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8.4.10. Producing single subject maps of seed-to-voxel correlation

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8.4.12. Group Sparse inverse covariance for multi-subject connectome

8.4.11. Ward clustering to learn a brain parcellation from rest fMRI

We use spatially-constrained Ward-clustering to create a set of parcels. These parcels are particularly interesting for creating a ‘compressed’ representation of the data, replacing the data in the fMRI images by mean on the parcellation.

This parcellation may be useful in a supervised learning, see for instance: A supervised clustering approach for fMRI-based inference of brain states, Michel et al, Pattern Recognition 2011.

The big picture discussion corresponding to this example can be found in the documentation section Clustering to parcellate the brain in regions. Download a rest dataset and turn it to a data matrix

We we download one subject of the ADHD dataset from Internet

from nilearn import datasets
dataset = datasets.fetch_adhd(n_subjects=1)

# print basic information on the dataset
print('First subject functional nifti image (4D) is at: %s' %
      dataset.func[0])  # 4D data


First subject functional nifti image (4D) is at: /home/parietal/gvaroqua/nilearn_data/adhd/data/0010042/0010042_rest_tshift_RPI_voreg_mni.nii.gz

Transform nifti files to a data matrix with the NiftiMasker

from nilearn import input_data

# The NiftiMasker will extract the data on a mask. We do not have a
# mask, hence we need to compute one.
# This is resting-state data: the background has not been removed yet,
# thus we need to use mask_strategy='epi' to compute the mask from the
# EPI images
nifti_masker = input_data.NiftiMasker(memory='nilearn_cache',
                                      mask_strategy='epi', memory_level=1,

func_filename = dataset.func[0]
# The fit_transform call computes the mask and extracts the time-series
# from the files:
fmri_masked = nifti_masker.fit_transform(func_filename)

# We can retrieve the numpy array of the mask
mask = nifti_masker.mask_img_.get_data().astype(bool) Perform Ward clustering

We use spatially-constrained Ward clustering. For this, we need to compute from the mask a matrix giving the voxel-to-voxel connectivity

# Compute connectivity matrix: which voxel is connected to which
from sklearn.feature_extraction import image
shape = mask.shape
connectivity = image.grid_to_graph(n_x=shape[0], n_y=shape[1],
                                   n_z=shape[2], mask=mask)

Then we use FeatureAgglomeration from scikit-learn. Indeed, the voxels are the features of the data matrix.

In addition, we use caching. As a result, the clustering doesn’t have to be recomputed later.

# Computing the ward for the first time, this is long...
from sklearn.cluster import FeatureAgglomeration
# If you have scikit-learn older than 0.14, you need to import
# WardAgglomeration instead of FeatureAgglomeration
import time
start = time.time()
ward = FeatureAgglomeration(n_clusters=1000, connectivity=connectivity,
                            linkage='ward', memory='nilearn_cache')
print("Ward agglomeration 1000 clusters: %.2fs" % (time.time() - start))

# Compute the ward with more clusters, should be faster as we are using
# the caching mechanism
start = time.time()
ward = FeatureAgglomeration(n_clusters=2000, connectivity=connectivity,
                            linkage='ward', memory='nilearn_cache')
print("Ward agglomeration 2000 clusters: %.2fs" % (time.time() - start))


Ward agglomeration 1000 clusters: 0.36s
Ward agglomeration 2000 clusters: 0.20s Visualize results

First we display the labels of the clustering in the brain.

To visualize results, we need to transform the clustering’s labels back to a neuroimaging volume. For this, we use the NiftiMasker’s inverse_transform method.

from nilearn.plotting import plot_roi, plot_epi, show

# Unmask the labels

# Avoid 0 label
labels = ward.labels_ + 1
labels_img = nifti_masker.inverse_transform(labels)

from nilearn.image import mean_img
mean_func_img = mean_img(func_filename)

first_plot = plot_roi(labels_img, mean_func_img, title="Ward parcellation",

# common cut coordinates for all plots
cut_coords = first_plot.cut_coords

labels_img is a Nifti1Image object, it can be saved to file with the following code:


Second, we illustrate the effect that the clustering has on the signal. We show the original data, and the approximation provided by the clustering by averaging the signal on each parcel.

As you can see below, this approximation is very good, although there are only 2000 parcels, instead of the original 60000 voxels

# Display the original data
         title='Original (%i voxels)' % fmri_masked.shape[1],
         vmax=fmri_masked.max(), vmin=fmri_masked.min(),

# A reduced data can be create by taking the parcel-level average:
# Note that, as many objects in the scikit-learn, the ward object exposes
# a transform method that modifies input features. Here it reduces their
# dimension
fmri_reduced = ward.transform(fmri_masked)

# Display the corresponding data compressed using the parcellation
fmri_compressed = ward.inverse_transform(fmri_reduced)
compressed_img = nifti_masker.inverse_transform(fmri_compressed[0])

plot_epi(compressed_img, cut_coords=cut_coords,
         title='Compressed representation (2000 parcels)',
         vmax=fmri_masked.max(), vmin=fmri_masked.min(),

  • ../../_images/sphx_glr_plot_rest_clustering_002.png
  • ../../_images/sphx_glr_plot_rest_clustering_003.png

Total running time of the script: ( 0 minutes 6.581 seconds)

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