def local_plot_hemispheres(values, label_text, color_range, cmap="viridis"):
# Plot cortical surfaces with values as the data, label_text as
# the labels, and color_range as the limits of the color bar.
return plot_hemispheres(
pial_left,
pial_right,
values,
color_bar=True,
color_range=color_range,
label_text=label_text,
cmap=cmap,
embed_nb=True,
size=(1400, 200),
zoom=1.45,
nan_color=(0.7, 0.7, 0.7, 1),
cb__labelTextProperty={"fontSize": 12},
interactive=False,
)
n_permutations = 1000
sp = SpinPermutations(n_rep=n_permutations, random_state=0)
sp.fit(sphere_lh, points_rh=sphere_rh)
t1wt2w_rotated = np.hstack(sp.randomize(t1wt2w_lh, t1wt2w_rh))
thickness_rotated = np.hstack(sp.randomize(thickness_lh, thickness_rh))
###############################################################################
# As an illustration of the rotation, let’s plot the original t1w/t2w data
# Plot original data
plot_hemispheres(surf_lh,
surf_rh,
array_name=t1wt2w,
size=(1200, 300),
cmap='viridis',
nan_color=(0.5, 0.5, 0.5, 1),
color_bar=True)
###############################################################################
# as well as a few rotated versions.
# sphinx_gallery_thumbnail_number = 2
# Plot some rotations
plot_hemispheres(surf_lh,
surf_rh,
array_name=t1wt2w_rotated[:3],
size=(1200, 800),
cmap='viridis',
nan_color=(0.5, 0.5, 0.5, 1),
gradients_kernel = [None] * len(kernels)
for i, k in enumerate(kernels):
gm = GradientMaps(kernel=k, approach='dm', random_state=0)
gm.fit(conn_matrix)
gradients_kernel[i] = map_to_labels(gm.gradients_[:, i],
labeling,
mask=mask,
fill=np.nan)
label_text = ['Pearson', 'Spearman', 'Normalized\nAngle']
plot_hemispheres(surf_lh,
surf_rh,
array_name=gradients_kernel,
size=(1200, 800),
cmap='viridis_r',
color_bar=True,
label_text=label_text)
###############################################################################
# It seems the gradients provided by these kernels are quite similar although
# their scaling is quite different. Do note that the gradients are in arbitrary
# units, so the smaller/larger axes across kernels do not imply anything.
# Similar to using different kernels, we can also use different dimensionality
# reduction techniques.
# PCA, Laplacian eigenmaps and diffusion mapping
embeddings = ['pca', 'le', 'dm']
gradients_embedding = [None] * len(embeddings)
# Visualization
from brainspace.datasets import load_group_fc, load_parcellation, load_conte69
from brainspace.gradient import GradientMaps
from brainspace.plotting import plot_hemispheres
from brainspace.utils.parcellation import map_to_labels
atlas = np.load("Z:\\hschoi\\backup\\hschoi\\template\\MMP\\MMP.10k_fs_LR.npy")
pc_num = 2
ref_PCs = ref_PC[:, pc_num]
X = ref_PCs
labeling = atlas
conn_matrix = X # X # ref_PCs
mask = labeling != 0
grad = map_to_labels(conn_matrix, labeling, mask=mask, fill=np.nan)
plot_hemispheres(
surf_lh,
surf_rh,
array_name=grad,
size=(1300, 200),
color_bar=True,
cmap='jet',
zoom=1.85
) #'jet' # 'viridis_r', 'Blues', , 'seismic' # color_range = (-0.1,0.16)
from brainspace.gradient import GradientMaps
import numpy as np
from brainspace.utils.parcellation import map_to_labels
import nibabel as nib
from nilearn import surface
import pandas as pd
surf_lh, surf_rh = hcp360()
labeling = load_hcp_parcellation('hcp', scale=360, join=True)
print(np.shape(labeling))
P = plot_hemispheres(surf_lh,
surf_rh,
array_name=labeling,
size=(1200, 200),
cmap='tab20',
zoom=1.85)
P.screenshot('test.png')
"""hcp_grad = pd.read_csv("gradients.csv")
mask = labeling != 0
grad = [None] * 4
for i in range(4):
L_grad = hcp_grad['L_grad_'+str(i+1)].tolist()
R_grad = hcp_grad['R_grad_'+str(i+1)].tolist()
grad_LR = L_grad + R_grad
# map the gradient to the parcels
grad[i] = map_to_labels(np.array(grad_LR), labeling, mask=mask, fill=np.nan)
# proc_freesurfer
try:
# Freesurfer native thickness
th = np.concatenate((nb.freesurfer.read_morph_data(dir_fS + subBIDS +
'/surf/lh.thickness'),
nb.freesurfer.read_morph_data(dir_fS + subBIDS +
'/surf/rh.thickness')),
axis=0)
plot_hemispheres(surf_lh,
surf_rh,
array_name=th,
size=(900, 250),
color_bar='bottom',
zoom=1.25,
embed_nb=True,
interactive=False,
share='both',
nan_color=(0, 0, 0, 1),
color_range=(1.5, 4),
cmap="inferno",
transparent_bg=False,
screenshot=True,
filename=dir_QC_png + subBIDS +
'_space-fsnative_desc-surf_thickness.png')
# Freesurfer native curvature
cv = np.concatenate(
(nb.freesurfer.read_morph_data(dir_fS + subBIDS + '/surf/lh.curv'),
nb.freesurfer.read_morph_data(dir_fS + subBIDS + '/surf/rh.curv')),
axis=0)
plot_hemispheres(wm_lh,
wm_rh,
gm = GradientMaps(n_components=2, random_state=0)
gm.fit(correlation_matrix)
###############################################################################
# Visualize results
from brainspace.datasets import load_fsa5
from brainspace.plotting import plot_hemispheres
from brainspace.utils.parcellation import map_to_labels
# Map gradients to original parcels
grad = [None] * 2
for i, g in enumerate(gm.gradients_.T):
grad[i] = map_to_labels(g, labeling, mask=mask, fill=np.nan)
# Load fsaverage5 surfaces
surf_lh, surf_rh = load_fsa5()
# sphinx_gallery_thumbnail_number = 2
plot_hemispheres(surf_lh,
surf_rh,
array_name=grad,
size=(1200, 400),
cmap='viridis_r',
color_bar=True,
label_text=['Grad1', 'Grad2'],
zoom=1.5)
