.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/03_connectivity/plot_signal_extraction.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_03_connectivity_plot_signal_extraction.py>`
        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_03_connectivity_plot_signal_extraction.py:


Extracting signals from a brain parcellation
============================================

Here we show how to extract signals from a brain parcellation and compute
a correlation matrix.

We also show the importance of defining good confounds signals: the
first correlation matrix is computed after regressing out simple
confounds signals: movement regressors, white matter and CSF signals, ...
The second one is without any confounds: all regions are connected to each
other. Finally we demonstrated the functionality of
:func:`nilearn.interfaces.fmriprep.load_confounds` to flexibly select confound
variables from :term:`fMRIPrep` outputs while following some implementation
guideline of :term:`fMRIPrep` confounds documentation
`<https://fmriprep.org/en/stable/outputs.html#confounds>`_.

One reference that discusses the importance of confounds is `Varoquaux and
Craddock, Learning and comparing functional connectomes across subjects,
NeuroImage 2013
<http://www.sciencedirect.com/science/article/pii/S1053811913003340>`_.

This is just a code example, see the :ref:`corresponding section in the
documentation <parcellation_time_series>` for more.

.. note::
    This example needs SciPy >= 1.0.0 for the reordering of the matrix.

.. include:: ../../../examples/masker_note.rst

.. GENERATED FROM PYTHON SOURCE LINES 34-36

Retrieve the atlas and the data
-------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 36-50

.. code-block:: default

    from nilearn import datasets

    dataset = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm')
    atlas_filename = dataset.maps
    labels = dataset.labels

    print('Atlas ROIs are located in nifti image (4D) at: %s' %
          atlas_filename)  # 4D data

    # One subject of brain development fmri data
    data = datasets.fetch_development_fmri(n_subjects=1, reduce_confounds=True)
    fmri_filenames = data.func[0]
    reduced_confounds = data.confounds[0]  # This is a preselected set of confounds





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Atlas ROIs are located in nifti image (4D) at: <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'




.. GENERATED FROM PYTHON SOURCE LINES 51-54

Extract signals on a parcellation defined by labels
---------------------------------------------------
Using the NiftiLabelsMasker

.. GENERATED FROM PYTHON SOURCE LINES 54-63

.. code-block:: default

    from nilearn.maskers import NiftiLabelsMasker
    masker = NiftiLabelsMasker(labels_img=atlas_filename, standardize=True,
                               memory='nilearn_cache', verbose=5)

    # Here we go from nifti files to the signal time series in a numpy
    # array. Note how we give confounds to be regressed out during signal
    # extraction
    time_series = masker.fit_transform(fmri_filenames, confounds=reduced_confounds)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    [NiftiLabelsMasker.fit_transform] loading data from Nifti1Image(
    shape=(91, 109, 91),
    affine=array([[   2.,    0.,    0.,  -90.],
           [   0.,    2.,    0., -126.],
           [   0.,    0.,    2.,  -72.],
           [   0.,    0.,    0.,    1.]])
    )
    Resampling labels
    ________________________________________________________________________________
    [Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
    _filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
    <nilearn.maskers.nifti_labels_masker._ExtractionFunctor object at 0x7fa50ce759a0>,
    { 'background_label': 0,
      'detrend': False,
      'dtype': None,
      'high_pass': None,
      'high_variance_confounds': False,
      'labels': None,
      'labels_img': <nibabel.nifti1.Nifti1Image object at 0x7fa50cdddd60>,
      'low_pass': None,
      'mask_img': None,
      'reports': True,
      'smoothing_fwhm': None,
      'standardize': True,
      'standardize_confounds': True,
      'strategy': 'mean',
      't_r': None,
      'target_affine': None,
      'target_shape': None}, confounds=[ '/home/nicolas/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=5)
    [NiftiLabelsMasker.transform_single_imgs] Loading data from /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
    [NiftiLabelsMasker.transform_single_imgs] Extracting region signals
    [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals
    _______________________________________________filter_and_extract - 0.8s, 0.0min




.. GENERATED FROM PYTHON SOURCE LINES 64-66

Compute and display a correlation matrix
----------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 66-83

.. code-block:: default

    from nilearn.connectome import ConnectivityMeasure
    correlation_measure = ConnectivityMeasure(kind='correlation')
    correlation_matrix = correlation_measure.fit_transform([time_series])[0]

