9.4.8. Comparing connectomes on different reference atlases

This examples shows how to turn a parcellation into connectome for visualization. This requires choosing centers for each parcel or network, via nilearn.plotting.find_parcellation_cut_coords for parcellation based on labels and nilearn.plotting.find_probabilistic_atlas_cut_coords for parcellation based on probabilistic values.

In the intermediary steps, we make use of nilearn.maskers.NiftiLabelsMasker and nilearn.maskers.NiftiMapsMasker to extract time series from nifti objects using different parcellation atlases.

The time series of all subjects of the brain development dataset are concatenated and given directly to nilearn.connectome.ConnectivityMeasure for computing parcel-wise correlation matrices for each atlas across all subjects.

Mean correlation matrix is displayed on glass brain on extracted coordinates.

# author: Amadeus Kanaan

Note

If you are using Nilearn with a version older than 0.9.0, then you should either upgrade your version or import maskers from the input_data module instead of the maskers module.

That is, you should manually replace in the following example all occurrences of:

from nilearn.maskers import NiftiMasker

with:

from nilearn.input_data import NiftiMasker

9.4.8.1. Load atlases

from nilearn import datasets

yeo = datasets.fetch_atlas_yeo_2011()
print('Yeo atlas nifti image (3D) with 17 parcels and liberal mask is located '
      'at: %s' % yeo['thick_17'])

Out:

Yeo atlas nifti image (3D) with 17 parcels and liberal mask is located at: /home/nicolas/nilearn_data/yeo_2011/Yeo_JNeurophysiol11_MNI152/Yeo2011_17Networks_MNI152_FreeSurferConformed1mm_LiberalMask.nii.gz

9.4.8.2. Load functional data

data = datasets.fetch_development_fmri(n_subjects=10)

print('Functional nifti images (4D, e.g., one subject) are located at : %r'
      % data['func'][0])
print('Counfound csv files (of same subject) are located at : %r'
      % data['confounds'][0])

Out:

Functional nifti images (4D, e.g., one subject) are located at : '/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
Counfound csv files (of same subject) are located at : '/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_desc-reducedConfounds_regressors.tsv'

9.4.8.3. Extract coordinates on Yeo atlas - parcellations

from nilearn.maskers import NiftiLabelsMasker
from nilearn.connectome import ConnectivityMeasure

# ConenctivityMeasure from Nilearn uses simple 'correlation' to compute
# connectivity matrices for all subjects in a list
connectome_measure = ConnectivityMeasure(kind='correlation')

# useful for plotting connectivity interactions on glass brain
from nilearn import plotting

# create masker to extract functional data within atlas parcels
masker = NiftiLabelsMasker(labels_img=yeo['thick_17'], standardize=True,
                           memory='nilearn_cache')

# extract time series from all subjects and concatenate them
time_series = []
for func, confounds in zip(data.func, data.confounds):
    time_series.append(masker.fit_transform(func, confounds=confounds))

# calculate correlation matrices across subjects and display
correlation_matrices = connectome_measure.fit_transform(time_series)

# Mean correlation matrix across 10 subjects can be grabbed like this,
# using connectome measure object
mean_correlation_matrix = connectome_measure.mean_

# grab center coordinates for atlas labels
coordinates = plotting.find_parcellation_cut_coords(labels_img=yeo['thick_17'])

# plot connectome with 80% edge strength in the connectivity
plotting.plot_connectome(mean_correlation_matrix, coordinates,
                         edge_threshold="80%",
                         title='Yeo Atlas 17 thick (func)')

Out:

<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fa502c0cd30>

9.4.8.4. Plot a directed connectome - asymmetric connectivity measure

In this section, we use the lag-1 correlation as the connectivity measure, which leads to an asymmetric connectivity matrix. The plot_connectome function accepts both symmetric and asymmetric matrices, but plot the latter as a directed graph.

import numpy as np

# Define a custom function to compute lag correlation on the time series
def lag_correlation(time_series, lag):
    n_subjects = len(time_series)
    n_samples, n_features = time_series[0].shape
    lag_cor = np.zeros((n_subjects, n_features, n_features))
    for subject, serie in enumerate(time_series):
        for i in range(n_features):
            for j in range(n_features):
                if lag == 0:
                    lag_cor[subject, i, j] = np.corrcoef(serie[:, i],
                                                         serie[:, j])[0, 1]
                else:
                    lag_cor[subject, i, j] = np.corrcoef(serie[lag:, i],
                                                         serie[:-lag, j])[0, 1]
    return np.mean(lag_cor, axis=0)

# Compute lag-0 and lag-1 correlations and plot associated connectomes
for lag in [0, 1]:
    lag_correlation_matrix = lag_correlation(time_series, lag)
    plotting.plot_connectome(lag_correlation_matrix, coordinates,
                             edge_threshold="90%",
                             title='Lag-{} correlation'.format(
                                 lag))

• 
• 

Out:

/home/nicolas/GitRepos/nilearn-fork/nilearn/plotting/displays/_projectors.py:166: UserWarning:

'adjacency_matrix' is not symmetric. A directed graph will be plotted.

9.4.8.5. Load probabilistic atlases - extracting coordinates on brain maps

dim = 64
difumo = datasets.fetch_atlas_difumo(
    dimension=dim, resolution_mm=2, legacy_format=False
)

9.4.8.6. Iterate over fetched atlases to extract coordinates - probabilistic

from nilearn.maskers import NiftiMapsMasker

# create masker to extract functional data within atlas parcels
masker = NiftiMapsMasker(maps_img=difumo.maps, standardize=True,
                         memory='nilearn_cache')

# extract time series from all subjects and concatenate them
time_series = []
for func, confounds in zip(data.func, data.confounds):
    time_series.append(masker.fit_transform(func, confounds=confounds))

# calculate correlation matrices across subjects and display
correlation_matrices = connectome_measure.fit_transform(time_series)

# Mean correlation matrix across 10 subjects can be grabbed like this,
# using connectome measure object
mean_correlation_matrix = connectome_measure.mean_

# grab center coordinates for probabilistic atlas
coordinates = plotting.find_probabilistic_atlas_cut_coords(maps_img=difumo.maps)

# plot connectome with 85% edge strength in the connectivity
plotting.plot_connectome(mean_correlation_matrix, coordinates,
                         edge_threshold="85%",
                         title='DiFuMo with {0} dimensions (probabilistic)'.format(dim))
plotting.show()

Out:

/home/nicolas/GitRepos/nilearn-fork/nilearn/_utils/cache_mixin.py:304: UserWarning:

memory_level is currently set to 0 but a Memory object has been provided. Setting memory_level to 1.

/home/nicolas/GitRepos/nilearn-fork/nilearn/maskers/nifti_maps_masker.py:460: UserWarning:

Persisting input arguments took 1.05s to run.
If this happens often in your code, it can cause performance problems
(results will be correct in all cases).
The reason for this is probably some large input arguments for a wrapped
 function (e.g. large strings).
THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an
 example so that they can fix the problem.

Estimated memory usage: 1622 MB