.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/06_manipulating_images/plot_nifti_labels_simple.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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 :term:`fMRI` data and brain regions defined through an atlas. More precisely, this example shows how to use the :class:`~nilearn.maskers.NiftiLabelsMasker` object to perform this operation in just a few lines of code. .. include:: ../../../examples/masker_note.rst .. GENERATED FROM PYTHON SOURCE LINES 14-20 .. code-block:: Python from nilearn._utils.helpers import check_matplotlib check_matplotlib() .. GENERATED FROM PYTHON SOURCE LINES 21-26 Retrieve the brain development functional dataset ------------------------------------------------- We start by fetching the brain development functional dataset and we restrict the example to one subject only. .. GENERATED FROM PYTHON SOURCE LINES 26-34 .. code-block:: Python from nilearn.datasets import fetch_atlas_harvard_oxford, fetch_development_fmri dataset = fetch_development_fmri(n_subjects=1) func_filename = dataset.func[0] # print basic information on the dataset print(f"First functional nifti image (4D) is at: {func_filename}") .. rst-class:: sphx-glr-script-out .. code-block:: none [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 First functional nifti image (4D) is at: /home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz .. GENERATED FROM PYTHON SOURCE LINES 35-41 Load an atlas ------------- We then load the Harvard-Oxford atlas to define the brain regions and the first label correspond to the background. .. GENERATED FROM PYTHON SOURCE LINES 41-45 .. code-block:: Python atlas = fetch_atlas_harvard_oxford("cort-maxprob-thr25-2mm") print(f"The atlas contains {len(atlas.labels) - 1} non-overlapping regions") .. rst-class:: sphx-glr-script-out .. code-block:: none [fetch_atlas_harvard_oxford] Dataset found in /home/runner/nilearn_data/fsl The atlas contains 48 non-overlapping regions .. GENERATED FROM PYTHON SOURCE LINES 46-51 Instantiate the mask and visualize atlas ---------------------------------------- Instantiate the masker with label image and label values .. GENERATED FROM PYTHON SOURCE LINES 51-57 .. code-block:: Python from nilearn.maskers import NiftiLabelsMasker masker = NiftiLabelsMasker( atlas.maps, lut=atlas.lut, standardize="zscore_sample", verbose=1 ) .. GENERATED FROM PYTHON SOURCE LINES 58-66 Visualize the atlas ------------------- We need to call fit prior to generating the mask. We can then generate a report to visualize the atlas. .. include:: ../../../examples/report_note.rst .. GENERATED FROM PYTHON SOURCE LINES 66-71 .. code-block:: Python masker.fit() report = masker.generate_report() report .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiLabelsMasker.fit] Loading regions from [NiftiLabelsMasker.fit] Finished fit /home/runner/work/nilearn/nilearn/examples/06_manipulating_images/plot_nifti_labels_simple.py:68: UserWarning: No image provided to fit in NiftiLabelsMasker. Plotting ROIs of label image on the MNI152Template for reporting. .. raw:: html

NiftiLabelsMasker Class for extracting data from Niimg-like objects using labels of non-overlapping brain regions. NiftiLabelsMasker is useful when data from non-overlapping volumes should be extracted (contrarily to :class:`nilearn.maskers.NiftiMapsMasker`). Use case: summarize brain signals from clusters that were obtained by prior K-means or Ward clustering. For more details on the definitions of labels in Nilearn, see the :ref:`region` section.