    # Plot the correlation matrix
    import numpy as np
    from nilearn import plotting
    # Make a large figure
    # Mask the main diagonal for visualization:
    np.fill_diagonal(correlation_matrix, 0)
    # The labels we have start with the background (0), hence we skip the
    # first label
    # matrices are ordered for block-like representation
    plotting.plot_matrix(correlation_matrix, figure=(10, 8), labels=labels[1:],
                         vmax=0.8, vmin=-0.8, title="Confounds",
                         reorder=True)




.. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_001.png
   :alt: plot signal extraction
   :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    <matplotlib.image.AxesImage object at 0x7fa50cf5e100>



.. GENERATED FROM PYTHON SOURCE LINES 84-90

Extract signals and compute a connectivity matrix without confounds removal
---------------------------------------------------------------------------
After covering the basic of signal extraction and functional connectivity
matrix presentation, let's look into the impact of confounds to :term:`fMRI`
signal and functional connectivity. Firstly let's find out what a functional
connectivity matrix looks like without confound removal.

.. GENERATED FROM PYTHON SOURCE LINES 90-101

.. code-block:: default


    time_series = masker.fit_transform(fmri_filenames)
    # Note how we did not specify confounds above. This is bad!

    correlation_matrix = correlation_measure.fit_transform([time_series])[0]

    np.fill_diagonal(correlation_matrix, 0)

    plotting.plot_matrix(correlation_matrix, figure=(10, 8), labels=labels[1:],
                         vmax=0.8, vmin=-0.8, title='No confounds', reorder=True)




.. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_002.png
   :alt: plot signal extraction
   :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_002.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    [NiftiLabelsMasker.fit_transform] loading data from Nifti1Image(
    shape=(91, 109, 91),
    affine=array([[   2.,    0.,    0.,  -90.],
           [   0.,    2.,    0., -126.],
           [   0.,    0.,    2.,  -72.],
           [   0.,    0.,    0.,    1.]])
    )
    ________________________________________________________________________________
    [Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
    _filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
    <nilearn.maskers.nifti_labels_masker._ExtractionFunctor object at 0x7fa5139413a0>,
    { 'background_label': 0,
      'detrend': False,
      'dtype': None,
      'high_pass': None,
      'high_variance_confounds': False,
      'labels': None,
      'labels_img': <nibabel.nifti1.Nifti1Image object at 0x7fa50cdddd60>,
      'low_pass': None,
      'mask_img': None,
      'reports': True,
      'smoothing_fwhm': None,
      'standardize': True,
      'standardize_confounds': True,
      'strategy': 'mean',
      't_r': None,
      'target_affine': None,
      'target_shape': None}, confounds=None, sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=5)
    [NiftiLabelsMasker.transform_single_imgs] Loading data from /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
    [NiftiLabelsMasker.transform_single_imgs] Extracting region signals
    [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals
    _______________________________________________filter_and_extract - 0.8s, 0.0min

    <matplotlib.image.AxesImage object at 0x7fa501dd8af0>



.. GENERATED FROM PYTHON SOURCE LINES 102-116

Load confounds from file using a flexible strategy with fmriprep interface
--------------------------------------------------------------------------
The :func:`nilearn.interfaces.fmriprep.load_confounds` function provides
flexible  parameters to retrieve the relevant columns from the TSV file
generated by :term:`fMRIPrep`.
:func:`nilearn.interfaces.fmriprep.load_confounds` ensures two things:

1. The correct regressors are selected with provided strategy, and

2. Volumes such as non-steady-state and/or high motion volumes are masked
   out correctly.

Let's try a simple strategy removing motion, white matter signal,
cerebrospinal fluid signal with high-pass filtering.

