def thresh_diff(dens_thresh, thr, conn_model, network, ID, dir_path, mask,
node_size, conn_matrix, parc):
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 dens_thresh is False:
print("%s%.2f%s" %
('\nThresholding proportionally at: ', thr_perc, '% ...\n'))
else:
print("%s%.2f%s" %
('\nThresholding to achieve density of: ', thr_perc, '% ...\n'))
if dens_thresh is False:
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
else:
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, float(thr))
# Save thresholded mat
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size)
np.save(est_path, conn_matrix_thr)
return conn_matrix_thr, edge_threshold, est_path, thr, node_size, network
def thresh_diff(dens_thresh, thr, conn_model, network, ID, dir_path, mask,
node_size, conn_matrix):
from pynets import utils, thresholding
if not dens_thresh:
print("%s%.2f%s" % ('\nThresholding proportionally at: ',
100 * float(thr), '% ...\n'))
else:
print("%s%.2f%s" % ('\nThresholding to achieve density of: ',
100 * float(thr), '% ...\n'))
if dens_thresh == False:
##Save thresholded
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
edge_threshold = str(float(thr) * 100) + '%'
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size)
else:
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, float(thr))
edge_threshold = str(float(thr) * 100) + '%'
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size)
np.save(est_path, conn_matrix_thr)
return (conn_matrix_thr, edge_threshold, est_path, thr, node_size, network)
def thresh_and_fit(dens_thresh, thr, ts_within_nodes, conn_model, network, ID,
dir_path, mask, node_size):
from pynets import utils, thresholding, graphestimation
thr_perc = 100 * float(thr)
edge_threshold = "%s%s" % (str(thr_perc), '%')
if not dens_thresh:
print("%s%.2f%s" %
('\nThresholding proportionally at: ', thr_perc, '% ...\n'))
else:
print("%s%.2f%s" %
('\nThresholding to achieve density of: ', thr_perc, '% ...\n'))
# Fit mat
conn_matrix = graphestimation.get_conn_matrix(ts_within_nodes, conn_model)
# Save unthresholded
unthr_path = utils.create_unthr_path(ID, network, conn_model, mask,
dir_path)
np.save(unthr_path, conn_matrix)
if dens_thresh is False:
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
else:
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, float(thr))
# Save thresholded mat
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size)
np.save(est_path, conn_matrix_thr)
return conn_matrix_thr, edge_threshold, est_path, thr, node_size, network
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_and_fit(adapt_thresh, dens_thresh, thr, ts_within_nodes, conn_model,
network, ID, dir_path, mask):
from pynets import utils, thresholding, graphestimation
##Adaptive thresholding scenario
if adapt_thresh is not False:
try:
est_path2 = dir_path + '/' + ID + '_structural_est.txt'
if os.path.isfile(est_path2) == True:
#[conn_matrix_thr, est_path, edge_threshold, thr] = adaptive_thresholding(ts_within_nodes, conn_model, network, ID, est_path2, dir_path)
##Save unthresholded
unthr_path = utils.create_unthr_path(ID, network, conn_model,
mask, dir_path)
#np.savetxt(unthr_path, conn_matrix_thr, delimiter='\t')
edge_threshold = str(float(thr) * 100) + '%'
else:
print('No structural mx found! Exiting...')
sys.exit()
except:
print('No structural mx assigned! Exiting...')
