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gft2_src.py
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gft2_src.py
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"""
functions to create source space
@author: giuliano giari, giuliano.giari@gmail.com
"""
import glob
import matplotlib
import mne
import numpy as np
import os
from mne.source_estimate import _volume_labels, _prepare_label_extraction
# to avoid figures to pop up
matplotlib.use('Agg')
def make_src(sub_id, opt_local):
"""
Create source space and save diagnostic figures.
If these files are already present the corresponding lines will be skipped
"""
# define this subject filenames
src_fname = f"{opt_local['srcPath']}{sub_id}_{opt_local['src_%s_spacing'%opt_local['src_type']]}_{opt_local['src_type']}-src.fif"
bem_fname = f"{opt_local['srcPath']}{sub_id}_{opt_local['src_bem_spacing']}-bem.fif"
# create bem head model
if not os.path.exists(bem_fname):
mne.set_log_file(f"{opt_local['logPath']}{sub_id}/ses-anat/{sub_id}_ses-anat_log.log",
output_format='%(asctime)s | %(levelname)s | %(message)s', overwrite=False)
model = mne.make_bem_model(subject=sub_id, ico=opt_local['src_bem_spacing'], conductivity=(0.3,), # single shell
subjects_dir=opt_local['fsPath'])
bem = mne.make_bem_solution(model)
mne.write_bem_solution(bem_fname, bem)
# plot bem
fig = mne.viz.plot_bem(subject=sub_id, subjects_dir=opt_local['fsPath'], orientation='coronal',
brain_surfaces='white', show=False)
fig.savefig(f"{opt_local['logPath']}{sub_id}/ses-anat/{sub_id}_bem.png", dpi=300)
if not os.path.exists(src_fname):
# create volume source space
src = mne.setup_volume_source_space(subject=sub_id, pos=opt_local['src_vol_spacing'],
subjects_dir=opt_local['fsPath'], mri='T1.mgz', bem=bem_fname)
src.save(src_fname)
# plot source space
fig = mne.viz.plot_bem(subject=sub_id, subjects_dir=opt_local['fsPath'], orientation='coronal',
brain_surfaces='white', src=src, show=False)
fig.savefig(f"{opt_local['logPath']}{sub_id}/ses-anat/{sub_id}_{opt_local['src_type']}.png", dpi=300)
def make_morpher(sub_id, ses_id, opt_local, info=None):
"""
Compute and save source morph to fsaverage
"""
morph_fname = f"{opt_local['srcPath']}{sub_id}_ses-{ses_id}_{opt_local['src_type']}_{opt_local['src_smooth']}-morph.h5"
if not os.path.exists(morph_fname):
mne.set_log_file(f"{opt_local['logPath']}{sub_id}/ses-anat/{sub_id}_ses-anat_log.log",
output_format='%(asctime)s | %(levelname)s | %(message)s', overwrite=False)
# load data
if info is None:
info = mne.io.read_info(glob.glob(f"{opt_local['epoPath']}{sub_id}_ses-{ses_id}_task-task_epo.fif.gz")[0])
fwd_ = make_forward(sub_id, ses_id, opt_local, info)
src_fs = mne.read_source_spaces(f"{opt_local['srcPath']}sub-fsaverage_"
f"{opt_local['src_%s_spacing' % opt_local['src_type']]}_{opt_local['src_type']}-src.fif")
morpher_ = mne.compute_source_morph(fwd_['src'], subject_from=sub_id, subject_to='sub-fsaverage',
subjects_dir=opt_local['fsPath'], smooth=opt_local['src_smooth'],
src_to=src_fs)
morpher_.save(morph_fname, overwrite=True)
else:
morpher_ = mne.read_source_morph(morph_fname)
return morpher_
def make_forward(sub_id, ses_id, opt_local, info=None):
"""
Compute and save forward solution
"""