.. GENERATED FROM PYTHON SOURCE LINES 116-138

.. code-block:: default


    from nilearn.interfaces.fmriprep import load_confounds
    confounds_simple, sample_mask = load_confounds(
        fmri_filenames,
        strategy=["high_pass", "motion", "wm_csf"],
        motion="basic", wm_csf="basic")

    print("The shape of the confounds matrix is:", confounds_simple.shape)
    print(confounds_simple.columns)

    time_series = masker.fit_transform(fmri_filenames,
                                       confounds=confounds_simple,
                                       sample_mask=sample_mask)

    correlation_matrix = correlation_measure.fit_transform([time_series])[0]

    np.fill_diagonal(correlation_matrix, 0)

    plotting.plot_matrix(correlation_matrix, figure=(10, 8), labels=labels[1:],
                         vmax=0.8, vmin=-0.8, title='Motion, WM, CSF',
                         reorder=True)




.. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_003.png
   :alt: plot signal extraction
   :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_003.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    The shape of the confounds matrix is: (168, 12)
    Index(['cosine00', 'cosine01', 'cosine02', 'cosine03', 'csf', 'rot_x', 'rot_y',
           'rot_z', 'trans_x', 'trans_y', 'trans_z', 'white_matter'],
          dtype='object')
    [NiftiLabelsMasker.fit_transform] loading data from Nifti1Image(
    shape=(91, 109, 91),
    affine=array([[   2.,    0.,    0.,  -90.],
           [   0.,    2.,    0., -126.],
           [   0.,    0.,    2.,  -72.],
           [   0.,    0.,    0.,    1.]])
    )
    ________________________________________________________________________________
    [Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
    _filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
    <nilearn.maskers.nifti_labels_masker._ExtractionFunctor object at 0x7fa4fe8793d0>,
    { 'background_label': 0,
      'detrend': False,
      'dtype': None,
      'high_pass': None,
      'high_variance_confounds': False,
      'labels': None,
      'labels_img': <nibabel.nifti1.Nifti1Image object at 0x7fa50cdddd60>,
      'low_pass': None,
      'mask_img': None,
      'reports': True,
      'smoothing_fwhm': None,
      'standardize': True,
      'standardize_confounds': True,
      'strategy': 'mean',
      't_r': None,
      'target_affine': None,
      'target_shape': None}, confounds=[      cosine00  cosine01  cosine02  cosine03  ...   trans_x   trans_y   trans_z  white_matter
    0    0.109104  0.109090  0.109066  0.109033  ...  0.006621 -0.026078  0.055006     -0.876886
    1    0.109066  0.108937  0.108723  0.108423  ...  0.000668 -0.027587  0.049458     -1.418909
    2    0.108990  0.108632  0.108038  0.107207  ...  0.006628 -0.019085  0.075787     -1.540842
    3    0.108875  0.108176  0.107012  0.105391  ...  0.009347 -0.023900  0.053022     -1.922085
    4    0.108723  0.107567  0.105651  0.102986  ...  0.006629 -0.033396  0.077764     -1.843388
    ..        ...       ...       ...       ...  ...       ...       ...       ...           ...
    163 -0.108723  0.107567 -0.105651  0.102986  ... -0.010477  0.064873 -0.022169      1.221217
    164 -0.108875  0.108176 -0.107012  0.105391  ... -0.012250 -0.064266 -0.042248      1.237783
    165 -0.108990  0.108632 -0.108038  0.107207  ... -0.004304  0.053241 -0.029500      1.590905
    166 -0.109066  0.108937 -0.108723  0.108423  ... -0.008227 -0.081559 -0.034236      1.122246
    167 -0.109104  0.109090 -0.109066  0.109033  ... -0.020041  0.069287 -0.007294      1.000828

    [168 rows x 12 columns]], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=5)
    [NiftiLabelsMasker.transform_single_imgs] Loading data from /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
    [NiftiLabelsMasker.transform_single_imgs] Extracting region signals
    [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals
    _______________________________________________filter_and_extract - 0.8s, 0.0min

    <matplotlib.image.AxesImage object at 0x7fa4fdf7b1c0>



.. GENERATED FROM PYTHON SOURCE LINES 139-150

Motion-based scrubbing
----------------------
With a scrubbing-based strategy,
:func:`~nilearn.interfaces.fmriprep.load_confounds` returns a `sample_mask`
that removes the index of volumes exceeding the framewise displacement and
standardised DVARS threshold, and all the continuous segment with less than
five volumes. Before applying scrubbing, it's important to access the
percentage of volumns scrubbed. Scrubbing is not a suitable strategy for
datasets with too many high motion subjects.
On top of the simple strategy above, let's add scrubbing to our
strategy.

