def sources_to_labels(stcs,
age=None,
template=None,
parc='aparc',
mode='mean_flip',
allow_empty=True,
return_generator=False,
subjects_dir=None,
include_vol_src=True):
template = __validate_template__(age, template, subjects_dir)
montage, trans, bem_model, bem_solution, src = get_bem_artifacts(
template, subjects_dir=subjects_dir, include_vol_src=include_vol_src)
labels_parc = mne.read_labels_from_annot(template,
subjects_dir=subjects_dir,
parc=parc)
labels_ts = mne.extract_label_time_course(
stcs,
labels_parc,
src,
mode=mode,
allow_empty=allow_empty,
return_generator=return_generator)
if include_vol_src:
labels_aseg = mne.get_volume_labels_from_src(src, template,
subjects_dir)
labels = labels_parc + labels_aseg
else:
labels = labels_parc
return labels_ts, labels
def _compute_mean_ROIs(stc, sbj_id, subjects_dir, parc, inverse_operator,
forward, aseg, is_fixed):
# these coo are in MRI space and we have to convert them to MNI space
labels_cortex = mne.read_labels_from_annot(sbj_id,
parc=parc,
subjects_dir=subjects_dir)
print(('\n*** %d ***\n' % len(labels_cortex)))
src = inverse_operator['src']
# allow_empty : bool -> Instead of emitting an error, return all-zero time
# courses for labels that do not have any vertices in the source estimate
if is_fixed:
mode = 'mean_flip'
else:
mode = 'mean'
label_ts = mne.extract_label_time_course(stc,
labels_cortex,
src,
mode=mode,
allow_empty=True,
return_generator=False)
# save results in .npy file that will be the input for spectral node
print('\n*** SAVE ROI TS ***\n')
print((len(label_ts)))
if aseg:
print(sbj_id)
labels_aseg = get_volume_labels_from_src(src, sbj_id, subjects_dir)
labels = labels_cortex + labels_aseg
else:
labels = labels_cortex
labels_aseg = None
print((labels[0].pos))
print((len(labels)))
labels_file, label_names_file, label_coords_file = \
_create_MNI_label_files(forward, labels_cortex, labels_aseg,
sbj_id, subjects_dir)
return label_ts, labels_file, label_names_file, label_coords_file
fmin = 8.
fmax = 13.
sfreq = raw.info['sfreq'] # the sampling frequency
con, freqs, times, n_epochs, n_tapers = spectral_connectivity(
label_ts,
method='pli',
mode='multitaper',
sfreq=sfreq,
fmin=fmin,
fmax=fmax,
faverage=True,
mt_adaptive=True,
n_jobs=1)
# We create a list of Label containing also the sub structures
labels_aseg = mne.get_volume_labels_from_src(src, subject, subjects_dir)
labels = labels_parc + labels_aseg
# read colors
node_colors = [label.color for label in labels]
# We reorder the labels based on their location in the left hemi
label_names = [label.name for label in labels]
lh_labels = [name for name in label_names if name.endswith('lh')]
rh_labels = [name for name in label_names if name.endswith('rh')]
# Get the y-location of the label
label_ypos_lh = list()
for name in lh_labels:
idx = label_names.index(name)
ypos = np.mean(labels[idx].pos[:, 1])
label_ts = mne.extract_label_time_course(stcs, labels_parc, src,
mode='mean_flip',
allow_empty=True,
return_generator=False)
# We compute the connectivity in the alpha band and plot it using a circular
# graph layout
fmin = 8.
fmax = 13.
