def multilayer_pc_degree(mlgraph: np.ndarray) -> np.ndarray:
""" Multilayer Participation Coefficient (Degree)
Parameters
----------
mlgraph : array-like, shape(n_layers, n_rois, n_rois)
A multilayer (undirected) graph. Each layer consists of a graph.
Returns
-------
mpc : array-like
Participation coefficient based on the degree of the layers' nodes.
"""
num_layers, num_rois, num_rois = np.shape(mlgraph)
degrees = np.zeros((num_layers, num_rois))
for i in range(num_layers):
a_layer = np.squeeze(mlgraph[i, :, :])
degrees[i] = bct.degrees_und(a_layer)
normal_degrees = np.zeros((num_layers, num_rois))
for i in range(num_rois):
normal_degrees[:, i] = degrees[:, i] / np.sum(degrees[:, i])
mpc = np.zeros((num_rois, 1))
for i in range(num_rois):
mpc[i] = (np.float32(num_layers) / (num_layers - 1)) * (
1.0 - np.sum(np.power(normal_degrees[:, i], 2.0)))
return mpc
def create_feature_matrix(self):
# Feature matrix with each element containing an NxN array
feature_matrix = []
# EDGE WEIGHT (Depth 0)
structural_connectivity_array = self.get_structure_and_function()
feature_matrix.append(structural_connectivity_array)
# DEGREE (Depth 1 & 2)
deg = bct.degrees_und(structural_connectivity_array)
self.fill_array_2D(feature_matrix, deg)
# Conversion of connection weights to connection lengths
connection_length_matrix = bct.weight_conversion(structural_connectivity_array, 'lengths')
# SHORTEST PATH LENGTH (Depth 3 & 4)
shortest_path = bct.distance_wei(connection_length_matrix)
feature_matrix.append(shortest_path[0]) # distance (shortest weighted path) matrix
feature_matrix.append(shortest_path[1]) # matrix of number of edges in shortest weighted path
# BETWEENNESS CENTRALITY (Depth 5 & 6)
bc = bct.betweenness_wei(connection_length_matrix)
from python_files.create_feature_matrix import fill_array_2D
self.fill_array_2D(feature_matrix, bc)
# CLUSTERING COEFFICIENTS (Depth 7 & 8)
cl = bct.clustering_coef_wu(connection_length_matrix)
self.fill_array_2D(feature_matrix, cl)
return feature_matrix
def compute(self):
centrality = bct.degrees_und(self.g)
self.stats['Degree'] = [v for v in centrality]
# Lobe grouping
plots.create_bar_lobe_grouped_descending(
"reports/plots/" + self.name + "_degree_descending.pdf",
self.name + " Degree Centrality", 'Degree', centrality)
plt.show()
'''
# Degree distribution
distribution = self.stats.groupby(['Degree']).size().reset_index(name='Frequency')
sum = distribution['Frequency'].sum()
distribution['Probability'] = distribution['Frequency'] / sum
distribution.head(10)
alpha = plots.create_bar("reports/plots/" + self.name + "_degree_distribution.pdf", self.name + " Degree Distribution",
'Degree', distribution['Degree'],
'Probability', distribution['Probability'],
yticks=[0, 0.05, 0.1, 0.15])
plt.show()
'''
average = statistics.mean(self.stats['Degree'])
#print('Degree distribution gamma= ' + str(alpha) + "\n")
print("Average Degree = " + str(average) + "\n")
return self.stats
def scale_free_tau(corrmat, skew_thresh, proportional=True):
''''
Calculates threshold at which network becomes scale-free, estimated from the skewness of the networks degree distribution.
Parameters
----------
corrmat : numpy.array
Correlation or other connectivity matrix from which tau_connected will be estimated.
Should be values between 0 and 1.
proportional : bool
Determines whether connectivity matrix is thresholded proportionally or absolutely.
Default is proportional as maintaining network density across participants is a priority
Returns
-------
tau : float
Lowest vaue of tau (threshold) at which network is scale-free.
'''
tau = 0.01
skewness = 1
while abs(skewness) > 0.3:
if proportional:
w = bct.threshold_proportional(corrmat, tau)
else:
w = bct.threshold_absolute(corrmat, tau)
skewness = skew(bct.degrees_und(w))
tau += 0.01
return tau
def compute(self):
self.centrality = bct.degrees_und(self.g)
self.region_names = pd.read_csv("data/lobes.node", " ", header='infer')['RegionName']
if str(self.algorithm).lower() == 'all' or self.algorithm is None:
self.louvain()
self.spinglass()
self.walktrap()
self.girvan_newman()
self.infomap()
if str(self.algorithm).lower() == 'louvain':
communities = self.louvain()
return communities
if str(self.algorithm).lower() == 'spinglass':
communities = self.spinglass()
return communities
if str(self.algorithm).lower() == 'walktrap':
communities = self.walktrap()
return communities
if str(self.algorithm).lower() == 'girvan_newman':
communities = self.girvan_newman()
return communities
if str(self.algorithm).lower() == 'infomap':
communities = self.infomap()
return communities
def k_core_decomposition(mtx, threshold):
""" Threshold a binary graph based on the detected k-cores.
