.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_06_manipulating_images_plot_nifti_labels_simple.py: Extracting signals from brain regions using the NiftiLabelsMasker ================================================================= This simple example shows how to extract signals from functional fmri data and brain regions defined through an atlas. More precisely, this example shows how to use the NiftiLabelsMasker object to perform this operation in just a few lines of code. Retrieve the brain development functional dataset We start by fetching the brain development functional dataset and we restrict the example to one subject only. .. code-block:: default from nilearn import datasets dataset = datasets.fetch_development_fmri(n_subjects=1) func_filename = dataset.func[0] # print basic information on the dataset print('First functional nifti image (4D) is at: %s' % func_filename) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none First functional nifti image (4D) is at: /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz Load an atlas We then load the Harvard-Oxford atlas to define the brain regions .. code-block:: default atlas = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm') # The first label correspond to the background print('The atlas contains {} non-overlapping regions'.format( len(atlas.labels) - 1)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none The atlas contains 48 non-overlapping regions Instantiate the mask and visualize atlas .. code-block:: default from nilearn.input_data import NiftiLabelsMasker # Instantiate the masker with label image and label values masker = NiftiLabelsMasker(atlas.maps, labels=atlas.labels, standardize=True) # Visualize the atlas # Note that we need to call fit prior to generating the mask masker.fit() # At this point, no functional image has been provided to the masker. # We can still generate a report which can be displayed in a Jupyter # Notebook, opened in a browser using the .open_in_browser() method, # or saved to a file using the .save_as_html(output_filepath) method. report = masker.generate_report() report .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/nicolas/GitRepos/nilearn-fork/nilearn/input_data/nifti_labels_masker.py:303: UserWarning: No image provided to fit in NiftiLabelsMasker. Plotting ROIs of label image on the MNI152Template for reporting. warnings.warn(msg) .. raw:: html

NiftiLabelsMasker Class for masking of Niimg-like objects. NiftiLabelsMasker is useful when data from non-overlapping volumes should be extracted (contrarily to NiftiMapsMasker). Use case: Summarize brain signals from clusters that were obtained by prior K-means or Ward clustering.

image

No image provided to fit in NiftiLabelsMasker. Plotting ROIs of label image on the MNI152Template for reporting.

This reports shows the regions defined by the labels of the mask.

The masker has 48 different non-overlapping regions.

