Note
Go to the end to download the full example code or to run this example in your browser via Binder.
Classification of age groups using functional connectivity¶
This example compares different kinds of functional connectivity between regions of interest : correlation, partial correlation, and tangent space embedding.
The resulting connectivity coefficients can be used to discriminate children from adults.In general, the tangent space embedding outperforms the standard correlations: see Dadi et al.[1] for a careful study.
Warning
If you are using Nilearn with a version older than 0.9.0,
then you should either upgrade your version or import maskers
from the input_data module instead of the maskers module.
That is, you should manually replace in the following example all occurrences of:
from nilearn.maskers import NiftiMasker
with:
from nilearn.input_data import NiftiMasker
Load brain development fMRI dataset and MSDL atlas¶
We study only 30 subjects from the dataset, to save computation time.
from nilearn.datasets import fetch_atlas_msdl, fetch_development_fmri
from nilearn.plotting import plot_connectome, plot_matrix, show
development_dataset = fetch_development_fmri(n_subjects=30)
[fetch_development_fmri] Dataset found in
/home/runner/nilearn_data/development_fmri
[fetch_development_fmri] Dataset found in
/home/runner/nilearn_data/development_fmri/development_fmri
[fetch_development_fmri] Dataset found in
/home/runner/nilearn_data/development_fmri/development_fmri
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3ea4712b400183b70b7/ ...
[fetch_development_fmri] ...done. (4 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3eb2286e80019c3c194/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3e52286e80018c3e439/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3e72286e80017c41b3d/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3e4a743a9001760814f/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3e54712b400183b70a5/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3e14712b400183b7097/ ...
[fetch_development_fmri] ...done. (4 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3e32286e80018c3e42c/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3e9a743a90017608158/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3e82286e80018c3e443/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb47052f2be3c0017057069/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46e5c353c5800199ac79f/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb47045a3bc970019f073a0/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46e913992690018133b1c/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3912286e80018c3e393/ ...
[fetch_development_fmri] ...done. (4 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3952286e80017c41a1b/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb47023353c58001c9ac02b/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46eaa39926900160f69af/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff391a743a900176080a9/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3914712b400173b5329/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb4702a353c58001b9cb5ae/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46e9b39926900190fad5c/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
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[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3914712b4001a3b5579/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb470413992690018133d8c/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46e9a353c58001c9abeac/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb47016a3bc970017efe44f/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46e43f2be3c0017056b8a/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff37da743a90018606df1/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff37c2286e80019c3c102/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb47056353c58001c9ac064/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46e5af2be3c001801f799/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff38ca743a90018606dfe/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff38ca743a9001760809e/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff389a743a9001660a016/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff38c2286e80016c3c2da/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3884712b400183b7023/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3884712b400193b5b5c/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3872286e80017c419ea/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3872286e80017c419e9/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb4702f39926900171090ee/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46e8b353c58001c9abe98/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3842286e80017c419e0/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3854712b4001a3b5568/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3814712b4001a3b5561/ ...
[fetch_development_fmri] ...done. (4 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3832286e80016c3c2d1/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3822286e80018c3e37b/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff382a743a90018606df8/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff37e2286e80016c3c2cb/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff3832286e80019c3c10f/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff37d4712b400193b5b54/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff37d4712b400183b7011/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb4701e3992690018133d4f/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46e6b353c58001b9cb34f/ ...
[fetch_development_fmri] ...done. (2 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb4703bf2be3c001801fa49/ ...
[fetch_development_fmri] ...done. (4 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5cb46e92a3bc970019f0717f/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff38c4712b4001a3b5573/ ...
[fetch_development_fmri] ...done. (1 seconds, 0 min)
[fetch_development_fmri] Downloading data from
https://osf.io/download/5c8ff38da743a900176080a2/ ...
