def task_dipole():
"""Step 5: Dipole source estimate (golden standard)"""
for subject in subjects:
yield dict(
name=str(subject),
file_dep=[
fname.epochs(subject=subject),
fname.fwd(subject=subject), '05_dipole.py'
],
targets=[fname.ecd(subject=subject)],
actions=[f'ipython 05_dipole.py {subject:d}'],
)
def task_dics():
"""Step 7: DICS source estimate"""
for subject in subjects:
yield dict(
name=str(subject),
file_dep=[
fname.epochs_long(subject=subject),
fname.fwd(subject=subject),
fname.ecd(subject=subject), '07_dics.py'
],
targets=[
fname.stc_dics(subject=subject),
fname.nii_dics(subject=subject)
],
actions=[f'ipython 07_dics.py {subject:d}'],
)
def task_lcmv():
"""Step 6: LCMV source estimate"""
for subject in subjects:
yield dict(
name=str(subject),
file_dep=[
fname.epochs(subject=subject),
fname.fwd(subject=subject),
fname.ecd(subject=subject), '06_lcmv.py'
],
targets=[
fname.stc_lcmv(subject=subject),
fname.nii_lcmv(subject=subject)
],
actions=[f'ipython 06_lcmv.py {subject:d}'],
)
def task_mne():
"""Step 8: MNE source estimate"""
for subject in subjects:
yield dict(
name=str(subject),
file_dep=[
fname.epochs(subject=subject),
fname.fwd(subject=subject),
fname.ecd(subject=subject), '08_mne.py'
],
targets=[
fname.stc_mne(subject=subject),
fname.nii_mne(subject=subject)
],
actions=[f'ipython 08_mne.py {subject:d}'],
)
import numpy as np
from config import fname
# Handle command line arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('subject', metavar='sub###', type=int, help='The subject to process')
args = parser.parse_args()
subject = args.subject
print('Processing subject:', subject)
epochs = mne.read_epochs(fname.epochs(subject=subject))
noise_cov = mne.compute_covariance(epochs, tmin=-0.2, tmax=0, method='shrunk')
fwd = mne.read_forward_solution(fname.fwd(subject=subject))
inv = mne.minimum_norm.make_inverse_operator(epochs.info, fwd, noise_cov, depth=None)
stc = mne.minimum_norm.apply_inverse(epochs.average(), inv)
stc.save(fname.stc_mne(subject=subject))
# Find the time point that corresponds to the best dipole fit
dip = mne.read_dipole(fname.ecd(subject=subject))
peak_time = dip[int(np.argmax(dip.gof))].times[0]
stc_peak = abs(stc.copy().crop(peak_time, peak_time).mean())
stc_peak.save(fname.stc_mne(subject=subject))
stc_peak.save_as_volume(fname.nii_mne(subject=subject), src=fwd['src'])
fig = stc.plot(initial_time=peak_time, subject=fname.subject_id(subject=subject), subjects_dir=fname.subjects_dir, src=fwd['src'],
clim=dict(kind='percent', lims=[99.9, 99.95, 100]))
with mne.open_report(fname.report(subject=subject)) as report:
report.add_figs_to_section(fig, f'MNE Source estimate at {peak_time}', 'Source level', replace=True)
report.save(fname.report_html(subject=subject), overwrite=True, open_browser=False)
# Find the slope of the onset
evoked = epochs.average().crop(0.03, 0.05)
_, mag_peak = evoked.get_peak('mag')
_, grad_peak = evoked.get_peak('grad')
peak_time = (mag_peak + grad_peak) / 2
evoked = epochs.average().crop(peak_time - 0.005, peak_time + 0.005)
print(evoked)
dip, res = mne.fit_dipole(evoked,
noise_cov,
bem,
trans,
n_jobs=n_jobs,
verbose=True)
dip = dip[int(np.argmax(dip.gof))]
dip.save(fname.ecd(subject=subject), overwrite=True)
# Plot the result in 3D brain with the MRI image using Nilearn
mri_pos = mne.head_to_mri(dip.pos,
mri_head_t=trans,
subject=fname.subject_id(subject=subject),
subjects_dir=fname.subjects_dir)
t1_fname = op.join(fname.subjects_dir, fname.subject_id(subject=subject),
'mri', 'T1.mgz')
fig = plt.figure()
plot_anat(t1_fname,
cut_coords=mri_pos[np.argmax(dip.gof)],
title='Dipole loc.',
figure=fig)
with mne.open_report(fname.report(subject=subject)) as report:
report.add_figs_to_section(fig,