sys.exit()
else:
if not dens_thresh:
print(
'\nRunning graph estimation and thresholding proportionally at: '
+ str(thr) + '% ...\n')
else:
print(
'\nRunning graph estimation and thresholding to achieve density of: '
+ str(100 * dens_thresh) + '% ...\n')
##Fit mat
conn_matrix = graphestimation.get_conn_matrix(ts_within_nodes,
conn_model)
##Save unthresholded
unthr_path = utils.create_unthr_path(ID, network, conn_model, mask,
dir_path)
np.savetxt(unthr_path, conn_matrix, delimiter='\t')
if not dens_thresh:
##Save thresholded
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
edge_threshold = str(float(thr) * 100) + '%'
est_path = utils.create_est_path(ID, network, conn_model, thr,
mask, dir_path)
else:
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, dens_thresh)
edge_threshold = str((1 - float(dens_thresh)) * 100) + '%'
est_path = utils.create_est_path(ID, network, conn_model,
dens_thresh, mask, dir_path)
np.savetxt(est_path, conn_matrix_thr, delimiter='\t')
return (conn_matrix_thr, edge_threshold, est_path, thr)
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 density_thresholding(conn_matrix, thr):
from pynets import thresholding
work_thr = 0.0
conn_matrix = thresholding.normalize(conn_matrix)
np.fill_diagonal(conn_matrix, 0)
i = 1
thr_max = 0.50
G = nx.from_numpy_matrix(conn_matrix)
density = nx.density(G)
while float(work_thr) <= float(thr_max) and float(density) > float(thr):
work_thr = float(work_thr) + float(0.01)
conn_matrix = thresholding.threshold_proportional(conn_matrix, work_thr)
G = nx.from_numpy_matrix(conn_matrix)
density = nx.density(G)
print("%s%d%s%.2f%s%.2f%s" % ('Iteratively thresholding -- Iteration ', i, ' -- with thresh: ', float(work_thr), ' and Density: ', float(density), '...'))
i = i + 1
return conn_matrix
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
def wb_connectome_with_us_atlas_coords(input_file, ID, atlas_select, NETWORK,
node_size, mask, thr, parlistfile,
all_nets, conn_model, dens_thresh, conf,
adapt_thresh, plot_switch,
bedpostx_dir):
nilearn_atlases = [
'atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009'
]
##Input is nifti file
func_file = input_file
##Test if atlas_select is a nilearn atlas
if atlas_select in nilearn_atlases:
try:
parlistfile = getattr(datasets, 'fetch_%s' % atlas_select)().maps
try:
label_names = getattr(datasets,
'fetch_%s' % atlas_select)().labels
except:
label_names = None
try:
networks_list = getattr(datasets,
'fetch_%s' % atlas_select)().networks
except:
networks_list = None
except:
print(
'PyNets is not ready for multi-scale atlases like BASC just yet!'
)
sys.exit()
##Fetch user-specified atlas coords
[coords, atlas_name,
par_max] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
atlas_select = atlas_name
try:
label_names
except:
label_names = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_select
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##Get coord membership dictionary if all_nets option triggered
if all_nets != None:
try:
networks_list
except:
networks_list = None
[membership,
membership_plotting] = nodemaker.get_mem_dict(func_file, coords,
networks_list)
##Describe user atlas coords
print('\n' + atlas_name + ' comes with {0} '.format(par_max) + 'parcels' +
'\n')
print('\n' + 'Stacked atlas coordinates in array of shape {0}.'.format(
coords.shape) + '\n')
##Mask coordinates
if mask is not None:
[coords, label_names] = nodemaker.coord_masker(mask, coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_wb_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(coords, f)
labels_path = dir_path + '/labelnames_wb_' + str(thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
FSLDIR = os.environ['FSLDIR']
try:
FSLDIR
except NameError:
print('FSLDIR environment variable not set!')
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, coords, node_size)
##extract time series from whole brain parcellaions:
parcellation = nib.load(parlistfile)
parcel_masker = input_data.NiftiLabelsMasker(labels_img=parcellation,
background_label=0,
memory='nilearn_cache',
memory_level=5,
standardize=True)
ts_within_parcels = parcel_masker.fit_transform(func_file, confounds=conf)
print('\n' +
'Time series has {0} samples'.format(ts_within_parcels.shape[0]) +
'\n')
##Save time series as txt file
out_path_ts = dir_path + '/' + ID + '_whole_brain_ts_within_parcels.txt'
np.savetxt(out_path_ts, ts_within_parcels)
##Fit connectivity model
if adapt_thresh is not False:
if os.path.isfile(est_path2) == True:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.adaptive_thresholding(ts_within_parcels,
conn_model, NETWORK, ID,
est_path2, dir_path)
else:
print('No structural mx found! Exiting...')