Regions summary
label value region name size (in mm^3) relative size (in %)
1 Frontal Pole 123176 11.75
2 Insular Cortex 18728 1.79
3 Superior Frontal Gyrus 40640 3.88
4 Middle Frontal Gyrus 42528 4.06
5 Inferior Frontal Gyrus, pars triangularis 8824 0.84
6 Inferior Frontal Gyrus, pars opercularis 11072 1.06
7 Precentral Gyrus 68584 6.54
8 Temporal Pole 37688 3.59
9 Superior Temporal Gyrus, anterior division 4168 0.4
10 Superior Temporal Gyrus, posterior division 14640 1.4
11 Middle Temporal Gyrus, anterior division 6784 0.65
12 Middle Temporal Gyrus, posterior division 20200 1.93
13 Middle Temporal Gyrus, temporooccipital part 16032 1.53
14 Inferior Temporal Gyrus, anterior division 5176 0.49
15 Inferior Temporal Gyrus, posterior division 15536 1.48
16 Inferior Temporal Gyrus, temporooccipital part 11760 1.12
17 Postcentral Gyrus 55160 5.26
18 Superior Parietal Lobule 23264 2.22
19 Supramarginal Gyrus, anterior division 13936 1.33
20 Supramarginal Gyrus, posterior division 18072 1.72
21 Angular Gyrus 19272 1.84
22 Lateral Occipital Cortex, superior division 78232 7.46
23 Lateral Occipital Cortex, inferior division 32712 3.12
24 Intracalcarine Cortex 11208 1.07
25 Frontal Medial Cortex 7808 0.74
26 Juxtapositional Lobule Cortex (formerly Supplementary Motor Cortex) 11872 1.13
27 Subcallosal Cortex 9136 0.87
28 Paracingulate Gyrus 23552 2.25
29 Cingulate Gyrus, anterior division 20736 1.98
30 Cingulate Gyrus, posterior division 19296 1.84
31 Precuneous Cortex 44984 4.29
32 Cuneal Cortex 9816 0.94
33 Frontal Orbital Cortex 25184 2.4
34 Parahippocampal Gyrus, anterior division 9984 0.95
35 Parahippocampal Gyrus, posterior division 5680 0.54
36 Lingual Gyrus 27048 2.58
37 Temporal Fusiform Cortex, anterior division 4880 0.47
38 Temporal Fusiform Cortex, posterior division 12752 1.22
39 Temporal Occipital Fusiform Cortex 11752 1.12
40 Occipital Fusiform Gyrus 14448 1.38
41 Frontal Operculum Cortex 5496 0.52
42 Central Opercular Cortex 15088 1.44
43 Parietal Operculum Cortex 8952 0.85
44 Planum Polare 5992 0.57
45 Heschl's Gyrus (includes H1 and H2) 4832 0.46
46 Planum Temporale 7616 0.73
47 Supracalcarine Cortex 2088 0.2
48 Occipital Pole 42208 4.03
Parameters
Parameter Value
background_label 0
detrend False
dtype None
high_pass None
high_variance_confounds False
labels ['Background', 'Frontal Pole', 'Insular Cortex', 'Superior Frontal Gyrus', 'Middle Frontal Gyrus', 'Inferior Frontal Gyrus, pars triangularis', 'Inferior Frontal Gyrus, pars opercularis', 'Precentral Gyrus', 'Temporal Pole', 'Superior Temporal Gyrus, anterior division', 'Superior Temporal Gyrus, posterior division', 'Middle Temporal Gyrus, anterior division', 'Middle Temporal Gyrus, posterior division', 'Middle Temporal Gyrus, temporooccipital part', 'Inferior Temporal Gyrus, anterior division', 'Inferior Temporal Gyrus, posterior division', 'Inferior Temporal Gyrus, temporooccipital part', 'Postcentral Gyrus', 'Superior Parietal Lobule', 'Supramarginal Gyrus, anterior division', 'Supramarginal Gyrus, posterior division', 'Angular Gyrus', 'Lateral Occipital Cortex, superior division', 'Lateral Occipital Cortex, inferior division', 'Intracalcarine Cortex', 'Frontal Medial Cortex', 'Juxtapositional Lobule Cortex (formerly Supplementary Motor Cortex)', 'Subcallosal Cortex', 'Paracingulate Gyrus', 'Cingulate Gyrus, anterior division', 'Cingulate Gyrus, posterior division', 'Precuneous Cortex', 'Cuneal Cortex', 'Frontal Orbital Cortex', 'Parahippocampal Gyrus, anterior division', 'Parahippocampal Gyrus, posterior division', 'Lingual Gyrus', 'Temporal Fusiform Cortex, anterior division', 'Temporal Fusiform Cortex, posterior division', 'Temporal Occipital Fusiform Cortex', 'Occipital Fusiform Gyrus', 'Frontal Operculum Cortex', 'Central Opercular Cortex', 'Parietal Operculum Cortex', 'Planum Polare', "Heschl's Gyrus (includes H1 and H2)", 'Planum Temporale', 'Supracalcarine Cortex', 'Occipital Pole']
labels_img <class 'nibabel.nifti1.Nifti1Image'> data shape (91, 109, 91) affine: [[ 2. 0. 0. -90.] [ 0. 2. 0. -126.] [ 0. 0. 2. -72.] [ 0. 0. 0. 1.]] metadata: <class 'nibabel.nifti1.Nifti1Header'> object, endian='<' sizeof_hdr : 348 data_type : b'' db_name : b'' extents : 0 session_error : 0 regular : b'' dim_info : 0 dim : [ 3 91 109 91 1 1 1 1] intent_p1 : 0.0 intent_p2 : 0.0 intent_p3 : 0.0 intent_code : none datatype : uint8 bitpix : 8 slice_start : 0 pixdim : [1. 2. 2. 2. 1. 1. 1. 1.] vox_offset : 0.0 scl_slope : nan scl_inter : nan slice_end : 0 slice_code : unknown xyzt_units : 0 cal_max : 0.0 cal_min : 0.0 slice_duration : 0.0 toffset : 0.0 glmax : 0 glmin : 0 descrip : b'' aux_file : b'' qform_code : unknown sform_code : aligned quatern_b : 0.0 quatern_c : 0.0 quatern_d : 0.0 qoffset_x : -90.0 qoffset_y : -126.0 qoffset_z : -72.0 srow_x : [ 2. 0. 0. -90.] srow_y : [ 0. 2. 0. -126.] srow_z : [ 0. 0. 2. -72.] intent_name : b'' magic : b'n+1'
low_pass None
mask_img None
memory Memory(location=None)
memory_level 1
reports True
resampling_target data
smoothing_fwhm None
standardize True
standardize_confounds True
strategy mean
t_r None
verbose 0

This report was generated based on information provided at instantiation and fit time. Note that the masker can potentially perform resampling at transform time.



