def dip_source_loc(evoked, cov, fs_subj, study_path, plot=False):
evo_crop = evoked.copy().crop(-0.003, 0.003)
subj = fs_subj.strip('_an') if fs_subj.find('_an') > 0 else fs_subj
img_type = 'anony' if fs_subj.find('_an') > 0 else 'orig'
trans_fname = op.join(study_path, 'source_stim', subj, 'source_files', img_type, '%s_fid-trans.fif' % fs_subj)
bem_fname = op.join(subjects_dir, fs_subj, 'bem', '%s-bem-sol.fif' % fs_subj)
cond = evoked.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
dip, res = mne.fit_dipole(evo_crop, cov, bem_fname, trans_fname, min_dist=10, n_jobs=3)
import mne.transforms as tra
from scipy import linalg
trans = mne.read_trans(trans_fname)
stim_point = stim_coords['surf'] # get point for plot in mm
#dip.pos[np.argmax(dip.gof)] = tra.apply_trans(surf_to_head, stim_coords['surf_ori']/1e3) # check stim loc (apply affine in m)
dip_surf = tra.apply_trans(trans['trans'], dip.pos[np.argmax(dip.gof)]) * 1e3 # transform from head to surface
dist_surf = euclidean(dip_surf, stim_point) # compute distance in surface space
print(dist_surf)
if plot:
# Plot the result in 3D brain with the MRI image.
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
dip.plot_locations(trans_fname, fs_subj, subjects_dir, mode='orthoview', coord_frame='mri', ax=ax, show_all=True,
idx='gof')
ax.scatter(stim_point[0], stim_point[1], stim_point[2])
ax.plot([stim_point[0], -128], [stim_point[1], stim_point[1]], [stim_point[2], stim_point[2]], color='g')
ax.plot([stim_point[0], stim_point[0]], [stim_point[1], -128], [stim_point[2], stim_point[2]], color='g')
ax.plot([stim_point[0], stim_point[0]], [stim_point[1], stim_point[1]], [stim_point[2], -128], color='g')
ax.text2D(0.05, 0.90, 'distance: %i mm \nstim coords = %0.1f %0.1f %0.1f' % (dist_surf, stim_point[0], stim_point[1], stim_point[2]),
transform=ax.transAxes)
red_patch = mpatches.Patch(color='red')
green_patch = mpatches.Patch(color='green')
fig.legend(handles=[red_patch, green_patch], labels=['dipole', 'electrode'])
fig.savefig(op.join(study_path, 'source_stim', subj, 'figures', 'dipole', '%s_dip_15mm.png' % cond))
plt.close()
plot_dipole_amplitudes(dip)
plot_dipole_locations(dip, trans, subj, subjects_dir=subjects_dir)
return dist_surf
def single_trial_source(epo_alig):
from mne.minimum_norm import apply_inverse_epochs
evoked = epo_alig.average()
cond = evoked.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
evo_crop = evoked.copy().crop(-0.001, 0.001)
inv = mne.minimum_norm.make_inverse_operator(evoked.info, fwd, cov, loose=1, depth=None)
snr = 30.
lambda2 = 1. / snr ** 2
method = 'dSPM'
stcs = apply_inverse_epochs(epo_alig, inv, lambda2, method,
nave=evoked.nave)
mean_stc = sum(stcs) / len(stcs)
def source_loc(cond_fname, subj, fwd):
eeg_epo = mne.read_epochs(cond_fname + '-epo.fif')
cov = mne.compute_covariance(eeg_epo, method='shrunk', tmin=-0.5, tmax=-0.3)
inv = mne.minimum_norm.make_inverse_operator(eeg_epo.info, fwd, cov, loose=0.2, depth=0.8)
evoked = eeg_epo.average()
method = "dSPM"
snr = 3.