[fetch_development_fmri] ...done. (3 seconds, 0 min)
We use probabilistic regions of interest (ROIs) from the MSDL atlas.
msdl_data = fetch_atlas_msdl()
msdl_coords = msdl_data.region_coords
n_regions = len(msdl_coords)
print(
f"MSDL has {n_regions} ROIs, "
f"part of the following networks:\n{msdl_data.networks}."
)
[fetch_atlas_msdl] Dataset found in /home/runner/nilearn_data/msdl_atlas
MSDL has 39 ROIs, part of the following networks:
['Aud', 'Aud', 'Striate', 'DMN', 'DMN', 'DMN', 'DMN', 'Occ post', 'Motor', 'R V Att', 'R V Att', 'R V Att', 'R V Att', 'Basal', 'L V Att', 'L V Att', 'L V Att', 'D Att', 'D Att', 'Vis Sec', 'Vis Sec', 'Vis Sec', 'Salience', 'Salience', 'Salience', 'Temporal', 'Temporal', 'Language', 'Language', 'Language', 'Language', 'Language', 'Cereb', 'Dors PCC', 'Cing-Ins', 'Cing-Ins', 'Cing-Ins', 'Ant IPS', 'Ant IPS'].
Region signals extraction¶
To extract regions time series, we instantiate a
NiftiMapsMasker object and pass the atlas the
file name to it, as well as filtering band-width and detrending option.
from nilearn.maskers import NiftiMapsMasker
masker = NiftiMapsMasker(
msdl_data.maps,
resampling_target="data",
t_r=development_dataset.t_r,
detrend=True,
low_pass=0.1,
high_pass=0.01,
memory="nilearn_cache",
memory_level=1,
standardize="zscore_sample",
standardize_confounds=True,
verbose=1,
)
Then we compute region signals and extract useful phenotypic information.
children = []
pooled_subjects = []
groups = [] # child or adult
for func_file, confound_file, phenotype in zip(
development_dataset.func,
development_dataset.confounds,
development_dataset.phenotypic["Child_Adult"],
strict=False,
):
time_series = masker.fit_transform(func_file, confounds=confound_file)
pooled_subjects.append(time_series)
if phenotype == "child":
children.append(time_series)
groups.append(phenotype)
print(f"Data has {len(children)} children.")
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
________________________________________________________________________________
[Memory] Calling nilearn.image.resampling.resample_img...
resample_img(<nibabel.nifti1.Nifti1Image object at 0x7fac59290a00>, interpolation='linear', target_shape=(50, 59, 50), target_affine=array([[ 4., 0., 0., -96.],
[ 0., 4., 0., -132.],
[ 0., 0., 4., -78.],
[ 0., 0., 0., 1.]]))
_____________________________________________________resample_img - 0.2s, 0.0min
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar128_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac59292e90>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar128_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar128_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.2s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar126_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36d00760>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar126_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar126_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar125_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac577cb6d0>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar125_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar125_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar124_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac577cb730>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar124_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar124_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac59389a80>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar127_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac590b9a20>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar127_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar127_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar024_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36d03f40>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar024_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar024_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar023_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac3d90bcd0>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar023_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar023_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar022_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac577cb730>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar022_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar022_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.2s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar021_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac59389cc0>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar021_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar021_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.2s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar020_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36cda290>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar020_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar020_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar019_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac577cb700>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar019_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar019_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar018_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36cd9600>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar018_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar018_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar017_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac590b9900>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar017_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar017_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar016_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36cd9ff0>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar016_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar016_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac577cb730>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar001_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar013_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36d01450>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar013_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar013_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar012_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36cd9600>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar012_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar012_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar011_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac590b9930>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar011_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar011_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar010_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36cd9ff0>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar010_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar010_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar009_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac577cb640>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar009_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar009_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar008_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36d021a0>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar008_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar008_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar007_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36d01d20>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar007_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar007_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar006_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac577cb700>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar006_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar006_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar005_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac590b9a20>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar005_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar005_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar004_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac590b9a20>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar004_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar004_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac36cd9600>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar003_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac5938a500>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar002_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar014_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac577cb7f0>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar014_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar014_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
[NiftiMapsMasker.wrapped] Loading regions from
'/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii'
[NiftiMapsMasker.wrapped] Resampling regions
[NiftiMapsMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar015_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7fac590b9d20>, { 'allow_overlap': True,
'clean_args': None,
'clean_kwargs': {},
'cmap': 'CMRmap_r',
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'keep_masked_maps': False,
'low_pass': 0.1,
'maps_img': '/home/runner/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
'mask_img': None,
'reports': True,
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2,
'target_affine': None,
'target_shape': None}, confounds=[ '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar015_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1, sklearn_output_config=None)
[NiftiMapsMasker.wrapped] Loading data from
'/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar015_task-p
ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
[NiftiMapsMasker.wrapped] Extracting region signals
[NiftiMapsMasker.wrapped] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:70: DeprecationWarning: From release 0.14.0, confounds will be standardized using the sample std instead of the population std.
