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 (Power et al.[1]), and the Dosenbach-160 atlas (Dosenbach et al.[2]).

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.

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
from nilearn import datasets, plotting

Retrieve the brain development fMRI dataset#

We are going to use a subject from the development functional connectivity dataset.

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/runner/work/nilearn/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="zscore_sample",
    standardize_confounds="zscore_sample",
    low_pass=0.1,
    high_pass=0.01,
    t_r=2,
    memory="nilearn_cache",
    memory_level=1,
    verbose=2,
    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...
_filter_and_extract('/home/runner/work/nilearn/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 0x7f30a4d2b320>,
{ '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,
  'reports': True,
  'seeds': [(0, -52, 18), (-46, -68, 32), (46, -68, 32), (1, 50, -5)],
  'smoothing_fwhm': None,
  'standardize': 'zscore_sample',
  'standardize_confounds': 'zscore_sample',
  't_r': 2}, confounds=[ '/home/runner/work/nilearn/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/runner/work/nilearn/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 - 1.6s, 0.0min

Display spheres summary report#

By default all spheres are displayed. This can be tweaked by passing an integer or list/array of indices to the displayed_spheres argument of generate_report.

NiftiSpheresMasker Class for masking of Niimg-like objects using seeds. NiftiSpheresMasker is useful when data from given seeds should be extracted. Use case: summarize brain signals from seeds that were obtained from prior knowledge.

All Spheres

image

This reports shows the regions defined by the spheres of the masker.

The masker has 4 spheres in total (displayed together on the first image).

They can be individually browsed using Previous and Next buttons.

Regions summary
seed number coordinates position radius size (in mm^3) size (in voxels) relative size (in %)
0 [0, -52, 18] [24 20 24] 8 2144.66 not implemented not implemented
1 [-46, -68, 32] [12 16 28] 8 2144.66 not implemented not implemented
2 [46, -68, 32] [36 16 28] 8 2144.66 not implemented not implemented
3 [1, 50, -5] [24 46 18] 8 2144.66 not implemented not implemented
Parameters
Parameter Value
allow_overlap False
detrend True
dtype None
high_pass 0.01
high_variance_confounds False
low_pass 0.1
mask_img None
memory Memory(location=nilearn_cache/joblib)
memory_level 1
radius 8
reports True
seeds [(0, -52, 18), (-46, -68, 32), (46, -68, 32), (1, 50, -5)]
smoothing_fwhm None
standardize zscore_sample
standardize_confounds zscore_sample
t_r 2
verbose 2


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()
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.

from nilearn.connectome import ConnectivityMeasure

connectivity_measure = ConnectivityMeasure(
    kind="partial correlation",
    standardize="zscore_sample",
)
partial_correlation_matrix = connectivity_measure.fit_transform([time_series])[
    0
]

Display connectome#

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

plotting.plot_connectome(
    partial_correlation_matrix,
    dmn_coords,
    title="Default Mode Network Connectivity",
)
plot sphere based connectome
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7f309e32cb30>

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()
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
view