def test_indices():
"""Test connectivity indexing methods"""
n_seeds_test = [1, 3, 4]
n_targets_test = [2, 3, 200]
for n_seeds in n_seeds_test:
for n_targets in n_targets_test:
idx = np.random.permutation(np.arange(n_seeds + n_targets))
seeds = idx[:n_seeds]
targets = idx[n_seeds:]
indices = seed_target_indices(seeds, targets)
assert_true(len(indices) == 2)
assert_true(len(indices[0]) == len(indices[1]))
assert_true(len(indices[0]) == n_seeds * n_targets)
for seed in seeds:
assert_true(np.sum(indices[0] == seed) == n_targets)
for target in targets:
assert_true(np.sum(indices[1] == target) == n_seeds)
inverse_operator,
lambda2,
method,
pick_ori="normal")
# Restrict the source estimate to the label in the left auditory cortex.
stc_label = stc.in_label(label_lh)
# Find number and index of vertex with most power.
src_pow = np.sum(stc_label.data**2, axis=1)
seed_vertno = stc_label.vertices[0][np.argmax(src_pow)]
seed_idx = np.searchsorted(stc.vertices[0], seed_vertno) # index in orig stc
# Generate index parameter for seed-based connectivity analysis.
n_sources = stc.data.shape[0]
indices = seed_target_indices([seed_idx], np.arange(n_sources))
###############################################################################
# Compute the inverse solution for each epoch. By using "return_generator=True"
# stcs will be a generator object instead of a list. This allows us so to
# compute the coherence without having to keep all source estimates in memory.
snr = 1.0 # use lower SNR for single epochs
lambda2 = 1.0 / snr**2
stcs = apply_inverse_epochs(epochs,
inverse_operator,
lambda2,
method,
pick_ori="normal",
return_generator=True)
label=v1_label)
stcs_fast_v4 = apply_inverse_epochs(allepochs,
inverse_operator,
lambda2,
method='sLORETA',
pick_ori="normal",
label=v4_label)
# Find index of vertices of interest in original stc
idx_v1 = np.searchsorted(stcs_fast[0].vertices[1],
stcs_fast_v1[0].vertices[1])
idx_v4 = np.searchsorted(stcs_fast[0].vertices[1],
stcs_fast_v4[0].vertices[1])
# Construct indices to estimate connectivity between the label time course
indices = seed_target_indices([idx_v1], [idx_v4])
# Compute the WPLI2_debiased in the frequency range
fmin = 2.
fmax = 40.
sfreq = allepochs.info['sfreq'] # the sampling frequency
wpli2, freqs, times, n_epochs, n_tapers = spectral_connectivity(
stcs_fast,
method='wpli2_debiased',
sfreq=sfreq,
indices=indices,
mode='fourier',
fmin=fmin,
fmax=fmax,
n_jobs=1)
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=dict(grad=4000e-13, eog=150e-6),
preload=True)
# Use 'MEG 2343' as seed
seed_ch = 'MEG 2343'
picks_ch_names = [raw.ch_names[i] for i in picks]
# Create seed-target indices for connectivity computation
seed = picks_ch_names.index(seed_ch)
targets = np.arange(len(picks))
indices = seed_target_indices(seed, targets)
# Define wavelet frequencies and number of cycles
cwt_freqs = np.arange(7, 30, 2)
cwt_n_cycles = cwt_freqs / 7.
# Run the connectivity analysis using 2 parallel jobs
sfreq = raw.info['sfreq'] # the sampling frequency
con, freqs, times, _, _ = spectral_connectivity(epochs,
indices=indices,
method='wpli2_debiased',
mode='cwt_morlet',
sfreq=sfreq,
cwt_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles,
n_jobs=1)
exclude='bads')
# Create epochs for left-visual condition
event_id, tmin, tmax = 3, -0.2, 0.5
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),
preload=True)
# Use 'MEG 2343' as seed
seed_ch = 'MEG 2343'
picks_ch_names = [raw.ch_names[i] for i in picks]
# Create seed-target indices for connectivity computation
seed = picks_ch_names.index(seed_ch)
targets = np.arange(len(picks))
indices = seed_target_indices(seed, targets)
# Define wavelet frequencies and number of cycles
cwt_freqs = np.arange(7, 30, 2)
cwt_n_cycles = cwt_freqs / 7.
