def thresh_raw_graph(conn_matrix, thr, min_span_tree, dens_thresh, disp_filt,
est_path):
from pynets.core import thresholding
[thr_type, edge_threshold, conn_matrix_thr
] = thresholding.perform_thresholding(conn_matrix, thr, min_span_tree,
dens_thresh, disp_filt)
return thr_type, edge_threshold, conn_matrix_thr, thr, est_path
def thresh_raw_graph(conn_matrix, thr, min_span_tree, dens_thresh, disp_filt,
est_path):
from pynets.core import thresholding
if 'rawgraph' in est_path:
est_path = est_path.replace('rawgraph', 'graph')
[thr_type, edge_threshold, conn_matrix_thr] = \
thresholding.perform_thresholding(
conn_matrix, thr, min_span_tree, dens_thresh, disp_filt)
return thr_type, edge_threshold, conn_matrix_thr, thr, est_path
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, directget,
min_length):
"""
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.
directget : str
The statistical approach to tracking. Options are: det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
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.
directget : str
The statistical approach to tracking. Options are: det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
"""
import gc
from pynets.core import utils, thresholding
thr_perc = 100 - np.abs(100 * float(thr))
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
utils.save_mat(
conn_matrix,
utils.create_raw_path_diff(ID, network, conn_model, roi, dir_path,
node_size, target_samples, track_type, parc,
directget, min_length))
[thr_type, edge_threshold, conn_matrix_thr, coords,
labels] = thresholding.perform_thresholding(conn_matrix, coords, labels,
thr, thr_perc, min_span_tree,
dens_thresh, disp_filt)
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,
directget, min_length)
utils.save_mat(conn_matrix_thr, est_path)
gc.collect()
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, directget, min_length)
def thresh_func(dens_thresh, thr, conn_matrix, conn_model, network, ID,
dir_path, roi, node_size, min_span_tree, smooth, disp_filt,
parc, prune, atlas, uatlas, labels, coords, c_boot, norm,
binary, hpass):
"""
Threshold a functional 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.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
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.
c_boot : int
Number of bootstraps if user specified circular-block bootstrapped resampling of the node-extracted time-series.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
hpass : float
High-pass filter values (Hz) to apply to node-extracted time-series.
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.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
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.
c_boot : int
Number of bootstraps if user specified circular-block bootstrapped resampling of the node-extracted time-series.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
hpass : float
High-pass filter values (Hz) to apply to node-extracted time-series.
"""
import gc
from pynets.core import utils, thresholding
thr_perc = 100 - np.abs(100 * float(thr))
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
utils.save_mat(
conn_matrix,
utils.create_raw_path_func(ID, network, conn_model, roi, dir_path,
node_size, smooth, c_boot, hpass, parc))
[thr_type, edge_threshold, conn_matrix_thr, coords,
labels] = thresholding.perform_thresholding(conn_matrix, coords, labels,
thr, thr_perc, min_span_tree,
dens_thresh, disp_filt)
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_func(ID, network, conn_model, thr, roi,
dir_path, node_size, smooth, c_boot,
thr_type, hpass, parc)
utils.save_mat(conn_matrix_thr, est_path)
gc.collect()
return (conn_matrix_thr, edge_threshold, est_path, thr, node_size, network,
conn_model, roi, smooth, prune, ID, dir_path, atlas, uatlas,
labels, coords, c_boot, norm, binary, hpass)
def thresh_func(
dens_thresh,
thr,
conn_matrix,
conn_model,
network,
ID,
dir_path,
roi,
node_size,
min_span_tree,
smooth,
disp_filt,
parc,
prune,
atlas,
uatlas,
labels,
coords,
norm,
binary,
hpass,
extract_strategy,
check_consistency=True,
):
"""
Threshold a functional 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.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting
signal from ROI's.
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.
hpass : float
High-pass filter values (Hz) to apply to node-extracted time-series.
extract_strategy : str
The name of a valid function used to reduce the time-series region
extraction.
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.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting
signal from ROI's.
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.
hpass : float
High-pass filter values (Hz) to apply to node-extracted time-series.
extract_strategy : str
The name of a valid function used to reduce the time-series region
extraction.
