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generate_figures.py
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# In[1]: Packages
import pandas as pd
from bids import BIDSLayout
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib.patches import Patch
import plotje
import scipy
from multipy.fdr import lsu
#In[2]: Data acquisition
#Specify used paths
data_dir = './bids/'
conn_dir = './bids/derivatives/connectivity/'
file_dir = './datasets/'
fig_dir = './figures/publication/'
analysis_dir = './analysis/nbs_results/100/'
# Grab bids structure
layout = BIDSLayout(data_dir)
# Add connectivity directory
layout.add_derivatives(conn_dir)
# Read dataset of interest
dataset = pd.read_csv(file_dir + 'b2b_dataset_20230626.csv', sep='\t')
#Load significant findings from NBS analysis (see NBS script)
nbs_surgcons_adj_t3 = np.array(pd.read_csv(analysis_dir + 'surgery_conservative/surgery_conservative_adjacency_matrix_t3.csv',index_col=0))
nbs_surgcons_t3_p = pd.read_csv(analysis_dir + 'surgery_conservative/surgery_conservative_p_values_t3.csv',index_col=0)
sig_idx_surgcons_t3_p = np.where(nbs_surgcons_t3_p < 0.05)
sig_idx_surgcons_t3_p = int(sig_idx_surgcons_t3_p[0])+1 # +1 because idx 0 would not match the value 0 (no adj) in the adjacency matrix
# Read MR-files
files = layout.derivatives['connectivity'].get(extension='.tsv', space = 'MNI152NLin2009cAsym', atlas = 'HOSubCortSchaefer2018100Parcels7Networks')
conn_list = list(np.zeros(len(files)))
control_df = []
surg_df = []
cons_df = []
surg_names = []
control_names = []
cons_names = []
for file in files:
entities = dict(file.entities)
file_df = pd.read_csv(file.path, sep='\t', index_col=[0])
idx = np.where(dataset['mr_brain'] == int(entities['subject']))
idx = int(idx[0]) # <-- Make sure index is an int
group = dataset['Group'][idx]
if group == 'Surgery':
surg_df.append(file_df)
surg_names.append(entities['subject'])
elif group =='Control':
control_df.append(file_df)
control_names.append(entities['subject'])
elif group =='Conservative':
cons_df.append(file_df)
cons_names.append(entities['subject'])
#Fischer transform data
surg_nodes = np.zeros((len(surg_df), len(file_df),len(file_df)))
cons_nodes = np.zeros((len(cons_df), len(file_df),len(file_df)))
control_nodes = np.zeros((len(control_df),len(file_df),len(file_df)))
for i in range(len(surg_df)):
surg_arr = np.array(surg_df[i])
surg_arr[surg_arr==1] = np.nan
surg_arr = np.arctanh(surg_arr)
surg_nodes[i,:,:] = surg_arr
for i in range(len(control_df)):
control_arr = np.array(control_df[i])
control_arr[control_arr==1] = np.nan
control_arr = np.arctanh(control_arr)
control_nodes[i,:,:] = control_arr
for i in range(len(cons_df)):
cons_arr = np.array(cons_df[i])
cons_arr[cons_arr==1] = np.nan
cons_arr = np.arctanh(cons_arr)
cons_nodes[i,:,:] = cons_arr
#Generate mean Matrices for each group
mean_nodes_surg = np.mean(surg_nodes, axis = (0))
mean_nodes_control = np.mean(control_nodes, axis = (0))
mean_nodes_cons = np.mean(cons_nodes, axis = (0))
delta_nodes_surgcont = mean_nodes_surg - mean_nodes_control
delta_nodes_surgcons = mean_nodes_surg - mean_nodes_cons
delta_nodes_conscont = mean_nodes_cons - mean_nodes_control
#In[3]:
#Create indices for each corresponding Network (Yeo7 + HOSPA + Cerebellum)
vis_index = np.hstack([np.arange(0,9), np.arange(50,58)])
sommot_index = np.hstack([np.arange(9,15), np.arange(58,66)])
dorsatt_index = np.hstack([np.arange(15,23), np.arange(66,73)])