# Run the connectivity analysis using 2 parallel jobs
sfreq = raw.info['sfreq'] # the sampling frequency
con, freqs, times, _, _ = spectral_connectivity(
epochs, indices=indices,
method='wpli2_debiased', mode='cwt_morlet', sfreq=sfreq,
cwt_freqs=cwt_freqs, cwt_n_cycles=cwt_n_cycles, n_jobs=1)
# Mark the seed channel with a value of 1.0, so we can see it in the plot
con[np.where(indices[1] == seed)] = 1.0
verbose='error')
# Combine the seed time course with the source estimates. There will be a total
# of 7500 signals:
# index 0: time course extracted from label
# index 1..7499: dSPM source space time courses
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
pick_ori="normal", return_generator=True)
comb_ts = list(zip(seed_ts, stcs))
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices = [src[i]['vertno'] for i in range(2)]
n_signals_tot = 1 + len(vertices[0]) + len(vertices[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
# Compute the PSI in the frequency range 8Hz..30Hz. We exclude the baseline
# period from the connectivity estimation
fmin = 8.
fmax = 30.
tmin_con = 0.
sfreq = raw.info['sfreq'] # the sampling frequency
psi, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts, mode='multitaper', indices=indices, sfreq=sfreq,
fmin=fmin, fmax=fmax, tmin=tmin_con)
# Generate a SourceEstimate with the PSI. This is simple since we used a single
# seed (inspect the indices variable to see how the PSI scores are arranged in
# the output)
verbose='error')
# Combine the seed time course with the source estimates. There will be a total
# of 7500 signals:
# index 0: time course extracted from label
# index 1..7499: dSPM source space time courses
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
pick_ori="normal", return_generator=True)
comb_ts = list(zip(seed_ts, stcs))
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices = [src[i]['vertno'] for i in range(2)]
n_signals_tot = 1 + len(vertices[0]) + len(vertices[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
# Compute the PSI in the frequency range 10Hz-20Hz. We exclude the baseline
# period from the connectivity estimation.
fmin = 10.
fmax = 20.
tmin_con = 0.
sfreq = epochs.info['sfreq'] # the sampling frequency
psi, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts, mode='multitaper', indices=indices, sfreq=sfreq,
fmin=fmin, fmax=fmax, tmin=tmin_con)
# Generate a SourceEstimate with the PSI. This is simple since we used a single
# seed (inspect the indices variable to see how the PSI scores are arranged in
# the output)
lambda2 = 1.0 / snr ** 2
evoked = epochs.average()
stc = apply_inverse(evoked, inverse_operator, lambda2, method,
pick_ori="normal")
# Restrict the source estimate to the label in the left auditory cortex
stc_label = stc.in_label(label_lh)
# Find number and index of vertex with most power
src_pow = np.sum(stc_label.data ** 2, axis=1)
seed_vertno = stc_label.vertno[0][np.argmax(src_pow)]
seed_idx = np.searchsorted(stc.vertno[0], seed_vertno) # index in original stc
# Generate index parameter for seed-based connectivity analysis
n_sources = stc.data.shape[0]
indices = seed_target_indices([seed_idx], np.arange(n_sources))
# Compute inverse solution and for each epoch. By using "return_generator=True"
# stcs will be a generator object instead of a list. This allows us so to
# compute the coherence without having to keep all source estimates in memory.
snr = 1.0 # use lower SNR for single epochs
lambda2 = 1.0 / snr ** 2
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
pick_ori="normal", return_generator=True)
# Now we are ready to compute the coherence in the alpha and beta band.
# fmin and fmax specify the lower and upper freq. for each band, resp.
fmin = (8., 13.)
fmax = (13., 30.)