.. [Alvarez2006] Alvarez-Hamelin, J. I., Dall'Asta, L., Barrat, A., & Vespignani, A. (2006). Large scale networks fingerprinting and visualization using the k-core decomposition. In Advances in neural information processing systems (pp. 41-50).
.. [Hagman2008] Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS biology, 6(7), e159.
Parameters
----------
mtx : array-like, shape(N, N)
Binary matrix.
threshold : int
Degree threshold.
Returns
-------
k_cores : array-like, shape(N, 1)
A binary matrix of the decomposed cores.
"""
imtx = mtx
N, _ = np.shape(mtx)
# in_degree = np.sum(mtx, 0)
# out_degree = np.sum(mtx, 1)
degree = bct.degrees_und(mtx)
for i in range(N):
if degree[i] < threshold:
for l in range(N):
imtx[i, l] = 0
# Recalculate the list of the degrees
degree = bct.degrees_und(imtx)
k_cores = np.zeros((N, 1), dtype=np.int32)
for i in range(N):
if degree[i] > 0:
k_cores[i] = 1
return k_cores
def compute(self):
centrality_A = bct.degrees_und(self.gA)
centrality_B = bct.degrees_und(self.gB)
plots.create_plot_lobecolored("reports/plots/" + self.nameA + '_' +
self.nameB + "_degree_correlation.pdf",
self.nameA + " Degree vs. " +
self.nameB + " Degree",
self.nameA + " Degree",
centrality_A,
self.nameB + " Degree",
centrality_B,
also_log_scale=False,
yticks=[0, 45])
plt.show()
return
def get_network_measures(fname_connectivity):
C = pd.read_table(fname_connectivity, header=None, dtype=object)
#cleaning up structural data
C = C.drop([0, 1], axis=1)
C = C.drop([0], axis=0)
C = C.iloc[:, :-1]
#C_electrode_names = np.array([e[-4:] for e in np.array(C.iloc[0])])
C = np.array(C.iloc[1:, :]).astype(
'float64') #finally turn into numpy array
#binarize connectivity matrix
C_binarize = bct.weight_conversion(C, "binarize")
#Calculate Network Measures:
# 1. Density
density = bct.density_und(C_binarize)[0]
# 2. Degree
degree_mean = np.mean(bct.degrees_und(C_binarize))
# 3. Clustering Coefficient
clustering_coefficient = bct.clustering_coef_bu(C_binarize)
clustering_coefficient_mean = np.mean(clustering_coefficient)
# 4. characteristic path length (i.e. average shortest path length)
#Get distance
C_dist = bct.distance_bin(C_binarize)
#If there are any disjointed nodes set them equal to the largest non-Inf length
C_dist_max = np.nanmax(
C_dist[C_dist != np.inf]) #find the max length (that's not infinity)
C_dist[np.where(C_dist == np.inf
)] = C_dist_max #find the inifnities, and replace with max
characteristic_path_length = bct.charpath(C_dist)[0]
# 5. Small Worldness
Cr = degree_mean / len(C_binarize)
Lr = np.log10(len(C_binarize)) / np.log10(degree_mean)
gamma = clustering_coefficient_mean / Cr
lamb = characteristic_path_length / Lr
sigma = gamma / lamb
small_worldness = sigma
network_measures = np.zeros(shape=(1, 5))
network_measures[0, :] = [
density, degree_mean, clustering_coefficient_mean,
characteristic_path_length, small_worldness
]
colLabels = [
"Density", "degree_mean", "clustering_coefficient_mean",
"characteristic_path_length", "small_worldness"
]
network_measures_df = pd.DataFrame(network_measures, columns=colLabels)
return network_measures_df
def get_network_measures(ifname_connectivity):
C = np.array(pd.DataFrame(loadmat(ifname_connectivity)['connectivity']))
#binarize connectivity matrix
C_binarize = bct.weight_conversion(C, "binarize")
#Calculate Network Measures:
# 1. Density
density = bct.density_und(C_binarize)[0]
# 2. Degree
degree_mean = np.mean(bct.degrees_und(C_binarize))
# 3. Clustering Coefficient
clustering_coefficient = bct.clustering_coef_bu(C_binarize)
clustering_coefficient_mean = np.mean(clustering_coefficient)
# 4. characteristic path length (i.e. average shortest path length)
#Get distance
C_dist = bct.distance_bin(C_binarize)
#If there are any disjointed nodes set them equal to the largest non-Inf length
C_dist_max = np.nanmax(
C_dist[C_dist != np.inf]) #find the max length (that's not infinity)
C_dist[np.where(C_dist == np.inf
)] = C_dist_max #find the inifnities, and replace with max
characteristic_path_length = bct.charpath(C_dist)[0]
# 5. Small Worldness
Cr = degree_mean / len(C_binarize)
Lr = np.log10(len(C_binarize)) / np.log10(degree_mean)
gamma = clustering_coefficient_mean / Cr
lamb = characteristic_path_length / Lr
sigma = gamma / lamb
small_worldness = sigma
network_measures = np.zeros(shape=(1, 5))
network_measures[0, :] = [
density, degree_mean, clustering_coefficient_mean,
characteristic_path_length, small_worldness
]
colLabels = [
"Density", "degree_mean", "clustering_coefficient_mean",
"characteristic_path_length", "small_worldness"
]
network_measures_df = pd.DataFrame(network_measures, columns=colLabels)
return network_measures_df
def laplacian_energy(mtx: np.ndarray) -> float:
""" Laplacian Energy
Parameters
----------
mtx : array-like, shape(N, N)
Symmetric, weighted and undirected connectivity matrix.
