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[WIP] Removed sub-example due to unfit for lasso dataset - unstable float values #2177

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Oct 17, 2019
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[WIP] Removed sub-example due to unfit for lasso dataset - unstable float values #2177

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f1a1cc2
Removed sub-example due to unfit for lasso dataset - unstable float v…
kchawla-pi Oct 14, 2019
6ae21d0
Removed remaining mention of Seitzman atlas from example
kchawla-pi Oct 16, 2019
cf77447
Updated Whats whats_new
kchawla-pi Oct 17, 2019
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4 changes: 4 additions & 0 deletions doc/whats_new.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,10 @@ Changes
- :func:`nilearn.plotting.view_connectome` now converts NaNs in the adjacency
matrix to 0.

- Removed the plotting connectomes example which used the Seitzman atlas
from `examples/03_connectivity/plot_sphere_based_connectome.py`.
The atlas data is unsuitable for the method & the example is redundant.

Fixes
-----

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58 changes: 2 additions & 56 deletions examples/03_connectivity/plot_sphere_based_connectome.py
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Expand Up @@ -4,8 +4,8 @@

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], the Dosenbach-160
atlas [2], and the Seitzman-300 atlas [3].
centered on coordinates from the Power-264 atlas [1], and the Dosenbach-160
atlas [2].

**References**

Expand All @@ -15,11 +15,6 @@
[2] Dosenbach N.U., Nardos B., et al. "Prediction of individual brain maturity
using fMRI.", 2010, Science 329, 1358-1361.

[3] `Seitzman, B. A., et al. "A set of functionally-defined brain regions with
improved representation of the subcortex and cerebellum.", 2018, bioRxiv,
450452
<http://doi.org/10.1101/450452>`_

We estimate connectomes using two different methods: **sparse inverse
covariance** and **partial_correlation**, to recover the functional brain
**networks structure**.
Expand Down Expand Up @@ -332,55 +327,6 @@

plotting.show()


###############################################################################
# Connectome extracted from Seitzman's atlas
# -----------------------------------------------------
# We repeat the same steps for Seitzman's atlas.
seitzman = datasets.fetch_coords_seitzman_2018()

coords = np.vstack((
seitzman.rois['x'],
seitzman.rois['y'],
seitzman.rois['z'],
)).T

###############################################################################
# Before calculating the connectivity matrix, let's look at the distribution
# of the regions of the default mode network.
dmn_rois = seitzman.networks == "DefaultMode"
dmn_coords = coords[dmn_rois]
zero_matrix = np.zeros((len(dmn_coords), len(dmn_coords)))
plotting.plot_connectome(zero_matrix, dmn_coords,
title='Seitzman default mode network',
node_color='darkred', node_size=20)

###############################################################################
# Now let's calculate connectivity for the Seitzman atlas.
spheres_masker = input_data.NiftiSpheresMasker(
seeds=coords, smoothing_fwhm=6, radius=4,
detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2,
allow_overlap=True)

timeseries = spheres_masker.fit_transform(func_filename,
confounds=confounds_filename)

covariance_estimator = GraphicalLassoCV()
covariance_estimator.fit(timeseries)
matrix = covariance_estimator.covariance_

plotting.plot_matrix(matrix, vmin=-1., vmax=1., colorbar=True,
title='Seitzman correlation matrix')

plotting.plot_connectome(matrix, coords, title='Seitzman correlation graph',
edge_threshold="99.7%", node_size=20, colorbar=True)


###############################################################################
# We can easily identify the networks from the matrix blocks.
print('Seitzman networks names are {0}'.format(np.unique(seitzman.networks)))
plotting.show()

###############################################################################
# .. seealso::
#
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