WARNING

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

This report 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 0.40
10 Superior Temporal Gyrus, posterior division 14640 1.40
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 2.40
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 Opercular Cortex 5496 0.52
42 Central Opercular Cortex 15088 1.44
43 Parietal Opercular 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 0.20
48 Occipital Pole 42208 4.03 
NiftiLabelsMasker(labels_img=<nibabel.nifti1.Nifti1Image object at 0x7f1ee360d180>,
                      lut=    index                                               name
    0       0                                         Background
    1       1                                       Frontal Pole
    2       2                                     Insular Cortex
    3       3                             Superior Frontal Gyrus
    4       4                               Middle Frontal Gyrus
    5       5          Inferior Frontal Gyrus, pars triangularis
    6       6           Inferior Frontal Gyrus, pars opercularis
    7       7                                   Precentral Gyrus
    8       8                                      Temporal Pole
    9       9         Superior Temporal Gyrus, anterior division
    10     10        Superior Temporal Gyrus, p...
    37     37        Temporal Fusiform Cortex, anterior division
    38     38       Temporal Fusiform Cortex, posterior division
    39     39                 Temporal Occipital Fusiform Cortex
    40     40                           Occipital Fusiform Gyrus
    41     41                           Frontal Opercular Cortex
    42     42                           Central Opercular Cortex
    43     43                          Parietal Opercular Cortex
    44     44                                      Planum Polare
    45     45                Heschl's Gyrus (includes H1 and H2)
    46     46                                   Planum Temporale
    47     47                              Supracalcarine Cortex
    48     48                                     Occipital Pole,
                      standardize='zscore_sample', verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

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



.. GENERATED FROM PYTHON SOURCE LINES 72-79 Fitting the masker on data and generating a report -------------------------------------------------- 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. .. GENERATED FROM PYTHON SOURCE LINES 79-84 .. code-block:: Python masker.fit(func_filename) report = masker.generate_report() report .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiLabelsMasker.fit] Loading regions from [NiftiLabelsMasker.fit] Resampling regions [NiftiLabelsMasker.fit] Finished fit .. raw:: html

NiftiLabelsMasker Class for extracting data from Niimg-like objects using labels of non-overlapping brain regions. NiftiLabelsMasker is useful when data from non-overlapping volumes should be extracted (contrarily to :class:`nilearn.maskers.NiftiMapsMasker`). Use case: summarize brain signals from clusters that were obtained by prior K-means or Ward clustering. For more details on the definitions of labels in Nilearn, see the :ref:`region` section.

image

This report 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 123008 11.76
2 Insular Cortex 18240 1.74
3 Superior Frontal Gyrus 40064 3.83
4 Middle Frontal Gyrus 42048 4.02
5 Inferior Frontal Gyrus, pars triangularis 8576 0.82
6 Inferior Frontal Gyrus, pars opercularis 10880 1.04
7 Precentral Gyrus 68352 6.53
8 Temporal Pole 38016 3.63
9 Superior Temporal Gyrus, anterior division 4160 0.40
10 Superior Temporal Gyrus, posterior division 14272 1.36
11 Middle Temporal Gyrus, anterior division 6528 0.62
12 Middle Temporal Gyrus, posterior division 20224 1.93
13 Middle Temporal Gyrus, temporooccipital part 15680 1.50
14 Inferior Temporal Gyrus, anterior division 5248 0.50
15 Inferior Temporal Gyrus, posterior division 15616 1.49
16 Inferior Temporal Gyrus, temporooccipital part 11648 1.11
17 Postcentral Gyrus 54400 5.20
18 Superior Parietal Lobule 24000 2.29
19 Supramarginal Gyrus, anterior division 14016 1.34
20 Supramarginal Gyrus, posterior division 17600 1.68
21 Angular Gyrus 19328 1.85
22 Lateral Occipital Cortex, superior division 78272 7.48
23 Lateral Occipital Cortex, inferior division 33600 3.21
24 Intracalcarine Cortex 11008 1.05
25 Frontal Medial Cortex 7744 0.74
26 Juxtapositional Lobule Cortex (formerly Supplementary Motor Cortex) 11968 1.14
27 Subcallosal Cortex 8960 0.86
28 Paracingulate Gyrus 23104 2.21
29 Cingulate Gyrus, anterior division 20480 1.96
30 Cingulate Gyrus, posterior division 19392 1.85
31 Precuneous Cortex 44800 4.28
32 Cuneal Cortex 10176 0.97
33 Frontal Orbital Cortex 26240 2.51
34 Parahippocampal Gyrus, anterior division 9728 0.93
35 Parahippocampal Gyrus, posterior division 5760 0.55
36 Lingual Gyrus 26816 2.56
37 Temporal Fusiform Cortex, anterior division 4864 0.47
38 Temporal Fusiform Cortex, posterior division 12224 1.17
39 Temporal Occipital Fusiform Cortex 11904 1.14
40 Occipital Fusiform Gyrus 14336 1.37
41 Frontal Opercular Cortex 5632 0.54
42 Central Opercular Cortex 14976 1.43
43 Parietal Opercular Cortex 9600 0.92
44 Planum Polare 5952 0.57
45 Heschl's Gyrus (includes H1 and H2) 4864 0.47
46 Planum Temporale 7680 0.73
47 Supracalcarine Cortex 1920 0.18
48 Occipital Pole 42112 4.03 
NiftiLabelsMasker(labels_img=<nibabel.nifti1.Nifti1Image object at 0x7f1ee360d180>,
                      lut=    index                                               name
    0       0                                         Background
    1       1                                       Frontal Pole
    2       2                                     Insular Cortex
    3       3                             Superior Frontal Gyrus
    4       4                               Middle Frontal Gyrus
    5       5          Inferior Frontal Gyrus, pars triangularis
    6       6           Inferior Frontal Gyrus, pars opercularis
    7       7                                   Precentral Gyrus
    8       8                                      Temporal Pole
    9       9         Superior Temporal Gyrus, anterior division
    10     10        Superior Temporal Gyrus, p...
    37     37        Temporal Fusiform Cortex, anterior division
    38     38       Temporal Fusiform Cortex, posterior division
    39     39                 Temporal Occipital Fusiform Cortex
    40     40                           Occipital Fusiform Gyrus
    41     41                           Frontal Opercular Cortex
    42     42                           Central Opercular Cortex
    43     43                          Parietal Opercular Cortex
    44     44                                      Planum Polare
    45     45                Heschl's Gyrus (includes H1 and H2)
    46     46                                   Planum Temporale
    47     47                              Supracalcarine Cortex
    48     48                                     Occipital Pole,
                      standardize='zscore_sample', verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