time_series = masker.fit_transform(func_file, confounds=confound_file)
_______________________________________________filter_and_extract - 1.1s, 0.0min
Data has 24 children.
ROI-to-ROI correlations of children¶
The simpler and most commonly used kind of connectivity is correlation. It
models the full (marginal) connectivity between pairwise ROIs. We can
estimate it using ConnectivityMeasure.
from nilearn.connectome import ConnectivityMeasure
correlation_measure = ConnectivityMeasure(
kind="correlation", standardize="zscore_sample", verbose=1
)
From the list of ROIs time-series for children, the correlation_measure computes individual correlation matrices.
correlation_matrices = correlation_measure.fit_transform(children)
# All individual coefficients are stacked in a unique 2D matrix.
print(
"Correlations of children are stacked "
f"in an array of shape {correlation_matrices.shape}"
)
[ConnectivityMeasure.wrapped] Finished fit
Correlations of children are stacked in an array of shape (24, 39, 39)
as well as the average correlation across all fitted subjects.
mean_correlation_matrix = correlation_measure.mean_
print(f"Mean correlation has shape {mean_correlation_matrix.shape}.")
Mean correlation has shape (39, 39).
We display the connectome matrices of the first 3 children
import numpy as np
from matplotlib import pyplot as plt
_, axes = plt.subplots(1, 3, figsize=(15, 5))
vmax = np.absolute(correlation_matrices).max()
for i, (matrix, ax) in enumerate(
zip(correlation_matrices, axes, strict=False)
):
plot_matrix(
matrix,
tri="lower",
axes=ax,
title=f"correlation, child {i}",
vmax=vmax,
vmin=-vmax,
)
The blocks structure that reflect functional networks are visible.
Now we display as a connectome the mean correlation matrix over all children.
plot_connectome(
mean_correlation_matrix,
msdl_coords,
title="mean correlation over all children",
)
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fac59210b80>
Studying partial correlations¶
We can also study direct connections, revealed by partial correlation coefficients. We just change the ConnectivityMeasure kind
partial_correlation_measure = ConnectivityMeasure(
kind="partial correlation", standardize="zscore_sample", verbose=1
)
partial_correlation_matrices = partial_correlation_measure.fit_transform(
children
)
[ConnectivityMeasure.wrapped] Finished fit
Most of direct connections are weaker than full connections.