salvent_index = np.hstack([np.arange(23,30), np.arange(73,78)])
limbic_index = np.hstack([np.arange(30,33), np.arange(78,80)])
cont_index = np.hstack([np.arange(33,37), np.arange(80,89)])
default_index = np.hstack([np.arange(37,50), np.arange(89,100)])
subcort_index = np.hstack([np.arange(100,112)])
cerebellum_index = np.hstack([np.arange(112,123)])
network_indices = {'vis' : vis_index,
'sommot' : sommot_index,
'dorsatt' : dorsatt_index,
'salvent' : salvent_index,
'limbic' : limbic_index,
'cont' : cont_index,
'default' : default_index,
'subcort' : subcort_index,
'cereb' : cerebellum_index}
#Sort according to networks
netsort = np.zeros(len(surg_arr))
for i, key_i in enumerate(network_indices):
netsort[network_indices[key_i]] = i
sortindex = np.argsort(netsort)
colors = [ 'purple',
'blue',
'green',
'violet',
'gray',
'orange',
'red',
'brown',
'olive']
labels = [ 'Visual',
'Somatomotor',
'Dorsal attention',
'Ventral attention',
'Limbic',
'Frontoparietal',
'Default mode',
'Subcortical',
'Cerebellum']
#Visualization setup on parcel-level
network_color={}
for i in vis_index:
network_color[i] = 'purple'
for i in sommot_index:
network_color[i] = 'blue'
for i in dorsatt_index:
network_color[i] = 'green'
for i in salvent_index:
network_color[i] = 'violet'
for i in limbic_index:
network_color[i] = 'gray'
for i in cont_index:
network_color[i] = 'orange'
for i in default_index:
network_color[i] = 'red'
for i in subcort_index:
network_color[i] = 'brown'
for i in cerebellum_index:
network_color[i] = 'olive'
network_color_full = []
for i in sorted(network_color):
network_color_full.append(network_color[i])
sorted_network_colors = []
for i in sortindex:
if netsort[i] == 0:
sorted_network_colors.append('purple')
elif netsort[i] == 1:
sorted_network_colors.append('blue')
elif netsort[i] == 2:
sorted_network_colors.append('green')
elif netsort[i] == 3:
sorted_network_colors.append('violet')
elif netsort[i] == 4:
sorted_network_colors.append('gray')
elif netsort[i] == 5:
sorted_network_colors.append('orange')
elif netsort[i] == 6:
sorted_network_colors.append('red')
elif netsort[i] == 7:
sorted_network_colors.append('brown')
elif netsort[i] == 8:
sorted_network_colors.append('olive')
#Visualization setup on network-level
network_color_net = [ 'purple',
'blue',
'green',
'violet',
'gray',
'orange',
'red',
'brown',
'olive']
ticklabels_net = [ 'Vis',
'S.M',
'D.A',
'V.A',
'Lim',
'F.P',
'D.M',
'Sub',
'Cer']
#Create specific legend for used networks
legend_elements = [Patch(facecolor = 'purple', edgecolor = None, label = 'Vis'),
Patch(facecolor = 'blue', edgecolor = None, label = 'S.M'),
Patch(facecolor = 'green', edgecolor = None, label = 'D.A'),
Patch(facecolor = 'violet', edgecolor = None, label = 'V.A'),
Patch(facecolor = 'gray', edgecolor = None, label = 'Lim'),
Patch(facecolor = 'orange', edgecolor = None, label = 'F.P'),
Patch(facecolor = 'red', edgecolor = None, label = 'D.M'),
Patch(facecolor = 'brown', edgecolor = None, label = 'Sub'),
Patch(facecolor = 'olive', edgecolor = None, label = 'Cer')]
#Generate color-coded line to illustrate location of networks within connectivity matrix
line_parcel_x = np.zeros(len(sorted_network_colors)+1)
line_parcel_y = np.zeros(len(sorted_network_colors)+1)
for i in range(len(line_parcel_x)):
line_parcel_x[i] = i-0.5
x_points_parcel_x = np.array([line_parcel_x, line_parcel_y]).T.reshape(-1, 1, 2)
segments_parcel_x = np.concatenate([x_points_parcel_x[:-1], x_points_parcel_x[1:]], axis=1)