###############################################################################
# This concludes the setup tutorial. The following tutorials can be run using
# either the output generated here or the example data.
# ## Functional gradients to fsaverage5 surface
# In[152]:
# Mask of the medial wall on fsaverage 5
mask_fs5 = labels_fs5 != 0
# Map gradients to original parcels
grad = [None] * 3
for i, g in enumerate(gm.gradients_.T[0:3,:]):
grad[i] = map_to_labels(g, labels_fs5, fill=np.nan, mask=mask_fs5)
# Plot Gradients RdYlBu
plot_hemispheres(fs5_lh, fs5_rh, array_name=grad, size=(1000, 600), cmap='coolwarm',
embed_nb=True, label_text={'left':['Grad1', 'Grad2','Grad3']}, color_bar='left',
zoom=1.25, nan_color=(1, 1, 1, 1) )
# ## Functional gradients to conte69 surface
# In[153]:
# mask of the medial wall
mask_c69 = labels_c69 != 0
# Map gradients to original parcels
grad = [None] * 3
for i, g in enumerate(gm.gradients_.T[0:3,:]):
grad[i] = map_to_labels(g, labels_c69, fill=np.nan, mask=mask_c69)
def test_plot_hemispheres():
s1 = to_data(vtk.vtkSphereSource())
s2 = to_data(vtk.vtkSphereSource())
plot_hemispheres(s1, s2, offscreen=True)
from brainspace.datasets import load_group_fc, load_parcellation, load_conte69
# First load mean connectivity matrix and Schaefer parcellation
conn_matrix = load_group_fc('schaefer', scale=400)
labeling = load_parcellation('schaefer', scale=400, join=True)
# and load the conte69 hemisphere surfaces
surf_lh, surf_rh = load_conte69()
###############################################################################
# Let’s first look at the parcellation scheme we’re using.
from brainspace.plotting import plot_hemispheres
plot_hemispheres(surf_lh, surf_rh, array_name=labeling, size=(1200, 300), cmap='tab20')
###############################################################################
# and let’s construct our gradients.
from brainspace.gradient import GradientMaps
# Ask for 10 gradients (default)
gm = GradientMaps(n_components=10, random_state=0)
gm.fit(conn_matrix)
###############################################################################
# Note that the default parameters are normalized angle kernel, diffusion
# embedding approach, 10 components. Once you have your gradients, a good first
# Load the data
th_lh = dir_morph + subjectID + '_space-fsnative_desc-lh_thickness.mgh'
th_rh = dir_morph + subjectID + '_space-fsnative_desc-rh_thickness.mgh'
th_nat = np.hstack(
np.concatenate(
(np.array(load(th_lh).get_fdata()), np.array(load(th_rh).get_fdata())),
axis=0))
# Plot the surface
plot_hemispheres(inf_lh,
inf_rh,
array_name=th_nat,
size=(900, 250),
color_bar='bottom',
zoom=1.25,
embed_nb=True,
interactive=False,
share='both',
nan_color=(0, 0, 0, 1),
color_range=(1.5, 4),
cmap="inferno",
transparent_bg=False)
# ### Thickness: Inflated fsaverage5
# In[6]:
# Load the data
th_lh_fs5 = dir_morph + subjectID + '_space-fsaverage5_desc-lh_thickness.mgh'
th_rh_fs5 = dir_morph + subjectID + '_space-fsaverage5_desc-rh_thickness.mgh'
th_fs5 = np.hstack(
gradients_kernel = [None] * len(kernels)
for i, k in enumerate(kernels):
gm = GradientMaps(kernel=k, approach='dm', random_state=0)
gm.fit(conn_matrix)
gradients_kernel[i] = map_to_labels(gm.gradients_[:, i],
labeling,
mask=mask,
fill=np.nan)
label_text = ['Pearson', 'Spearman', 'Normalized\nAngle']
plot_hemispheres(surf_lh,
surf_rh,
array_name=gradients_kernel,
size=(1200, 600),
cmap='viridis_r',
color_bar=True,
label_text=label_text,
zoom=1.45)
###############################################################################
# It seems the gradients provided by these kernels are quite similar although
# their scaling is quite different. Do note that the gradients are in arbitrary
# units, so the smaller/larger axes across kernels do not imply anything.
# Similar to using different kernels, we can also use different dimensionality
# reduction techniques.
# PCA, Laplacian eigenmaps and diffusion mapping
embeddings = ['pca', 'le', 'dm']
gradients_embedding = [None] * len(embeddings)