vmax = np.max(mean_func_img.get_data())
plotting.plot_epi(mean_func_img, cut_coords=cut_coords,
title='Original (%i voxels)' % original_voxels,
vmax=vmax, vmin=vmin, display_mode='xz')
# A reduced dataset can be created by taking the parcel-level average:
# Note that Parcellation objects with any method have the opportunity to
# use a `transform` call that modifies input features. Here it reduces their
# dimension. Note that we `fit` before calling a `transform` so that average
# signals can be created on the brain parcellations with fit call.
fmri_reduced = ward.transform(dataset.func)
# Display the corresponding data compressed using the parcellation using
# parcels=2000.
fmri_compressed = ward.inverse_transform(fmri_reduced)
plotting.plot_epi(index_img(fmri_compressed, 0),
cut_coords=cut_coords,
title='Ward compressed representation (2000 parcels)',
vmin=vmin, vmax=vmax, display_mode='xz')
# As you can see below, this approximation is almost good, although there
# are only 2000 parcels, instead of the original 60000 voxels
#########################################################################
# Brain parcellations with KMeans Clustering
# ------------------------------------------
#
# We use the same approach as with building parcellations using Ward
# clustering. But, in the range of a small number of clusters,
# it is most likely that we want to use standardization. Indeed with
vmax = np.max(get_data(mean_func_img))
plotting.plot_epi(mean_func_img, cut_coords=cut_coords,
title='Original (%i voxels)' % original_voxels,
vmax=vmax, vmin=vmin, display_mode='xz')
# A reduced dataset can be created by taking the parcel-level average:
# Note that Parcellation objects with any method have the opportunity to
# use a `transform` call that modifies input features. Here it reduces their
# dimension. Note that we `fit` before calling a `transform` so that average
# signals can be created on the brain parcellations with fit call.
fmri_reduced = ward.transform(dataset.func)
# Display the corresponding data compressed using the parcellation using
# parcels=2000.
fmri_compressed = ward.inverse_transform(fmri_reduced)
plotting.plot_epi(index_img(fmri_compressed, 0),
cut_coords=cut_coords,
title='Ward compressed representation (2000 parcels)',
vmin=vmin, vmax=vmax, display_mode='xz')
# As you can see below, this approximation is almost good, although there
# are only 2000 parcels, instead of the original 60000 voxels
#########################################################################
# Brain parcellations with KMeans Clustering
# ------------------------------------------
#
# We use the same approach as with building parcellations using Ward
# clustering. But, in the range of a small number of clusters,
# it is most likely that we want to use standardization. Indeed with
cut_coords=cut_coords,
title='Original (%i voxels)' % original_voxels,
vmax=vmax,
vmin=vmin,
display_mode='xz')
# A reduced dataset can be created by taking the parcel-level average:
# Note that Parcellation objects with any method have the opportunity to
# use a `transform` call that modifies input features. Here it reduces their
# dimension. Note that we `fit` before calling a `transform` so that average
# signals can be created on the brain parcellations with fit call.
fmri_reduced = ward.transform(dataset.func)
# Display the corresponding data compressed using the parcellation using
# parcels=2000.
fmri_compressed = ward.inverse_transform(fmri_reduced)
plotting.plot_epi(index_img(fmri_compressed, 0),
cut_coords=cut_coords,
title='Ward compressed representation (2000 parcels)',
vmin=vmin,
vmax=vmax,
display_mode='xz')
# As you can see below, this approximation is almost good, although there
# are only 2000 parcels, instead of the original 60000 voxels
#########################################################################
# Brain parcellations with KMeans Clustering
# ------------------------------------------
#
# We use the same approach as with building parcellations using Ward