Returns
-------
le : float
The Laplacian Energy.
"""
lmtx = scipy.sparse.csgraph.laplacian(mtx, normed=False)
w, _ = np.linalg.eig(lmtx)
avg_degree = np.mean(bct.degrees_und(mtx))
le = np.sum(np.abs(w - avg_degree))
return le
sns.heatmap(pearson[:,:,100], square=True , vmin = -0.4, vmax = 1)
threshPos = 0.4
threshNeg = -0.2
pearsonBinary = copy.deepcopy(pearson)
pearsonBinary[ np.abs(pearsonBinary) < threshPos] = 0
pearsonBinaryPos = copy.deepcopy(pearson)
pearsonBinaryPos[ pearsonBinaryPos < threshPos] = 0
pearsonBinaryNeg = copy.deepcopy(pearson)
pearsonBinaryNeg[ pearsonBinaryNeg > threshNeg] = 0
degree = bct.degrees_und(pearsonBinary[:,:,:]).T
degreePos = bct.degrees_und(pearsonBinaryPos[:,:,:]).T
degreeNeg = bct.degrees_und(pearsonBinaryNeg[:,:,:]).T
strength = bct.strengths_und(np.abs( pearson[:,:,:])).T
strengthPos = np.zeros(shape = (windows, nchan))
strengthNeg = np.zeros(shape = (windows, nchan))
for win in range(windows):
strengthPos[win, :], strengthNeg[win, :] , _, _= bct.strengths_und_sign( pearson[:,:,win])
strengthNegAbs = np.abs(strengthNeg)
#normalize
degreeNorm = degree / degree.max(axis=0)
for group in groups:
if group == 'patient':
participants = ['z1','z2','z3','z4','z5','z6','z8']
elif group == 'control':
participants = ['c1','c2','c3','c5','c6','c7','c8']
all_measures = np.empty(shape=[68,len(participants),5])
adjmats = np.empty(shape=[68,68,len(participants)])
counter = 0
for participant in participants:
adjmat = sio.loadmat(participant + '_FA.mat')
adjmat = adjmat['adjacency_matrix']
labels = get_parcellation_labels(generate_ROI_file(FreeSurfer_ROI_file)).values
labels,adjmat = remove_non_cortical_ROIs(labels,adjmat)
all_measures[:,counter,0] = bct.degrees_und(adjmat)
all_measures[:,counter,1] = bct.strengths_und(adjmat)
all_measures[:,counter,2] = bct.clustering_coef_wu(adjmat)
all_measures[:,counter,3] = bct.betweenness_wei(adjmat)
all_measures[:,counter,4] = bct.efficiency_wei(adjmat,local=True)
adjmats[:,:,counter] = adjmat
counter += 1
mean_measures = np.mean(all_measures,axis=1)
if group == 'patient':
patient = pd.DataFrame(mean_measures, index=labels,columns=['patient.NodeDegree','patient.Strength','patient.ClustCoeff','patient.BetweenCent','patient.LocEff'])
patient_measures = all_measures
patient_adjmats = adjmats
elif group == 'control':
control = pd.DataFrame(mean_measures, index=labels,columns=['control.NodeDegree','control.Strength','control.ClustCoeff','control.BetweenCent','control.LocEff'])
2: '0 255 0',
3: '255 255 0',
4: '0 0 255',
5: '255 0 255',
6: '51 255 255'
}
count = 0
for line in fp_roi:
# if line[0] != '#':
elms = line.split()
vc[int(elms[1])] = module_rgb_dict[Ci[count]] # id -> 'r g b'
count += 1
vc[0] = '255 255 255' # vertex id not assoc'd with any parcel gets colored white
degs = bct.degrees_und(adjMtx)
#-------------------------------
# 1st pass through the .obj file: count # of verts and faces (triangles)
in_fname = '../anatomy/' + root_fname + '.obj'
try:
fp = open(in_fname, 'r')
except:
print("Error opening " + in_fname)
numv = 0
numf = 0
for line in fp:
if line[0] == 'v':
numv += 1
elif line[0] == 'f':
##
projected_erm_cov = mne.compute_raw_covariance(projected_erm_raw, tmin=0, tmax=None)
cov.plot(raw.info)
erm_cov.plot(erm_raw.info)
projected_erm_cov.plot(projected_erm_raw.info)
corr_zz = corr_z.copy()
corr_zz[corr_zz<0] = 0
stc = get_stc(labels_fname, corr_z[temporal_labels_indices,:].mean(0))
brain = stc.plot(subject='fsaverageSK', time_viewer=True,hemi='split', colormap='gnuplot',