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



.. GENERATED FROM PYTHON SOURCE LINES 85-90 Process the data with the NiftiLablesMasker ------------------------------------------- In order to extract the signals, we need to call transform on the functional data. .. GENERATED FROM PYTHON SOURCE LINES 90-95 .. code-block:: Python signals = masker.transform(func_filename) # signals is a 2D numpy array, (n_time_points x n_regions) print(f"{signals.shape=}") .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiLabelsMasker.wrapped] Loading data from '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-p ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz' [NiftiLabelsMasker.wrapped] Extracting region signals [NiftiLabelsMasker.wrapped] Cleaning extracted signals signals.shape=(168, 48) .. GENERATED FROM PYTHON SOURCE LINES 96-102 Output to dataframe and plot ---------------------------- You can use 'set_output()' to decide the output format of 'transform'. If you want to output to a DataFrame, you an choose pandas and polars. .. GENERATED FROM PYTHON SOURCE LINES 102-109 .. code-block:: Python masker.set_output(transform="pandas") signals_df = masker.transform(func_filename) print(signals_df.head) signals_df[["Frontal Pole", "Insular Cortex", "Superior Frontal Gyrus"]].plot( title="Signals from 3 regions", figsize=(15, 5) ) .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_labels_simple_001.png :alt: Signals from 3 regions :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_nifti_labels_simple_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiLabelsMasker.wrapped] Loading data from '/home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-p ixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz' [NiftiLabelsMasker.wrapped] Extracting region signals [NiftiLabelsMasker.wrapped] Cleaning extracted signals .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 9.965 seconds) **Estimated memory usage:** 305 MB .. _sphx_glr_download_auto_examples_06_manipulating_images_plot_nifti_labels_simple.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn/0.13.1?urlpath=lab/tree/notebooks/auto_examples/06_manipulating_images/plot_nifti_labels_simple.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nifti_labels_simple.ipynb ` .. 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-zip :download:`Download zipped: plot_nifti_labels_simple.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_