sfreq = raw.info['sfreq'] # the sampling frequency
def SN_functional_connectivity_bands(i, method):
s = time.time()
meg = subjects[i]
sub_to = MRI_sub[i][1:15]
stc_SD_file_name = os.path.expanduser(
'~'
) + '/my_semnet/json_files/connectivity/stc_' + method + '200_equalized_bands_SD_sub' + str(
i) + '.json'
stc_LD_file_name = os.path.expanduser(
'~'
) + '/my_semnet/json_files/connectivity/stc_' + method + '200_equalized_bands_LD_sub' + str(
i) + '.json'
# stc_SD_file_name=os.path.expanduser('~') +'/my_semnet/json_files/connectivity/stc_'+method+'bl_bands_SD_sub'+str(i)+'.json'
# stc_LD_file_name=os.path.expanduser('~') +'/my_semnet/json_files/connectivity/stc_'+method+'bl_bands_LD_sub'+str(i)+'.json'
morphed_labels = mne.morph_labels(SN_ROI,subject_to=data_path+sub_to,\
subject_from='fsaverage',subjects_dir=data_path)
# Reading epochs
epo_name_SD = data_path + meg + 'block_SD_words_epochs-epo.fif'
epo_name_LD = data_path + meg + 'block_LD_words_epochs-epo.fif'
epochs_sd = mne.read_epochs(epo_name_SD, preload=True)
epochs_ld = mne.read_epochs(epo_name_LD, preload=True)
epochs_SD = epochs_sd['words'].copy().resample(500)
epochs_LD = epochs_ld['words'].copy().resample(500)
equalize_epoch_counts([epochs_SD, epochs_LD])
# Reading inverse operator
inv_fname_SD = data_path + meg + 'InvOp_SD_EMEG-inv.fif'
inv_fname_LD = data_path + meg + 'InvOp_LD_EMEG-inv.fif'
inv_op_SD = read_inverse_operator(inv_fname_SD)
inv_op_LD = read_inverse_operator(inv_fname_LD)
stc_sd = apply_inverse_epochs(epochs_SD,
inv_op_SD,
lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
stc_ld = apply_inverse_epochs(epochs_LD,
inv_op_LD,
lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
src_SD = inv_op_SD['src']
src_LD = inv_op_LD['src']
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices_SD = [src_SD[j]['vertno'] for j in range(2)]
n_signals_tot = 1 + len(vertices_SD[0]) + len(vertices_SD[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
morph_SD = mne.compute_source_morph(src=inv_op_SD['src'],\
subject_from=sub_to, subject_to=C.subject_to,\
spacing=C.spacing_morph, subjects_dir=C.data_path)
morph_LD = mne.compute_source_morph(src= inv_op_LD['src'],\
subject_from=sub_to, subject_to=C.subject_to,\
spacing=C.spacing_morph, subjects_dir=C.data_path)
for win in np.arange(0, len(C.con_time_window) - 1):
print('[i,win]: ', i, win)
t_min = C.con_time_window[win]
t_max = C.con_time_window[win + 1]
stc_SD = []
stc_LD = []
for n in np.arange(0, len(stc_sd)):
stc_SD.append(stc_sd[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
for n in np.arange(0, len(stc_ld)):
stc_LD.append(stc_ld[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
for k in np.arange(0, 6):
print('[i,win,k]: ', i, win, k)
morphed_labels[k].name = C.rois_labels[k]
seed_ts_sd = mne.extract_label_time_course(stc_SD, morphed_labels[k], \
src_SD, mode='mean_flip',return_generator=False)
seed_ts_ld = mne.extract_label_time_course(stc_LD, morphed_labels[k], \
src_LD, mode='mean_flip',return_generator=False)
for f in np.arange(0, len(C.con_freq_band) - 1):
print('[i,win,k,f]: ', i, win, k, f)
f_min = C.con_freq_band[f]
f_max = C.con_freq_band[f + 1]
print(f_min, f_max)
comb_ts_sd = zip(seed_ts_sd, stc_SD)
comb_ts_ld = zip(seed_ts_ld, stc_LD)
con_SD, freqs, times, n_epochs, n_tapers = spectral_connectivity(
comb_ts_sd,
method=method,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
faverage=True,
