Note
Click here to download the full example code or to run this example in your browser via Binder
Extract signals on spheres and plot a connectome#
This example shows how to extract signals from spherical regions. We show how to build spheres around user-defined coordinates, as well as centered on coordinates from the Power-264 atlas [1], and the Dosenbach-160 atlas [2].
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
References
[1] Power, Jonathan D., et al. “Functional network organization of the human brain.” Neuron 72.4 (2011): 665-678.
[2] Dosenbach N.U., Nardos B., et al. “Prediction of individual brain maturity using fMRI.”, 2010, Science 329, 1358-1361.
We estimate connectomes using two different methods: sparse inverse covariance and partial_correlation, to recover the functional brain networks structure.
We’ll start by extracting signals from Default Mode Network regions and computing a connectome from them.
Retrieve the brain development fmri dataset#
We are going to use a subject from the development functional connectivity dataset.
from nilearn import datasets
dataset = datasets.fetch_development_fmri(n_subjects=10)
# print basic information on the dataset
print('First subject functional nifti image (4D) is at: %s' %
dataset.func[0]) # 4D data
First subject functional nifti image (4D) is at: /home/yasmin/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
Coordinates of Default Mode Network#
dmn_coords = [(0, -52, 18), (-46, -68, 32), (46, -68, 32), (1, 50, -5)]
labels = [
'Posterior Cingulate Cortex',
'Left Temporoparietal junction',
'Right Temporoparietal junction',
'Medial prefrontal cortex',
]
Extracts signal from sphere around DMN seeds#
We can compute the mean signal within spheres of a fixed radius
around a sequence of (x, y, z) coordinates with the object
nilearn.maskers.NiftiSpheresMasker
.
The resulting signal is then prepared by the masker object: Detrended,
band-pass filtered and standardized to 1 variance.
from nilearn.maskers import NiftiSpheresMasker
masker = NiftiSpheresMasker(
dmn_coords, radius=8, detrend=True, standardize=True,
low_pass=0.1, high_pass=0.01, t_r=2,
memory='nilearn_cache', memory_level=1, verbose=2)
# Additionally, we pass confound information to ensure our extracted
# signal is cleaned from confounds.
func_filename = dataset.func[0]
confounds_filename = dataset.confounds[0]
time_series = masker.fit_transform(func_filename,
confounds=[confounds_filename])
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
_filter_and_extract('/home/yasmin/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_spheres_masker._ExtractionFunctor object at 0x7fc1ef1d8580>,
{ 'allow_overlap': False,
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'low_pass': 0.1,
'mask_img': None,
'radius': 8,
'seeds': [(0, -52, 18), (-46, -68, 32), (46, -68, 32), (1, 50, -5)],
'smoothing_fwhm': None,
'standardize': True,
'standardize_confounds': True,
't_r': 2}, confounds=[ '/home/yasmin/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_desc-reducedConfounds_regressors.tsv'], sample_mask=None, dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=2)
[NiftiSpheresMasker.transform_single_imgs] Loading data from /home/yasmin/nilearn/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
[NiftiSpheresMasker.transform_single_imgs] Extracting region signals
[NiftiSpheresMasker.transform_single_imgs] Cleaning extracted signals
_______________________________________________filter_and_extract - 2.4s, 0.0min
Display time series#
import matplotlib.pyplot as plt
for time_serie, label in zip(time_series.T, labels):
plt.plot(time_serie, label=label)
plt.title('Default Mode Network Time Series')
plt.xlabel('Scan number')
plt.ylabel('Normalized signal')
plt.legend()
plt.tight_layout()

Compute partial correlation matrix#
Using object nilearn.connectome.ConnectivityMeasure
: Its
default covariance estimator is Ledoit-Wolf, allowing to obtain accurate
partial correlations.
from nilearn.connectome import ConnectivityMeasure
connectivity_measure = ConnectivityMeasure(kind='partial correlation')
partial_correlation_matrix = connectivity_measure.fit_transform(
[time_series])[0]
Display connectome#
We display the graph of connections with :func: nilearn.plotting.plot_connectome.
from nilearn import plotting
plotting.plot_connectome(partial_correlation_matrix, dmn_coords,
title="Default Mode Network Connectivity")

<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fc1aac40070>
Display connectome with hemispheric projections. Notice (0, -52, 18) is included in both hemispheres since x == 0.
plotting.plot_connectome(partial_correlation_matrix, dmn_coords,
title="Connectivity projected on hemispheres",
display_mode='lyrz')
plotting.show()

3D visualization in a web browser#
An alternative to nilearn.plotting.plot_connectome
is to use
nilearn.plotting.view_connectome
, which gives more interactive
visualizations in a web browser. See 3D Plots of connectomes
for more details.
view = plotting.view_connectome(partial_correlation_matrix, dmn_coords)
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
view