
# nilearn.plotting.plot_glass_brain(nilearn.image.smooth_img(con_image, 6),
# display_mode='lyrz', colorbar=True, plot_abs=False, threshold=2.3, bg_img=antimg)
#%% Gourp analysis - based on SPM - should condifer the fsl Randomize option (other script)
# OneSampleTTestDesign - creates one sample T-Test Design
onesamplettestdes = Node(spm.OneSampleTTestDesign(),
name="onesampttestdes")
# EstimateModel - estimates the model
level2estimate = Node(spm.EstimateModel(estimation_method={'Classical': 1}),
name="level2estimate")
# EstimateContrast - estimates group contrast
level2conestimate = Node(spm.EstimateContrast(group_contrast=True),
name="level2conestimate")
cont1 = ['Group', 'T', ['mean'], [1]]
level2conestimate.inputs.contrasts = [cont1]
# Which contrasts to use for the 2nd-level analysis
contrast_list = ['con_0001', 'con_0002', 'con_0003', 'con_0004', 'con_0005']
contrast_ids = list(range(1, len(contrasts) + 1))
l2source = pe.Node(nio.DataGrabber(infields=['fwhm', 'con']), name="l2source")
# we use .*i* to capture both .img (SPM8) and .nii (SPM12)
l2source.inputs.template = os.path.abspath(
'spm_tutorial/l1output/*/con*/*/_fwhm_%d/con_%04d.*i*')
# iterate over all contrast images
l2source.iterables = [('fwhm', fwhmlist), ('con', contrast_ids)]
l2source.inputs.sort_filelist = True
"""Use :class:`nipype.interfaces.spm.OneSampleTTestDesign` to perform a
simple statistical analysis of the contrasts from the group of
subjects (n=2 in this example).
"""
# setup a 1-sample t-test node
onesamplettestdes = pe.Node(
interface=spm.OneSampleTTestDesign(), name="onesampttestdes")
l2estimate = pe.Node(interface=spm.EstimateModel(), name="level2estimate")
l2estimate.inputs.estimation_method = {'Classical': 1}
l2conestimate = pe.Node(
interface=spm.EstimateContrast(), name="level2conestimate")
cont1 = ('Group', 'T', ['mean'], [1])
l2conestimate.inputs.contrasts = [cont1]
l2conestimate.inputs.group_contrast = True
"""As before, we setup a pipeline to connect these two nodes (l2source
-> onesamplettest).
"""
l2pipeline = pe.Workflow(name="level2")
l2pipeline.base_dir = os.path.abspath('spm_tutorial/l2output')
l2pipeline.connect([
(l2source, onesamplettestdes, [('outfiles', 'in_files')]),
def create_2lvl(do_one_sample, name="group", mask=None):
import nipype.interfaces.fsl as fsl
import nipype.interfaces.spm as spm
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
wk = pe.Workflow(name=name)
inputspec = pe.Node(niu.IdentityInterface(fields=[
'copes', 'estimation_method', 'template', "contrasts",
"include_intercept", "regressors", "p_thresh", "height_thresh",
'min_cluster_size'
]),
name='inputspec')
if do_one_sample:
model = pe.Node(spm.OneSampleTTestDesign(), name='onesample')
else:
model = pe.Node(spm.MultipleRegressionDesign(), name='l2model')
wk.connect(inputspec, 'regressors', model, "user_covariates")
wk.connect(inputspec, 'include_intercept', model, 'include_intercept')
est_model = pe.Node(spm.EstimateModel(), name='estimate_model')
wk.connect(inputspec, 'copes', model, 'in_files')
wk.connect(inputspec, 'estimation_method', est_model, 'estimation_method')
wk.connect(model, 'spm_mat_file', est_model, 'spm_mat_file')
if mask == None:
bet = pe.Node(fsl.BET(mask=True, frac=0.3, output_type='NIFTI'),
name="template_brainmask")
wk.connect(inputspec, 'template', bet, 'in_file')
wk.connect(bet, 'mask_file', model, 'explicit_mask_file')
else:
wk.connect(inputspec, 'template', model, 'explicit_mask_file')
est_cont = pe.Node(spm.EstimateContrast(group_contrast=True),
name='estimate_contrast')
wk.connect(inputspec, 'contrasts', est_cont, "contrasts")
wk.connect(est_model, 'spm_mat_file', est_cont, "spm_mat_file")
wk.connect(est_model, 'residual_image', est_cont, "residual_image")
wk.connect(est_model, 'beta_images', est_cont, "beta_images")
thresh = pe.MapNode(spm.Threshold(use_fwe_correction=False,
use_topo_fdr=True,
height_threshold_type='p-value'),
name='fdr',
iterfield=['stat_image', 'contrast_index'])
wk.connect(est_cont, 'spm_mat_file', thresh, 'spm_mat_file')
wk.connect(est_cont, 'spmT_images', thresh, 'stat_image')
wk.connect(inputspec, 'min_cluster_size', thresh, 'extent_threshold')
count = lambda x: range(1, len(x) + 1)
wk.connect(inputspec, ('contrasts', count), thresh, 'contrast_index')
wk.connect(inputspec, 'p_thresh', thresh, 'extent_fdr_p_threshold')
wk.connect(inputspec, 'height_thresh', thresh, 'height_threshold')
outputspec = pe.Node(niu.IdentityInterface(fields=[
'RPVimage', 'beta_images', 'mask_image', 'residual_image',
'con_images', 'ess_images', 'spmF_images', 'spmT_images',
'spm_mat_file', 'pre_topo_fdr_map', 'thresholded_map'
]),
name='outputspec')
wk.connect(est_model, 'RPVimage', outputspec, 'RPVimage')
wk.connect(est_model, 'beta_images', outputspec, 'beta_images')
wk.connect(est_model, 'mask_image', outputspec, 'mask_image')
wk.connect(est_model, 'residual_image', outputspec, 'residual_image')
wk.connect(est_cont, 'con_images', outputspec, 'con_images')
wk.connect(est_cont, 'ess_images', outputspec, 'ess_images')
wk.connect(est_cont, 'spmF_images', outputspec, 'spmF_images')
wk.connect(est_cont, 'spmT_images', outputspec, 'spmT_images')
wk.connect(est_cont, 'spm_mat_file', outputspec, 'spm_mat_file')
wk.connect(thresh, 'pre_topo_fdr_map', outputspec, 'pre_topo_fdr_map')
wk.connect(thresh, 'thresholded_map', outputspec, 'thresholded_map')
return wk