_, axes = plt.subplots(1, 3, figsize=(15, 5))
vmax = np.absolute(partial_correlation_matrices).max()
for i, (matrix, ax) in enumerate(
zip(partial_correlation_matrices, axes, strict=False)
):
plot_matrix(
matrix,
tri="lower",
axes=ax,
title=f"partial correlation, child {i}",
vmax=vmax,
vmin=-vmax,
)
plot_connectome(
partial_correlation_measure.mean_,
msdl_coords,
title="mean partial correlation over all children",
)
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fac34346a40>
Extract subjects variabilities around a group connectivity¶
We can use both correlations and partial correlations to capture reproducible connectivity patterns at the group-level. This is done by the tangent space embedding.
tangent_measure = ConnectivityMeasure(
kind="tangent", standardize="zscore_sample", verbose=1
)
We fit our children group and get the group connectivity matrix stored as in tangent_measure.mean_, and individual deviation matrices of each subject from it.
tangent_matrices model individual connectivities as perturbations of the group connectivity matrix tangent_measure.mean_. Keep in mind that these subjects-to-group variability matrices do not directly reflect individual brain connections. For instance negative coefficients can not be interpreted as anticorrelated regions.
_, axes = plt.subplots(1, 3, figsize=(15, 5))
for i, (matrix, ax) in enumerate(zip(tangent_matrices, axes, strict=False)):
plot_matrix(
matrix,
tri="lower",
axes=ax,
title=f"tangent offset, child {i}",
)
The average tangent matrix cannot be interpreted, as individual matrices represent deviations from the mean, which is set to 0.
What kind of connectivity is most powerful for classification?¶
We will use connectivity matrices as features to distinguish children from adults. We use cross-validation and measure classification accuracy to compare the different kinds of connectivity matrices. We use random splits of the subjects into training/testing sets. StratifiedShuffleSplit allows preserving the proportion of children in the test set.
from sklearn.metrics import accuracy_score
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.svm import LinearSVC
kinds = ["correlation", "partial correlation", "tangent"]
_, classes = np.unique(groups, return_inverse=True)
cv = StratifiedShuffleSplit(n_splits=15, random_state=0, test_size=5)
pooled_subjects = np.asarray(pooled_subjects)
scores = {}
for kind in kinds:
scores[kind] = []
for train, test in cv.split(pooled_subjects, classes):
# *ConnectivityMeasure* can output the estimated subjects coefficients
# as a 1D arrays through the parameter *vectorize*.
connectivity = ConnectivityMeasure(
kind=kind, vectorize=True, standardize="zscore_sample", verbose=1
)
# build vectorized connectomes for subjects in the train set
connectomes = connectivity.fit_transform(pooled_subjects[train])
# fit the classifier
classifier = LinearSVC(dual=True).fit(connectomes, classes[train])
# make predictions for the left-out test subjects
predictions = classifier.predict(
connectivity.transform(pooled_subjects[test])
)
# store the accuracy for this cross-validation fold
scores[kind].append(accuracy_score(classes[test], predictions))
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
[ConnectivityMeasure.wrapped] Finished fit
display the results
mean_scores = [np.mean(scores[kind]) for kind in kinds]
scores_std = [np.std(scores[kind]) for kind in kinds]
plt.figure(figsize=(6, 4), constrained_layout=True)
positions = np.arange(len(kinds)) * 0.1 + 0.1
plt.barh(positions, mean_scores, align="center", height=0.05, xerr=scores_std)
yticks = [k.replace(" ", "\n") for k in kinds]
plt.yticks(positions, yticks)
plt.gca().grid(True)
plt.gca().set_axisbelow(True)
plt.gca().axvline(0.8, color="red", linestyle="--")
plt.xlabel("Classification accuracy\n(red line = chance level)")
Text(0.5, 34.367, 'Classification accuracy\n(red line = chance level)')
This is a small example to showcase nilearn features. In practice such comparisons need to be performed on much larger cohorts and several datasets. Dadi et al.[1] showed that across many cohorts and clinical questions, the tangent kind should be preferred.
show()
/home/runner/work/nilearn/nilearn/examples/03_connectivity/plot_group_level_connectivity.py:272: UserWarning: You are using the 'agg' matplotlib backend that is non-interactive.
No figure will be plotted when calling matplotlib.pyplot.show() or nilearn.plotting.show().
You can fix this by installing a different backend: for example via
pip install PyQt6
show()
References¶
Total running time of the script: (3 minutes 8.615 seconds)
Estimated memory usage: 1235 MB
