Extracting signals of a probabilistic atlas of functional regions

This example extracts the signal on regions defined via a probabilistic atlas, to construct a functional connectome.

We use the MSDL atlas of functional regions in movie-watching.

The key to extract signals is to use the nilearn.maskers.NiftiMapsMasker that can transform nifti objects to time series using a probabilistic atlas.

As the MSDL atlas comes with (x, y, z) MNI coordinates for the different regions, we can visualize the matrix as a graph of interaction in a brain. To avoid having too dense a graph, we represent only the 20% edges with the highest values.

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

Retrieve the atlas and the data

from nilearn import datasets
atlas = datasets.fetch_atlas_msdl()
# Loading atlas image stored in 'maps'
atlas_filename = atlas['maps']
# Loading atlas data stored in 'labels'
labels = atlas['labels']

# Load the functional datasets
data = datasets.fetch_development_fmri(n_subjects=1)

print('First subject resting-state nifti image (4D) is located at: %s' %
      data.func[0])

Dataset created in /home/alexis/nilearn_data/development_fmri


Dataset created in /home/alexis/nilearn_data/development_fmri/development_fmri

Downloading data from https://osf.io/yr3av/download ...
 ...done. (1 seconds, 0 min)
Downloading data from https://osf.io/download/5c8ff3df4712b400183b7092/ ...
 ...done. (2 seconds, 0 min)
Downloading data from https://osf.io/download/5c8ff3e04712b400193b5bdf/ ...
 ...done. (2 seconds, 0 min)
First subject resting-state nifti image (4D) is located at: /home/alexis/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz

Extract the time series

from nilearn.maskers import NiftiMapsMasker
masker = NiftiMapsMasker(maps_img=atlas_filename, standardize=True,
                         memory='nilearn_cache', verbose=5)
masker.fit(data.func[0])
time_series = masker.transform(data.func[0],
                               confounds=data.confounds)

[NiftiMapsMasker.fit] loading regions from /home/alexis/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii
Resampling maps
/home/alexis/miniconda3/envs/nilearn/lib/python3.10/site-packages/nilearn/_utils/cache_mixin.py:304: UserWarning:

memory_level is currently set to 0 but a Memory object has been provided. Setting memory_level to 1.

________________________________________________________________________________
[Memory] Calling nilearn.image.resampling.resample_img...
resample_img(<nibabel.nifti1.Nifti1Image object at 0x7ff8fb20f0d0>, interpolation='continuous', target_shape=(50, 59, 50), target_affine=array([[   4.,    0.,    0.,  -96.],
       [   0.,    4.,    0., -132.],
       [   0.,    0.,    4.,  -78.],
       [   0.,    0.,    0.,    1.]]))
_____________________________________________________resample_img - 1.4s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker._filter_and_extract...
_filter_and_extract('/home/alexis/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
<nilearn.maskers.nifti_maps_masker._ExtractionFunctor object at 0x7ff8fb20f5e0>, { 'allow_overlap': True,
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'maps_img': '/home/alexis/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii',
  'mask_img': None,
  'reports': True,
  'smoothing_fwhm': None,
  'standardize': True,
  'standardize_confounds': True,
  't_r': None,
  'target_affine': None,
  'target_shape': None}, confounds=[ '/home/alexis/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=5)
[NiftiMapsMasker.transform_single_imgs] Loading data from /home/alexis/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
[NiftiMapsMasker.transform_single_imgs] Extracting region signals
[NiftiMapsMasker.transform_single_imgs] Cleaning extracted signals
_______________________________________________filter_and_extract - 1.7s, 0.0min

We can generate an HTML report and visualize the components of the NiftiMapsMasker. You can pass the indices of the spatial maps you want to include in the report in the order you want them to appear. Here, we only include maps 2, 6, 7, 16, and 21 in the report:

report = masker.generate_report(displayed_maps=[2, 6, 7, 16, 21])
report

/home/alexis/miniconda3/envs/nilearn/lib/python3.10/site-packages/numpy/ma/core.py:2826: UserWarning:

Warning: converting a masked element to nan.

NiftiMapsMasker Class for masking of Niimg-like objects. NiftiMapsMasker is useful when data from overlapping volumes should be extracted (contrarily to :class:`nilearn.maskers.NiftiLabelsMasker`). Use case: Summarize brain signals from large-scale networks obtained by prior PCA or :term:`ICA`. Note that, Inf or NaN present in the given input images are automatically put to zero rather than considered as missing data.

Previous Map

Component

Next Map

This reports shows the spatial maps provided to the mask.

The masker has 39 spatial maps in total.

Only 5 spatial maps are shown in this report.

Parameters

Parameter	Value
allow_overlap	True
detrend	False
dtype	None
high_pass	None
high_variance_confounds	False
low_pass	None
maps_img	/home/alexis/nilearn_data/msdl_atlas/MSDL_rois/msdl_rois.nii
mask_img	None
memory	Memory(location=nilearn_cache/joblib)
memory_level	1
reports	True
resampling_target	data
smoothing_fwhm	None
standardize	True
standardize_confounds	True
t_r	None
verbose	5

time_series is now a 2D matrix, of shape (number of time points x number of regions)

print(time_series.shape)

(168, 39)

Build and display a correlation matrix

from nilearn.connectome import ConnectivityMeasure
correlation_measure = ConnectivityMeasure(kind='correlation')
correlation_matrix = correlation_measure.fit_transform([time_series])[0]

# Display the correlation matrix
import numpy as np
from nilearn import plotting
# Mask out the major diagonal
np.fill_diagonal(correlation_matrix, 0)
plotting.plot_matrix(correlation_matrix, labels=labels, colorbar=True,
                     vmax=0.8, vmin=-0.8)

<matplotlib.image.AxesImage object at 0x7ff8ed826da0>

And now display the corresponding graph

from nilearn import plotting
coords = atlas.region_coords

# We threshold to keep only the 20% of edges with the highest value
# because the graph is very dense
plotting.plot_connectome(correlation_matrix, coords,
                         edge_threshold="80%", colorbar=True)

plotting.show()

3D visualization in a web browser

An alternative to nilearn.plotting.plot_connectome is to use nilearn.plotting.view_connectome that gives more interactive visualizations in a web browser. See 3D Plots of connectomes for more details.

view = plotting.view_connectome(correlation_matrix, coords, edge_threshold='80%')

# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
view

# uncomment this to open the plot in a web browser:
# view.open_in_browser()

Estimated memory usage: 1066 MB