views=['lateral','medial'],
surface='inflated10', subjects_dir=subjects_dir,clim={'kind':'value','lims':[5,7.5,10]})
stc = get_stc(labels_fname, corr_rest_median[temporal_labels_indices,:].mean(0))
brain = stc.plot(subject='fsaverageSK', time_viewer=True,hemi='split', colormap='gnuplot',
views=['lateral','medial'],
surface='inflated10', subjects_dir=subjects_dir,clim={'kind':'value','lims':[0,.5,1]})
corr = np.int32(bct.utils.threshold_proportional(corr_z,.15) > 0)
deg = bct.degrees_und(corr)
stc = get_stc(labels_fname, corr_z[254,:])
brain = stc.plot(subject='fsaverageSK', time_viewer=True,hemi='split', colormap='gnuplot',
views=['lateral','medial'],
surface='inflated10', subjects_dir=subjects_dir,clim={'kind':'value','lims':[5,6,7]})
brain.save_image('beta_projected_erm_corr.png')
vc = {} # vertex color dictionary
count = 0
# colors (rgb) for modules (Q: what's the max # of modules we'll have??)
# rf. http://www.rapidtables.com/web/color/RGB_Color.htm
# Red,Green,Yellow,Blue,...
module_rgb_dict = {1:'255 0 0', 2: '0 255 0', 3: '255 255 0', 4: '0 0 255', 5: '255 0 255', 6: '51 255 255'}
count = 0
for line in fp_roi:
# if line[0] != '#':
elms = line.split()
vc[int(elms[1])] = module_rgb_dict[Ci[count]] # id -> 'r g b'
count += 1
vc[0] = '255 255 255' # vertex id not assoc'd with any parcel gets colored white
degs = bct.degrees_und(adjMtx)
#-------------------------------
# 1st pass through the .obj file: count # of verts and faces (triangles)
in_fname = '../anatomy/' + root_fname + '.obj'
try:
fp = open(in_fname, 'r')
except:
print("Error opening " + in_fname)
numv = 0
numf = 0
for line in fp:
if line[0] == 'v':
numv += 1
elif line[0] == 'f':
subject,
"{0}-session-{1}_{2}-{3}_{4}-corrmat.csv".
format(subject, i, task, conditions[j],
mask),
),
corrmat,
)
# reset kappa starting point
# calculate proportion of connections that can be retained
# before degree dist. ceases to be scale-free
kappa = 0.01
skewness = 1
while abs(skewness) > 0.3:
w = bct.threshold_proportional(corrmat, kappa)
skewness = skew(bct.degrees_und(w))
kappa += 0.01
df.at[(subject, sessions[i], task, conds[j], mask),
"k_scale-free", ] = kappa
# reset kappa starting point
# calculate proportion of connections that need to be retained
# for node connectedness
kappa = 0.01
num = 2
while num > 1:
w = bct.threshold_proportional(corrmat, kappa)
[comp, num] = bct.get_components(w)
num = np.unique(comp).shape[0]
kappa += 0.01
df.at[(subject, sessions[i], task, conds[j], mask),
def create_feature_matrix(structure_matrix_file):
# Feature matrix with each element containing an NxN array
feature_matrix = []
# EDGE WEIGHT (Depth 0)
# weighted & undirected network
structural_connectivity_array = np.array(
pd.DataFrame(loadmat(structure_matrix_file)['connectivity']))
feature_matrix.append(structural_connectivity_array)
# DEGREE (Depth 1 & 2)
# Node degree is the number of links connected to the node.
deg = bct.degrees_und(structural_connectivity_array)
fill_array_2D(feature_matrix, deg)
# *** Conversion of connection weights to connection lengths ***
connection_length_matrix = bct.weight_conversion(
structural_connectivity_array, 'lengths')
# print(connection_length_matrix)
# SHORTEST PATH LENGTH (Depth 3 & 4)
'''
The distance matrix contains lengths of shortest paths between all pairs of nodes.
An entry (u,v) represents the length of shortest path from node u to node v.
The average shortest path length is the characteristic path length of the network.