References
----------
.. [1] van Wijk, B. C. M., Stam, C. J., & Daffertshofer, A. (2010).
Comparing brain networks of different size and connectivity
density using graph theory. PLoS ONE.
https://doi.org/10.1371/journal.pone.0013701
"""
import gc
from pynets.core import utils, thresholding
if np.count_nonzero(conn_matrix) == 0:
print(UserWarning("Raw connectivity matrix contains only" " zeros."))
[thr_type, edge_threshold, conn_matrix_thr] = \
thresholding.perform_thresholding(
conn_matrix, thr, min_span_tree, dens_thresh, disp_filt)
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_func(
ID,
network,
conn_model,
thr,
roi,
dir_path,
node_size,
smooth,
thr_type,
hpass,
parc,
extract_strategy,
)
utils.save_mat(conn_matrix_thr, est_path)
gc.collect()
if check_consistency is True:
assert len(coords) == len(labels) == conn_matrix_thr.shape[0]
if network is not None:
atlas_name = f"{atlas}_{network}_stage-post_thr"
else:
atlas_name = f"{atlas}_stage-post_thr"
utils.save_coords_and_labels_to_json(coords,
labels,
dir_path,
atlas_name,
indices=None)
return (
edge_threshold,
est_path,
thr,
node_size,
network,
conn_model,
roi,
smooth,
prune,
ID,
dir_path,
atlas,
uatlas,
labels,
coords,
norm,
binary,
hpass,
extract_strategy,
)
def thresh_struct(
dens_thresh,
thr,
conn_matrix,
conn_model,
subnet,
ID,
dir_path,
roi,
node_radius,
min_span_tree,
disp_filt,
parc,
prune,
atlas,
parcellation,
labels,
coords,
norm,
binary,
track_type,
atlas_for_streams,
streams,
traversal,
min_length,
error_margin,
check_consistency=True,
):
"""
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.
subnet : str
Resting-state subnet 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_radius : 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 subnet' 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.
parcellation : 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.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
atlas_for_streams : str
File path to atlas parcellation Nifti1Image in the morphological
space of the streamlines.
streams : str
File path to save streamline array sequence in .trk format.
traversal : str
The statistical approach to tracking. Options are:
det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
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_radius : int
Spherical centroid node size in the case that coordinate-based
centroids are used as ROI's.
subnet : str
Resting-state subnet 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.
parcellation : 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.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
atlas_for_streams : str
File path to atlas parcellation Nifti1Image in the morphological
space of the streamlines.
streams : str
File path to save streamline array sequence in .trk format.
traversal : str
The statistical approach to tracking. Options are:
det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
References
----------
.. [1] van Wijk, B. C. M., Stam, C. J., & Daffertshofer, A. (2010).
Comparing brain networks of different size and connectivity
density using graph theory. PLoS ONE.
https://doi.org/10.1371/journal.pone.0013701
"""
import gc
from pynets.core import utils, thresholding
if parc is True:
node_radius = "parc"
if np.count_nonzero(conn_matrix) == 0:
print(UserWarning("Raw connectivity matrix contains only"
" zeros."))
[thr_type, edge_threshold, conn_matrix_thr] = \
thresholding.perform_thresholding(
conn_matrix, thr, min_span_tree, dens_thresh, disp_filt)
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,
subnet,
conn_model,
thr,
roi,
dir_path,
node_radius,
track_type,
thr_type,
parc,
traversal,
min_length,
error_margin
)
utils.save_mat(conn_matrix_thr, est_path)
gc.collect()
if check_consistency is True:
assert len(coords) == len(labels) == conn_matrix_thr.shape[0]
if subnet is not None:
atlas_name = f"{atlas}_{subnet}_stage-post_thr"
else:
atlas_name = f"{atlas}_stage-post_thr"
utils.save_coords_and_labels_to_json(coords, labels, dir_path,
atlas_name, indices=None)
return (
edge_threshold,
est_path,
thr,
node_radius,
subnet,
conn_model,
roi,
prune,
ID,
dir_path,
atlas,
parcellation,
labels,
coords,
norm,
binary,
track_type,
atlas_for_streams,
streams,
traversal,
min_length,
error_margin
)