sfreq = raw.info['sfreq'] # the sampling frequency
con, freqs, times, n_epochs, n_tapers = spectral_connectivity(
label_ts, method='pli', mode='multitaper', sfreq=sfreq, fmin=fmin,
fmax=fmax, faverage=True, mt_adaptive=True, n_jobs=1)
# We create a list of Label containing also the sub structures
labels_aseg = mne.get_volume_labels_from_src(src, subject, subjects_dir)
labels = labels_parc + labels_aseg
# read colors
node_colors = [label.color for label in labels]
# We reorder the labels based on their location in the left hemi
label_names = [label.name for label in labels]
lh_labels = [name for name in label_names if name.endswith('lh')]
rh_labels = [name for name in label_names if name.endswith('rh')]
# Get the y-location of the label
label_ypos_lh = list()
for name in lh_labels:
idx = label_names.index(name)
ypos = np.mean(labels[idx].pos[:, 1])
def compute_rois_inv_sol(raw_filename,
sbj_id,
sbj_dir,
fwd_filename,
cov_fname,
is_epoched=False,
events_id=[],
t_min=None,
t_max=None,
is_evoked=False,
snr=1.0,
inv_method='MNE',
parc='aparc',
aseg=False,
aseg_labels=[],
save_stc=False,
is_fixed=False):
"""Compute the inverse solution on raw/epoched data.
This function return the average time series computed in the N_r regions of
the source space defined by the specified cortical parcellation
Parameters
----------
raw_filename : str
filename of the raw/epoched data
sbj_id : str
subject name
sbj_dir : str
Freesurfer directory
fwd_filename : str
filename of the forward operator
cov_filename : str
filename of the noise covariance matrix
is_epoched : bool
if True and events_id = None the input data are epoch data
in the format -epo.fif
if True and events_id is not None, the raw data are epoched
according to events_id and t_min and t_max values
events_id: dict
the dict of events
t_min, t_max: int
define the time interval in which to epoch the raw data
is_evoked: bool
if True the raw data will be averaged according to the events
contained in the dict events_id
inv_method : str
the inverse method to use; possible choices: MNE, dSPM, sLORETA
snr : float
the SNR value used to define the regularization parameter
parc: str
the parcellation defining the ROIs atlas in the source space
aseg: bool
if True a mixed source space will be created and the sub cortical
regions defined in aseg_labels will be added to the source space
aseg_labels: list
list of substructures we want to include in the mixed source space
save_stc: bool
if True the stc will be saved
Returns
-------
ts_file : str
filename of the file where are saved the ROIs time series
labels_file : str
filename of the file where are saved the ROIs of the parcellation
label_names_file : str
filename of the file where are saved the name of the ROIs of the
parcellation
label_coords_file : str
filename of the file where are saved the coordinates of the
centroid of the ROIs of the parcellation
"""
import os.path as op
import numpy as np
import mne
from mne.io import read_raw_fif
from mne import read_epochs
from mne.minimum_norm import make_inverse_operator, apply_inverse_raw
from mne.minimum_norm import apply_inverse_epochs, apply_inverse
from mne import get_volume_labels_from_src
from nipype.utils.filemanip import split_filename as split_f
from ephypype.preproc import create_reject_dict
from ephypype.source_space import create_mni_label_files
try:
traits.undefined(events_id)
except NameError:
events_id = None
print(('\n*** READ raw filename %s ***\n' % raw_filename))
if is_epoched and events_id is None:
epochs = read_epochs(raw_filename)
info = epochs.info
else:
raw = read_raw_fif(raw_filename, preload=True)
# raw.set_eeg_reference()
info = raw.info
subj_path, basename, ext = split_f(raw_filename)
print(('\n*** READ noise covariance %s ***\n' % cov_fname))
noise_cov = mne.read_cov(cov_fname)
print(('\n*** READ FWD SOL %s ***\n' % fwd_filename))
forward = mne.read_forward_solution(fwd_filename)
if not aseg:
print(('\n*** fixed orientation {} ***\n'.format(is_fixed)))
forward = mne.convert_forward_solution(forward,
surf_ori=True,
force_fixed=is_fixed)
lambda2 = 1.0 / snr**2
# compute inverse operator
print('\n*** COMPUTE INV OP ***\n')
if is_fixed:
loose = None
depth = None
pick_ori = None
elif aseg:
loose = 1
depth = None
pick_ori = None
else:
loose = 0.2
depth = 0.8
pick_ori = 'normal'
print(('\n *** loose {} depth {} ***\n'.format(loose, depth)))
inverse_operator = make_inverse_operator(info,
forward,
noise_cov,
loose=loose,
depth=depth,
fixed=is_fixed)
# apply inverse operator to the time windows [t_start, t_stop]s
print('\n*** APPLY INV OP ***\n')
if is_epoched and events_id is not None:
events = mne.find_events(raw)
picks = mne.pick_types(info, meg=True, eog=True, exclude='bads')
reject = create_reject_dict(info)
if is_evoked:
epochs = mne.Epochs(raw,
events,
events_id,
t_min,
t_max,
picks=picks,
baseline=(None, 0),
reject=reject)
evoked = [epochs[k].average() for k in events_id]
snr = 3.0
lambda2 = 1.0 / snr**2
ev_list = list(events_id.items())
for k in range(len(events_id)):
stc = apply_inverse(evoked[k],
inverse_operator,
lambda2,
inv_method,
pick_ori=pick_ori)
print(('\n*** STC for event %s ***\n' % ev_list[k][0]))
stc_file = op.abspath(basename + '_' + ev_list[k][0])
print('***')
print(('stc dim ' + str(stc.shape)))
print('***')
if not aseg:
stc.save(stc_file)
else:
epochs = mne.Epochs(raw,
events,
events_id,
t_min,
t_max,
picks=picks,
baseline=(None, 0),
reject=reject)
stc = apply_inverse_epochs(epochs,
inverse_operator,
lambda2,
inv_method,
pick_ori=pick_ori)
print('***')
print(('len stc %d' % len(stc)))
print('***')
elif is_epoched and events_id is None:
stc = apply_inverse_epochs(epochs,
inverse_operator,
lambda2,
inv_method,
pick_ori=pick_ori)
print('***')
print(('len stc %d' % len(stc)))
print('***')
else:
stc = apply_inverse_raw(raw,
inverse_operator,
lambda2,
inv_method,
label=None,
start=None,
stop=None,
buffer_size=1000,
pick_ori=pick_ori) # None 'normal'
print('***')
print(('stc dim ' + str(stc.shape)))
print('***')
if not isinstance(stc, list):
stc = [stc]
if save_stc:
for i in range(len(stc)):
stc_file = op.abspath(basename + '_stc_' + str(i) + '.npy')
np.save(stc_file, stc[i].data)
# these coo are in MRI space and we have to convert to MNI space
labels_cortex = mne.read_labels_from_annot(sbj_id,
parc=parc,
subjects_dir=sbj_dir)
print(('\n*** %d ***\n' % len(labels_cortex)))
src = inverse_operator['src']
# allow_empty : bool -> Instead of emitting an error, return all-zero time
# courses for labels that do not have any vertices in the source estimate
if is_fixed:
mode = 'mean_flip'
else:
mode = 'mean'
label_ts = mne.extract_label_time_course(stc,
labels_cortex,
src,
mode=mode,
allow_empty=True,
return_generator=False)
# save results in .npy file that will be the input for spectral node
print('\n*** SAVE ROI TS ***\n')
print((len(label_ts)))
ts_file = op.abspath(basename + '_ROI_ts.npy')
np.save(ts_file, label_ts)
if aseg:
print(sbj_id)
labels_aseg = get_volume_labels_from_src(src, sbj_id, sbj_dir)
labels = labels_cortex + labels_aseg
else:
labels = labels_cortex
labels_aseg = None
print((labels[0].pos))
print((len(labels)))
