def plot_connectome(covs, tv=False):
# g = np.zeros(shape=(160, 160))
g = covs
dos_coords = datasets.fetch_coords_dosenbach_2010()
dos_coords = dos_coords.rois
dos_coords_table = [[x, y, z] for (x, y, z) in dos_coords
] # Reformat the atlas coordinates
f = plt.figure(figsize=(2.3, 3.5)) # 2.2,2.3
if tv:
plotting.plot_connectome(g,
dos_coords_table,
display_mode='z',
output_file='figs/connectome_tv.pdf',
edge_threshold="0.0005%",
annotate=True,
figure=f,
node_size=18)
else:
plotting.plot_connectome(g,
dos_coords_table,
display_mode='z',
output_file='figs/connectome.pdf',
edge_threshold="0.0005%",
annotate=True,
figure=f,
node_size=18)
def save_dists(path):
# Get the MNI coordinates for the Dosenbach atlas
# Here I load the dosenbach atlas directly from nilearn, but you will need to provide a list of the MNI coordinates as a 2d array (see reformatting below)
dos_coords = datasets.fetch_coords_dosenbach_2010()
print("networks", dos_coords.networks)
labels = numpy.array(
[str(x[2:x.rfind(' ') - 1]) for x in dos_coords.labels])
networks = numpy.array([x.decode("utf-8") for x in dos_coords.networks])
print("networks shape", dos_coords.networks.shape)
dos_coords = dos_coords.rois
dos_coords_table = [[x, y, z] for (x, y, z) in dos_coords
] # Reformat the atlas coordinates
dists = numpy.zeros((160, 160))
for i in range(160):
for j in range(160):
dists[i, j] = dist(dos_coords_table[i], dos_coords_table[j])
# print("coords", dos_coords_table)
numpy.savetxt(path + '/data/dists.csv', dists, delimiter=',')
numpy.savetxt(path + '/data/labels.csv', labels, fmt="%s", delimiter=',')
numpy.savetxt(path + '/data/networks.csv',
networks,
fmt="%s",
delimiter=',')
title='Power correlation graph',
edge_threshold='99.8%',
node_size=20,
colorbar=True)
###############################################################################
# Note the 1. on the matrix diagonal: These are the signals variances, set to
# 1. by the `spheres_masker`. Hence the covariance of the signal is a
# correlation matrix
###############################################################################
# Connectome extracted from Dosenbach's atlas
# -------------------------------------------
#
# We repeat the same steps for Dosenbach's atlas.
dosenbach = datasets.fetch_coords_dosenbach_2010()
coords = np.vstack((
dosenbach.rois['x'],
dosenbach.rois['y'],
dosenbach.rois['z'],
)).T
spheres_masker = input_data.NiftiSpheresMasker(seeds=coords,
smoothing_fwhm=4,
radius=4.5,
detrend=True,
standardize=True,
low_pass=0.1,
high_pass=0.01,
t_r=2.5)
# Tweak edge_threshold to keep only the strongest connections.
plotting.plot_connectome(
matrix, coords, title="Power correlation graph", edge_threshold="99.8%", node_size=20, colorbar=True
)
###############################################################################
# Note the 1. on the matrix diagonal: These are the signals variances, set to
# 1. by the `spheres_masker`. Hence the covariance of the signal is a
# correlation matrix
###############################################################################
# Connectome extracted from Dosenbach's atlas
# -------------------------------------------
#
# We repeat the same steps for Dosenbach's atlas.
dosenbach = datasets.fetch_coords_dosenbach_2010()
coords = np.vstack((dosenbach.rois["x"], dosenbach.rois["y"], dosenbach.rois["z"])).T
spheres_masker = input_data.NiftiSpheresMasker(
seeds=coords, smoothing_fwhm=4, radius=4.5, detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2.5
)
timeseries = spheres_masker.fit_transform(fmri_filename, confounds=confounds_filename)
covariance_estimator = GraphLassoCV()
covariance_estimator.fit(timeseries)
matrix = covariance_estimator.covariance_
plt.figure()
plt.imshow(matrix, vmin=-1.0, vmax=1.0, cmap="RdBu_r", interpolation="nearest")
title='Node strength for the positive edges for Power atlas',
node_cmap=cm.YlOrRd)
# plot nodes' strength for negative edges
plotting.plot_markers(
node_strength_negative,
coords,
title='Node strength for the negative edges for Power atlas',
node_cmap=cm.PuBu)
