def morph_labels_from_fsaverage(subject,
subjects_dir,
mmvt_dir,
aparc_name='aparc250',
fs_labels_fol='',
sub_labels_fol='',
n_jobs=6,
fsaverage='fsaverage',
overwrite=False):
fsaverage = find_template_brain_with_annot_file(aparc_name, fsaverage,
subjects_dir)
if fsaverage == '':
return False
if subject == fsaverage:
return True
subject_dir = op.join(subjects_dir, subject)
labels_fol = op.join(subjects_dir, fsaverage, 'label',
aparc_name) if fs_labels_fol == '' else fs_labels_fol
sub_labels_fol = op.join(
subject_dir, 'label',
aparc_name) if sub_labels_fol == '' else sub_labels_fol
if not op.isdir(sub_labels_fol):
os.makedirs(sub_labels_fol)
labels = read_labels(fsaverage, subjects_dir, aparc_name, n_jobs=n_jobs)
verts = utils.load_surf(subject, mmvt_dir, subjects_dir)
# Make sure we have a morph map, and if not, create it here, and not in the parallel function
mne.surface.read_morph_map(subject, fsaverage, subjects_dir=subjects_dir)
indices = np.array_split(np.arange(len(labels)), n_jobs)
chunks = [([labels[ind] for ind in chunk_indices], subject, fsaverage,
labels_fol, sub_labels_fol, verts, subjects_dir, overwrite)
for chunk_indices in indices]
results = utils.run_parallel(_morph_labels_parallel, chunks, n_jobs)
return all(results)
def save_labels_from_vertices_lookup(subject,
atlas,
subjects_dir,
mmvt_dir,
surf_type='pial',
read_labels_from_fol='',
overwrite_vertices_labels_lookup=False,
n_jobs=6):
lookup = create_vertices_labels_lookup(
subject,
atlas,
read_labels_from_fol=read_labels_from_fol,
overwrite=overwrite_vertices_labels_lookup)
labels_fol = op.join(subjects_dir, subject, 'label', atlas)
surf = utils.load_surf(subject, mmvt_dir, subjects_dir)
utils.delete_folder_files(labels_fol)
ok = True
for hemi in utils.HEMIS:
labels_vertices = defaultdict(list)
# surf_fname = op.join(subjects_dir, subject, 'surf', '{}.{}'.format(hemi, surf_type))
# surf, _ = mne.surface.read_surface(surf_fname)
for vertice, label in lookup[hemi].items():
labels_vertices[label].append(vertice)
chunks_indices = np.array_split(np.arange(len(labels_vertices)),
n_jobs)
labels_vertices_items = list(labels_vertices.items())
chunks = [([labels_vertices_items[ind] for ind in chunk_indices],
subject, labels_vertices, surf, hemi, labels_fol)
for chunk_indices in chunks_indices]
results = utils.run_parallel(_save_labels_from_vertices_lookup_hemi,
chunks, n_jobs)
ok = ok and all(results)
return ok
def get_t1_vertices_data(subject):
trans_fname = op.join(MMVT_DIR, subject, 't1_trans.npz')
trans_dict = utils.Bag(np.load(trans_fname))
ras_tkr2vox = np.linalg.inv(trans_dict.vox2ras_tkr)
pial_verts = utils.load_surf(subject, MMVT_DIR, SUBJECTS_DIR)
t1_data, t1_header = anat.get_data_and_header(subject, 'brainmask.mgz')
for hemi in utils.HEMIS:
output_fname = op.join(MMVT_DIR, subject, 'surf', 'T1-{}.npy'.format(hemi))
verts = pial_verts[hemi]
t1_surf_hemi = np.zeros((len(verts)))
hemi_pial_voxels = np.rint(utils.apply_trans(ras_tkr2vox, verts)).astype(int)
for vert_ind, t1_vox in zip(range(len(verts)), hemi_pial_voxels):
t1_surf_hemi[vert_ind] = t1_data[tuple(t1_vox)]
np.save(output_fname, t1_surf_hemi)
def project_cbf_on_cortex(subject,
site,
scan_rescan,
overwrite=False,
print_only=False):
mmvt_subject = subject if scan_rescan == SCAN else '{}_rescan'.format(
subject)
fmri_fol = utils.make_dir(op.join(FMRI_DIR, mmvt_subject))
mmvt_cbf_fname = op.join(fmri_fol, 'CBF_{}.nii'.format(scan_rescan))
cics_cbf_fname = op.join(HOME_FOL, site, subject, scan_rescan, 'CBF.nii')
reg_file = op.join(HOME_FOL, site, subject, scan_rescan,
'control_to_T1.lta')
if not op.islink(mmvt_cbf_fname) and op.isfile(cics_cbf_fname):
utils.make_link(cics_cbf_fname, mmvt_cbf_fname)
if not op.islink(mmvt_cbf_fname):
print('Cannot file the link to CBF! ({}-{})'.format(
cics_cbf_fname, mmvt_cbf_fname))
return False
if not op.isfile(reg_file):
print('Cannot find the registration file! ({})'.format(reg_file))
return False
verts = utils.load_surf(subject, MMVT_DIR, SUBJECTS_DIR)
for hemi in utils.HEMIS:
surf_output_fname = op.join(FMRI_DIR, subject,
'CBF_{}_{}.mgz'.format(scan_rescan, hemi))
npy_output_fname = op.join(
MMVT_DIR, subject, 'fmri',
'fmri_CBF_{}_{}.npy'.format(scan_rescan, hemi))
if op.isfile(npy_output_fname) and not overwrite:
continue
fu.project_volume_data(cics_cbf_fname,
hemi,
reg_file=reg_file,
smooth_fwhm=0,
output_fname=surf_output_fname,
print_only=print_only)
if print_only:
continue
surf_data = np.squeeze(nib.load(surf_output_fname).get_data())
np.save(npy_output_fname, surf_data)
if len(verts[hemi]) != len(surf_data):
print(
'*** Wrong number of vertices in {} data! surf vertices ({}) != data vertices ({})'
.format(hemi, len(verts[hemi]), len(surf_data)))
def check_labels(subject, atlas, subjects_dir, mmvt_dir):
labels = read_labels(subject, subjects_dir, atlas)
verts = utils.load_surf(subject, mmvt_dir, subjects_dir)
verts = {hemi: range(len(verts[hemi])) for hemi in utils.HEMIS}
ok = True
for hemi in utils.HEMIS:
labels_indices = []
for l in labels:
if l.hemi != hemi:
continue
labels_indices.extend(l.vertices.tolist())
labels_indices = set(labels_indices)
print('{}: labels vertices len: {}, verts len: {}'.format(
hemi, len(labels_indices), len(verts[hemi])))
for label in labels:
if not all(np.in1d(label.vertices, verts[label.hemi])):
print('Not all {} vertices are in {} verts!'.format(
label.name, label.hemi))
ok = False
return ok