def task_select_vertices():
"""Step 08: Select the vertices for which to do the analyses."""
fwd_fnames = [fname.fwd(subject=subject) for subject in subjects]
fwd_r_fnames = [fname.fwd_r(subject=subject) for subject in subjects]
src_fnames = [fname.src(subject=subject) for subject in subjects]
pairs_fname = fname.pairs
return dict(file_dep=['08_select_vertices.py'] + fwd_fnames + src_fnames,
targets=fwd_r_fnames + [pairs_fname],
actions=['python 08_select_vertices.py'])
def task_figures():
"""Make all figures. Each figure is a sub-task."""
# Make figure 1: plot of the CSD matrices.
yield dict(
name='csd',
task_dep=['connectivity_stats'],
file_dep=[fname.epo(subject=subjects[0]),
fname.csd(subject=subjects[0], condition='face')],
targets=['../paper/figures/csd.pdf'],
actions=['python figure_csd.py'],
)
# Make figure 2: plot of the source space and forward model.
yield dict(
name='forward',
file_dep=[fname.fwd(subject=subjects[0]),
fname.fwd_r(subject=subjects[0]),
fname.trans(subject=subjects[0])],
targets=['../paper/figures/forward1.png',
'../paper/figures/forward2.png'],
actions=['python figure_forward.py'],
)
# Make figure 3: grand average power maps.
file_dep = [fname.ga_power_hemi(condition=cond, hemi='lh') for cond in conditions]
file_dep += [fname.ga_power_hemi(condition=cond, hemi='rh') for cond in conditions]
targets = ['../paper/figures/power_face_lh.png',
'../paper/figures/power_face_rh.png',
'../paper/figures/power_scrambled_lh.png',
'../paper/figures/power_scrambled_rh.png']
targets += ['../paper/figures/power_contrast_%s-%s-lh.png' % (freq[0], freq[1]) for freq in freq_bands]
yield dict(
name='power',
file_dep=file_dep,
targets=targets,
actions=['python figure_power.py'],
)
# Make figure 4: plot of the functional connectivity.
yield dict(
name='connectivity',
file_dep=[fname.ga_con(condition='pruned'),
fname.ga_con(condition='parcelled')],
targets=['../paper/figures/degree_lh.png',
'../paper/figures/degree_rh.png',
'../paper/figures/squircle.pdf'],
actions=['python figure_connectivity.py'],
)
def task_connectivity():
"""Step 10: Compute DICS connectivity."""
for subject in subjects:
fwd_r_fname = fname.fwd_r(subject=subject)
csd_fnames = []
con_fnames = []
for cond in conditions:
csd_fnames.append(fname.csd(subject=subject, condition=cond))
con_fnames.append(fname.con(subject=subject, condition=cond))
yield dict(
name=subject,
task_dep=['power'],
file_dep=[fwd_r_fname, fname.pairs, '10_connectivity.py'] + csd_fnames,
targets=con_fnames,
actions=['python 10_connectivity.py %s' % subject],
)
def task_power():
"""Step 09: Compute DICS power maps."""
for subject in subjects:
fwd_r_fname = fname.fwd_r(subject=subject)
csd_fnames = []
stc_fnames = []
for cond in conditions + ['baseline']:
csd_fnames.append(fname.csd(subject=subject, condition=cond))
stc_fnames.append(fname.power_hemi(subject=subject, condition=cond, hemi='lh'))
stc_fnames.append(fname.power_hemi(subject=subject, condition=cond, hemi='rh'))
yield dict(
name=subject,
task_dep=['select_vertices'],
file_dep=[fwd_r_fname, '09_power.py'] + csd_fnames,
targets=stc_fnames,
actions=['python 09_power.py %s' % subject],
)
from mayavi import mlab
import mne
from mne.bem import _fit_sphere
import conpy
from config import fname, subjects
mne.set_log_level('ERROR')
fwd = mne.read_forward_solution(fname.fwd(subject=subjects[0]))
fwd_r = mne.read_forward_solution(fname.fwd_r(subject=subjects[0]))
trans = fname.trans(subject=subjects[0])
fig1 = mne.viz.plot_alignment(fwd['info'],
trans=trans,
src=fwd_r['src'],
meg='sensors',
surfaces='white')
fig1.scene.background = (1, 1, 1) # white
fig1.children[-1].children[0].children[0].glyph.glyph.scale_factor = 0.008
mlab.view(135, 120, 0.3, [0.01, 0.015, 0.058])
mlab.savefig('../paper/figures/forward1.png', magnification=4, figure=fig1)
radius, center = _fit_sphere(fwd_r['source_rr'])
rad, tan1, tan2 = conpy.forward._make_radial_coord_system(
fwd_r['source_rr'], center)
fig2 = conpy.forward._plot_coord_system(fwd_r['source_rr'],
rad,
tan1,
tan2,
scale=0.003,
fwds.append(mne.read_forward_solution(fname.fwd(subject=subject)))
# Compute the vertices that are shared across the forward operators for all
# subjects. The first one we restricted ourselves, the other ones may have
# dropped vertices which were too close to the inner skull surface. We use the
# fsaverage brain as a reference to determine corresponding vertices across
# subjects.
fsaverage = mne.read_source_spaces(fname.fsaverage_src)
vert_inds = conpy.select_shared_vertices(fwds,
ref_src=fsaverage,
subjects_dir=fname.subjects_dir)
# Restrict all forward operators to the same vertices and save them.
for fwd, vert_ind, subject in zip(fwds, vert_inds, subjects):
fwd_r = conpy.restrict_forward_to_vertices(fwd, vert_ind)
mne.write_forward_solution(fname.fwd_r(subject=subject),
fwd_r,
overwrite=True)
# Update the forward operator of the first subject
if subject == subjects[0]:
fwd1 = fwd_r
# Save a visualization of the restricted forward operator to the subject's
# HTML report
with mne.open_report(fname.report(subject=subject)) as report:
fig = mne.viz.plot_alignment(fwd['info'],
trans=fname.trans(subject=subject),
src=fwd_r['src'],
meg='sensors',
surfaces='white')