###############################################################################
# Connectome extracted from Dosenbach's atlas
# -------------------------------------------
#
# We repeat the same steps for Dosenbach's atlas.
dosenbach = datasets.fetch_coords_dosenbach_2010(legacy_format=False)
coords = np.vstack((
dosenbach.rois['x'],
dosenbach.rois['y'],
dosenbach.rois['z'],
)).T
spheres_masker = NiftiSpheresMasker(seeds=coords,
smoothing_fwhm=6,
radius=4.5,
detrend=True,
standardize=True,
low_pass=0.1,
high_pass=0.01,
t_r=2)
def plot_connectome(path):
# Each of these is a p X p matrix, where p = the number of brain regions
# In this dataset, we're looking at 160 ROIs. Each matrix graphs the functional dependencies between pairs of ROIs (building a connectome)
# for 2 groups - patients with ASD and control subjects
folder = "data/full_1/nsimule_dist_2/"
autism = numpy.loadtxt(
path + folder + "autism_0.2_1.csv", delimiter=','
) # File containing the output graph from running SIMULE - autism
control = numpy.loadtxt(
path + folder + "control_0.2_1.csv", delimiter=','
) # File containing the output graph from running SIMULE - control
autism = numpy.around(autism, decimals=3) != 0
control = numpy.around(control, decimals=3) != 0
print("shape", autism.shape)
print("topleft", autism[:3, :3])
# find diff
diff = autism - control
print("diff shape", diff.shape)
print("nonzeros autism", numpy.sum(diff != 0))
# Get the MNI coordinates for the Dosenbach atlas
# Here I load the dosenbach atlas directly from nilearn, but you will need to provide a list of the MNI coordinates as a 2d array (see reformatting below)
dos_coords = datasets.fetch_coords_dosenbach_2010()
dos_coords = dos_coords.rois
dos_coords_table = [[x, y, z] for (x, y, z) in dos_coords
] # Reformat the atlas coordinates
# WARNING: The more coordinates, the slower this becomes. I haven't tried plotting with >160 coordinates, and it takes about a minute to plot.
# You might need to consider filtering your coordinates down to fewer ROIs
out = 'pdf'
tit = {'fontsize': 14, 'horizontalalignment': 'center'}
f = plt.figure(figsize=(2.3, 3.5)) # 2.2,2.3
plotting.plot_connectome(
-1 * autism,
dos_coords_table,
display_mode='z',
# output_file=path + "plots/connectomes/autism." + out,
# title="$\mathrm{\Omega^{Autism}}$",
annotate=False,
figure=f,
node_size=18)
plt.title("$\mathrm{\hat{\Omega}^{Autism}}$", **tit)
plt.savefig(path + "plots/connectomes/autism." + out)
# print('out', plot_out)
# plt.show()
f = plt.figure(figsize=(2.3, 3.5)) # 2.2,2.3
plotting.plot_connectome(
-1 * control,
dos_coords_table,
display_mode='z',
# output_file=path + "plots/connectomes/autism." + out,
# title="$\mathrm{\Omega^{Autism}}$",
annotate=False,
figure=f,
node_size=18)
plt.title("$\mathrm{\hat{\Omega}^{Control}}$", **tit)
plt.savefig(path + "plots/connectomes/control." + out)
f = plt.figure(figsize=(2.3, 3.5)) # 2.2,2.3
plotting.plot_connectome(
-1 * diff,
dos_coords_table,
display_mode='z',
# output_file=path + "plots/connectomes/autism." + out,
# title="$\mathrm{\Omega^{Autism}}$",
annotate=False,
figure=f,
node_size=18)
plt.title("$\mathrm{\hat{\Omega}^{Autism}-\hat{\Omega}^{Control}}$", **tit)
plt.savefig(path + "plots/connectomes/diff." + out)