'''
shortest_path = bct.distance_wei(connection_length_matrix)
feature_matrix.append(
shortest_path[0]) # distance (shortest weighted path) matrix
feature_matrix.append(
shortest_path[1]
) # matrix of number of edges in shortest weighted path
# BETWEENNESS CENTRALITY (Depth 5 & 6)
'''
Node betweenness centrality is the fraction of all shortest paths in
the network that contain a given node. Nodes with high values of
betweenness centrality participate in a large number of shortest paths.
'''
bc = bct.betweenness_wei(connection_length_matrix)
fill_array_2D(feature_matrix, bc)
# CLUSTERING COEFFICIENTS (Depth 7 & 8)
'''
The weighted clustering coefficient is the average "intensity" of
triangles around a node.
'''
cl = bct.clustering_coef_wu(connection_length_matrix)
fill_array_2D(feature_matrix, cl)
# Find disconnected nodes - component size set to 1
new_array = structural_connectivity_array
W_bin = bct.weight_conversion(structural_connectivity_array, 'binarize')
[comps, comp_sizes] = bct.get_components(W_bin)
print('comp: ', comps)
print('sizes: ', comp_sizes)
for i in range(len(comps)):
if (comps[i] != statistics.mode(comps)):
new_array = np.delete(new_array, new_array[i])
return feature_matrix
label_data_orig = np.abs(label_data)
label_data_cont = np.transpose(
np.dstack((label_data_orig, np.transpose(label_data_orth, (1, 2, 0)))),
(1, 2, 0))
corr_mats[index] = np.array([np.corrcoef(dat)
for dat in label_data_cont])[:, 0, 1:].T
print(float(index) / len(labels) * 100)
corr_mats = np.transpose(corr_mats, (2, 0, 1))
corr = np.median(np.array([(np.abs(corr_mat) + np.abs(corr_mat).T) / 2.
for corr_mat in corr_mats]),
axis=0)
corr = np.int32(bct.utils.threshold_proportional(corr, .15) > 0)
deg = np.array(bct.degrees_und(corr))
data = {'corr_mats': corr_mats, 'labels': labels, 'corr': corr, 'deg': deg}
pkl_fname = os.path.join(
data_path, subject + '_lf_' + str(int(lf)) + '_hf_' + str(int(hf)) +
'_labels_' + str(len(labels)) + '_timestamp_' + '_'.join(
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S").split(' ')) +
'_corr_orth.pkl')
joblib.dump(data, pkl_fname)
labels_path = '/cluster/transcend/sheraz/Dropbox/mne-camcan-data/recons/fsaverageSK/2009_labels/'
labels_fname = [
str(labels_path + label.name[-2:] + '.' + label.name[:-3] + '.label')
for label in labels
def threshold_mean_degree(mtx, threshold_mean_degree):
""" Threshold a graph based on the mean degree.
Parameters
----------
mtx : array-like, shape(N, N)
Symmetric, weighted and undirected connectivity matrix.
threshold_mean_degree : int
Mean degree threshold.
Returns
-------
binary_mtx : array-like, shape(N, N)
A binary mask matrix.
"""
binary_mtx = np.zeros_like(mtx, dtype=np.int32)
N, _ = np.shape(mtx)
iterations = 100
step = 1.0 / iterations
thres = 0.0
thresdeg = np.zeros((iterations, 2))
for i in range(iterations):
thres += step
tmp_binary = np.zeros_like(binary_mtx)
for k in range(N):
for l in range(k + 1, N):
if mtx[k, l] > thres:
tmp_binary[k, l] = 1
tmp_binary[l, k] = 1
degree = bct.degrees_und(tmp_binary)
thresdeg[i, 0] = np.mean(degree)
thresdeg[i, 1] = thres
# find the nearest mean degree to kk
diff = np.zeros((iterations, 1))
for i in range(iterations):
diff[i] = np.abs(thresdeg[i, 0] - threshold_mean_degree)
# find the mean degree with the min difference from kk
r = np.argmin(diff)
# find the threhold corresponds to the mean degree
# mdegree = thresdeg[r, 0]
thres = thresdeg[r, 1]
for k in range(N):
for l in range(k + 1, N):
if mtx[k, l] > thres:
binary_mtx[k, l] = 1
binary_mtx[l, k] = 1
return binary_mtx
def threshold_global_cost_efficiency(mtx, iterations):
""" Threshold a graph based on the Global Efficiency - Cost formula.
.. [Basset2009] Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R. (2009). Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences, 106(28), 11747-11752.
Parameters
----------
mtx : array-like, shape(N, N)
Symmetric, weighted and undirected connectivity matrix.
iterations : int
Number of steps, as a resolution when search for optima.