sys.exit(0)
elif dens_thresh is None:
edge_threshold = str(float(thr) * 100) + '%'
[conn_matrix,
est_path] = graphestimation.get_conn_matrix(ts_within_parcels,
conn_model, NETWORK, ID,
dir_path, thr)
conn_matrix = thresholding.threshold_proportional(
conn_matrix, float(thr), dir_path)
conn_matrix = thresholding.normalize(conn_matrix)
elif dens_thresh is not None:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.density_thresholding(ts_within_parcels,
conn_model, NETWORK, ID,
dens_thresh, dir_path)
if plot_switch == True:
##Plot connectogram
plotting.plot_connectogram(conn_matrix, conn_model, atlas_name,
dir_path, ID, NETWORK, label_names)
##Plot adj. matrix based on determined inputs
atlast_graph_title = plotting.plot_conn_mat(conn_matrix, conn_model,
atlas_name, dir_path, ID,
NETWORK, label_names, mask)
##Plot connectome viz for all Yeo networks
if all_nets != False:
plotting.plot_membership(membership_plotting, conn_matrix,
conn_model, coords, edge_threshold,
atlas_name, dir_path)
else:
out_path_fig = dir_path + '/' + ID + '_connectome_viz.png'
niplot.plot_connectome(conn_matrix,
coords,
title=atlast_graph_title,
edge_threshold=edge_threshold,
node_size=20,
colorbar=True,
output_file=out_path_fig)
return est_path, thr
def network_connectome(input_file, ID, atlas_select, NETWORK, node_size, mask,
thr, parlistfile, all_nets, conn_model, dens_thresh,
conf, adapt_thresh, plot_switch, bedpostx_dir):
nilearn_atlases = [
'atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009'
]
##Input is nifti file
func_file = input_file
##Test if atlas_select is a nilearn atlas
if atlas_select in nilearn_atlases:
atlas = getattr(datasets, 'fetch_%s' % atlas_select)()
try:
parlistfile = atlas.maps
try:
label_names = atlas.labels
except:
label_names = None
try:
networks_list = atlas.networks
except:
networks_list = None
except RuntimeError:
print('Error, atlas fetching failed.')
sys.exit()
if parlistfile == None and atlas_select not in nilearn_atlases:
##Fetch nilearn atlas coords
[coords, atlas_name, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
if atlas_name == 'Power 2011 atlas':
##Reference RSN list
import pkgutil
import io
network_coords_ref = NETWORK + '_coords.csv'
atlas_coords = pkgutil.get_data("pynets",
"rsnrefs/" + network_coords_ref)
df = pd.read_csv(io.BytesIO(atlas_coords)).ix[:, 0:4]
i = 1
net_coords = []
ix_labels = []
for i in range(len(df)):
#print("ROI Reference #: " + str(i))
x = int(df.ix[i, 1])
y = int(df.ix[i, 2])
z = int(df.ix[i, 3])
#print("X:" + str(x) + " Y:" + str(y) + " Z:" + str(z))
net_coords.append((x, y, z))
ix_labels.append(i)
i = i + 1
#print(net_coords)
label_names = ix_labels
elif atlas_name == 'Dosenbach 2010 atlas':
coords = list(tuple(x) for x in coords)
##Get coord membership dictionary
[membership, membership_plotting
] = nodemaker.get_mem_dict(func_file, coords, networks_list)
##Convert to membership dataframe
mem_df = membership.to_frame().reset_index()
nets_avail = list(set(list(mem_df['index'])))
##Get network name equivalents
if NETWORK == 'DMN':
NETWORK = 'default'
elif NETWORK == 'FPTC':
NETWORK = 'fronto-parietal'
elif NETWORK == 'CON':
NETWORK = 'cingulo-opercular'
elif NETWORK not in nets_avail:
print('Error: ' + NETWORK + ' not available with this atlas!')