lambda2 = 1. / snr ** 2
stc = mne.minimum_norm.apply_inverse(evoked, inv, lambda2, method=method, pick_ori=None)
# from surfer import Brain # noqa
# src_fname = op.join(study_path, 'source_stim', 'images', subj, '%s-oct5-src.fif' % subj)
# src = mne.read_source_spaces(src_fname, patch_stats=True)
#
# brain = Brain(subj, 'lh', 'inflated', subjects_dir=subjects_dir)
# surf = brain.geo['lh']
#
# vertidx = np.where(src[0]['inuse'])[0]
#
# mlab.points3d(surf.x[vertidx[:-1]], surf.y[vertidx[:-1]],
# surf.z[vertidx[:-1]], color=(1, 1, 0), scale_factor=1.5)
# mlab.show()
cond = eeg_epo.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
vertno_max, time_max = stc.get_peak(hemi='lh')
stc_max = np.max(stc.data)
brain_inf = stc.plot(surface='inflated', hemi='lh', subjects_dir=subjects_dir,
clim=dict(kind='value', lims=[0, stc_max*0.75, stc_max]),
initial_time=time_max, time_unit='s', alpha=1, background='w', foreground='k')
brain_inf.add_foci(vertno_max, coords_as_verts=True, hemi='lh', color='blue',
scale_factor=0.8)
brain_inf.add_foci(stim_coords, map_surface='inflated', hemi='lh', color='green', scale_factor=0.8)
brain_inf.show_view('lateral') # mlab.view(130, 90)
fig_fname = op.join(study_path, 'source_stim', 'figures', '%s_inf_foci.eps' % cond)
brain_inf.save_image(fig_fname)
mlab.show()
def source_loc(evoked, cov, fwd, subj, img_type, study_path, plot=False):
cond = evoked.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
stim_params = get_stim_params(cond)
inv = mne.minimum_norm.make_inverse_operator(evoked.info,
fwd,
cov,
loose=0.2,
depth=0.8)
method = "dSPM"
snr = 300.
lambda2 = 1. / snr**2
stc = mne.minimum_norm.apply_inverse(evoked,
inv,
lambda2,
method=method,
pick_ori=None)
stc.save(
op.join(study_path, 'source_stim', subj, 'source_files', img_type,
'stc', '%s_%s' % (subj, cond)))
# plot_source_space(subj, study_path, subjects_dir)
hemi = 'lh' if stim_params['is_left'] else 'rh'
contact_nr = int(re.findall('\d+', stim_params['ch'])[0])
view = 'medial' if contact_nr < 5 else 'lateral' # improve view selection
vertno_max, time_max = stc.get_peak(hemi=hemi)
stc_max = np.max(stc.data)
if plot:
brain_inf = stc.plot(surface='inflated',
hemi=hemi,
subjects_dir=subjects_dir,
subject=fs_subj,
clim=dict(kind='value',
lims=[0, stc_max * 0.75, stc_max]),
initial_time=time_max,
time_unit='s',
alpha=1,
background='w',
foreground='k')
brain_inf.add_foci(vertno_max,
coords_as_verts=True,
hemi=hemi,
color='blue',
scale_factor=0.8)
brain_inf.add_foci(stim_coords['surf_ori'],
map_surface='pial',
hemi=hemi,
color='green',
scale_factor=0.8)
brain_inf.show_view(view) # mlab.view(130, 90)
fig_fname = op.join(study_path, 'source_stim', subj, 'figures',
'distributed', '%s_inf_foci.eps' % cond)
#brain_inf.save_image(fig_fname)
mlab.show()
#mlab.clf()
max_mni = mne.vertex_to_mni(
vertno_max, hemis=0, subject=subj, subjects_dir=subjects_dir
) # mni coords of vertex (ojo vertex resolution ico-5)
stim_mni = stim_coords['mni'] # mni coords of stimulation
dist_mni = euclidean(max_mni, stim_mni)
print(dist_mni)
return dist_mni
def dipole_source_loc(evoked, cov, fs_subj, study_path, plot=False):
evo_crop = evoked.copy().crop(-0.005, 0.005)
subj = fs_subj.strip('_an') if fs_subj.find('_an') > 0 else fs_subj
img_type = 'anony' if fs_subj.find('_an') > 0 else 'orig'
seeg_ch_info = pd.read_csv(
op.join(study_path, 'physio_data', subj, 'chan_info',
'%s_seeg_ch_info.csv' % subj))
# ori_to_an_fname = op.join(study_path, 'freesurfer_subjects', subj, 'mri', 'ori_to_an_trans.lta') # mri to mri
# ori_to_an_trans = np.genfromtxt(ori_to_an_fname, skip_header=8, skip_footer=18)
trans_fname = op.join(study_path, 'source_stim', subj, 'source_files',
img_type, '%s_static-trans.fif' % fs_subj)
bem_fname = op.join(subjects_dir, fs_subj, 'bem',
'%s-bem-sol.fif' % fs_subj)
cond = eeg_epo.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
# Fit a dipole
dip = mne.fit_dipole(evo_crop,
cov,
bem_fname,
trans_fname,
min_dist=5,
n_jobs=2)[0]
from mne.beamformer import rap_music
dip = rap_music(evo_crop,
fwd,
cov,
n_dipoles=1,
return_residual=True,
verbose=True)[0][0]
import mne.transforms as tra
from scipy import linalg
trans = mne.read_trans(trans_fname)
surf_to_head = linalg.inv(trans['trans'])
# stim_point = tra.apply_trans(surf_to_head, stim_coords['surf']/1e3)
stim_point = stim_coords['surf_ori'] # get point for plot in mm
#dip.pos[np.argmax(dip.gof)] = tra.apply_trans(surf_to_head, stim_coords['surf_ori']/1e3) # check stim loc (apply affine in m)
#stim_point = tra.apply_trans(ori_to_an_trans, stim_point)
dip_surf = tra.apply_trans(trans['trans'], dip.pos[np.argmax(
dip.gof)]) * 1e3 # transform from head to surface
dist_surf = euclidean(dip_surf,
stim_point) # compute distance in surface space
print(dist_surf)
if plot:
