8.4.10. Regions extraction using Dictionary Learning and functional connectomes

This example shows how to use nilearn.regions.RegionExtractor to extract spatially constrained brain regions from whole brain maps decomposed using dictionary learning and use them to build a functional connectome.

We used 20 movie-watching functional datasets from nilearn.datasets.fetch_development_fmri and nilearn.decomposition.DictLearning for set of brain atlas maps.

This example can also be inspired to apply the same steps to even regions extraction using ICA maps. In that case, idea would be to replace dictionary learning to canonical ICA decomposition using nilearn.decomposition.CanICA

Please see the related documentation of nilearn.regions.RegionExtractor for more details.


The use of the attribute components_img_ from dictionary learning estimator is implemented from version 0.4.1. For older versions, unmask the deprecated attribute components_ to get the components image using attribute masker_ embedded in estimator. See the section Inverse transform: unmasking data. Fetch brain development functional datasets

We use nilearn’s datasets downloading utilities

from nilearn import datasets

rest_dataset = datasets.fetch_development_fmri(n_subjects=20)
func_filenames = rest_dataset.func
confounds = rest_dataset.confounds Extract functional networks with DictionaryLearning

# Import dictionary learning algorithm from decomposition module and call the
# object and fit the model to the functional datasets
from nilearn.decomposition import DictLearning

# Initialize DictLearning object
dict_learn = DictLearning(n_components=8, smoothing_fwhm=6.,
                          memory="nilearn_cache", memory_level=2,
# Fit to the data
# Resting state networks/maps in attribute `components_img_`
# Note that this attribute is implemented from version 0.4.1.
# For older versions, see the note section above for details.
components_img = dict_learn.components_img_

# Visualization of functional networks
# Show networks using plotting utilities
from nilearn import plotting

plotting.plot_prob_atlas(components_img, view_type='filled_contours',
                         title='Dictionary Learning maps')


/home/emdupre/Desktop/open_source/nilearn/nilearn/plotting/displays.py:99: UserWarning: linewidths is ignored by contourf
/home/emdupre/Desktop/open_source/nilearn/nilearn/plotting/displays.py:99: UserWarning: No contour levels were found within the data range.

<nilearn.plotting.displays.OrthoSlicer object at 0x7f872ee88b50> Extract regions from networks

# Import Region Extractor algorithm from regions module
# threshold=0.5 indicates that we keep nominal of amount nonzero voxels across all
# maps, less the threshold means that more intense non-voxels will be survived.
from nilearn.regions import RegionExtractor

extractor = RegionExtractor(components_img, threshold=0.5,
                            standardize=True, min_region_size=1350)
# Just call fit() to process for regions extraction
# Extracted regions are stored in regions_img_
regions_extracted_img = extractor.regions_img_
# Each region index is stored in index_
regions_index = extractor.index_
# Total number of regions extracted
n_regions_extracted = regions_extracted_img.shape[-1]

# Visualization of region extraction results
title = ('%d regions are extracted from %d components.'
         '\nEach separate color of region indicates extracted region'
         % (n_regions_extracted, 8))
plotting.plot_prob_atlas(regions_extracted_img, view_type='filled_contours',


/home/emdupre/miniconda3/envs/nilearn/lib/python3.7/site-packages/numpy/ma/core.py:2795: UserWarning: Warning: converting a masked element to nan.
  order=order, subok=True, ndmin=ndmin)
/home/emdupre/Desktop/open_source/nilearn/nilearn/plotting/displays.py:99: UserWarning: linewidths is ignored by contourf
/home/emdupre/Desktop/open_source/nilearn/nilearn/plotting/displays.py:99: UserWarning: No contour levels were found within the data range.

<nilearn.plotting.displays.OrthoSlicer object at 0x7f8755decc90> Compute correlation coefficients

# First we need to do subjects timeseries signals extraction and then estimating
# correlation matrices on those signals.
# To extract timeseries signals, we call transform() from RegionExtractor object
# onto each subject functional data stored in func_filenames.
# To estimate correlation matrices we import connectome utilities from nilearn
from nilearn.connectome import ConnectivityMeasure

correlations = []
# Initializing ConnectivityMeasure object with kind='correlation'
connectome_measure = ConnectivityMeasure(kind='correlation')
for filename, confound in zip(func_filenames, confounds):
    # call transform from RegionExtractor object to extract timeseries signals
    timeseries_each_subject = extractor.transform(filename, confounds=confound)
    # call fit_transform from ConnectivityMeasure object
    correlation = connectome_measure.fit_transform([timeseries_each_subject])
    # saving each subject correlation to correlations

# Mean of all correlations
import numpy as np
mean_correlations = np.mean(correlations, axis=0).reshape(n_regions_extracted,
                                                          n_regions_extracted) Plot resulting connectomes

title = 'Correlation between %d regions' % n_regions_extracted

# First plot the matrix
display = plotting.plot_matrix(mean_correlations, vmax=1, vmin=-1,
                               colorbar=True, title=title)

# Then find the center of the regions and plot a connectome
regions_img = regions_extracted_img
coords_connectome = plotting.find_probabilistic_atlas_cut_coords(regions_img)

plotting.plot_connectome(mean_correlations, coords_connectome,
                         edge_threshold='90%', title=title)
  • ../../_images/sphx_glr_plot_extract_regions_dictlearning_maps_003.png
  • ../../_images/sphx_glr_plot_extract_regions_dictlearning_maps_004.png


<nilearn.plotting.displays.OrthoProjector object at 0x7f87599b1810> Plot regions extracted for only one specific network

# First, we plot a network of index=4 without region extraction (left plot)
from nilearn import image

img = image.index_img(components_img, 4)
coords = plotting.find_xyz_cut_coords(img)
display = plotting.plot_stat_map(img, cut_coords=coords, colorbar=False,
                                 title='Showing one specific network')

Now, we plot (right side) same network after region extraction to show that connected regions are nicely seperated. Each brain extracted region is identified as separate color.

# For this, we take the indices of the all regions extracted related to original
# network given as 4.
regions_indices_of_map3 = np.where(np.array(regions_index) == 4)

display = plotting.plot_anat(cut_coords=coords,
                             title='Regions from this network')

# Add as an overlay all the regions of index 4
colors = 'rgbcmyk'
for each_index_of_map3, color in zip(regions_indices_of_map3[0], colors):
    display.add_overlay(image.index_img(regions_extracted_img, each_index_of_map3),



/home/emdupre/miniconda3/envs/nilearn/lib/python3.7/site-packages/numpy/ma/core.py:2795: UserWarning: Warning: converting a masked element to nan.
  order=order, subok=True, ndmin=ndmin)

Total running time of the script: ( 1 minutes 40.259 seconds)

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