This example covers the normalization of data. Some people prefer to normalize the data during the preprocessing, just before smoothing. I prefer to do the 1st-level analysis completely in subject space and only normalize the contrasts for the 2nd-level analysis. But both approaches are fine.
For the current example, we will take the computed 1st-level contrasts from the previous experiment (again once done with fwhm=4mm and fwhm=8mm) and normalize them into MNI-space. To show two different approaches, we will do the normalization once with ANTs and once with SPM.
The normalization with ANTs requires that you first compute the transformation matrix that would bring the anatomical images of each subject into template space. Depending on your system this might take a few hours per subject. To facilitate this step, the transformation matrix is already computed for the T1 images.
The data for it can be found under:
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!ls /data/ds000114/derivatives/fmriprep/sub-*/anat/*h5
If you want to compute the transformation matrix yourself, either use fmriprep, as in our example dataset.
Alternatively, you can also create a ANTS registration pipeline yourself. An example of such a pipeline would look as follows (note, that you can load the script to see the workflow, but you don't have to run it, we will NOT use the output of the workflow in this nothebook):
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%load scripts/ANTS_registration.py
Now, let's start with the ANTs normalization workflow!
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from os.path import join as opj
from nipype.interfaces.ants import ApplyTransforms
from nipype.interfaces.utility import IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
from nipype.pipeline.engine import Workflow, Node, MapNode
from nipype.interfaces.fsl import Info
It's always a good idea to specify all parameters that might change between experiments at the beginning of your script. And remember that we decided to run the group analysis without subject sub-01, sub-06 and sub-10 because they are left handed (see this section).
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experiment_dir = '/output'
output_dir = 'datasink'
working_dir = 'workingdir'
# list of subject identifiers (remember we use only right handed subjects)
subject_list = ['sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-07', 'sub-08', 'sub-09']
# task name
task_name = "fingerfootlips"
# Smoothing widths used during preprocessing
fwhm = [4, 8]
# Template to normalize to
template = '/data/ds000114/derivatives/fmriprep/mni_icbm152_nlin_asym_09c/1mm_T1.nii.gz'
Note that the template file might not be in your data directory. To get mni_icbm152_nlin_asym_09c, either download it from this website, unpack it and move it to /data/ds000114/derivatives/fmriprep/ or run the following command in a cell:
%%bash
curl -L https://files.osf.io/v1/resources/fvuh8/providers/osfstorage/580705089ad5a101f17944a9 \
-o /data/ds000114/derivatives/fmriprep/mni_icbm152_nlin_asym_09c.tar.gz
tar xf /data/ds000114/derivatives/fmriprep/mni_icbm152_nlin_asym_09c.tar.gz \
-C /data/ds000114/derivatives/fmriprep/.
rm /data/ds000114/derivatives/fmriprep/mni_icbm152_nlin_asym_09c.tar.gz
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# Apply Transformation - applies the normalization matrix to contrast images
apply2con = MapNode(ApplyTransforms(args='--float',
input_image_type=3,
interpolation='BSpline',
invert_transform_flags=[False],
num_threads=1,
reference_image=template,
terminal_output='file'),
name='apply2con', iterfield=['input_image'])
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# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'fwhm_id']),
name="infosource")
infosource.iterables = [('subject_id', subject_list),
('fwhm_id', fwhm)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
templates = {'con': opj(output_dir, '1stLevel',
'{subject_id}/fwhm-{fwhm_id}', '???_00??.nii'),
'transform': opj('/data/ds000114/derivatives/fmriprep/', '{subject_id}', 'anat',
'{subject_id}_t1w_space-mni152nlin2009casym_warp.h5')}
selectfiles = Node(SelectFiles(templates,
base_directory=experiment_dir,
sort_filelist=True),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
# Use the following DataSink output substitutions
substitutions = [('_subject_id_', '')]
subjFolders = [('_fwhm_id_%s%s' % (f, sub), '%s_fwhm%s' % (sub, f))
for f in fwhm
for sub in subject_list]
subjFolders += [('_apply2con%s/' % (i), '') for i in range(9)] # number of contrast used in 1stlevel an.
substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
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# Initiation of the ANTs normalization workflow
antsflow = Workflow(name='antsflow')
antsflow.base_dir = opj(experiment_dir, working_dir)
# Connect up the ANTs normalization components
antsflow.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('fwhm_id', 'fwhm_id')]),
(selectfiles, apply2con, [('con', 'input_image'),
('transform', 'transforms')]),
(apply2con, datasink, [('output_image', 'norm_ants.@con')]),
])
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# Create ANTs normalization graph
antsflow.write_graph(graph2use='colored', format='png', simple_form=True)
# Visualize the graph
from IPython.display import Image
Image(filename=opj(antsflow.base_dir, 'antsflow', 'graph.dot.png'))
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antsflow.run('MultiProc', plugin_args={'n_procs': 4})
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from os.path import join as opj
from nipype.interfaces.spm import Normalize12
from nipype.interfaces.utility import IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
from nipype.algorithms.misc import Gunzip
from nipype.pipeline.engine import Workflow, Node
It's always a good idea to specify all parameters that might change between experiments at the beginning of your script. And remember that we decided to run the group analysis without subject sub-01, sub-06 and sub-10 because they are left handed (see this section).