# Plot the result in 3D brain with the MRI image.
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
dip.plot_locations(trans_fname,
fs_subj,
subjects_dir,
mode='orthoview',
coord_frame='mri',
ax=ax,
show_all=True,
idx='gof')
ax.scatter(stim_point[0], stim_point[1], stim_point[2])
ax.plot([stim_point[0], -128], [stim_point[1], stim_point[1]],
[stim_point[2], stim_point[2]],
color='g')
ax.plot([stim_point[0], stim_point[0]], [stim_point[1], -128],
[stim_point[2], stim_point[2]],
color='g')
ax.plot([stim_point[0], stim_point[0]], [stim_point[1], stim_point[1]],
[stim_point[2], -128],
color='g')
ax.text2D(0.05,
0.90,
'distance: %i mm \nstim coords = %0.1f %0.1f %0.1f' %
(dist_surf, stim_point[0], stim_point[1], stim_point[2]),
transform=ax.transAxes)
red_patch = mpatches.Patch(color='red')
green_patch = mpatches.Patch(color='green')
fig.legend(handles=[red_patch, green_patch],
labels=['dipole', 'electrode'])
fig.savefig(
op.join(study_path, 'source_stim', subj, 'figures', 'dipole',
'%s_dip_15mm.png' % cond))
plt.close()
from mne.viz import plot_dipole_locations, plot_dipole_amplitudes
plot_dipole_amplitudes(dip)
plot_dipole_locations(dip, trans, subj, subjects_dir=subjects_dir)
return dist_surf
def vol_source_loc(evoked, fwd, cov, fs_subj, method='sLORETA', plot=False):
cond = evoked.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
evo_crop = evoked.copy().crop(-0.003, 0.003)
inv = mne.minimum_norm.make_inverse_operator(evoked.info, fwd, cov, loose=1, depth=0.8)
snr = 30.
lambda2 = 1. / snr ** 2
stc = mne.minimum_norm.apply_inverse(evo_crop, inv, lambda2, method=method, pick_ori=None)
from nilearn.plotting import plot_stat_map
from nilearn.image import index_img
import nilearn as ni
nii_fname = op.join(study_path, 'source_stim', subj, 'source_files', img_type, 'stc', '%s_%s_stc.nii' % (subj, cond))
img = mne.save_stc_as_volume(nii_fname, stc.copy().crop(-0.003, 0.003), fwd['src'], dest='mri', mri_resolution=True)
mri_file = op.join(subjects_dir, fs_subj, 'mri', 'T1.mgz')
from nibabel.affines import apply_affine
mri = nib.load(mri_file)
img_dat = img.get_data()
stim_loc_vox = np.unravel_index(img_dat.argmax(), img_dat.shape)
t_max = stim_loc_vox[-1]
img_max = index_img(img, t_max)
loc_coords = apply_affine(mri.affine, stim_loc_vox[:3])
dist = euclidean(stim_coords['scanner_RAS'], loc_coords)
max_coords = ni.plotting.find_xyz_cut_coords(img_max, activation_threshold=99.95)
print('Distance: %0.1f mm' % dist)
if plot:
thr = np.percentile(img_max.get_data(), 99.95)
st_map = plot_stat_map(img_max, mri_file, threshold=thr, display_mode='ortho', cmap=plt.cm.plasma,
cut_coords=max_coords)
img_thr = ni.image.threshold_img(img_max, '99%')
from scipy import linalg
inv_aff = linalg.inv(mri.affine)
stim_scan_coords = apply_affine(inv_aff, stim_loc_vox[:3])
img_thr_dat = img_thr.get_data()
dat = img_max.get_data()
plt.hist(dat[np.nonzero(dat)].flatten())
plot_stat_map(img_max, mri_file)
