def extract_ts_parc(net_parcels_map_nifti, conf, func_file, coords, mask,
dir_path, ID, network):
from nilearn import input_data
from pynets.graphestimation import extract_ts_parc_fast
from pynets import utils
##extract time series from whole brain parcellaions:
fast = False
#import time
#start_time = time.time()
if fast == True:
ts_within_nodes = extract_ts_parc_fast(net_parcels_map_nifti, conf,
func_file, dir_path)
else:
parcel_masker = input_data.NiftiLabelsMasker(
labels_img=net_parcels_map_nifti,
background_label=0,
standardize=True)
ts_within_nodes = parcel_masker.fit_transform(func_file,
confounds=conf)
print("%s%s%d%s" %
('\nTime series has {0} samples'.format(ts_within_nodes.shape[0]),
' and ', len(coords), ' volumetric ROI\'s\n'))
##Save time series as txt file
utils.save_ts_to_file(mask, network, ID, dir_path, ts_within_nodes)
return (ts_within_nodes)
def extract_ts_coords(node_size, conf, func_file, coords, dir_path, ID, mask,
network):
from nilearn import input_data
from pynets.graphestimation import extract_ts_coords_fast
from pynets import utils
fast = False
#import time
#start_time = time.time()
if fast == True:
ts_within_nodes = extract_ts_coords_fast(node_size, conf, func_file,
coords, dir_path)
else:
spheres_masker = input_data.NiftiSpheresMasker(seeds=coords,
radius=float(node_size),
allow_overlap=True,
standardize=True,
verbose=1)
ts_within_nodes = spheres_masker.fit_transform(func_file,
confounds=conf)
#print(time.time()-start_time)
print("%s%s%d%s" %
('\nTime series has {0} samples'.format(ts_within_nodes.shape[0]),
' and ', len(coords), ' coordinate ROI\'s\n'))
##Save time series as txt file
utils.save_ts_to_file(mask, network, ID, dir_path, ts_within_nodes)
return (ts_within_nodes, node_size)
def extract_ts_parc(net_parcels_map_nifti, conf, func_file, coords, roi,
dir_path, ID, network, smooth, atlas_select, uatlas_select,
label_names, c_boot, block_size, mask):
import os.path
from nilearn import input_data
# from pynets.fmri.estimation import extract_ts_parc_fast
from pynets import utils
#from sklearn.externals.joblib import Memory
if not os.path.isfile(func_file):
raise ValueError(
'\nERROR: Functional data input not found! Check that the file(s) specified with the -i flag '
'exist(s)')
if conf:
if not os.path.isfile(conf):
raise ValueError(
'\nERROR: Confound regressor file not found! Check that the file(s) specified with the '
'-conf flag exist(s)')
# if fast is True:
# ts_within_nodes = extract_ts_parc_fast(net_parcels_map_nifti, conf, func_file, dir_path)
# else:
detrending = True
# parcel_masker = input_data.NiftiLabelsMasker(labels_img=net_parcels_map_nifti, background_label=0,
# standardize=True, smoothing_fwhm=float(smooth),
# detrend=detrending,
# memory=Memory(cachedir="%s%s%s" % (dir_path,
# '/SpheresMasker_cache_',
# str(ID)), verbose=2),
# memory_level=1)
parcel_masker = input_data.NiftiLabelsMasker(
labels_img=net_parcels_map_nifti,
background_label=0,
standardize=True,
smoothing_fwhm=float(smooth),
detrend=detrending,
verbose=2,
mask_img=mask)
# parcel_masker = input_data.NiftiLabelsMasker(labels_img=net_parcels_map_nifti, background_label=0,
# standardize=True)
ts_within_nodes = parcel_masker.fit_transform(func_file, confounds=conf)
if ts_within_nodes is None:
raise RuntimeError('\nERROR: Time-series extraction failed!')
if float(c_boot) > 0:
print("%s%s%s" %
('Performing circular block bootstrapping iteration: ', c_boot,
'...'))