# labels_file, label_names_file, label_coords_file = \
# create_label_files(labels)
labels_file, label_names_file, label_coords_file = \
create_mni_label_files(forward, labels_cortex, labels_aseg,
sbj_id, sbj_dir)
return ts_file, labels_file, label_names_file, label_coords_file
def compute_ROIs_inv_sol(raw_filename, sbj_id, sbj_dir, fwd_filename,
cov_fname, is_epoched=False, event_id=None,
t_min=None, t_max=None,
is_evoked=False, events_id=[],
snr=1.0, inv_method='MNE',
parc='aparc', aseg=False, aseg_labels=[],
is_blind=False, labels_removed=[], save_stc=False):
import os
import os.path as op
import numpy as np
import mne
import pickle
from mne.io import read_raw_fif
from mne import read_epochs
from mne.minimum_norm import make_inverse_operator, apply_inverse_raw
from mne.minimum_norm import apply_inverse_epochs, apply_inverse
from mne import get_volume_labels_from_src
from nipype.utils.filemanip import split_filename as split_f
from neuropype_ephy.preproc import create_reject_dict
try:
traits.undefined(event_id)
except NameError:
event_id = None
print '\n*** READ raw filename %s ***\n' % raw_filename
if is_epoched and event_id is None:
epochs = read_epochs(raw_filename)
info = epochs.info
else:
raw = read_raw_fif(raw_filename)
info = raw.info
subj_path, basename, ext = split_f(info['filename'])
print '\n*** READ noise covariance %s ***\n' % cov_fname
noise_cov = mne.read_cov(cov_fname)
print '\n*** READ FWD SOL %s ***\n' % fwd_filename
forward = mne.read_forward_solution(fwd_filename)
if not aseg:
forward = mne.convert_forward_solution(forward, surf_ori=True,
force_fixed=False)
lambda2 = 1.0 / snr ** 2
# compute inverse operator
print '\n*** COMPUTE INV OP ***\n'
if not aseg:
loose = 0.2
depth = 0.8
else:
loose = None
depth = None
inverse_operator = make_inverse_operator(info, forward, noise_cov,
loose=loose, depth=depth,
fixed=False)
# apply inverse operator to the time windows [t_start, t_stop]s
print '\n*** APPLY INV OP ***\n'
if is_epoched and event_id is not None:
events = mne.find_events(raw)
picks = mne.pick_types(info, meg=True, eog=True, exclude='bads')
reject = create_reject_dict(info)
if is_evoked:
epochs = mne.Epochs(raw, events, events_id, t_min, t_max,
picks=picks, baseline=(None, 0), reject=reject)
evoked = [epochs[k].average() for k in events_id]
snr = 3.0
lambda2 = 1.0 / snr ** 2
ev_list = events_id.items()
for k in range(len(events_id)):
stc = apply_inverse(evoked[k], inverse_operator, lambda2,
inv_method, pick_ori=None)
print '\n*** STC for event %s ***\n' % ev_list[k][0]
stc_file = op.abspath(basename + '_' + ev_list[k][0])
print '***'
print 'stc dim ' + str(stc.shape)
print '***'
if not aseg:
stc.save(stc_file)
else:
epochs = mne.Epochs(raw, events, event_id, t_min, t_max,
picks=picks, baseline=(None, 0), reject=reject)
stc = apply_inverse_epochs(epochs, inverse_operator, lambda2,
inv_method, pick_ori=None)
print '***'
print 'len stc %d' % len(stc)
print '***'
elif is_epoched and event_id is None:
stc = apply_inverse_epochs(epochs, inverse_operator, lambda2,
inv_method, pick_ori=None)
print '***'
print 'len stc %d' % len(stc)
print '***'
else:
stc = apply_inverse_raw(raw, inverse_operator, lambda2, inv_method,
label=None,
start=None, stop=None,
buffer_size=1000,
pick_ori=None) # None 'normal'
print '***'
print 'stc dim ' + str(stc.shape)
print '***'
if save_stc:
if aseg:
for i in range(len(stc)):
try:
os.mkdir(op.join(subj_path, 'TS'))
except OSError:
pass
stc_file = op.join(subj_path, 'TS', basename + '_' +
inv_method + '_stc_' + str(i) + '.npy')
if not op.isfile(stc_file):
np.save(stc_file, stc[i].data)