.. GENERATED FROM PYTHON SOURCE LINES 150-175

.. code-block:: default


    confounds_scrub, sample_mask = load_confounds(
        fmri_filenames,
        strategy=["high_pass", "motion", "wm_csf", "scrub"],
        motion="basic", wm_csf="basic",
        scrub=5, fd_threshold=0.2, std_dvars_threshold=3)

    print("After scrubbing, {} out of {} volumes remains".format(
        sample_mask.shape[0], confounds_scrub.shape[0]))
    print("The shape of the confounds matrix is:", confounds_simple.shape)
    print(confounds_scrub.columns)

    time_series = masker.fit_transform(fmri_filenames,
                                       confounds=confounds_scrub,
                                       sample_mask=sample_mask)

    correlation_matrix = correlation_measure.fit_transform([time_series])[0]

    np.fill_diagonal(correlation_matrix, 0)

    plotting.plot_matrix(correlation_matrix, figure=(10, 8), labels=labels[1:],
                         vmax=0.8, vmin=-0.8,
                         title='Motion, WM, CSF, Scrubbing',
                         reorder=True)




.. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_004.png
   :alt: plot signal extraction
   :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_004.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    After scrubbing, 119 out of 168 volumes remains
    The shape of the confounds matrix is: (168, 12)
    Index(['cosine00', 'cosine01', 'cosine02', 'cosine03', 'csf', 'rot_x', 'rot_y',
           'rot_z', 'trans_x', 'trans_y', 'trans_z', 'white_matter'],
          dtype='object')
    [NiftiLabelsMasker.fit_transform] loading data from Nifti1Image(
    shape=(91, 109, 91),
    affine=array([[   2.,    0.,    0.,  -90.],
           [   0.,    2.,    0., -126.],
           [   0.,    0.,    2.,  -72.],
           [   0.,    0.,    0.,    1.]])
    )
    ________________________________________________________________________________
    [Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
    _filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
    <nilearn.maskers.nifti_labels_masker._ExtractionFunctor object at 0x7fa4fe0549a0>,
    { 'background_label': 0,
      'detrend': False,
      'dtype': None,
      'high_pass': None,
      'high_variance_confounds': False,
      'labels': None,
      'labels_img': <nibabel.nifti1.Nifti1Image object at 0x7fa50cdddd60>,
      'low_pass': None,
      'mask_img': None,
      'reports': True,
      'smoothing_fwhm': None,
      'standardize': True,
      'standardize_confounds': True,
      'strategy': 'mean',
      't_r': None,
      'target_affine': None,
      'target_shape': None}, confounds=[      cosine00  cosine01  cosine02  cosine03  ...   trans_x   trans_y   trans_z  white_matter
    0    0.097319  0.110228  0.100980  0.127209  ...  0.005385 -0.024472  0.048861     -0.796008
    1    0.097281  0.110075  0.100637  0.126600  ... -0.000568 -0.025982  0.043313     -1.338031
    2    0.097205  0.109770  0.099952  0.125384  ...  0.005391 -0.017480  0.069643     -1.459963
    3    0.097090  0.109313  0.098926  0.123568  ...  0.008110 -0.022294  0.046877     -1.841207
    4    0.096938  0.108705  0.097565  0.121163  ...  0.005392 -0.031791  0.071620     -1.762509
    ..        ...       ...       ...       ...  ...       ...       ...       ...           ...
    163 -0.120508  0.108705 -0.113737  0.121163  ... -0.011713  0.066478 -0.028314      1.302095
    164 -0.120661  0.109313 -0.115099  0.123568  ... -0.013487 -0.062660 -0.048393      1.318661
    165 -0.120775  0.109770 -0.116124  0.125384  ... -0.005541  0.054846 -0.035645      1.671784
    166 -0.120851  0.110075 -0.116809  0.126600  ... -0.009463 -0.079953 -0.040381      1.203124
    167 -0.120889  0.110228 -0.117152  0.127209  ... -0.021277  0.070892 -0.013439      1.081706

    [168 rows x 12 columns]], sample_mask=array([  0, ..., 152]), dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=5)
    [NiftiLabelsMasker.transform_single_imgs] Loading data from /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
    [NiftiLabelsMasker.transform_single_imgs] Extracting region signals
    [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals
    _______________________________________________filter_and_extract - 0.8s, 0.0min

    <matplotlib.image.AxesImage object at 0x7fa4fd61d7f0>



.. GENERATED FROM PYTHON SOURCE LINES 176-183

The impact of global signal removal
-----------------------------------
Global signal removes the grand mean from your signal. The benefit is that
it can remove impacts of physiological artifacts with minimal impact on the
degrees of freedom. The downside is that one cannot get insight into variance
explained by certain sources of noise. Now let's add global signal to the
simple strategy and see its impact.

