def load_fibers(main_folder,n,index_to_text_file,fig_type,nii_file):
tract_file = main_folder + r'\streamlines' + n + '_'+fig_type+'.trk'
streamlines = load_ft(tract_file,nii_file)
lab_labels_index, affine = nodes_by_index_general(main_folder, atlas='yeo7_200')
labels_headers, idx = nodes_labels_yeo7(index_to_text_file)
new_data, m, grouping = non_weighted_con_mat_mega(streamlines, lab_labels_index, affine, idx, main_folder)
h = labels_headers
return labels_headers, idx, m, grouping, h
def atlas_and_idx(atlas_type, atlas_main_folder):
if 'yeo' in atlas_type:
mni_atlas_label = f'{atlas_main_folder}{os.sep}{atlas_type}{os.sep}{atlas_type}_atlas.nii'
atlas_labels = nib.load(mni_atlas_label).get_fdata()
idx_to_txt = f'{atlas_main_folder}{os.sep}{atlas_type}{os.sep}index2label.txt'
idx = nodes_labels_yeo7(idx_to_txt)[1]
elif 'bna' in atlas_type:
mni_atlas_label = f'{atlas_main_folder}{os.sep}BN_Atlas_274_combined_1mm.nii'
atlas_labels = nib.load(mni_atlas_label).get_fdata()
idx_to_txt = f'{atlas_main_folder}{os.sep}BNA_with_cerebellum.csv'
idx = nodes_labels_bna(idx_to_txt)[1]
return atlas_labels, mni_atlas_label, idx
def _calc_weights(self):
'''
Calculate connectivity matrices based on chosen atlas and than calculate the
weighted average to compute mean ADD in each label
'''
import numpy as np
from weighted_tracts import nodes_by_index_general, nodes_labels_yeo7, nodes_labels_bna, non_weighted_con_mat_mega, weighted_con_mat_mega
lab_labels_index, affine = nodes_by_index_general(
self.folder_name, atlas=self.atlas_type)
if 'yeo' in self.atlas_type:
labels_headers, idx = nodes_labels_yeo7(self.idx_to_txt)
elif 'bna' in self.atlas_type:
labels_headers, idx = nodes_labels_bna(self.idx_to_txt)
new_data, num_mat, grouping = non_weighted_con_mat_mega(
self.streamlines,
lab_labels_index,
affine,
idx,
self.folder_name,
fig_type=self.atlas_type)
add_mat = weighted_con_mat_mega(self.weight_name,
grouping,
idx,
self.folder_name,
fig_type=self.atlas_type)[1]
mutual_mat = add_mat * num_mat
#mutual_mat[mutual_mat<=0]= np.nan
weights = np.sum(mutual_mat, 1) / np.sum(num_mat, 1)
weights[np.isnan(weights)] = 0
weights_dict = {idx[i]: weights[i] for i in range(len(idx))}
self.labels_weights = weights_dict
def create_labels_dict(atlas='yeo7'):
if atlas == 'yeo7':
labels_headers, idx = nodes_labels_yeo7(index_to_text_file)
labels_dict = {}
sides_dict = {}
net_dict = {}
for l, i in zip(labels_headers, idx):
lparts = l.split('_')
labels_dict[i] = f'{lparts[1]}_{lparts[2]}_{lparts[-1]}'
net_dict[i] = f'{lparts[1]}_{lparts[2]}'
#net_dict[i] = f'{lparts[2]}'
if '_LH_' in l:
sides_dict[i] = 'LH'
elif '_RH_' in l:
sides_dict[i] = 'RH'
else:
sides_dict[i] = 'C'
return sides_dict, labels_dict, net_dict
elif atlas == 'aal3':
labels_headers, idx = nodes_labels_aal3(index_to_text_file)
labels_dict = {}
sides_dict = {}
for l, i in zip(labels_headers, idx):
labels_dict[i] = l
if '_L' in l:
sides_dict[i] = 'LH'
elif '_R' in l:
sides_dict[i] = 'RH'
else:
sides_dict[i] = 'C'
return sides_dict, labels_dict
mat_file_ax = subj_folder + s + r'\weighted_wholebrain_4d_labmask_yeo7_200_nonnorm.npy'
mat_ax = np.load(mat_file_ax)
mat_dist_ax = mat_ax / np.nanmax(mat_ax[:])
mat_norm_ax = 1 / mat_dist_ax
mat_norm_ax[np.isinf(mat_norm_ax)] = 0
mat_norm_ax[np.isnan(mat_norm_ax)] = 0
mat_file_num = subj_folder + s + r'\non-weighted_wholebrain_4d_labmask_yeo7_200_nonnorm.npy'
mat_num = np.load(mat_file_num)
mat_dist_num = mat_num / np.nanmax(mat_num[:])
#mat_norm_num = 1 / mat_dist_num
mat_norm_num = -1 * np.log(mat_dist_num)
mat_norm_num[np.isinf(mat_norm_num)] = 0
mat_norm_num[np.isnan(mat_norm_num)] = 0
idx = nodes_labels_yeo7(index_to_text_file)[1]
id = np.argsort(idx)
g_ax = from_mat_2_graph(mat_norm_ax, id)
g_num = from_mat_2_graph(mat_norm_num, id)
aspl_ax = nx.average_shortest_path_length(g_ax,
weight='weight',
method='dijkstra')
aspl_num = nx.average_shortest_path_length(g_num,
weight='weight',
method='dijkstra')
aspl_nw = nx.average_shortest_path_length(g_num, weight=None)
aspl[:, i] = [aspl_nw, aspl_num, aspl_ax]
eff_ax = 1 / aspl_ax
import networkx as nx
import nxviz as nv
import matplotlib.pyplot as plt
import numpy as np
from all_subj import index_to_text_file
from weighted_tracts import nodes_labels_yeo7,nodes_labels_aal3
from network_analysis.specific_functional_yeo7networks import network_id_list
atlas = 'yeo7'
network = 'sommot'
side = 'both'
if atlas == 'yeo7':
labels_headers, idx = nodes_labels_yeo7(index_to_text_file)
n = len(idx)
start = int(n/2-1)
cont = start+1
id = idx[start::-1]+idx[cont::]
elif atlas == 'aal3':
labels_headers, idx = nodes_labels_aal3(index_to_text_file)
n = len(idx)
id = list(range(n))
start = 77 #end of left sided areas
cont = start+1
id = id[start::-1]+id[cont::]
nodes_labels={}
for l,i in zip(labels_headers,id):
lparts = l.split('_')
#nodes_labels[i] = '_'.join(lparts[1:])
nodes_labels[i]=f'{lparts[1]}_{lparts[2]}_{lparts[-1]}'
#nodes_labels[i]=l