labels_cortex = mne.read_labels_from_annot(sbj_id, parc=parc,
subjects_dir=sbj_dir)
if is_blind:
for l in labels_cortex:
if l.name in labels_removed:
print l.name
labels_cortex.remove(l)
print '\n*** %d ***\n' % len(labels_cortex)
src = inverse_operator['src']
# allow_empty : bool -> Instead of emitting an error, return all-zero time
# courses for labels that do not have any vertices in the source estimate
label_ts = mne.extract_label_time_course(stc, labels_cortex, src,
mode='mean',
allow_empty=True,
return_generator=False)
# save results in .npy file that will be the input for spectral node
print '\n*** SAVE ROI TS ***\n'
print len(label_ts)
ts_file = op.abspath(basename + '_ROI_ts.npy')
np.save(ts_file, label_ts)
if aseg:
print sbj_id
labels_aseg = get_volume_labels_from_src(src, sbj_id, sbj_dir)
labels = labels_cortex + labels_aseg
else:
labels = labels_cortex
print labels[0].pos
print len(labels)
labels_file = op.abspath('labels.dat')
with open(labels_file, "wb") as f:
pickle.dump(len(labels), f)
for value in labels:
pickle.dump(value, f)
label_names_file = op.abspath('label_names.txt')
label_coords_file = op.abspath('label_coords.txt')
label_names = []
label_coords = []
for value in labels:
label_names.append(value.name)
# label_coords.append(value.pos[0])
label_coords.append(np.mean(value.pos, axis=0))
np.savetxt(label_names_file, np.array(label_names, dtype=str),
fmt="%s")
np.savetxt(label_coords_file, np.array(label_coords, dtype=float),
fmt="%f %f %f")
return ts_file, labels_file, label_names_file, label_coords_file
os.chdir(filedir + '/' + SubjID)
fwdfile = 'MixedSourceSpace_%s_3shell_forICA-fwd.fif' % SubjID
fwd = mne.read_forward_solution(fwdfile)
# convert to surface-based source orientation
fwd = mne.convert_forward_solution(fwd, surf_ori=True, force_fixed=False)
# preparation for subcortical sources
srcfile = [i for i in os.listdir(os.getcwd())
if '_forICA-oct-6-src.fif' in i][0]
srcspace = mne.read_source_spaces(srcfile)
nvert_insurf = srcspace[0]['nuse'] + srcspace[1]['nuse']
vertices = [srcspace[0]['vertno'], srcspace[1]['vertno']]
locVal = locals()
vollabels = mne.get_volume_labels_from_src(srcspace, MRIsubject, subjects_dir)
nVert = [len(i.vertices) for i in vollabels]
vertNo = np.cumsum(nVert)
# make inverse operators:
InvOperator = make_inverse_operator(rawdata.info,
forward=fwd,
noise_cov=NoiseCov,
loose='auto',
depth=0.8,
fixed=False,
limit_depth_chs=False,
verbose=None)
snr = 3.0
lambda2 = 1.0 / snr**2
roi_power = []
# load the epochs, and restrict them to the experimental trials
epo_all = mne.read_epochs("{}{}-epo.fif".format(proc_dir, meg))
epos = epo_all['negative', 'positive']
# load the forward model and labels
fwd = mne.read_forward_solution("{}{}-fwd.fif".format(proc_dir, meg))
labels = mne.read_labels_from_annot(mri,
parc='aparc',
hemi='both',
surf_name='white',
annot_fname=None,
regexp=None,
subjects_dir=mri_dir,
sort=False,
verbose=None)
labels_sub = mne.get_volume_labels_from_src(fwd['src'], mri, mri_dir)
labels_all = labels + labels_sub
# load filters for DICS beamformer
filters = mne.beamformer.read_beamformer('{}{}-dics.h5'.format(
proc_dir, meg))
filters_g = mne.beamformer.read_beamformer('{}{}-gamma-dics.h5'.format(
proc_dir, meg))
# create a file to save the values
filename = "{}{}_trial_roi_power.txt".format(proc_dir, meg)
with open(filename, "w") as file:
file.write("Tri_Ord\tEvent_ID\tTrig\tFreq")
for l in labels_all:
file.write("\t{}".format(l.name))
file.write("\n")
# now loop through the single epochs and calculate the power values
for i in range(len(epos)):
