old_contents: # -*- coding: utf-8 -*-
  """
  ============================
  Plot a cortical parcellation
  ============================
  
  In this example, we download the HCP-MMP1.0 parcellation [1]_ and show it
  on ``fsaverage``.
  We will also download the customized 448-label aparc parcellation from [2]_
  
  .. note:: The HCP-MMP dataset has license terms restricting its use.
            Of particular relevance:
  
                "I will acknowledge the use of WU-Minn HCP data and data
                derived from WU-Minn HCP data when publicly presenting any
                results or algorithms that benefitted from their use."
  
  References
  ----------
  .. [1] Glasser MF et al. (2016) A multi-modal parcellation of human
         cerebral cortex. Nature 536:171-178.
  .. [2] Khan S et al. (2018) Maturation trajectories of cortical
         resting-state networks depend on the mediating frequency band.
         Neuroimage 174 57-68.
  
  """
  # Author: Eric Larson <larson.eric.d@gmail.com>
  #         Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  
  import mne
  Brain = mne.viz.get_brain_class()
  
  subjects_dir = mne.datasets.sample.data_path() + '/subjects'
  mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
                                          verbose=True)
  
  mne.datasets.fetch_aparc_sub_parcellation(subjects_dir=subjects_dir,
                                            verbose=True)
  
  labels = mne.read_labels_from_annot(
      'fsaverage', 'HCPMMP1', 'lh', subjects_dir=subjects_dir)
  
  brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
                cortex='low_contrast', background='white', size=(800, 600))
  brain.add_annotation('HCPMMP1')
  aud_label = [label for label in labels if label.name == 'L_A1_ROI-lh'][0]
  brain.add_label(aud_label, borders=False)
  
  ###############################################################################
  # We can also plot a combined set of labels (23 per hemisphere).
  
  brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
                cortex='low_contrast', background='white', size=(800, 600))
  brain.add_annotation('HCPMMP1_combined')
  
  ###############################################################################
  # We can add another custom parcellation
  
  brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
                cortex='low_contrast', background='white', size=(800, 600))
  brain.add_annotation('aparc_sub')
  
new_contents: # -*- coding: utf-8 -*-
  """
  ============================
  Plot a cortical parcellation
  ============================
  
  In this example, we download the HCP-MMP1.0 parcellation
  :footcite:`GlasserEtAl2016` and show it on ``fsaverage``.
  We will also download the customized 448-label aparc
  parcellation from :footcite:`KhanEtAl2018`.
  
  .. note:: The HCP-MMP dataset has license terms restricting its use.
            Of particular relevance:
  
                "I will acknowledge the use of WU-Minn HCP data and data
                derived from WU-Minn HCP data when publicly presenting any
                results or algorithms that benefitted from their use."
  
  """
  # Author: Eric Larson <larson.eric.d@gmail.com>
  #         Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  
  import mne
  Brain = mne.viz.get_brain_class()
  
  subjects_dir = mne.datasets.sample.data_path() + '/subjects'
  mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
                                          verbose=True)
  
  mne.datasets.fetch_aparc_sub_parcellation(subjects_dir=subjects_dir,
                                            verbose=True)
  
  labels = mne.read_labels_from_annot(
      'fsaverage', 'HCPMMP1', 'lh', subjects_dir=subjects_dir)
  
  brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
                cortex='low_contrast', background='white', size=(800, 600))
  brain.add_annotation('HCPMMP1')
  aud_label = [label for label in labels if label.name == 'L_A1_ROI-lh'][0]
  brain.add_label(aud_label, borders=False)
  
  ###############################################################################
  # We can also plot a combined set of labels (23 per hemisphere).
  
  brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
                cortex='low_contrast', background='white', size=(800, 600))
  brain.add_annotation('HCPMMP1_combined')
  
  ###############################################################################
  # We can add another custom parcellation
  
  brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
                cortex='low_contrast', background='white', size=(800, 600))
  brain.add_annotation('aparc_sub')
  
  ###############################################################################
  # References
  # ----------
  # .. footbibliography::
  
subject: convert references in plot_parcellation to use footcite (#9032)