counter = 0
line_parcel_x = np.zeros(len(sorted_network_colors)+1)
line_parcel_y = np.zeros(len(sorted_network_colors)+1)
for i in (range(len(line_parcel_y))):
line_parcel_y[counter] = i-0.5
counter = counter+1
y_points_parcel_y = np.array([line_parcel_x, line_parcel_y]).T.reshape(-1, 1, 2)
segments_parcel_y = np.concatenate([y_points_parcel_y[:-1], y_points_parcel_y[1:]], axis=1)
#In[4]: Illustrations for Figure 2
#Generate parcel-level connectivity matrices per group (Surgery)
mask = np.triu(mean_nodes_surg[sortindex,:][:,sortindex])
masked_matrix = np.ma.array(mean_nodes_surg[sortindex,:][:,sortindex],mask=mask)
fig, ax = plt.subplots(1,figsize=(6,5))
im = ax.imshow(masked_matrix, cmap='RdBu_r', vmin = -1, vmax=1)
ax.tick_params(top = False,
bottom = False,
left = False,
right = False,
labelleft = False,
labelbottom = False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-5))
ax.add_collection(lc_parcel_x)
lc_parcel_y = LineCollection(segments_parcel_y,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_yaxis_transform(),
clip_on = False,
offsets = (-5,0))
ax.add_collection(lc_parcel_y)
plotje.styler(ax, colorbar = im,
colorbarlabel = 'Mean connectivity',
cbaroffset = 0.4,
title = 'Surgery',
leftaxis = None,
bottomaxis = None,
xlabel = 'Nodes',
ylabel = 'Nodes')
plt.savefig(fig_dir +'surgery_conn_parcel_100.svg', dpi= 300)
#Controls
mask = np.triu(mean_nodes_control[sortindex,:][:,sortindex])
masked_matrix = np.ma.array(mean_nodes_control[sortindex,:][:,sortindex],mask=mask)
fig, ax = plt.subplots(1,figsize=(6,5))
im = ax.imshow(masked_matrix, cmap='RdBu_r', vmin = -1, vmax=1)
ax.tick_params(top = False,
bottom = False,
left = False,
right = False,
labelleft = False,
labelbottom = False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-5))
ax.add_collection(lc_parcel_x)
lc_parcel_y = LineCollection(segments_parcel_y,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_yaxis_transform(),
clip_on = False,
offsets = (-5,0))
ax.add_collection(lc_parcel_y)
plotje.styler(ax, colorbar = im,
colorbarlabel = 'Mean connectivity',
cbaroffset = 0.4,
title = 'Control',
leftaxis = None,
bottomaxis = None,
xlabel = 'Nodes',
ylabel = 'Nodes')
plt.savefig(fig_dir +'control_conn_parcel_100.svg', dpi= 300)
#Conservative
mask = np.triu(mean_nodes_cons[sortindex,:][:,sortindex])
masked_matrix = np.ma.array(mean_nodes_cons[sortindex,:][:,sortindex],mask=mask)
fig, ax = plt.subplots(1,figsize=(6,5))
im = ax.imshow(masked_matrix, cmap='RdBu_r', vmin = -1, vmax=1)
ax.tick_params(top = False,
bottom = False,
left = False,
right = False,
labelleft = False,
labelbottom = False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-5))
ax.add_collection(lc_parcel_x)
lc_parcel_y = LineCollection(segments_parcel_y,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_yaxis_transform(),
clip_on = False,
offsets = (-5,0))
ax.add_collection(lc_parcel_y)
plotje.styler(ax, colorbar = im,
colorbarlabel = 'Mean connectivity',
cbaroffset = 0.4,
title = 'Conservative',
leftaxis = None,
bottomaxis = None,
xlabel = 'Nodes',
ylabel = 'Nodes')
plt.savefig(fig_dir +'conservative_conn_parcel_100.svg', dpi= 300)
#Delta Matrix Surgery vs. Conservative
mask = np.triu(delta_nodes_surgcons[sortindex,:][:,sortindex])
masked_matrix = np.ma.array(delta_nodes_surgcons[sortindex,:][:,sortindex],mask=mask)
fig, ax = plt.subplots(1,figsize=(6,5))
im = ax.imshow(masked_matrix, cmap='RdBu_r', vmin = -0.3, vmax=0.3)