n_jobs=10)
con_LD, freqs, times, n_epochs, n_tapers = spectral_connectivity(
comb_ts_ld,
method=method,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
faverage=True,
n_jobs=10)
con_stc_SD = mne.SourceEstimate(con_SD, vertices=vertices_SD,\
tmin=t_min*1e-3, tstep=2e-3,subject=sub_to)
con_stc_LD = mne.SourceEstimate(con_LD, vertices=vertices_SD,\
tmin=t_min*1e-3, tstep=2e-3,subject=sub_to)
stc_total_SD[win][k][f] = morph_SD.apply(con_stc_SD)
stc_total_LD[win][k][f] = morph_LD.apply(con_stc_LD)
with open(stc_SD_file_name, "wb") as fp: #Pickling
pickle.dump(stc_total_SD, fp)
with open(stc_LD_file_name, "wb") as fp: #Pickling
pickle.dump(stc_total_LD, fp)
e = time.time()
print(e - s)
# Find number and index of vertex with most power
src_pow = np.sum(stc_label.data ** 2, axis=1)
print "src pow"
print len(src_pow)
print src_pow
print np.argmax(src_pow)
print
seed_vertno = stc_label.vertno[0][np.argmax(src_pow)] #Indices of the vertex with max power in label
print seed_vertno
seed_idx = np.searchsorted(stc.vertno[0], seed_vertno) # index in original stc ##np.searchsorted: Find indices where elements should be inserted to maintain order.
print "Seed Index:", seed_idx
# Generate index parameter for seed-based connectivity analysis
n_sources = stc.data.shape[0]
print "N_Sources:", n_sources
indices = seed_target_indices([seed_idx], np.arange(n_sources))#array of seed indices and Targets indices
# Compute inverse solution and for each epoch. By using "return_generator=True"
# stcs will be a generator object instead of a list. This allows us so to
# compute the coherence without having to keep all source estimates in memory.
snr = 1.0 # use lower SNR for single epochs
lambda2 = 1.0 / snr ** 2
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
pick_ori="normal", return_generator=True)
# Now we are ready to compute the coherence in the alpha and beta band.
# fmin and fmax specify the lower and upper freq. for each band, resp.
fmin = (8., 13.)
fmax = (13., 25.)
def SN_functional_connectivity_bands_runs(i, method, SN_ROI):
s = time.time()
meg = subjects[i]
sub_to = MRI_sub[i][1:15]
stc_F_file_name = os.path.expanduser(
'~'
) + '/old_semnet/my_semnet/json_files/connectivity/stc_' + method + '200_F_bands_SD_sub' + str(
i) + '.json'
stc_O_file_name = os.path.expanduser(
'~'
) + '/old_semnet/my_semnet/json_files/connectivity/stc_' + method + '200_O_bands_LD_sub' + str(
i) + '.json'
stc_M_file_name = os.path.expanduser(
'~'
) + '/old_semnet/my_semnet/json_files/connectivity/stc_' + method + '200_M_bands_SD_sub' + str(
i) + '.json'
stc_SD_file_name = os.path.expanduser(
'~'
) + '/old_semnet/my_semnet/json_files/connectivity/stc_' + method + '200_mean_bands_SD_sub' + str(
i) + '.json'
stc_LD_file_name = os.path.expanduser(
'~'
) + '/old_semnet/my_semnet/json_files/connectivity/stc_' + method + '200_mean_bands_LD_sub' + str(
i) + '.json'
morphed_labels = mne.morph_labels(SN_ROI,
subject_to=sub_to,
subject_from='fsaverage',
subjects_dir=data_path)
# Reading epochs
# Reading epochs
epo_name_LD = data_path + meg + 'block_LD_words_epochs-epo.fif'
epochs_ld = mne.read_epochs(epo_name_LD, preload=True)
epochs_LD = epochs_ld['words'].copy().resample(500)
epoch_fname_fruit = data_path + meg + 'block_fruit_epochs-epo.fif'
epoch_fname_odour = data_path + meg + 'block_odour_epochs-epo.fif'
epoch_fname_milk = data_path + meg + 'block_milk_epochs-epo.fif'
