Note
Go to the end to download the full example code. or to run this example in your browser via Binder
Extracting signals of a probabilistic atlas of functional regions¶
This example extracts the signal on regions defined via a probabilistic atlas, to construct a functional connectome.
We use the MSDL atlas of functional regions in movie-watching.
The key to extract signals is to use the
nilearn.maskers.NiftiMapsMasker
that can transform nifti
objects to time series using a probabilistic atlas.
As the MSDL atlas comes with (x, y, z) MNI coordinates for the different regions, we can visualize the matrix as a graph of interaction in a brain. To avoid having too dense a graph, we represent only the 20% edges with the highest values.
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
Retrieve the atlas and the data¶
from nilearn import datasets
atlas = datasets.fetch_atlas_msdl()
# Loading atlas image stored in 'maps'
atlas_filename = atlas["maps"]
# Loading atlas data stored in 'labels'
labels = atlas["labels"]
# Load the functional datasets
data = datasets.fetch_development_fmri(n_subjects=1)
print(
"First subject resting-state nifti image (4D) is located "
f"at: {data.func[0]}"
)
[get_dataset_dir] Dataset found in /home/runner/nilearn_data/msdl_atlas
[get_dataset_dir] Dataset found in /home/runner/nilearn_data/development_fmri
[get_dataset_dir] Dataset found in
/home/runner/nilearn_data/development_fmri/development_fmri
[get_dataset_dir] Dataset found in
/home/runner/nilearn_data/development_fmri/development_fmri
First subject resting-state nifti image (4D) is located at: /home/runner/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
Extract the time series¶
from nilearn.maskers import NiftiMapsMasker
masker = NiftiMapsMasker(
maps_img=atlas_filename,
standardize="zscore_sample",
standardize_confounds="zscore_sample",
memory="nilearn_cache",
verbose=5,
)
masker.fit(data.func[0])
time_series = masker.transform(data.func[0], confounds=data.confounds)
[NiftiMapsMasker.fit] loading regions from None
[NiftiMapsMasker.wrapped] Resampling maps
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/base_masker.py:269: UserWarning:
memory_level is currently set to 0 but a Memory object has been provided. Setting memory_level to 1.
[Memory]0.0s, 0.0min : Loading resample_img...
________________________________________resample_img cache loaded - 0.0s, 0.0min
[Memory]0.1s, 0.0min : Loading _filter_and_extract...
__________________________________filter_and_extract cache loaded - 0.0s, 0.0min
We can generate an HTML report and visualize the components of the
NiftiMapsMasker
.
You can pass the indices of the spatial maps you want to include in the
report in the order you want them to appear.
Here, we only include maps 2, 6, 7, 16, and 21 in the report:
report = masker.generate_report(displayed_maps=[2, 6, 7, 16, 21])
report
time_series is now a 2D matrix, of shape (number of time points x number of regions)
print(time_series.shape)
(168, 39)
Build and display a correlation matrix¶
from nilearn.connectome import ConnectivityMeasure
correlation_measure = ConnectivityMeasure(
kind="correlation",
standardize="zscore_sample",
)
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
# Display the correlation matrix
import numpy as np
from nilearn import plotting
# Mask out the major diagonal
np.fill_diagonal(correlation_matrix, 0)
plotting.plot_matrix(
correlation_matrix, labels=labels, colorbar=True, vmax=0.8, vmin=-0.8
)
<matplotlib.image.AxesImage object at 0x7f9b43ee89d0>
And now display the corresponding graph¶
coords = atlas.region_coords
# We threshold to keep only the 20% of edges with the highest value
# because the graph is very dense
plotting.plot_connectome(
correlation_matrix, coords, edge_threshold="80%", colorbar=True
)
plotting.show()
3D visualization in a web browser¶
An alternative to nilearn.plotting.plot_connectome
is to use
nilearn.plotting.view_connectome
that gives more interactive
visualizations in a web browser. See 3D Plots of connectomes
for more details.
view = plotting.view_connectome(
correlation_matrix, coords, edge_threshold="80%"
)
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
view