In [ ]:
experiment_dir = '/output'
output_dir = 'datasink'
working_dir = 'workingdir'
# list of subject identifiers
subject_list = ['sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-07', 'sub-08', 'sub-09']
# task name
task_name = "fingerfootlips"
# Smoothing withds used during preprocessing
fwhm = [4, 8]
template = '/opt/spm12/spm12_mcr/spm/spm12/tpm/TPM.nii'
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# Gunzip - unzip the anatomical image
gunzip = Node(Gunzip(), name="gunzip")
# Normalize - normalizes functional and structural images to the MNI template
normalize = Node(Normalize12(jobtype='estwrite',
tpm=template,
write_voxel_sizes=[1, 1, 1]),
name="normalize")
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# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'fwhm_id']),
name="infosource")
infosource.iterables = [('subject_id', subject_list),
('fwhm_id', fwhm)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
templates = {'con': opj(output_dir, '1stLevel',
'{subject_id}/fwhm-{fwhm_id}', '???_00??.nii'),
'anat': opj('/data/ds000114/derivatives', 'fmriprep', '{subject_id}',
'anat', '{subject_id}_t1w_preproc.nii.gz')}
selectfiles = Node(SelectFiles(templates,
base_directory=experiment_dir,
sort_filelist=True),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
# Use the following DataSink output substitutions
substitutions = [('_subject_id_', '')]
subjFolders = [('_fwhm_id_%s%s' % (f, sub), '%s_fwhm%s' % (sub, f))
for f in fwhm
for sub in subject_list]
substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
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# Specify Normalization-Workflow & Connect Nodes
spmflow = Workflow(name='spmflow')
spmflow.base_dir = opj(experiment_dir, working_dir)
# Connect up SPM normalization components
spmflow.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('fwhm_id', 'fwhm_id')]),
(selectfiles, normalize, [('con', 'apply_to_files')]),
(selectfiles, gunzip, [('anat', 'in_file')]),
(gunzip, normalize, [('out_file', 'image_to_align')]),
(normalize, datasink, [('normalized_files', 'norm_spm.@files'),
('normalized_image', 'norm_spm.@image'),
]),
])
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# Create SPM normalization graph
spmflow.write_graph(graph2use='colored', format='png', simple_form=True)
# Visualize the graph
from IPython.display import Image
Image(filename=opj(spmflow.base_dir, 'spmflow', 'graph.dot.png'))
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spmflow.run('MultiProc', plugin_args={'n_procs': 4})
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%matplotlib inline
from nilearn.plotting import plot_stat_map
anatimg = '/data/ds000114/derivatives/fmriprep/mni_icbm152_nlin_asym_09c/1mm_T1.nii.gz'
First, let's compare the normalization of the anatomical images:
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plot_stat_map(
'/data/ds000114/derivatives/fmriprep/sub-02/anat/sub-02_t1w_space-mni152nlin2009casym_preproc.nii.gz',
title='anatomy - ANTs (normalized to ICBM152)', bg_img=anatimg,
threshold=200, display_mode='ortho', cut_coords=(-50, 0, -10))
plot_stat_map(
'/output/datasink/norm_spm/sub-02_fwhm4/wsub-02_t1w_preproc.nii',
title='anatomy - SPM (normalized to SPM\'s TPM)', bg_img=anatimg,
threshold=200, display_mode='ortho', cut_coords=(-50, 0, -10))
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And what about the contrast images for Finger > others?
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plot_stat_map(
'/output/datasink/norm_ants/sub-02_fwhm8/con_0005_trans.nii', title='contrast5 - fwhm=8 - ANTs',
bg_img=anatimg, threshold=2, vmax=5, display_mode='ortho', cut_coords=(-39, -37, 56))
plot_stat_map(
'/output/datasink/norm_spm/sub-02_fwhm8/wcon_0005.nii', title='contrast5 - fwhm=8 - SPM',
bg_img=anatimg, threshold=2, vmax=5, display_mode='ortho', cut_coords=(-39, -37, 56))
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from nilearn.plotting import plot_glass_brain
plot_glass_brain(
'/output/datasink/norm_ants/sub-02_fwhm8/con_0005_trans.nii', colorbar=True,
threshold=3, display_mode='lyrz', black_bg=True, vmax=6, title='contrast5 - fwhm=8 - ANTs')
plot_glass_brain(
'/output/datasink/norm_spm/sub-02_fwhm8/wcon_0005.nii', colorbar=True,
threshold=3, display_mode='lyrz', black_bg=True, vmax=6, title='contrast5 - fwhm=8 - SPM')
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