Returns
-------
binary_mtx : array-like, shape(N, N)
A binary mask matrix.
threshold : float
The threshold that maximizes the global cost efficiency.
global_cost_eff_max : float
Global cost efficiency.
efficiency : float
Global efficiency.
cost_max : float
Cost of the network at the maximum global cost efficiency
"""
binary_mtx = np.zeros_like(mtx, dtype=np.int32)
step = 1.0 / iterations
thresholds = np.arange(0, 1 + step, step)
N, _ = np.shape(mtx)
num_connections = (N * (N - 1)) / 2.0
global_cost_eff = np.zeros((iterations, 1))
cost = np.zeros((iterations, 1))
for i in range(iterations):
tmp_binary = np.zeros_like(binary_mtx)
for k in range(N):
for l in range(k + 1, N):
if mtx[k, l] > thresholds[i]:
tmp_binary[k, l] = 1
tmp_binary[l, k] = 1
global_eff = bct.efficiency_bin(tmp_binary)
degree = bct.degrees_und(tmp_binary)
total_degree = np.sum(degree)
cost[i] = (0.5 * total_degree) / num_connections
global_cost_eff[i] = global_eff - cost[i]
indx_max = np.argmax(global_cost_eff)
threshold = thresholds[indx_max]
for k in range(N):
for l in range(k + 1, N):
if mtx[k, l] >= threshold:
binary_mtx[k, l] = 1
binary_mtx[l, k] = 1
cost_max = cost[indx_max]
global_cost_eff_max = global_cost_eff[indx_max]
efficiency = bct.efficiency_bin(binary_mtx)
# import matplotlib.pyplot as plt
# plt.figure()
# plt.plot(cost, global_cost_eff)
# plt.plot(cost_max, global_cost_eff_max, 'b*', label='Max Global Cost Efficiency')
# plt.title('Economical small-world network at max Global Cost Efficiency')
# plt.xlabel('Cost')
# plt.ylabel('Global Cost Efficiency')
# plt.legend()
# plt.show()
return binary_mtx, threshold, global_cost_eff_max, efficiency, cost_max
def process(data):
return bct.degrees_und(data)
corr_pln += [CorrelationAnalyzer(nits)]
corr_cls_coef = [d.corrcoef for d in corr_cls]
corr_pln_coef = [d.corrcoef for d in corr_pln]
full_matrix = np.concatenate(
[np.abs(corr_cls_coef),
np.abs(corr_pln_coef)], axis=0)
threshold = np.median(full_matrix[np.nonzero(full_matrix)]) + \
np.std(full_matrix[np.nonzero(full_matrix)])
data_cls_bin = np.abs(corr_cls_coef) > threshold
data_pln_bin = np.abs(corr_pln_coef) > threshold
deg_cls_tmp = np.asarray(
[bct.degrees_und(g) for g in data_cls_bin])
deg_pln_tmp = np.asarray(
[bct.degrees_und(g) for g in data_pln_bin])
trans_cls_tmp = np.asarray(
[bct.transitivity_bu(g) for g in data_cls_bin])
trans_pln_tmp = np.asarray(
[bct.transitivity_bu(g) for g in data_pln_bin])
cp_cls_tmp = np.asarray(
[bct.distance.charpath(g)[0] for g in data_cls_bin])
cp_pln_tmp = np.asarray(
[bct.distance.charpath(g)[0] for g in data_pln_bin])
# Add measure to results list
def threshold_omst_global_cost_efficiency(mtx, n_msts=None):
""" Threshold a graph by optimizing the formula GE-C via orthogonal MSTs.
.. [Dimitriadis2017a] Dimitriadis, S. I., Salis, C., Tarnanas, I., & Linden, D. E. (2017). Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs). Frontiers in neuroinformatics, 11.
.. [Dimitriadis2017n] Dimitriadis, S. I., Antonakakis, M., Simos, P., Fletcher, J. M., & Papanicolaou, A. C. (2017). Data-driven Topological Filtering based on Orthogonal Minimal Spanning Trees: Application to Multi-Group MEG Resting-State Connectivity. Brain Connectivity, (ja).
.. [Basset2009] Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R. (2009). Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences, 106(28), 11747-11752.
Parameters
----------
mtx : array-like, shape(N, N)
Symmetric, weighted and undirected connectivity matrix.
n_msts : int or None
Maximum number of OMSTs to compute. Default `None`; an exhaustive
computation will be performed.
Returns
-------
nCIJtree : array-like, shape(n_msts, N, N)
A matrix containing all the orthogonal MSTs.
CIJtree : array-like, shape(N, N)
Resulting graph.
degree : float
The mean degree of the resulting graph.
global_eff : float
Global efficiency of the resulting graph.
global_cost_eff_max : float
The value where global efficiency - cost is maximized.
cost_max : float
Cost of the network at the maximum global cost efficiency.