# img_smo = ni.image.smooth_img(index_img(img, t_max), fwhm=50)
# smo_dat = img_smo.get_data()
# stim_loc_vox_smo = np.unravel_index(smo_dat.argmax(), smo_dat.shape)
# loc_coords = apply_affine(mri.affine, stim_loc_vox_smo[:3])
# dist = euclidean(stim_coords['scanner_RAS'], loc_coords)
# print('Distance: %0.1f mm' % dist)
#
# thr = np.percentile(img_smo.get_data(), 99.99)
# st_map = plot_stat_map(img_smo, mri_file, threshold=thr, display_mode='ortho', cmap=plt.cm.plasma,
# cut_coords=loc_coords)
# fname_fig = op.join(study_path, 'source_stim', subj, 'figures', 'distributed', '%s_%s_vol.pdf' % (subj, cond))
# st_map.savefig(fname_fig)
# plt.close()
#
# stc_dspm = mne.minimum_norm.apply_inverse(evo_crop, inv, lambda2=lambda2,
# method='dSPM')
#
# Compute TF-MxNE inverse solution with dipole output
# from mne.inverse_sparse import tf_mixed_norm, make_stc_from_dipoles
# alpha_space = 50
# alpha_time = 0
# loose = 1
# depth = 0.2
# dipoles, residual = tf_mixed_norm(
# evo_crop, fwd, cov, alpha_space, alpha_time, loose=loose, depth=depth,
# maxit=200, tol=1e-6, weights=stc_dspm, weights_min=8., debias=True,
# wsize=16, tstep=4, window=0.05, return_as_dipoles=True,
# return_residual=True)
#
# stim_coords = find_stim_coords(cond, subj, study_path)
#
# import mne.transforms as tra
# trans_fname = op.join(study_path, 'source_stim', subj, 'source_files', img_type, '%s_fid-trans.fif' % fs_subj)
# trans = mne.read_trans(trans_fname)
#
# stim_point = stim_coords['surf'] # get point for plot in mm
# #dip.pos[np.argmax(dip.gof)] = tra.apply_trans(surf_to_head, stim_coords['surf_ori']/1e3) # check stim loc (apply affine in m)
#
# idx = np.argmax([np.max(np.abs(dip.amplitude)) for dip in dipoles])
# dip_surf = tra.apply_trans(trans['trans'], dipoles[idx].pos[0]) * 1e3 # transform from head to surface
# dist_surf = euclidean(dip_surf, stim_point) # compute distance in surface space
# print(dist_surf)
#
# plot_dipole_amplitudes(dipoles)
#
# # Plot dipole location of the strongest dipole with MRI slices
#
# plot_dipole_locations(dipoles[idx], fwd['mri_head_t'], subj,
# subjects_dir=subjects_dir, mode='orthoview',
# idx='amplitude')
return dist
fwd_fname = op.join(study_path, 'source_stim', subj, 'source_files', 'orig',
'%s-fwd.fif' % subj)
trans = op.join(study_path, 'source_stim', subj, 'source_files', 'orig', '%s_fid-trans.fif' % subj)
epo_path = op.join(study_path, 'source_stim', subj, 'epochs', 'fif')
conds = glob.glob(epo_path + '/*-epo.fif')
cond_fname = conds[0]
epochs = mne.read_epochs(cond_fname)
epochs.interpolate_bads(reset_bads=True)
epochs = epochs_fine_alignment(epochs)
cond = epochs.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
fwd = mne.read_forward_solution(fwd_fname)
fwd1 = mne.convert_forward_solution(fwd, force_fixed=True)
cov = mne.compute_covariance(epochs, method='shrunk', tmin=-0.5, tmax=-0.3) # use method='auto' for final computation
evoked = epochs.average()
info = evoked.info
from nibabel.freesurfer.io import read_geometry
fname_surf = op.join(subjects_dir, subj, 'surf', 'lh.pial')
surf = read_geometry(fname_surf)
def closest_nodes(node, all_nodes, used_nodes):