sys.exit()
##Get coords for network-of-interest
mem_df.loc[mem_df['index'] == NETWORK]
net_coords = mem_df.loc[mem_df['index'] == NETWORK][[0]].values[:,
0]
net_coords = list(tuple(x) for x in net_coords)
ix_labels = mem_df.loc[mem_df['index'] == NETWORK].index.values
####Add code for any special RSN reference lists for the nilearn atlases here#####
##If labels_names are not indices and NETWORK is specified, sub-list label names
if label_names != ix_labels:
try:
label_names = label_names.tolist()
except:
pass
label_names = [label_names[i] for i in ix_labels]
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_select
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##If masking, remove those coords that fall outside of the mask
if mask != None:
[net_coords,
label_names] = nodemaker.coord_masker(mask, net_coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_' + NETWORK + '_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(net_coords, f)
labels_path = dir_path + '/labelnames_' + NETWORK + '_' + str(
thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
FSLDIR = os.environ['FSLDIR']
try:
FSLDIR
except NameError:
print('FSLDIR environment variable not set!')
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, net_coords, node_size)
else:
##Fetch user-specified atlas coords
[coords_all, atlas_name,
par_max] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
coords = list(tuple(x) for x in coords_all)
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_name
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##Get coord membership dictionary
try:
networks_list
except:
networks_list = None
[membership,
membership_plotting] = nodemaker.get_mem_dict(func_file, coords,
networks_list)
##Convert to membership dataframe
mem_df = membership.to_frame().reset_index()
##Get coords for network-of-interest
mem_df.loc[mem_df['index'] == NETWORK]
net_coords = mem_df.loc[mem_df['index'] == NETWORK][[0]].values[:, 0]
net_coords = list(tuple(x) for x in net_coords)
ix_labels = mem_df.loc[mem_df['index'] == NETWORK].index.values
try:
label_names = [label_names[i] for i in ix_labels]
except:
label_names = ix_labels
if mask != None:
[net_coords,
label_names] = nodemaker.coord_masker(mask, net_coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_' + NETWORK + '_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(net_coords, f)
labels_path = dir_path + '/labelnames_' + NETWORK + '_' + str(
thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, net_coords, node_size)
##Generate network parcels image (through refinement, this could be used
##in place of the 3 lines above)
#net_parcels_img_path = gen_network_parcels(parlistfile, NETWORK, labels)
#parcellation = nib.load(net_parcels_img_path)
#parcel_masker = input_data.NiftiLabelsMasker(labels_img=parcellation, background_label=0, memory='nilearn_cache', memory_level=5, standardize=True)
#ts_within_parcels = parcel_masker.fit_transform(func_file)
#net_ts = ts_within_parcels
##Grow ROIs
masker = input_data.NiftiSpheresMasker(seeds=net_coords,
radius=float(node_size),
allow_overlap=True,
memory_level=5,
memory='nilearn_cache',
verbose=2,
standardize=True)
ts_within_spheres = masker.fit_transform(func_file, confounds=conf)
net_ts = ts_within_spheres
##Save time series as txt file
out_path_ts = dir_path + '/' + ID + '_' + NETWORK + '_net_ts.txt'
np.savetxt(out_path_ts, net_ts)
##Fit connectivity model
if adapt_thresh is not False:
if os.path.isfile(est_path2) == True:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.adaptive_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
est_path2, dir_path)
else:
print('No structural mx found! Exiting...')
