Note
Go to the end 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 (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]}")
[get_dataset_dir] Dataset found in /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri
[get_dataset_dir] Dataset found in /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri
[get_dataset_dir] Dataset found in /home/runner/work/nilearn/nilearn/nilearn_data/development_fmri/development_fmri
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 0x7f6ec7922ba0>,
{ '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.wrapped] 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.wrapped] Extracting region signals
[NiftiSpheresMasker.wrapped] 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
.
Display time series¶
import matplotlib.pyplot as plt
plt.figure(layout="constrained")
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()
<matplotlib.legend.Legend object at 0x7f6ec7fe5e80>
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",
)
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7f6eccd051f0>
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