ax.tick_params(top = False,
bottom = False,
left = False,
right = False,
labelleft = False,
labelbottom = False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-5))
ax.add_collection(lc_parcel_x)
lc_parcel_y = LineCollection(segments_parcel_y,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_yaxis_transform(),
clip_on = False,
offsets = (-5,0))
ax.add_collection(lc_parcel_y)
ax.legend(handles=legend_elements, frameon=False, bbox_to_anchor=(1,1))
plotje.styler(ax, colorbar = im,
colorbarlabel = 'Delta connectivity',
cbaroffset = 0.4,
title = '(Surgery - Conservative)',
leftaxis = None,
bottomaxis = None,
xlabel = 'Nodes',
ylabel = 'Nodes')
plt.savefig(fig_dir +'delta_surgcons_parcel_100.svg', dpi= 300)
#NBS Cluster from Surgery vs. Conservative
matrix = nbs_surgcons_adj_t3.copy()
matrix[matrix<sig_idx_surgcons_t3_p] = 0
#generate border
for i in range(123):
matrix[i,i] = sig_idx_surgcons_t3_p/4
#Plot significant findings from NBS
# Surgery vs Conservative
mask = np.triu(matrix[sortindex,:][:,sortindex], k=1)
masked_matrix = np.ma.array(matrix[sortindex,:][:,sortindex], mask=mask)
fig, ax = plt.subplots(1,figsize=(5,5))
im = ax.imshow(masked_matrix, cmap='Greys')
ax.tick_params (top = False,
bottom = False,
left = False,
right = False,
labelleft = False,
labelbottom = False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-5))
ax.add_collection(lc_parcel_x)
lc_parcel_y = LineCollection(segments_parcel_y,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_yaxis_transform(),
clip_on = False,
offsets = (-5,0))
ax.add_collection(lc_parcel_y)
plotje.styler(ax,
title = 'Significant clusters (Surgery vs. Conservative)',
leftaxis = None,
bottomaxis = None,
xlabel = 'Nodes',
ylabel = 'Nodes')
plt.savefig(fig_dir +'surgery_vs_conservative_t3.svg', dpi= 300)
#Highlight proportion of edges within networks in Bar graph
adj = nbs_surgcons_adj_t3.copy()
sigmap = adj==4
sum_network = np.zeros(len(network_indices.keys()))
for i, key_i in enumerate(network_indices):
for j, key_j in enumerate(network_indices):
if key_i == key_j:
sum_network[i] += np.sum(sigmap[network_indices[key_i],:][:,network_indices[key_j]])/2
else:
sum_network[i] += np.sum(sigmap[network_indices[key_i],:][:,network_indices[key_j]])
perc_network = sum_network/sum(sum_network)*100
fig, ax = plt.subplots(1, figsize = (5,6))
ax.bar(labels,sum_network, label = labels, color = colors)
ax.set_xticklabels(ax.get_xticks(),rotation=90)
plotje.styler(ax,
title = '',
ylabel = 'Number of edges in significant cluster',
xticklabels = labels,
rotatexticks = True)
plt.savefig(fig_dir + 'barplot_count.svg', dpi=300)
fig, ax = plt.subplots(1, figsize = (5,6))
ax.bar(labels,perc_network, label = labels, color = colors)
ax.set_xticklabels(ax.get_xticks(),rotation=90)
plotje.styler(ax,
title = '',
ylabel = 'Proportion of edges in significant cluster',
xticklabels = labels,
rotatexticks = True)
plt.savefig(fig_dir + 'barplot_perc.svg', dpi=300)
#In[5]: Figure 3 - Mean Somatomotor-specific Network connectivities
mean_network_matrix_surgcont = np.zeros([len(network_indices), len(network_indices)])
mean_network_matrix_surgcons = np.zeros([len(network_indices), len(network_indices)])
mean_network_matrix_conscont = np.zeros([len(network_indices), len(network_indices)])
#Method for condensing parcellations into averaged network connectivities
for i, key_i in enumerate(network_indices):
for j, key_j in enumerate(network_indices):
