3.4. Region Extraction for better brain parcellations#
3.4.1. Fetching movie-watching based functional datasets#
We use a naturalistic stimuli based movie-watching functional connectivity dataset
of 20 subjects, which is already preprocessed, downsampled to 4mm isotropic resolution, and publicly available at
https://osf.io/5hju4/files/. We use utilities
fetch_development_fmri implemented in nilearn for automatic fetching of this
from nilearn import datasets rest_dataset = datasets.fetch_development_fmri(n_subjects=20) func_filenames = rest_dataset.func confounds = rest_dataset.confounds
3.4.2. Brain maps using Dictionary learning#
Here, we use object
DictLearning, a multi subject model to decompose multi
subjects fMRI datasets into functionally defined maps. We do this by setting
the parameters and calling
DictLearning.fit on the filenames of datasets without
necessarily converting each file to
from nilearn.decomposition import DictLearning # Initialize DictLearning object dict_learn = DictLearning(n_components=8, 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 in attribute `components_img_` components_img = dict_learn.components_img_
3.4.3. Visualization of Dictionary learning maps#
Showing maps stored in
components_img using nilearn plotting utilities.
Here, we use
plot_prob_atlas for easy visualization of 4D atlas maps
onto the anatomical standard template. Each map is displayed in different
color and colors are random and automatically picked.
from nilearn import plotting plotting.plot_prob_atlas(components_img, view_type='filled_contours', title='Dictionary Learning maps')
3.4.4. Region Extraction with Dictionary learning maps#
We use object
RegionExtractor for extracting brain connected regions
from dictionary maps into separated brain activation regions with automatic
thresholding strategy selected as
We use thresholding strategy to first get foreground information present in the
maps and then followed by robust region extraction on foreground information using
Random Walker algorithm selected as
Here, we control foreground extraction using parameter
represents the expected proportion of voxels included in the regions
(i.e. with a non-zero value in one of the maps). If you need to keep more
proportion of voxels then threshold should be tweaked according to
the maps data.
min_region_size=1350 mm^3 is to keep the minimum number of extracted
regions. We control the small spurious regions size by thresholding in voxel
units to adapt well to the resolution of the image. Please see the documentation of
connected_regions for more details.
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]
3.4.5. Visualization of Region Extraction results#
Showing region extraction results. The same function
plot_prob_atlas is used
for visualizing extracted regions on a standard template. Each extracted brain
region is assigned a color and as you can see that visual cortex area is extracted
quite nicely into each hemisphere.
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', title=title)
3.4.6. Computing functional connectivity matrices#
Here, we use the object called
ConnectivityMeasure to compute
functional connectivity measured between each extracted brain regions. Many different
kinds of measures exists in nilearn such as “correlation”, “partial correlation”, “tangent”,
“covariance”, “precision”. But, here we show how to compute only correlations by
selecting parameter as
kind='correlation' as initialized in the object.
The first step to do is to extract subject specific time series signals using
functional data stored in
func_filenames and the second step is to call
ConnectivityMeasure.fit_transform on the time series signals.
Here, for each subject we have time series signals of
where 168 is the length of time series and
n_regions_extracted is the number of
extracted regions. Likewise, we have a total of 20 subject specific time series signals.
The third step, we compute the mean correlation across all subjects.
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)
3.4.7. Visualization of functional connectivity matrices#
Showing mean of correlation matrices computed between each extracted brain regions.
At this point, we make use of nilearn image and plotting utilities to find
automatically the coordinates required, for plotting connectome relations.
Left image is the correlations in a matrix form and right image is the
connectivity relations to brain regions plotted using
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)
3.4.8. Validating results#
Showing only one specific network regions before and after region extraction. The first image displays the regions of one specific functional network without region extraction.
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')
The second image displays the regions split apart after region extraction. Here, we can validate that regions are nicely separated identified by each extracted region in different color.
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()
The full code can be found as an example: Regions extraction using dictionary learning and functional connectomes