8.4.13. Regions extraction using Dictionary Learning and functional connectomes¶
This example shows how to use
to extract spatially constrained brain regions from whole brain maps decomposed
using dictionary learning and use them to build a functional connectome.
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
Please see the related documentation of
for more details.
220.127.116.11. Fetch ADHD resting state functional datasets¶
We use nilearn’s datasets downloading utilities
from nilearn import datasets adhd_dataset = datasets.fetch_adhd(n_subjects=20) func_filenames = adhd_dataset.func confounds = adhd_dataset.confounds
18.104.22.168. Extract resting-state 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=5, smoothing_fwhm=6., memory="nilearn_cache", memory_level=2, random_state=0) # Fit to the data dict_learn.fit(func_filenames) # Resting state networks/maps components_img = dict_learn.masker_.inverse_transform(dict_learn.components_) # Visualization of resting state networks # Show networks using plotting utilities from nilearn import plotting plotting.plot_prob_atlas(components_img, view_type='filled_contours', title='Dictionary Learning maps')
22.214.171.124. 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, thresholding_strategy='ratio_n_voxels', extractor='local_regions', standardize=True, min_region_size=1350) # Just call fit() to process for regions extraction extractor.fit() # 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, 5)) plotting.plot_prob_atlas(regions_extracted_img, view_type='filled_contours', title=title)
126.96.36.199. 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 correlations.append(correlation) # Mean of all correlations import numpy as np mean_correlations = np.mean(correlations, axis=0).reshape(n_regions_extracted, n_regions_extracted)
188.8.131.52. Plot resulting connectomes¶
import matplotlib.pyplot as plt from nilearn import image regions_imgs = image.iter_img(regions_extracted_img) coords_connectome = [plotting.find_xyz_cut_coords(img) for img in regions_imgs] title = 'Correlation interactions between %d regions' % n_regions_extracted plt.figure() plt.imshow(mean_correlations, interpolation="nearest", vmax=1, vmin=-1, cmap=plt.cm.bwr) plt.colorbar() plt.title(title) plotting.plot_connectome(mean_correlations, coords_connectome, edge_threshold='90%', title=title)
184.108.40.206. Plot regions extracted for only 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, colors): display.add_overlay(image.index_img(regions_extracted_img, each_index_of_map3), cmap=plotting.cm.alpha_cmap(color)) plotting.show()
Total running time of the script: ( 2 minutes 13.525 seconds)