mean_network_matrix_surgcont[i,j] = np.nanmean(delta_nodes_surgcont[network_indices[key_i],:][:,network_indices[key_j]])
for i, key_i in enumerate(network_indices):
for j, key_j in enumerate(network_indices):
mean_network_matrix_surgcons[i,j] = np.nanmean(delta_nodes_surgcons[network_indices[key_i],:][:,network_indices[key_j]])
for i, key_i in enumerate(network_indices):
for j, key_j in enumerate(network_indices):
mean_network_matrix_conscont[i,j] = np.nanmean(delta_nodes_conscont[network_indices[key_i],:][:,network_indices[key_j]])
sommot_matrix_surgcons = np.zeros([1,9])
sommot_matrix_surgcons[0,:] = mean_network_matrix_surgcons[1,:]
sommot_matrix_surgcont = np.zeros([1,9])
sommot_matrix_surgcont[0,:] = mean_network_matrix_surgcont[1,:]
sommot_matrix_conscont = np.zeros([1,9])
sommot_matrix_conscont[0,:] = mean_network_matrix_conscont[1,:]
#Generate network-specific labels and colors
network_color_net2 = ['blue']
ticklabels_net2 = ['S.M']
#Generate color-coded lines to illustrate location of networks within connectivity matrix
line_net9_x = np.zeros(len(network_color_net)+1)
line_net9_y = np.zeros(len(network_color_net)+1)
for i in range(len(line_net9_x)):
line_net9_x[i] = i-0.5
x_points_net9_x = np.array([line_net9_x, line_net9_y]).T.reshape(-1, 1, 2)
segments_net9_x = np.concatenate([x_points_net9_x[:-1], x_points_net9_x[1:]], axis=1)
counter = 0
line_net9_x = np.zeros(len(network_color_net2)+1)
line_net9_y = np.zeros(len(network_color_net2)+1)
for i in (range(len(line_net9_y))):
line_net9_y[counter] = i-0.5
counter = counter+1
y_points_net9_y = np.array([line_net9_x, line_net9_y]).T.reshape(-1, 1, 2)
segments_net9_y = np.concatenate([y_points_net9_y[:-1], y_points_net9_y[1:]], axis=1)
masked_matrix = np.ma.array(sommot_matrix_surgcons)
fig, ax = plt.subplots(1,figsize=(7,4))
im = ax.imshow(masked_matrix, cmap='RdBu_r', vmin = -0.07, vmax=0.07)
ax.set_xticks(np.arange(len(network_color_net)), labels=ticklabels_net)
ax.set_yticks(np.arange(len(network_color_net2)), labels=ticklabels_net2, )
for ticklabel, tickcolor in zip(plt.gca().get_xticklabels(), network_color_net):
ticklabel.set_color(tickcolor)
for ticklabel, tickcolor in zip(plt.gca().get_yticklabels(), network_color_net2):
ticklabel.set_color(tickcolor)
ax.tick_params(top=False,
bottom=False,
left=False,
right=False,
labelleft=True,
labelbottom=True)
lc_net9_x = LineCollection(segments_net9_x,colors=network_color_net, linewidth=2.5,
transform=ax.get_xaxis_transform(), clip_on=False )
ax.add_collection(lc_net9_x)
lc_net9_y = LineCollection(segments_net9_y, colors=network_color_net2, linewidth=2.5,
transform=ax.get_yaxis_transform(), clip_on=False)
ax.add_collection(lc_net9_y)
plotje.styler(ax, colorbar=im,
colorbarlabel='Connectivity',
cbaroffset=0.4,
title = 'Mean delta connectivity for (Surgery - Conservative)',
leftaxis=None,
bottomaxis = None,
xlabel = 'Networks')
plt.savefig(fig_dir +'delta_sommot_surgcons_network_100.svg', dpi= 300)
masked_matrix = np.ma.array(sommot_matrix_surgcont)
fig, ax = plt.subplots(1,figsize=(7,4))
im = ax.imshow(masked_matrix, cmap='RdBu_r', vmin = -0.07, vmax=0.07)
ax.set_xticks(np.arange(len(network_color_net)), labels=ticklabels_net)
ax.set_yticks(np.arange(len(network_color_net2)), labels=ticklabels_net2, )
for ticklabel, tickcolor in zip(plt.gca().get_xticklabels(), network_color_net):
ticklabel.set_color(tickcolor)
for ticklabel, tickcolor in zip(plt.gca().get_yticklabels(), network_color_net2):