ts_within_nodes = utils.timeseries_bootstrap(ts_within_nodes,
block_size)[0]
print("%s%s%d%s" %
('\nTime series has {0} samples'.format(ts_within_nodes.shape[0]),
' mean extracted from ', len(coords), ' volumetric ROI\'s'))
print("%s%s%s" % ('Smoothing FWHM: ', smooth, ' mm\n'))
# Save time series as txt file
utils.save_ts_to_file(roi, network, ID, dir_path, ts_within_nodes, c_boot)
node_size = None
return ts_within_nodes, node_size, smooth, dir_path, atlas_select, uatlas_select, label_names, coords, c_boot
def test_save_ts_to_file():
base_dir = str(Path(__file__).parent / "examples")
#base_dir = '/Users/rxh180012/PyNets-development/tests/examples'
mask = None
network = None
ID = '997'
dir_path = base_dir + '/997'
ts_within_nodes = '/tmp/'
utils.save_ts_to_file(mask, network, ID, dir_path, ts_within_nodes)
def test_save_ts_to_file():
base_dir = str(Path(__file__).parent / "examples")
roi = None
c_boot = 3
network = None
ID = '002'
dir_path = base_dir + '/002/fmri'
ts_within_nodes = '/tmp/'
out_path_ts = utils.save_ts_to_file(roi, network, ID, dir_path,
ts_within_nodes, c_boot)
assert os.path.isfile(out_path_ts) is True
def extract_ts_coords(node_size, conf, func_file, coords, dir_path, ID, mask,
network, smooth, atlas_select, uatlas_select,
label_names):
from nilearn import input_data
# from pynets.graphestimation import extract_ts_coords_fast
from pynets import utils
#from sklearn.externals.joblib import Memory
# if fast is True:
# ts_within_nodes = extract_ts_coords_fast(node_size, conf, func_file, coords, dir_path)
# else:
detrending = True
# spheres_masker = input_data.NiftiSpheresMasker(seeds=coords, radius=float(node_size), allow_overlap=True,
# standardize=True, smoothing_fwhm=float(smooth),
# detrend=detrending,
# memory=Memory(cachedir="%s%s%s" % (dir_path,
# '/SpheresMasker_cache_',
# str(ID)), verbose=2),
# memory_level=1)
spheres_masker = input_data.NiftiSpheresMasker(
seeds=coords,
radius=float(node_size),
allow_overlap=True,
standardize=True,
smoothing_fwhm=float(smooth),
detrend=detrending)
# spheres_masker = input_data.NiftiSpheresMasker(seeds=coords, radius=float(node_size), allow_overlap=True,
# standardize=True, verbose=1)
ts_within_nodes = spheres_masker.fit_transform(func_file, confounds=conf)
print("%s%s%d%s" %
('\nTime series has {0} samples'.format(ts_within_nodes.shape[0]),
' mean extracted from ', len(coords), ' coordinate ROI\'s'))
print("%s%s%s" % ('Using node radius: ', node_size, ' mm'))
print("%s%s%s" % ('Smoothing FWHM: ', smooth, ' mm\n'))
# Save time series as txt file
utils.save_ts_to_file(mask, network, ID, dir_path, ts_within_nodes)
return ts_within_nodes, node_size, smooth, dir_path, atlas_select, uatlas_select, label_names, coords
def extract_ts_parc(net_parcels_map_nifti, conf, func_file, coords, mask,
dir_path, ID, network, smooth, atlas_select, uatlas_select,
label_names):
from nilearn import input_data
# from pynets.graphestimation import extract_ts_parc_fast
from pynets import utils
#from sklearn.externals.joblib import Memory
# if fast is True:
# ts_within_nodes = extract_ts_parc_fast(net_parcels_map_nifti, conf, func_file, dir_path)
# else:
detrending = True
# parcel_masker = input_data.NiftiLabelsMasker(labels_img=net_parcels_map_nifti, background_label=0,
# standardize=True, smoothing_fwhm=float(smooth),
# detrend=detrending,
# memory=Memory(cachedir="%s%s%s" % (dir_path,
# '/SpheresMasker_cache_',
# str(ID)), verbose=2),
# memory_level=1)
parcel_masker = input_data.NiftiLabelsMasker(
labels_img=net_parcels_map_nifti,
background_label=0,
standardize=True,
smoothing_fwhm=float(smooth),
detrend=detrending,
verbose=2)
# parcel_masker = input_data.NiftiLabelsMasker(labels_img=net_parcels_map_nifti, background_label=0,
# standardize=True)
ts_within_nodes = parcel_masker.fit_transform(func_file, confounds=conf)
print("%s%s%d%s" %
('\nTime series has {0} samples'.format(ts_within_nodes.shape[0]),
' mean extracted from ', len(coords), ' volumetric ROI\'s'))
print("%s%s%s" % ('Smoothing FWHM: ', smooth, ' mm\n'))
# Save time series as txt file
utils.save_ts_to_file(mask, network, ID, dir_path, ts_within_nodes)
node_size = None
return ts_within_nodes, node_size, smooth, dir_path, atlas_select, uatlas_select, label_names, coords
def extract_ts_coords(node_size, conf, func_file, coords, dir_path, ID, roi,
network, smooth, atlas_select, uatlas_select,
label_names, c_boot, block_size, mask):
import os.path
from nilearn import input_data
# from pynets.fmri.estimation import extract_ts_coords_fast
from pynets import utils
#from sklearn.externals.joblib import Memory
if not os.path.isfile(func_file):
raise ValueError(
'\nERROR: Functional data input not found! Check that the file(s) specified with the -i flag '
'exist(s)')
if conf:
if not os.path.isfile(conf):
raise ValueError(
'\nERROR: Confound regressor file not found! Check that the file(s) specified with the '
'-conf flag exist(s)')
# if fast is True:
# ts_within_nodes = extract_ts_coords_fast(node_size, conf, func_file, coords, dir_path)
# else:
detrending = True
# spheres_masker = input_data.NiftiSpheresMasker(seeds=coords, radius=float(node_size), allow_overlap=True,
# standardize=True, smoothing_fwhm=float(smooth),
# detrend=detrending,
# memory=Memory(cachedir="%s%s%s" % (dir_path,
# '/SpheresMasker_cache_',
# str(ID)), verbose=2),
# memory_level=1)
if len(coords) > 0:
spheres_masker = input_data.NiftiSpheresMasker(
seeds=coords,
radius=float(node_size),
allow_overlap=True,
standardize=True,
smoothing_fwhm=float(smooth),
detrend=detrending,
verbose=2,
mask_img=mask)
# spheres_masker = input_data.NiftiSpheresMasker(seeds=coords, radius=float(node_size), allow_overlap=True,
# standardize=True, verbose=1)
ts_within_nodes = spheres_masker.fit_transform(func_file,
confounds=conf)
if float(c_boot) > 0:
print("%s%s%s" %
('Performing circular block bootstrapping iteration: ',
c_boot, '...'))