fwd_fname = f"{opt_local['srcPath']}{sub_id}_ses-{ses_id}_{opt_local['src_%s_spacing' % opt_local['src_type']]}_{opt_local['src_type']}-fwd.fif"
if os.path.exists(fwd_fname):
fwd_ = mne.read_forward_solution(fwd_fname) # is possible to select only few channels https://mne.tools/stable/generated/mne.read_forward_solution.html
else:
mne.set_log_file(f"{opt_local['logPath']}{sub_id}/ses-anat/{sub_id}_ses-anat_log.log",
output_format='%(asctime)s | %(levelname)s | %(message)s', overwrite=False)
if info is None:
info = mne.io.read_info(f"{opt_local['epoPath']}{sub_id}_ses-{ses_id}_task-task_epo.fif.gz")
src_fname = f"{opt_local['srcPath']}{sub_id}_{opt_local['src_%s_spacing' % opt_local['src_type']]}_{opt_local['src_type']}-src.fif"
bem_fname = f"{opt_local['srcPath']}{sub_id}_{opt_local['src_bem_spacing']}-bem.fif"
trans_fname = f"{opt_local['srcPath']}{sub_id}_ses-{ses_id}_trans.fif"
fwd_ = mne.make_forward_solution(info, trans_fname, src_fname, bem_fname, meg=True)
mne.write_forward_solution(fwd_fname, fwd_)
# select only one sensor type, if necessary
if opt_local['stc_ch_type'] != 'meg':
fwd_ = mne.pick_types_forward(fwd_, meg=opt_local['stc_ch_type'])
return fwd_
def copy_fsaverage(opt_local):
""" Copies fsaverage folder to the project folder and creates the relevant surfaces """
# copy its folder if its not there
if not os.path.exists(f"{opt_local['fsPath']}sub-fsaverage"):
mne.coreg.create_default_subject(fs_home=None, update=False, subjects_dir=opt_local['fsPath'],
verbose=None)
# run the watershed algorhythm
os.system(f"mne watershed_bem -s sub-fsaverage -d {opt_local['fsPath']} --overwrite")
# move files where mne expects them
files_list = glob.glob(f"{opt_local['fsPath']}sub-fsaverage/bem/watershed/*")
for fname in files_list:
# extract the surface name
surf_name = fname.split('fsaverage_')[-1].split('_surface')[0]
# remove the symbolic link to file to avoid errors
os.system(f"rm -f {opt_local['fsPath']}/sub-fsaverage/bem/{surf_name}.surf")
# copy and rename the original file
os.system(f"cp -rf {fname} {opt_local['fsPath']}/sub-fsaverage/bem/{surf_name}.surf")
# create the source space
os.mkdir(f"{opt_local['logPath']}sub-fsaverage/")
os.mkdir(f"{opt_local['logPath']}sub-fsaverage/ses-anat/")
make_src('fsaverage', opt_local)
def make_cortical_mask(opt_local):
roi_list = ['Left-Cerebellum-Exterior', 'Left-Cerebellum-White-Matter', 'Left-Cerebellum-Cortex',
'Right-Cerebellum-Exterior', 'Right-Cerebellum-White-Matter', 'Right-Cerebellum-Cortex',
'Brain-Stem', '4th-Ventricle']
# get the labels from the freesurfer parcellation
mri_fname = f"{opt_local['fsPath']}sub-fsaverage/mri/aparc+aseg.mgz"
# https://github.com/mne-tools/mne-python/blob/79ea57a4318d8d045f5966c26360b079f40a4865/mne/source_estimate.py#L3077
src_fs = mne.read_source_spaces(f"{opt_local['srcPath']}sub-fsaverage_"
f"{opt_local['src_%s_spacing' % opt_local['src_type']]}_{opt_local['src_type']}-src.fif")
labels = _volume_labels(src_fs, mri_fname, mri_resolution=True)
# get the corresponding indices in the source space
label_vertidx, _ = _prepare_label_extraction(
None, labels, src_fs, mode='mean', allow_empty=True, use_sparse=True)
ind = np.zeros_like(src_fs[0]['vertno'], dtype=bool)
for i, l in enumerate(labels):
if l['name'] in roi_list:
ind[label_vertidx[i].nonzero()[1]] = True
return ind