ticklabel.set_color(tickcolor)
ax.tick_params(top=False,
bottom=False,
left=False,
right=False,
labelleft=True,
labelbottom=True)
lc_net9_x = LineCollection(segments_net9_x,colors=network_color_net, linewidth=2.5,
transform=ax.get_xaxis_transform(), clip_on=False )
ax.add_collection(lc_net9_x)
lc_net9_y = LineCollection(segments_net9_y, colors=network_color_net2, linewidth=2.5,
transform=ax.get_yaxis_transform(), clip_on=False)
ax.add_collection(lc_net9_y)
plotje.styler(ax, colorbar=im,
colorbarlabel='Connectivity',
cbaroffset=0.4,
title = 'Mean delta connectivity for (Surgery - Control)',
leftaxis=None,
bottomaxis = None,
xlabel = 'Networks')
plt.savefig(fig_dir +'delta_sommot_surgcont_network_100.svg', dpi= 300)
masked_matrix = np.ma.array(sommot_matrix_conscont)
fig, ax = plt.subplots(1,figsize=(7,4))
im = ax.imshow(masked_matrix, cmap='RdBu_r', vmin = -0.07, vmax=0.07)
ax.set_xticks(np.arange(len(network_color_net)), labels=ticklabels_net)
ax.set_yticks(np.arange(len(network_color_net2)), labels=ticklabels_net2, )
for ticklabel, tickcolor in zip(plt.gca().get_xticklabels(), network_color_net):
ticklabel.set_color(tickcolor)
for ticklabel, tickcolor in zip(plt.gca().get_yticklabels(), network_color_net2):
ticklabel.set_color(tickcolor)
ax.tick_params(top=False,
bottom=False,
left=False,
right=False,
labelleft=True,
labelbottom=True)
lc_net9_x = LineCollection(segments_net9_x,colors=network_color_net, linewidth=2.5,
transform=ax.get_xaxis_transform(), clip_on=False )
ax.add_collection(lc_net9_x)
lc_net9_y = LineCollection(segments_net9_y, colors=network_color_net2, linewidth=2.5,
transform=ax.get_yaxis_transform(), clip_on=False)
ax.add_collection(lc_net9_y)
plotje.styler(ax, colorbar=im,
colorbarlabel='Connectivity',
cbaroffset=0.4,
title = 'Mean delta connectivity for (Conservative - Control)',
leftaxis=None,
bottomaxis = None,
xlabel = 'Networks')
plt.savefig(fig_dir +'delta_sommot_conscont_network_100.svg', dpi= 300)
#In[6]: Figure 4 - Seed-based analysis
#Left seed = 15, with idx[0]. Hence, left seed becomes 14.
#Right seed = 66, with idx[0]. Hence, right seed becomes 65
seed_surg_lh = surg_nodes[:,14,:].transpose()
seed_surg_rh = surg_nodes[:,65,:].transpose()
seed_cont_lh = control_nodes[:,14,:].transpose()
seed_cont_rh = control_nodes[:,65,:].transpose()
seed_cons_lh = cons_nodes[:,14,:].transpose()
seed_cons_rh = cons_nodes[:,65,:].transpose()
t_val_surgcons_lh = np.zeros(len(seed_surg_lh))
p_val_surgcons_raw_lh = np.zeros(len(seed_surg_lh))
t_val_surgcons_rh = np.zeros(len(seed_surg_lh))
p_val_surgcons_raw_rh = np.zeros(len(seed_surg_lh))
t_val_surgcont_lh = np.zeros(len(seed_surg_lh))
p_val_surgcont_raw_lh = np.zeros(len(seed_surg_lh))
t_val_surgcont_rh = np.zeros(len(seed_surg_lh))
p_val_surgcont_raw_rh = np.zeros(len(seed_surg_lh))
t_val_conscont_lh = np.zeros(len(seed_surg_lh))
p_val_conscont_raw_lh = np.zeros(len(seed_surg_lh))
t_val_conscont_rh = np.zeros(len(seed_surg_lh))
p_val_conscont_raw_rh = np.zeros(len(seed_surg_lh))
for i in range(len(seed_surg_lh)):
#Surgery vs. Conservative
t_val_surgcons_lh[i] , p_val_surgcons_raw_lh[i] = scipy.stats.ttest_ind(seed_surg_lh[i], seed_cons_lh[i])
t_val_surgcons_rh[i] , p_val_surgcons_raw_rh[i] = scipy.stats.ttest_ind(seed_surg_rh[i], seed_cons_rh[i])
#Surgery vs. Control
t_val_surgcont_lh[i] , p_val_surgcont_raw_lh[i] = scipy.stats.ttest_ind(seed_surg_lh[i], seed_cont_lh[i])