ts_within_nodes = utils.timeseries_bootstrap(
ts_within_nodes, block_size)[0]
if ts_within_nodes is None:
raise RuntimeError('\nERROR: Time-series extraction failed!')
else:
raise RuntimeError(
'\nERROR: Cannot extract time-series from an empty list of coordinates. \nThis usually means '
'that no nodes were generated based on the specified conditions at runtime (e.g. atlas was '
'overly restricted by an RSN or some user-defined mask.')
print("%s%s%d%s" %
('\nTime series has {0} samples'.format(ts_within_nodes.shape[0]),
' mean extracted from ', len(coords), ' coordinate ROI\'s'))
print("%s%s%s" % ('Using node radius: ', node_size, ' mm'))
print("%s%s%s" % ('Smoothing FWHM: ', smooth, ' mm\n'))
# Save time series as txt file
utils.save_ts_to_file(roi, network, ID, dir_path, ts_within_nodes, c_boot)
return ts_within_nodes, node_size, smooth, dir_path, atlas_select, uatlas_select, label_names, coords, c_boot
def extract_ts_coords(node_size, conf, func_file, coords, dir_path, ID, roi, network, smooth, atlas,
uatlas, labels, c_boot, block_size, hpass, detrending=True):
"""
API for employing Nilearn's NiftiSpheresMasker to extract fMRI time-series data from spherical ROI's based on a
given list of seed coordinates. The resulting time-series can then optionally be resampled using circular-block
bootrapping. The final 2D m x n array is ultimately saved to file in .npy format
Parameters
----------
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's for tracking.
conf : str
File path to a confound regressor file for reduce noise in the time-series when extracting from ROI's.
func_file : str
File path to a preprocessed functional Nifti1Image in standard space.
coords : list
List of (x, y, z) tuples corresponding to an a-priori defined set (e.g. a coordinate atlas).
dir_path : str
Path to directory containing subject derivative data for given run.
ID : str
A subject id or other unique identifier.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
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.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
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 graph nodes.
c_boot : int
Number of bootstraps if user specified circular-block bootstrapped resampling of the node-extracted time-series.
block_size : int
Size bootstrap blocks if bootstrapping (c_boot) is performed.
hpass : bool
High-pass filter values (Hz) to apply to node-extracted time-series.
detrending : bool
Indicates whether to remove linear trends from time-series when extracting across nodes. Default is True.
Returns
-------
ts_within_nodes : array
2D m x n array consisting of the time-series signal for each ROI node where m = number of scans and
n = number of ROI's, where ROI's are spheres.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's for tracking.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
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.
hpass : bool
High-pass filter values (Hz) to apply to node-extracted time-series.