Fitting the mask and generating a report .. code-block:: default masker.fit(func_filename) # We can again generate a report, but this time, the provided functional # image is displayed with the ROI of the atlas. # The report also contains a summary table giving the region sizes in mm3 report = masker.generate_report() report .. raw:: html

NiftiLabelsMasker Class for masking of Niimg-like objects. NiftiLabelsMasker is useful when data from non-overlapping volumes should be extracted (contrarily to NiftiMapsMasker). Use case: Summarize brain signals from clusters that were obtained by prior K-means or Ward clustering.

image

This reports shows the regions defined by the labels of the mask.

The masker has 48 different non-overlapping regions.

Regions summary
label value region name size (in mm^3) relative size (in %)
1 Frontal Pole 123176 11.75
2 Insular Cortex 18728 1.79
3 Superior Frontal Gyrus 40640 3.88
4 Middle Frontal Gyrus 42528 4.06
5 Inferior Frontal Gyrus, pars triangularis 8824 0.84
6 Inferior Frontal Gyrus, pars opercularis 11072 1.06
7 Precentral Gyrus 68584 6.54
8 Temporal Pole 37688 3.59
9 Superior Temporal Gyrus, anterior division 4168 0.4
10 Superior Temporal Gyrus, posterior division 14640 1.4
11 Middle Temporal Gyrus, anterior division 6784 0.65
12 Middle Temporal Gyrus, posterior division 20200 1.93
13 Middle Temporal Gyrus, temporooccipital part 16032 1.53
14 Inferior Temporal Gyrus, anterior division 5176 0.49
15 Inferior Temporal Gyrus, posterior division 15536 1.48
16 Inferior Temporal Gyrus, temporooccipital part 11760 1.12
17 Postcentral Gyrus 55160 5.26
18 Superior Parietal Lobule 23264 2.22
19 Supramarginal Gyrus, anterior division 13936 1.33
20 Supramarginal Gyrus, posterior division 18072 1.72
21 Angular Gyrus 19272 1.84
22 Lateral Occipital Cortex, superior division 78232 7.46
23 Lateral Occipital Cortex, inferior division 32712 3.12
24 Intracalcarine Cortex 11208 1.07
25 Frontal Medial Cortex 7808 0.74
26 Juxtapositional Lobule Cortex (formerly Supplementary Motor Cortex) 11872 1.13
27 Subcallosal Cortex 9136 0.87
28 Paracingulate Gyrus 23552 2.25
29 Cingulate Gyrus, anterior division 20736 1.98
30 Cingulate Gyrus, posterior division 19296 1.84
31 Precuneous Cortex 44984 4.29
32 Cuneal Cortex 9816 0.94
33 Frontal Orbital Cortex 25184 2.4
34 Parahippocampal Gyrus, anterior division 9984 0.95
35 Parahippocampal Gyrus, posterior division 5680 0.54
36 Lingual Gyrus 27048 2.58
37 Temporal Fusiform Cortex, anterior division 4880 0.47
38 Temporal Fusiform Cortex, posterior division 12752 1.22
39 Temporal Occipital Fusiform Cortex 11752 1.12
40 Occipital Fusiform Gyrus 14448 1.38
41 Frontal Operculum Cortex 5496 0.52
42 Central Opercular Cortex 15088 1.44
43 Parietal Operculum Cortex 8952 0.85
44 Planum Polare 5992 0.57
45 Heschl's Gyrus (includes H1 and H2) 4832 0.46
46 Planum Temporale 7616 0.73
47 Supracalcarine Cortex 2088 0.2
48 Occipital Pole 42208 4.03
Parameters
Parameter Value
background_label 0
detrend False
dtype None
high_pass None
high_variance_confounds False
labels ['Background', 'Frontal Pole', 'Insular Cortex', 'Superior Frontal Gyrus', 'Middle Frontal Gyrus', 'Inferior Frontal Gyrus, pars triangularis', 'Inferior Frontal Gyrus, pars opercularis', 'Precentral Gyrus', 'Temporal Pole', 'Superior Temporal Gyrus, anterior division', 'Superior Temporal Gyrus, posterior division', 'Middle Temporal Gyrus, anterior division', 'Middle Temporal Gyrus, posterior division', 'Middle Temporal Gyrus, temporooccipital part', 'Inferior Temporal Gyrus, anterior division', 'Inferior Temporal Gyrus, posterior division', 'Inferior