sys.exit(0)
elif dens_thresh is None:
edge_threshold = str(float(thr) * 100) + '%'
[conn_matrix,
est_path] = graphestimation.get_conn_matrix(ts_within_spheres,
conn_model, NETWORK, ID,
dir_path, thr)
conn_matrix = thresholding.threshold_proportional(
conn_matrix, float(thr), dir_path)
conn_matrix = thresholding.normalize(conn_matrix)
elif dens_thresh is not None:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.density_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
dens_thresh, dir_path)
if plot_switch == True:
##Plot connectogram
plotting.plot_connectogram(conn_matrix, conn_model, atlas_name,
dir_path, ID, NETWORK, label_names)
##Plot adj. matrix based on determined inputs
plotting.plot_conn_mat(conn_matrix, conn_model, atlas_name, dir_path,
ID, NETWORK, label_names, mask)
##Plot network time-series
plotting.plot_timeseries(net_ts, NETWORK, ID, dir_path, atlas_name,
label_names)
##Plot connectome viz for specific Yeo networks
title = "Connectivity Projected on the " + NETWORK
out_path_fig = dir_path + '/' + ID + '_' + NETWORK + '_connectome_plot.png'
niplot.plot_connectome(conn_matrix,
net_coords,
edge_threshold=edge_threshold,
title=title,
display_mode='lyrz',
output_file=out_path_fig)
return est_path, thr
def wb_connectome_with_nl_atlas_coords(input_file, ID, atlas_select, NETWORK,
node_size, mask, thr, all_nets,
conn_model, dens_thresh, conf,
adapt_thresh, plot_switch,
bedpostx_dir):
nilearn_atlases = [
'atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009'
]
##Input is nifti file
func_file = input_file
##Fetch nilearn atlas coords
[coords, atlas_name, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_select
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##Get coord membership dictionary if all_nets option triggered
if all_nets != False:
try:
networks_list
except:
networks_list = None
[membership,
membership_plotting] = nodemaker.get_mem_dict(func_file, coords,
networks_list)
##Mask coordinates
if mask is not None:
[coords, label_names] = nodemaker.coord_masker(mask, coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_wb_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(coords, f)
labels_path = dir_path + '/labelnames_wb_' + str(thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
FSLDIR = os.environ['FSLDIR']
try:
FSLDIR
except NameError:
print('FSLDIR environment variable not set!')
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, coords, node_size)
##Extract within-spheres time-series from funct file
spheres_masker = input_data.NiftiSpheresMasker(seeds=coords,
radius=float(node_size),
memory='nilearn_cache',
memory_level=5,
verbose=2,
standardize=True)
ts_within_spheres = spheres_masker.fit_transform(func_file, confounds=conf)
print('\n' +
'Time series has {0} samples'.format(ts_within_spheres.shape[0]) +
'\n')
##Save time series as txt file
out_path_ts = dir_path + '/' + ID + '_whole_brain_ts_within_spheres.txt'
np.savetxt(out_path_ts, ts_within_spheres)
##Fit connectivity model
if adapt_thresh is not False:
if os.path.isfile(est_path2) == True:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.adaptive_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
est_path2, dir_path)
else:
print('No structural mx found! Exiting...')
sys.exit(0)
elif dens_thresh is None:
edge_threshold = str(float(thr) * 100) + '%'
[conn_matrix,
est_path] = graphestimation.get_conn_matrix(ts_within_spheres,
conn_model, NETWORK, ID,
dir_path, thr)
conn_matrix = thresholding.threshold_proportional(
conn_matrix, float(thr), dir_path)
conn_matrix = thresholding.normalize(conn_matrix)
elif dens_thresh is not None:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.density_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
dens_thresh, dir_path)
if plot_switch == True:
##Plot connectogram
plotting.plot_connectogram(conn_matrix, conn_model, atlas_name,
dir_path, ID, NETWORK, label_names)
##Plot adj. matrix based on determined inputs
plotting.plot_conn_mat(conn_matrix, conn_model, atlas_name, dir_path,
ID, NETWORK, label_names, mask)
##Plot connectome viz for all Yeo networks
if all_nets != False:
plotting.plot_membership(membership_plotting, conn_matrix,
conn_model, coords, edge_threshold,
atlas_name, dir_path)
else:
out_path_fig = dir_path + '/' + ID + '_' + atlas_name + '_connectome_viz.png'
niplot.plot_connectome(conn_matrix,
coords,
title=atlas_name,
edge_threshold=edge_threshold,
node_size=20,
colorbar=True,
output_file=out_path_fig)
return est_path, thr