def thresh_diff(dens_thresh, thr, conn_model, network, ID, dir_path, mask,
node_size, conn_matrix, parc, min_span_tree, disp_filt,
atlas_select, uatlas_select, label_names, coords):
from pynets import utils, thresholding
thr_perc = 100 * float(thr)
edge_threshold = "%s%s" % (str(thr_perc), '%')
if parc is True:
node_size = 'parc'
if min_span_tree is True:
print(
'Using local thresholding option with the Minimum Spanning Tree (MST)...\n'
)
if dens_thresh is False:
thr_type = 'MSTprop'
conn_matrix_thr = thresholding.local_thresholding_prop(
conn_matrix, thr)
else:
thr_type = 'MSTdens'
conn_matrix_thr = thresholding.local_thresholding_dens(
conn_matrix, thr)
elif disp_filt is True:
thr_type = 'DISPα'
G1 = thresholding.disparity_filter(nx.from_numpy_array(conn_matrix))
# G2 = nx.Graph([(u, v, d) for u, v, d in G1.edges(data=True) if d['alpha'] < thr])
print('Computing edge disparity significance with alpha = %s' % thr)
print('Filtered graph: nodes = %s, edges = %s' %
(G1.number_of_nodes(), G1.number_of_edges()))
# print('Backbone graph: nodes = %s, edges = %s' % (G2.number_of_nodes(), G2.number_of_edges()))
#print(G2.edges(data=True))
conn_matrix_thr = nx.to_numpy_array(G1)
else:
if dens_thresh is False:
thr_type = 'prop'
print("%s%.2f%s" %
('\nThresholding proportionally at: ', thr_perc, '% ...\n'))
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
else:
thr_type = 'dens'
print("%s%.2f%s" % ('\nThresholding to achieve density of: ',
thr_perc, '% ...\n'))
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, float(thr))
if not nx.is_connected(nx.from_numpy_matrix(conn_matrix_thr)):
print('Warning: Fragmented graph')
# Save thresholded mat
smooth = 0
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size, smooth, thr_type)
np.save(est_path, conn_matrix_thr)
return conn_matrix_thr, edge_threshold, est_path, thr, node_size, network, conn_model, mask, atlas_select, uatlas_select, label_names, coords
def thresh_diff(dens_thresh, thr, conn_matrix, conn_model, network, ID, dir_path, roi, node_size, min_span_tree,
disp_filt, parc, prune, atlas_select, uatlas_select, label_names, coords, norm, binary, target_samples,
track_type, atlas_mni, streams):
from pynets import utils, thresholding
thr_perc = 100 * float(thr)
edge_threshold = "%s%s" % (str(thr_perc), '%')
if parc is True:
node_size = 'parc'
if np.count_nonzero(conn_matrix) == 0:
raise ValueError('ERROR: Raw connectivity matrix contains only zeros.')
# Save unthresholded
unthr_path = utils.create_unthr_path(ID, network, conn_model, roi, dir_path)
utils.save_mat(conn_matrix, unthr_path)
if min_span_tree is True:
print('Using local thresholding option with the Minimum Spanning Tree (MST)...\n')
if dens_thresh is False:
thr_type = 'MSTprop'
conn_matrix_thr = thresholding.local_thresholding_prop(conn_matrix, thr)
else:
thr_type = 'MSTdens'
conn_matrix_thr = thresholding.local_thresholding_dens(conn_matrix, thr)
elif disp_filt is True:
thr_type = 'DISP_alpha'
G1 = thresholding.disparity_filter(nx.from_numpy_array(conn_matrix))
# G2 = nx.Graph([(u, v, d) for u, v, d in G1.edges(data=True) if d['alpha'] < thr])
print('Computing edge disparity significance with alpha = %s' % thr)
print('Filtered graph: nodes = %s, edges = %s' % (G1.number_of_nodes(), G1.number_of_edges()))
# print('Backbone graph: nodes = %s, edges = %s' % (G2.number_of_nodes(), G2.number_of_edges()))
#print(G2.edges(data=True))
conn_matrix_thr = nx.to_numpy_array(G1)
else:
if dens_thresh is False:
thr_type='prop'
print("%s%.2f%s" % ('\nThresholding proportionally at: ', thr_perc, '% ...\n'))
conn_matrix_thr = thresholding.threshold_proportional(conn_matrix, float(thr))
else:
thr_type = 'dens'
print("%s%.2f%s" % ('\nThresholding to achieve density of: ', thr_perc, '% ...\n'))
conn_matrix_thr = thresholding.density_thresholding(conn_matrix, float(thr))
if not nx.is_connected(nx.from_numpy_matrix(conn_matrix_thr)):
print('Warning: Fragmented graph')
# Save thresholded mat
est_path = utils.create_est_path_diff(ID, network, conn_model, thr, roi, dir_path, node_size, target_samples,
track_type, thr_type)
utils.save_mat(conn_matrix_thr, est_path)
return conn_matrix_thr, edge_threshold, est_path, thr, node_size, network, conn_model, roi, prune, ID, dir_path, atlas_select, uatlas_select, label_names, coords, norm, binary, target_samples, track_type, atlas_mni, streams
def thresh_struct(dens_thresh, thr, conn_matrix, conn_model, network, ID,
dir_path, roi, node_size, min_span_tree, disp_filt, parc,
prune, atlas, uatlas, labels, coords, norm, binary,
target_samples, track_type, atlas_mni, streams):
"""
Threshold a structural connectivity matrix using any of a variety of methods.
