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].


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


from nilearn.input_data import NiftiMasker


[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(f"First subject functional nifti image (4D) is at: {dataset.func[0]}")
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(
    clean__butterworth__padtype="even",  # kwarg to modify Butterworth filter

# 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...
<nilearn.maskers.nifti_spheres_masker._ExtractionFunctor object at 0x7fb585a17b80>,
{ 'allow_overlap': False,
  'clean_kwargs': {'butterworth__padtype': 'even'},
  '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': 'zscore_sample',
  '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
/home/yasmin/miniconda3/envs/nilearn-dev/lib/python3.10/site-packages/joblib/memory.py:349: FutureWarning:

The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.

_______________________________________________filter_and_extract - 2.1s, 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")
Default Mode Network Time Series

Compute partial correlation matrix#

Using object nilearn.connectome.ConnectivityMeasure: Its default covariance estimator is Ledoit-Wolf, allowing to obtain accurate partial correlations.

Display connectome#

We display the graph of connections with :func: nilearn.plotting.plot_connectome.

from nilearn import plotting

    title="Default Mode Network Connectivity",
plot sphere based connectome
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fb5abf1d5a0>

Display connectome with hemispheric projections. Notice (0, -52, 18) is included in both hemispheres since x == 0.

    title="Connectivity projected on hemispheres",

plot sphere based connectome

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