.. GENERATED FROM PYTHON SOURCE LINES 183-205

.. code-block:: default


    confounds_minimal_no_gsr, sample_mask = load_confounds(
        fmri_filenames,
        strategy=["high_pass", "motion", "wm_csf", "global_signal"],
        motion="basic", wm_csf="basic", global_signal="basic")
    print("The shape of the confounds matrix is:",
          confounds_minimal_no_gsr.shape)
    print(confounds_minimal_no_gsr.columns)

    time_series = masker.fit_transform(fmri_filenames,
                                       confounds=confounds_minimal_no_gsr,
                                       sample_mask=sample_mask)

    correlation_matrix = correlation_measure.fit_transform([time_series])[0]

    np.fill_diagonal(correlation_matrix, 0)

    plotting.plot_matrix(correlation_matrix, figure=(10, 8), labels=labels[1:],
                         vmax=0.8, vmin=-0.8,
                         title='Motion, WM, CSF, GSR',
                         reorder=True)




.. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_005.png
   :alt: plot signal extraction
   :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_005.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    The shape of the confounds matrix is: (168, 13)
    Index(['cosine00', 'cosine01', 'cosine02', 'cosine03', 'csf', 'global_signal',
           'rot_x', 'rot_y', 'rot_z', 'trans_x', 'trans_y', 'trans_z',
           'white_matter'],
          dtype='object')
    [NiftiLabelsMasker.fit_transform] loading data from Nifti1Image(
    shape=(91, 109, 91),
    affine=array([[   2.,    0.,    0.,  -90.],
           [   0.,    2.,    0., -126.],
           [   0.,    0.,    2.,  -72.],
           [   0.,    0.,    0.,    1.]])
    )
    ________________________________________________________________________________
    [Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
    _filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
    <nilearn.maskers.nifti_labels_masker._ExtractionFunctor object at 0x7fa4fe879fa0>,
    { 'background_label': 0,
      'detrend': False,
      'dtype': None,
      'high_pass': None,
      'high_variance_confounds': False,
      'labels': None,
      'labels_img': <nibabel.nifti1.Nifti1Image object at 0x7fa50cdddd60>,
      'low_pass': None,
      'mask_img': None,
      'reports': True,
      'smoothing_fwhm': None,
      'standardize': True,
      'standardize_confounds': True,
      'strategy': 'mean',
      't_r': None,
      'target_affine': None,
      'target_shape': None}, confounds=[      cosine00  cosine01  cosine02  cosine03  ...   trans_x   trans_y   trans_z  white_matter
    0    0.109104  0.109090  0.109066  0.109033  ...  0.006621 -0.026078  0.055006     -0.876886
    1    0.109066  0.108937  0.108723  0.108423  ...  0.000668 -0.027587  0.049458     -1.418909
    2    0.108990  0.108632  0.108038  0.107207  ...  0.006628 -0.019085  0.075787     -1.540842
    3    0.108875  0.108176  0.107012  0.105391  ...  0.009347 -0.023900  0.053022     -1.922085
    4    0.108723  0.107567  0.105651  0.102986  ...  0.006629 -0.033396  0.077764     -1.843388
    ..        ...       ...       ...       ...  ...       ...       ...       ...           ...
    163 -0.108723  0.107567 -0.105651  0.102986  ... -0.010477  0.064873 -0.022169      1.221217
    164 -0.108875  0.108176 -0.107012  0.105391  ... -0.012250 -0.064266 -0.042248      1.237783
    165 -0.108990  0.108632 -0.108038  0.107207  ... -0.004304  0.053241 -0.029500      1.590905
    166 -0.109066  0.108937 -0.108723  0.108423  ... -0.008227 -0.081559 -0.034236      1.122246
    167 -0.109104  0.109090 -0.109066  0.109033  ... -0.020041  0.069287 -0.007294      1.000828