Temporal Gyrus, temporooccipital part', 'Postcentral Gyrus', 'Superior Parietal Lobule', 'Supramarginal Gyrus, anterior division', 'Supramarginal Gyrus, posterior division', 'Angular Gyrus', 'Lateral Occipital Cortex, superior division', 'Lateral Occipital Cortex, inferior division', 'Intracalcarine Cortex', 'Frontal Medial Cortex', 'Juxtapositional Lobule Cortex (formerly Supplementary Motor Cortex)', 'Subcallosal Cortex', 'Paracingulate Gyrus', 'Cingulate Gyrus, anterior division', 'Cingulate Gyrus, posterior division', 'Precuneous Cortex', 'Cuneal Cortex', 'Frontal Orbital Cortex', 'Parahippocampal Gyrus, anterior division', 'Parahippocampal Gyrus, posterior division', 'Lingual Gyrus', 'Temporal Fusiform Cortex, anterior division', 'Temporal Fusiform Cortex, posterior division', 'Temporal Occipital Fusiform Cortex', 'Occipital Fusiform Gyrus', 'Frontal Operculum Cortex', 'Central Opercular Cortex', 'Parietal Operculum Cortex', 'Planum Polare', "Heschl's Gyrus (includes H1 and H2)", 'Planum Temporale', 'Supracalcarine Cortex', 'Occipital Pole']
labels_img <class 'nibabel.nifti1.Nifti1Image'> data shape (91, 109, 91) affine: [[ 2. 0. 0. -90.] [ 0. 2. 0. -126.] [ 0. 0. 2. -72.] [ 0. 0. 0. 1.]] metadata: <class 'nibabel.nifti1.Nifti1Header'> object, endian='<' sizeof_hdr : 348 data_type : b'' db_name : b'' extents : 0 session_error : 0 regular : b'' dim_info : 0 dim : [ 3 91 109 91 1 1 1 1] intent_p1 : 0.0 intent_p2 : 0.0 intent_p3 : 0.0 intent_code : none datatype : uint8 bitpix : 8 slice_start : 0 pixdim : [1. 2. 2. 2. 1. 1. 1. 1.] vox_offset : 0.0 scl_slope : nan scl_inter : nan slice_end : 0 slice_code : unknown xyzt_units : 0 cal_max : 0.0 cal_min : 0.0 slice_duration : 0.0 toffset : 0.0 glmax : 0 glmin : 0 descrip : b'' aux_file : b'' qform_code : unknown sform_code : aligned quatern_b : 0.0 quatern_c : 0.0 quatern_d : 0.0 qoffset_x : -90.0 qoffset_y : -126.0 qoffset_z : -72.0 srow_x : [ 2. 0. 0. -90.] srow_y : [ 0. 2. 0. -126.] srow_z : [ 0. 0. 2. -72.] intent_name : b'' magic : b'n+1'
low_pass None
mask_img None
memory Memory(location=None)
memory_level 1
reports True
resampling_target data
smoothing_fwhm None
standardize True
standardize_confounds True
strategy mean
t_r None
verbose 0

This report was generated based on information provided at instantiation and fit time. Note that the masker can potentially perform resampling at transform time.



Process the data with the NiftiLablesMasker In order to extract the signals, we need to call transform on the functional data .. code-block:: default signals = masker.transform(func_filename) # signals is a 2D matrix, (n_time_points x n_regions) signals.shape .. rst-class:: sphx-glr-script-out Out: .. code-block:: none (168, 48) Plot the signals .. code-block:: default import matplotlib.pyplot as plt fig = plt.figure(figsize=(15, 5)) ax = fig.add_subplot(111) for label_idx in range(3): ax.plot(signals[:, label_idx], linewidth=2, label=atlas.labels[label_idx + 1]) # 0 is background ax.legend(loc=2) ax.set_title("Signals for first 3 regions") plt.show() .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_labels_simple_001.png :alt: Signals for first 3 regions :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 4.936 seconds) .. _sphx_glr_download_auto_examples_06_manipulating_images_plot_nifti_labels_simple.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/06_manipulating_images/plot_nifti_labels_simple.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nifti_labels_simple.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nifti_labels_simple.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_