Parameters
----------
dens_thresh : bool
Indicates whether a target graph density is to be used as the basis for
thresholding.
thr : float
A value, between 0 and 1, to threshold the graph using any variety of methods
triggered through other options.
conn_matrix : array
Adjacency matrix stored as an m x n array of nodes and edges.
conn_model : str
Connectivity estimation model (e.g. corr for correlation, cov for covariance, sps for precision covariance,
partcorr for partial correlation). sps type is used by default.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default') used to filter nodes in the study of
brain subgraphs.
ID : str
A subject id or other unique identifier.
dir_path : str
Path to directory containing subject derivative data for given run.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's.
min_span_tree : bool
Indicates whether local thresholding from the Minimum Spanning Tree
should be used.
disp_filt : bool
Indicates whether local thresholding using a disparity filter and
'backbone network' should be used.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
atlas : str
Name of atlas parcellation used.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to ROI nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
target_samples : int
Total number of streamline samples specified to generate streams.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
atlas_mni : str
File path to atlas parcellation Nifti1Image in T1w-warped MNI space.
streams : str
File path to save streamline array sequence in .trk format.
Returns
-------
conn_matrix_thr : array
Weighted, thresholded, NxN matrix.
edge_threshold : str
The string percentage representation of thr.
est_path : str
File path to the thresholded graph, conn_matrix_thr, saved as a numpy array in .npy format.
thr : float
The value, between 0 and 1, used to threshold the graph using any variety of methods
triggered through other options.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default') used to filter nodes in the study of
brain subgraphs.
conn_model : str
Connectivity estimation model (e.g. corr for correlation, cov for covariance, sps for precision covariance,
partcorr for partial correlation). sps type is used by default.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
ID : str
A subject id or other unique identifier.
dir_path : str
Path to directory containing subject derivative data for given run.
atlas : str
Name of atlas parcellation used.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to ROI nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
target_samples : int
Total number of streamline samples specified to generate streams.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
atlas_mni : str
File path to atlas parcellation Nifti1Image in T1w-warped MNI space.
streams : str
File path to save streamline array sequence in .trk format.
"""
from pynets import utils, thresholding
thr_perc = 100 * float(thr)
edge_threshold = "%s%s" % (str(thr_perc), '%')
if parc is True:
node_size = 'parc'
if np.count_nonzero(conn_matrix) == 0:
raise ValueError('ERROR: Raw connectivity matrix contains only zeros.')
# Save unthresholded
unthr_path = utils.create_unthr_path(ID, network, conn_model, roi,
dir_path)
utils.save_mat(conn_matrix, unthr_path)
if min_span_tree is True:
print(
'Using local thresholding option with the Minimum Spanning Tree (MST)...\n'
)
if dens_thresh is False:
thr_type = 'MSTprop'
conn_matrix_thr = thresholding.local_thresholding_prop(
conn_matrix, thr)
else:
thr_type = 'MSTdens'
conn_matrix_thr = thresholding.local_thresholding_dens(
conn_matrix, thr)
elif disp_filt is True:
thr_type = 'DISP_alpha'
G1 = thresholding.disparity_filter(nx.from_numpy_array(conn_matrix))
# G2 = nx.Graph([(u, v, d) for u, v, d in G1.edges(data=True) if d['alpha'] < thr])
print('Computing edge disparity significance with alpha = %s' % thr)
print('Filtered graph: nodes = %s, edges = %s' %
(G1.number_of_nodes(), G1.number_of_edges()))
# print('Backbone graph: nodes = %s, edges = %s' % (G2.number_of_nodes(), G2.number_of_edges()))
# print(G2.edges(data=True))
conn_matrix_thr = nx.to_numpy_array(G1)
else:
if dens_thresh is False:
thr_type = 'prop'
print("%s%.2f%s" %
('\nThresholding proportionally at: ', thr_perc, '% ...\n'))
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
else:
thr_type = 'dens'
print("%s%.2f%s" % ('\nThresholding to achieve density of: ',
thr_perc, '% ...\n'))
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, float(thr))
if not nx.is_connected(nx.from_numpy_matrix(conn_matrix_thr)):
print('Warning: Fragmented graph')
# Save thresholded mat
est_path = utils.create_est_path_diff(ID, network, conn_model, thr, roi,
dir_path, node_size, target_samples,
track_type, thr_type, parc)
utils.save_mat(conn_matrix_thr, est_path)
return conn_matrix_thr, edge_threshold, est_path, thr, node_size, network, conn_model, roi, prune, ID, dir_path, atlas, uatlas, labels, coords, norm, binary, target_samples, track_type, atlas_mni, streams