    [168 rows x 13 columns]], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=5)
    [NiftiLabelsMasker.transform_single_imgs] Loading data from /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
    [NiftiLabelsMasker.transform_single_imgs] Extracting region signals
    [NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals
    _______________________________________________filter_and_extract - 0.8s, 0.0min

    <matplotlib.image.AxesImage object at 0x7fa501e55490>



.. GENERATED FROM PYTHON SOURCE LINES 206-216

Using predefined strategies
---------------------------
Instead of customising the strategy through
:func:`nilearn.interfaces.fmriprep.load_confounds`, one can use a predefined
strategy with :func:`nilearn.interfaces.fmriprep.load_confounds_strategy`.
Based on the confound variables generated through :term:`fMRIPrep`, and past
benchmarks studies (:footcite:`Ciric2017`, :footcite:`Parker2018`): `simple`,
`scrubbing`, `compcor`, `ica_aroma`.
The following examples shows how to use the `simple` strategy and overwrite
the motion default to basic.

.. GENERATED FROM PYTHON SOURCE LINES 216-256

.. code-block:: default


    from nilearn.interfaces.fmriprep import load_confounds_strategy

    # use default parameters
    confounds, sample_mask = load_confounds_strategy(
        fmri_filenames, denoise_strategy="simple", motion="basic"
    )
    time_series = masker.fit_transform(fmri_filenames,
                                       confounds=confounds,
                                       sample_mask=sample_mask)

    correlation_matrix = correlation_measure.fit_transform([time_series])[0]

    np.fill_diagonal(correlation_matrix, 0)

    plotting.plot_matrix(correlation_matrix, figure=(10, 8), labels=labels[1:],
                         vmax=0.8, vmin=-0.8,
                         title='simple',
                         reorder=True)

    # add optional parameter global signal
    confounds, sample_mask = load_confounds_strategy(
        fmri_filenames, denoise_strategy="simple",
        motion="basic", global_signal="basic"
    )
    time_series = masker.fit_transform(fmri_filenames,
                                       confounds=confounds,
                                       sample_mask=sample_mask)

    correlation_matrix = correlation_measure.fit_transform([time_series])[0]

    np.fill_diagonal(correlation_matrix, 0)

    plotting.plot_matrix(correlation_matrix, figure=(10, 8), labels=labels[1:],
                         vmax=0.8, vmin=-0.8,
                         title='simple with global signal',
                         reorder=True)

    plotting.show()




.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_006.png
         :alt: plot signal extraction
         :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_006.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_007.png
         :alt: plot signal extraction
         :srcset: /auto_examples/03_connectivity/images/sphx_glr_plot_signal_extraction_007.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    [NiftiLabelsMasker.fit_transform] loading data from Nifti1Image(
    shape=(91, 109, 91),
    affine=array([[   2.,    0.,    0.,  -90.],
           [   0.,    2.,    0., -126.],
           [   0.,    0.,    2.,  -72.],
           [   0.,    0.,    0.,    1.]])
    )
    [Memory]9.4s, 0.2min    : Loading _filter_and_extract...
    __________________________________filter_and_extract cache loaded - 0.0s, 0.0min
    [NiftiLabelsMasker.fit_transform] loading data from Nifti1Image(
    shape=(91, 109, 91),
    affine=array([[   2.,    0.,    0.,  -90.],
           [   0.,    2.,    0., -126.],
           [   0.,    0.,    2.,  -72.],
           [   0.,    0.,    0.,    1.]])
    )
    [Memory]9.8s, 0.2min    : Loading _filter_and_extract...
    __________________________________filter_and_extract cache loaded - 0.0s, 0.0min




.. GENERATED FROM PYTHON SOURCE LINES 257-261

References
----------

 .. footbibliography::


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  11.524 seconds)

**Estimated memory usage:**  395 MB


.. _sphx_glr_download_auto_examples_03_connectivity_plot_signal_extraction.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/03_connectivity/plot_signal_extraction.ipynb
      :alt: Launch binder
      :width: 150 px


  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_signal_extraction.py <plot_signal_extraction.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_signal_extraction.ipynb <plot_signal_extraction.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_