t_val_surgcont_rh[i] , p_val_surgcont_raw_rh[i] = scipy.stats.ttest_ind(seed_surg_rh[i], seed_cont_rh[i])
#Conservative vs. Control
t_val_conscont_lh[i] , p_val_conscont_raw_lh[i] = scipy.stats.ttest_ind(seed_cons_lh[i], seed_cont_lh[i])
t_val_conscont_rh[i] , p_val_conscont_raw_rh[i] = scipy.stats.ttest_ind(seed_cons_rh[i], seed_cont_rh[i])
#In[7]: Figure 4 - Plot network colorbar with significant arrows
#For brain illlustration, see "seedbrain.py"
#Surger vs. Conservative (LH)
p_surgcons_adj_lh = p_val_surgcons_raw_lh.copy()
sig_p = lsu(p_val_surgcons_raw_lh, q=0.05)
sig_idx = np.where(sig_p==True)
arr = np.array(range(123))
res = np.delete(arr, sig_idx)
p_surgcons_adj_lh[res] = 1
p_surgcons_adj_lh[sig_idx] = 0
colors=[]
for i in p_surgcons_adj_lh[sortindex]:
if i>0:
colors.append('white')
else:
colors.append('black')
fig, ax = plt.subplots(1,figsize=(11,2))
im = plt.scatter(range(123),p_surgcons_adj_lh[sortindex],color = colors, marker = 'v')
ax.tick_params (top=False,
bottom=False,
left=False,
right=False,
labelleft=False,
labelbottom=False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-3))
ax.add_collection(lc_parcel_x)
lgd = ax.legend(handles=legend_elements, loc='lower center', ncol= 9, frameon = False, bbox_to_anchor=(0.5,-0.4))
plotje.styler(ax,
title = 'Significant nodes from LH hip/trunk-area seed (Surgery vs. Conservative)',
leftaxis=None,
bottomaxis = None,
xlabel='Nodes')
plt.savefig(fig_dir+'lhseed_surgcons.svg', bbox_extra_artists=(lgd,), bbox_inches='tight',dpi=300)
#Surgery vs. Conservative (RH)
p_surgcons_adj_rh = p_val_surgcons_raw_rh.copy()
sig_p = lsu(p_val_surgcons_raw_rh, q=0.05)
sig_idx = np.where(sig_p==True)
arr = np.array(range(123))
res = np.delete(arr, sig_idx)
p_surgcons_adj_rh[res] = 1
p_surgcons_adj_rh[sig_idx] = 0
colors=[]
for i in p_surgcons_adj_rh[sortindex]:
if i>0:
colors.append('white')
else:
colors.append('black')
fig, ax = plt.subplots(1,figsize=(11,2))
im = plt.scatter(range(123),p_surgcons_adj_rh[sortindex],color = colors, marker = 'v')
ax.tick_params (top=False,
bottom=False,
left=False,
right=False,
labelleft=False,
labelbottom=False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-3))
ax.add_collection(lc_parcel_x)
lgd = ax.legend(handles=legend_elements, loc='lower center', ncol= 9, frameon = False, bbox_to_anchor=(0.5,-0.4))
plotje.styler(ax,
title = 'Significant nodes from RH hip/trunk-area seed (Surgery vs. Conservative)',
leftaxis=None,
bottomaxis = None,
xlabel='Nodes')
plt.savefig(fig_dir+'rhseed_surgcons.svg', bbox_extra_artists=(lgd,), bbox_inches='tight',dpi=300)
#Surgery vs. Control (LH)
p_surgcont_adj_lh = p_val_surgcont_raw_lh.copy()
sig_p = lsu(p_val_surgcont_raw_lh, q=0.05)
sig_idx = np.where(sig_p==True)
arr = np.array(range(123))
res = np.delete(arr, sig_idx)
p_surgcont_adj_lh[res] = 1
p_surgcont_adj_lh[sig_idx] = 0
colors=[]
for i in p_surgcont_adj_lh[sortindex]:
if i>0:
colors.append('white')
else:
colors.append('black')
fig, ax = plt.subplots(1,figsize=(11,2))
im = plt.scatter(range(123),p_surgcont_adj_lh[sortindex],color=colors, marker = 'v')
ax.tick_params (top=False,
bottom=False,
left=False,
right=False,
labelleft=False,
labelbottom=False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-3))
ax.add_collection(lc_parcel_x)
lgd = ax.legend(handles=legend_elements, loc='lower center', ncol= 9, frameon = False, bbox_to_anchor=(0.5,-0.4))
plotje.styler(ax,
title = 'Significant nodes from LH hip/trunk-area seed (Surgery vs. Control)',
