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8.2.2. Glass brain plotting in nilearn

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8.2.4. Visualizing multiscale functional brain parcellations

8.2.3. Visualizing Megatrawls Network Matrices from Human Connectome ProjectΒΆ

This example shows how to fetch network matrices data from HCP beta-release of the Functional Connectivity Megatrawl project.

See nilearn.datasets.fetch_megatrawls_netmats documentation for more details.

Fetching the partial correlation matrices of dimensionality d=300 with timeseries method ‘eigen regression’ by importing datasets module

from nilearn import datasets

netmats = datasets.fetch_megatrawls_netmats(dimensionality=300,
                                            timeseries='eigen_regression',
                                            matrices='partial_correlation')
# Partial correlation matrices array of size (300, 300) are stored in the name
# of 'correlation_matrices'
partial_correlation = netmats.correlation_matrices

Visualization Importing matplotlib and nilearn plotting modules to use its utilities for plotting correlation matrices

import matplotlib.pyplot as plt
from nilearn import plotting

title = "Partial correlation matrices of d=300 with timeseries='eigen_regression'"
plt.figure()
plt.imshow(partial_correlation, interpolation="nearest", cmap=plotting.cm.bwr)
plt.colorbar()
plt.title(title)
plt.show()
../../_images/sphx_glr_plot_visualize_megatrawls_netmats_001.png

Total running time of the script: ( 0 minutes 0.184 seconds)

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