"""
imtx = np.copy(mtx)
imtx_uptril = np.copy(mtx)
N, _ = np.shape(imtx)
for k in range(N):
for l in range(k + 1, N):
imtx_uptril[l, k] = 0.0
np.fill_diagonal(imtx_uptril, 0.0)
# Find the number of orthogonal msts according to the desired mean degree
num_edges = len(np.where(imtx > 0.0)[0])
if n_msts is None:
num_msts = np.round(num_edges / (N - 1)) + 1
else:
num_msts = n_msts
pos_num_msts = np.round(num_edges / (N - 1))
if num_msts > pos_num_msts:
num_msts = pos_num_msts
CIJnotintree = imtx
# Keep the N-1 connections of the num_msts MSTs
num_msts = np.int32(num_msts)
mst_conn = np.zeros((num_msts * (N - 1), 2))
nCIJtree = np.zeros((num_msts, N, N)) #, dtype=np.int32)
omst = np.zeros((num_msts, N, N), dtype=np.float32)
# Repeat N-2 times
count = 0
CIJtree = np.zeros((N, N))
for no in range(num_msts):
tmp_mtx = 1.0 / CIJnotintree
# ugly code ~_~
graph = nx.from_numpy_matrix(tmp_mtx)
# graph = nx.Graph()
# for x in range(N):
# for y in range(x+1, N):
# graph.add_edge(x, y, weight=tmp_mtx[x][y])
mst = nx.minimum_spanning_tree(graph)
links = list(mst.edges())
new_mst = np.zeros((N, N))
mst_num_links = len(links)
for k in range(mst_num_links):
link1 = links[k][0]
link2 = links[k][1]
CIJtree[link1, link2] = imtx[link1, link2]
CIJtree[link2, link1] = imtx[link1, link2]
mst_conn[count, 0] = link1
mst_conn[count, 1] = link2
new_mst[link1, link2] = imtx[link1, link2]
new_mst[link2, link1] = imtx[link1, link2]
count += 1
iCIJtree = np.ones((N, N))
iCIJtree[np.where(CIJtree != 0.0)] = 0
CIJnotintree = CIJnotintree * iCIJtree
nCIJtree[no, :, :] = CIJtree
omst[no, :, :] = new_mst
global_eff_ini = bct.efficiency_wei(imtx_uptril) * 2.0
cost_ini = np.sum(imtx_uptril[:])
# Insert the 1st MST
graph = np.zeros((N, N))
global_cost_eff = np.zeros((num_msts, 1))
degrees = np.zeros((num_msts, 1))
cost = np.zeros((num_msts, 1))
for k in range(num_msts):
graph = nCIJtree[k, :, :]
degree = bct.degrees_und(graph)
mean_degree = np.mean(degree)
degrees[k] = mean_degree
cost[k] = np.sum(graph) / cost_ini
global_eff = bct.efficiency_wei(graph)
global_cost_eff[k] = global_eff / global_eff_ini - cost[k]
# Get the OMST where the formula GE-C is maximized
indx_max = np.argmax(global_cost_eff)
# Final output
degree = degrees[indx_max]
CIJtree = nCIJtree[indx_max, :, :]
cost_max = cost[indx_max]
global_eff = bct.efficiency_wei(1.0 / CIJtree)
global_cost_eff_max = global_cost_eff[indx_max]
# import matplotlib.pyplot as plt
# plt.figure()
# plt.plot(cost, global_cost_eff)
# plt.plot(cost_max, global_cost_eff_max, 'b*', label='Max Global Cost Efficiency')
# plt.title('Economical small-world network at max Global Cost Efficiency')
# plt.xlabel('Cost')
# plt.ylabel('Global Cost Efficiency')
# plt.legend()
# plt.show()
# return nCIJtree, CIJtree, degree, global_eff, global_cost_eff_max, cost_max
return nCIJtree, CIJtree, degree, global_eff, global_cost_eff_max, cost_max, cost, global_cost_eff
corr_pln += [CorrelationAnalyzer(nits)]
corr_cls_coef = [d.corrcoef for d in corr_cls]
corr_pln_coef = [d.corrcoef for d in corr_pln]
full_matrix = np.concatenate(
[np.abs(corr_cls_coef), np.abs(corr_pln_coef)], axis=0)
threshold = np.median(full_matrix[np.nonzero(full_matrix)]) + \
np.std(full_matrix[np.nonzero(full_matrix)])
data_cls_bin = np.abs(corr_cls_coef) > threshold
data_pln_bin = np.abs(corr_pln_coef) > threshold
deg_cls_tmp = np.asarray(
[bct.degrees_und(g) for g in data_cls_bin])
deg_pln_tmp = np.asarray(
[bct.degrees_und(g) for g in data_pln_bin])
trans_cls_tmp = np.asarray(
[bct.transitivity_bu(g) for g in data_cls_bin])
trans_pln_tmp = np.asarray(
[bct.transitivity_bu(g) for g in data_pln_bin])
cp_cls_tmp = np.asarray(
[bct.distance.charpath(g)[0] for g in data_cls_bin])
cp_pln_tmp = np.asarray(
[bct.distance.charpath(g)[0] for g in data_pln_bin])
# Add measure to results list
def rich_feeder_peripheral(x, sc, stat='median'):
"""
Calculates connectivity values in rich, feeder, and peripheral edges.