leftaxis=None,
bottomaxis = None,
xlabel='Nodes')
plt.savefig(fig_dir+'lhseed_surgcont.svg', bbox_extra_artists=(lgd,), bbox_inches='tight',dpi=300)
#Surgery vs. Control (RH)
p_surgcont_adj_rh = p_val_surgcont_raw_rh.copy()
sig_p = lsu(p_val_surgcont_raw_rh, q=0.05)
sig_idx = np.where(sig_p==True)
arr = np.array(range(123))
res = np.delete(arr, sig_idx)
p_surgcont_adj_rh[res] = 1
p_surgcont_adj_rh[sig_idx] = 0
colors=[]
for i in p_surgcont_adj_rh[sortindex]:
if i>0:
colors.append('white')
else:
colors.append('black')
fig, ax = plt.subplots(1,figsize=(11,2))
im = plt.scatter(range(123),p_surgcont_adj_rh[sortindex],color = colors, marker = 'v')
ax.tick_params (top=False,
bottom=False,
left=False,
right=False,
labelleft=False,
labelbottom=False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-3))
ax.add_collection(lc_parcel_x)
lgd = ax.legend(handles=legend_elements, loc='lower center', ncol= 9, frameon = False, bbox_to_anchor=(0.5,-0.4))
plotje.styler(ax,
title = 'Significant nodes from RH hip/trunk-area seed (Surgery vs. Control)',
leftaxis=None,
bottomaxis = None,
xlabel='Nodes')
plt.savefig(fig_dir+'rhseed_surgcont.svg', bbox_extra_artists=(lgd,), bbox_inches='tight',dpi=300)
#Conservative vs. Control (LH)
p_conscont_adj_lh = p_val_conscont_raw_lh.copy()
sig_p = lsu(p_val_conscont_raw_lh, q=0.05)
sig_idx = np.where(sig_p==True)
arr = np.array(range(123))
res = np.delete(arr, sig_idx)
p_conscont_adj_lh[res] = 1
p_conscont_adj_lh[sig_idx] = 0
colors=[]
for i in p_conscont_adj_lh[sortindex]:
if i>0:
colors.append('white')
else:
colors.append('black')
fig, ax = plt.subplots(1,figsize=(11,2))
im = plt.scatter(range(123),p_conscont_adj_lh[sortindex],color = colors, marker = 'v')
ax.tick_params (top=False,
bottom=False,
left=False,
right=False,
labelleft=False,
labelbottom=False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-3))
ax.add_collection(lc_parcel_x)
lgd = ax.legend(handles=legend_elements, loc='lower center', ncol= 9, frameon = False, bbox_to_anchor=(0.5,-0.4))
plotje.styler(ax,
title = 'Significant nodes from LH hip/trunk-area seed (Conservative vs. Control)',
leftaxis=None,
bottomaxis = None,
xlabel='Nodes')
plt.savefig(fig_dir+'lhseed_conscont.svg', bbox_extra_artists=(lgd,), bbox_inches='tight',dpi=300)
#Conservative vs. Control (RH)
p_conscont_adj_rh = p_val_conscont_raw_rh.copy()
sig_p = lsu(p_val_conscont_raw_rh, q=0.05)
sig_idx = np.where(sig_p==True)
arr = np.array(range(123))
res = np.delete(arr, sig_idx)
p_conscont_adj_rh[res] = 1
p_conscont_adj_rh[sig_idx] = 0
colors=[]
for i in p_conscont_adj_rh[sortindex]:
if i>0:
colors.append('white')
else:
colors.append('black')
fig, ax = plt.subplots(1,figsize=(11,2))
im = plt.scatter(range(123),p_conscont_adj_rh[sortindex],color = colors, marker = 'v')
ax.tick_params (top=False,
bottom=False,
left=False,
right=False,
labelleft=False,
labelbottom=False)
lc_parcel_x = LineCollection(segments_parcel_x,
colors = sorted_network_colors,
linewidth = 8,
transform = ax.get_xaxis_transform(),
clip_on = False,
offsets = (0,-3))
ax.add_collection(lc_parcel_x)
lgd = ax.legend(handles=legend_elements, loc='lower center', ncol= 9, frameon = False, bbox_to_anchor=(0.5,-0.4))
plotje.styler(ax,
title = 'Significant nodes from RH hip/trunk-area seed (Conservative vs. Control)',
leftaxis=None,
bottomaxis = None,
xlabel='Nodes')
plt.savefig(fig_dir+'rhseed_conscont.svg', bbox_extra_artists=(lgd,), bbox_inches='tight',dpi=300)