"""
import os.path as op
from nilearn import input_data
from pynets import utils
if not op.isfile(func_file):
raise ValueError('\nERROR: Functional data input not found! Check that the file(s) specified with the -i flag '
'exist(s)')
if conf:
if not op.isfile(conf):
raise ValueError('\nERROR: Confound regressor file not found! Check that the file(s) specified with the '
'-conf flag exist(s)')
if len(coords) > 0:
spheres_masker = input_data.NiftiSpheresMasker(seeds=coords, radius=float(node_size), allow_overlap=True,
standardize=True, smoothing_fwhm=float(smooth), high_pass=hpass,
detrend=detrending, verbose=2)
ts_within_nodes = spheres_masker.fit_transform(func_file, confounds=conf)
if float(c_boot) > 0:
print("%s%s%s" % ('Performing circular block bootstrapping iteration: ', c_boot, '...'))
ts_within_nodes = utils.timeseries_bootstrap(ts_within_nodes, block_size)[0]
if ts_within_nodes is None:
raise RuntimeError('\nERROR: Time-series extraction failed!')
else:
raise RuntimeError(
'\nERROR: Cannot extract time-series from an empty list of coordinates. \nThis usually means '
'that no nodes were generated based on the specified conditions at runtime (e.g. atlas was '
'overly restricted by an RSN or some user-defined mask.')
print("%s%s%d%s" % ('\nTime series has {0} samples'.format(ts_within_nodes.shape[0]), ' mean extracted from ',
len(coords), ' coordinate ROI\'s'))
print("%s%s%s" % ('Using node radius: ', node_size, ' mm'))
print("%s%s%s" % ('Smoothing FWHM: ', smooth, ' mm\n'))
print("%s%s%s" % ('Applying high-pass filter: ', hpass, ' Hz\n'))
# Save time series as txt file
utils.save_ts_to_file(roi, network, ID, dir_path, ts_within_nodes, c_boot)
return ts_within_nodes, node_size, smooth, dir_path, atlas, uatlas, labels, coords, c_boot, hpass
def extract_ts_parc(net_parcels_map_nifti, conf, func_file, coords, roi, dir_path, ID, network, smooth, atlas,
uatlas, labels, c_boot, block_size, hpass, detrending=True):
"""
API for employing Nilearn's NiftiLabelsMasker to extract fMRI time-series data from spherical ROI's based on a
given 3D atlas image of integer-based voxel intensities. The resulting time-series can then optionally be resampled
using circular-block bootrapping. The final 2D m x n array is ultimately saved to file in .npy format
Parameters
----------
net_parcels_map_nifti : Nifti1Image
A nibabel-based nifti image consisting of a 3D array with integer voxel intensities corresponding to ROI
membership.
conf : str
File path to a confound regressor file for reduce noise in the time-series when extracting from ROI's.
func_file : str
File path to a preprocessed functional Nifti1Image in standard space.
coords : list
List of (x, y, z) tuples corresponding to the center-of-mass of each parcellation node.
dir_path : str
Path to directory containing subject derivative data for given run.
ID : str
A subject id or other unique identifier.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
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.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
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 graph nodes.
c_boot : int
Number of bootstraps if user specified circular-block bootstrapped resampling of the node-extracted time-series.
block_size : int
Size bootstrap blocks if bootstrapping (c_boot) is performed.
hpass : bool
High-pass filter values (Hz) to apply to node-extracted time-series.
detrending : bool
Indicates whether to remove linear trends from time-series when extracting across nodes. Default is True.
Returns
-------
ts_within_nodes : array
2D m x n array consisting of the time-series signal for each ROI node where m = number of scans and
n = number of ROI's, where ROI's are parcel volumes.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
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 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.
hpass : bool
High-pass filter values (Hz) to apply to node-extracted time-series.
"""
import os.path as op
from nilearn import input_data
from pynets import utils
if not op.isfile(func_file):
raise ValueError('\nERROR: Functional data input not found! Check that the file(s) specified with the -i flag '
'exist(s)')
if conf:
if not op.isfile(conf):
raise ValueError('\nERROR: Confound regressor file not found! Check that the file(s) specified with the '
'-conf flag exist(s)')
parcel_masker = input_data.NiftiLabelsMasker(labels_img=net_parcels_map_nifti, background_label=0,
standardize=True, smoothing_fwhm=float(smooth), high_pass=hpass,
detrend=detrending, verbose=2, resampling_target='data')
ts_within_nodes = parcel_masker.fit_transform(func_file, confounds=conf)
if ts_within_nodes is None:
raise RuntimeError('\nERROR: Time-series extraction failed!')
if float(c_boot) > 0:
print("%s%s%s" % ('Performing circular block bootstrapping iteration: ', c_boot, '...'))
ts_within_nodes = utils.timeseries_bootstrap(ts_within_nodes, block_size)[0]
print("%s%s%d%s" % ('\nTime series has {0} samples'.format(ts_within_nodes.shape[0]), ' mean extracted from ',
len(coords), ' volumetric ROI\'s'))
print("%s%s%s" % ('Smoothing FWHM: ', smooth, ' mm\n'))
print("%s%s%s" % ('Applying high-pass filter: ', hpass, ' Hz\n'))
# Save time series as txt file
utils.save_ts_to_file(roi, network, ID, dir_path, ts_within_nodes, c_boot)
node_size = None
return ts_within_nodes, node_size, smooth, dir_path, atlas, uatlas, labels, coords, c_boot, hpass