Parameters
----------
x : (N, N) numpy.ndarray
Symmetric correlation or connectivity matrix
sc : (N, N) numpy.ndarray
Binary structural connectivity matrix
stat : {'mean', 'median'}, optional
Statistic to use over rich/feeder/peripheral links. Default: 'median'
Returns
-------
rfp : (3, k) numpy.ndarray
Array of median rich (0), feeder (1), and peripheral (2)
values, defined by `x`. `k` is the maximum degree defined on `sc`.
pvals : (3, k) numpy.ndarray
p-value for each link, computed using Welch's t-test.
Rich links are compared against non-rich links. Feeder links are
compared against peripheral links. Peripheral links are compared
against feeder links. T-test is one-sided.
Author
------
This code was written by Justine Hansen who promises to fix and even
optimize the code should any issues arise, provided you let her know.
"""
stats = ['mean', 'median']
if stat not in stats:
raise ValueError(f'Provided stat {stat} not valid.\
Must be one of {stats}')
nnodes = len(sc)
mask = np.triu(np.ones(nnodes), 1) > 0
node_degree = degrees_und(sc)
k = np.max(node_degree).astype(np.int64)
rfp_label = np.zeros((len(sc[mask]), k))
for degthresh in range(k): # for each degree threshold
hub_idx = np.where(node_degree >= degthresh) # find the hubs
hub = np.zeros([nnodes, 1])
hub[hub_idx, :] = 1
rfp = np.zeros([nnodes, nnodes]) # for each link, define rfp
for edge1 in range(nnodes):
for edge2 in range(nnodes):
if hub[edge1] + hub[edge2] == 2:
rfp[edge1, edge2] = 1 # rich
if hub[edge1] + hub[edge2] == 1:
rfp[edge1, edge2] = 2 # feeder
if hub[edge1] + hub[edge2] == 0:
rfp[edge1, edge2] = 3 # peripheral
rfp_label[:, degthresh] = rfp[mask]
rfp = np.zeros([3, k])
pvals = np.zeros([3, k])
for degthresh in range(k):
redfunc = np.median if stat == 'median' else np.mean
for linktype in range(3):
rfp[linktype, degthresh] = redfunc(
x[mask][rfp_label[:, degthresh] == linktype + 1])
# p-value (one-sided Welch's t-test)
_, pvals[0,
degthresh] = ttest_ind(x[mask][rfp_label[:, degthresh] == 1],
x[mask][rfp_label[:, degthresh] != 1],
equal_var=False,
alternative='greater')
_, pvals[1,
degthresh] = ttest_ind(x[mask][rfp_label[:, degthresh] == 2],
x[mask][rfp_label[:, degthresh] == 3],
equal_var=False,
alternative='greater')
_, pvals[2,
degthresh] = ttest_ind(x[mask][rfp_label[:, degthresh] == 3],
x[mask][rfp_label[:, degthresh] == 2],
equal_var=False,
alternative='greater')
return rfp, pvals
def test_degrees_und():
x = load_sample()
s = bct.degrees_und(bct.threshold_proportional(x, .26))
assert np.sum(s) == 4916
if group == 'patient':
participants = ['z1', 'z2', 'z3', 'z4', 'z5', 'z6', 'z8']
elif group == 'control':
participants = ['c1', 'c2', 'c3', 'c5', 'c6', 'c7', 'c8']
all_measures = np.empty(shape=[68, len(participants), 5])
adjmats = np.empty(shape=[68, 68, len(participants)])
counter = 0
for participant in participants:
adjmat = sio.loadmat(participant + '_FA.mat')
adjmat = adjmat['adjacency_matrix']
labels = get_parcellation_labels(
generate_ROI_file(FreeSurfer_ROI_file)).values
labels, adjmat = remove_non_cortical_ROIs(labels, adjmat)
all_measures[:, counter, 0] = bct.degrees_und(adjmat)
all_measures[:, counter, 1] = bct.strengths_und(adjmat)
all_measures[:, counter, 2] = bct.clustering_coef_wu(adjmat)
all_measures[:, counter, 3] = bct.betweenness_wei(adjmat)
all_measures[:, counter, 4] = bct.efficiency_wei(adjmat, local=True)
adjmats[:, :, counter] = adjmat
counter += 1
mean_measures = np.mean(all_measures, axis=1)
if group == 'patient':
patient = pd.DataFrame(mean_measures,
index=labels,
columns=[
'patient.NodeDegree', 'patient.Strength',
'patient.ClustCoeff', 'patient.BetweenCent',
'patient.LocEff'