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.MultiNiftiLabelsMasker and nilearn.maskers.MultiNiftiMapsMasker to extract time series from nifti objects from multiple subjects 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

Load atlases

from nilearn import datasets

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

[get_dataset_dir] Dataset found in /home/remi/nilearn_data/yeo_2011
Yeo atlas nifti image (3D) with 17 parcels and liberal mask  is located at: /home/remi/nilearn_data/yeo_2011/Yeo_JNeurophysiol11_MNI152/Yeo2011_17Networks_MNI152_FreeSurferConformed1mm_LiberalMask.nii.gz

Load functional data

data = datasets.fetch_development_fmri(n_subjects=10)

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

[get_dataset_dir] Dataset found in /home/remi/nilearn_data/development_fmri
[get_dataset_dir] Dataset found in /home/remi/nilearn_data/development_fmri/development_fmri
[get_dataset_dir] Dataset found in /home/remi/nilearn_data/development_fmri/development_fmri
Functional nifti images (4D, e.g., one subject) are located at : '/home/remi/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/remi/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_desc-reducedConfounds_regressors.tsv'

Extract coordinates on Yeo atlas - parcellations

from nilearn.connectome import ConnectivityMeasure
from nilearn.maskers import MultiNiftiLabelsMasker

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

# create masker using MultiNiftiLabelsMasker to extract functional data within
# atlas parcels from multiple subjects using parallelization to speed up the
# computation
masker = MultiNiftiLabelsMasker(
    labels_img=yeo["thick_17"],  # Both hemispheres
    standardize="zscore_sample",
    standardize_confounds="zscore_sample",
    memory="nilearn_cache",
    n_jobs=2,
)

# extract time series from all subjects
time_series = masker.fit_transform(data.func, confounds=data.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_

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

# 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
left_connectome = plotting.plot_connectome(
    mean_correlation_matrix, coordinates, edge_threshold="80%"
)

Note that the approach above will extract time series and compute a single connectivity matrix for both hemispheres. However, the connectome is plotted only for the left hemisphere. If your aim is to compute and plot hemisphere-wise connectivity, you can follow the example below. First, create a separate atlas image for each hemisphere:

import nibabel as nb
import numpy as np

from nilearn.image import get_data, new_img_like
from nilearn.image.resampling import coord_transform

# load the atlas image first
label_image = nb.load(yeo["thick_17"])

# extract the affine matrix of the image
labels_affine = label_image.affine

# generate image coordinates using affine
x, y, z = coord_transform(0, 0, 0, np.linalg.inv(labels_affine))

# generate an separate image for the left hemisphere
# left/right split is done along x-axis
left_hemi = get_data(label_image).copy()
left_hemi[: int(x)] = 0
label_image_left = new_img_like(label_image, left_hemi, labels_affine)

# same for the right hemisphere
right_hemi = get_data(label_image).copy()
right_hemi[int(x) :] = 0
label_image_right = new_img_like(label_image, right_hemi, labels_affine)

Then, create a masker object, compute a connectivity matrix and plot the results for each hemisphere:

for hemi, img in zip(["right", "left"], [label_image_right, label_image_left]):
    masker = MultiNiftiLabelsMasker(
        labels_img=img,
        standardize="zscore_sample",
        standardize_confounds="zscore_sample",
    )

    time_series = masker.fit_transform(data.func, confounds=data.confounds)

    correlation_matrices = connectome_measure.fit_transform(time_series)
    mean_correlation_matrix = connectome_measure.mean_

    coordinates = plotting.find_parcellation_cut_coords(
        labels_img=img, label_hemisphere=hemi
    )

    plotting.plot_connectome(
        mean_correlation_matrix,
        coordinates,
        edge_threshold="80%",
        title=f"Yeo Atlas 17 thick (func) - {hemi}",
    )

plotting.show()

• 
• 

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.

# Define a custom function to compute lag correlation on the time series
def lag_correlation(time_series, lag):
    n_subjects = len(time_series)
    _, 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=f"Lag-{lag} correlation",
    )

• 
• 

/home/remi/github/nilearn/nilearn_doc_build/examples/03_connectivity/plot_atlas_comparison.py:194: UserWarning:

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

Load probabilistic atlases - extracting coordinates on brain maps

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

[get_dataset_dir] Dataset found in /home/remi/nilearn_data/difumo_atlases

Iterate over fetched atlases to extract coordinates - probabilistic

from nilearn.maskers import MultiNiftiMapsMasker

# create masker using MultiNiftiMapsMasker to extract functional data within
# atlas parcels from multiple subjects using parallelization to speed up the
# # computation
masker = MultiNiftiMapsMasker(
    maps_img=difumo.maps,
    standardize="zscore_sample",
    standardize_confounds="zscore_sample",
    memory="nilearn_cache",
    n_jobs=2,
)

# extract time series from all subjects
time_series = masker.fit_transform(data.func, confounds=data.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=f"DiFuMo with {dim} dimensions (probabilistic)",
)
plotting.show()

/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/multi_nifti_maps_masker.py:225: UserWarning:

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

Estimated memory usage: 2060 MB