9.4.12. 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].

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.

9.4.12.1. 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

Out:

First subject functional nifti image (4D) is at: /home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz

9.4.12.2. 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',
    ]

9.4.12.3. 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.input_data.NiftiSpheresMasker. The resulting signal is then prepared by the masker object: Detrended, band-pass filtered and standardized to 1 variance.

from nilearn import input_data

masker = input_data.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])

Out:

________________________________________________________________________________
[Memory] Calling nilearn.input_data.base_masker.filter_and_extract...
filter_and_extract('/home/nicolas/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.input_data.nifti_spheres_masker._ExtractionFunctor object at 0x7fc022723b50>,
{ '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/nicolas/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/nicolas/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 - 1.6s, 0.0min

9.4.12.4. 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()

9.4.12.5. 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]

9.4.12.6. 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")

Out:

<nilearn.plotting.displays.OrthoProjector object at 0x7fc022b84550>

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()

9.4.12.7. 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