epochs_fruit = mne.read_epochs(epoch_fname_fruit, preload=True)
epochs_odour = mne.read_epochs(epoch_fname_odour, preload=True)
epochs_milk = mne.read_epochs(epoch_fname_milk, preload=True)
epochs_f = mne.epochs.combine_event_ids(
epochs_fruit, ['visual', 'hear', 'hand', 'neutral', 'emotional'],
{'words': 15})
epochs_o = mne.epochs.combine_event_ids(
epochs_odour, ['visual', 'hear', 'hand', 'neutral', 'emotional'],
{'words': 15})
epochs_m = mne.epochs.combine_event_ids(
epochs_milk, ['visual', 'hear', 'hand', 'neutral', 'emotional'],
{'words': 15})
epochs_f = epochs_f['words'].copy().resample(500)
epochs_o = epochs_o['words'].copy().resample(500)
epochs_m = epochs_m['words'].copy().resample(500)
# Reading inverse operator
inv_fname_SD = data_path + meg + 'InvOp_SD_EMEG-inv.fif'
inv_fname_LD = data_path + meg + 'InvOp_LD_EMEG-inv.fif'
inv_op_SD = read_inverse_operator(inv_fname_SD)
inv_op_LD = read_inverse_operator(inv_fname_LD)
stc_f = apply_inverse_epochs(epochs_f,
inv_op_SD,
lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
stc_o = apply_inverse_epochs(epochs_o,
inv_op_SD,
lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
stc_m = apply_inverse_epochs(epochs_m,
inv_op_SD,
lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
stc_ld = apply_inverse_epochs(epochs_LD,
inv_op_LD,
lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
src_SD = inv_op_SD['src']
src_LD = inv_op_LD['src']
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices_SD = [src_SD[j]['vertno'] for j in range(2)]
n_signals_tot = 1 + len(vertices_SD[0]) + len(vertices_SD[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
morph_SD = mne.compute_source_morph(src=inv_op_SD['src'],
subject_from=sub_to,
subject_to=C.subject_to,
spacing=C.spacing_morph,
subjects_dir=C.data_path)
morph_LD = mne.compute_source_morph(src=inv_op_LD['src'],
subject_from=sub_to,
subject_to=C.subject_to,
spacing=C.spacing_morph,
subjects_dir=C.data_path)
for win in np.arange(0, len(C.con_time_window) - 1):
print('[i,win]: ', i, win)
t_min = C.con_time_window[win]
t_max = C.con_time_window[win + 1]
stc_F = []
stc_O = []
stc_M = []
stc_LD = []
for n in np.arange(0, len(stc_f)):
stc_F.append(stc_f[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
for n in np.arange(0, len(stc_o)):
stc_O.append(stc_o[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
for n in np.arange(0, len(stc_m)):
stc_M.append(stc_m[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
for n in np.arange(0, len(stc_ld)):
stc_LD.append(stc_ld[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
for k in np.arange(0, 6):
print('[i,win,k]: ', i, win, k)
morphed_labels[k].name = C.rois_labels[k]
seed_ts_f = mne.extract_label_time_course(stc_F,
morphed_labels[k],
src_SD,
mode='mean_flip',
return_generator=False)
seed_ts_o = mne.extract_label_time_course(stc_O,
morphed_labels[k],
src_SD,
mode='mean_flip',
return_generator=False)
seed_ts_m = mne.extract_label_time_course(stc_M,
morphed_labels[k],
src_SD,
mode='mean_flip',
return_generator=False)
seed_ts_ld = mne.extract_label_time_course(stc_LD,
morphed_labels[k],
src_LD,
mode='mean_flip',
return_generator=False)
for f in np.arange(0, len(C.con_freq_band) - 1):
print('[i,win,k,f]: ', i, win, k, f)
f_min = C.con_freq_band[f]
f_max = C.con_freq_band[f + 1]
print(f_min, f_max)
comb_ts_f = zip(seed_ts_f, stc_F)
comb_ts_o = zip(seed_ts_o, stc_O)
comb_ts_m = zip(seed_ts_m, stc_M)
comb_ts_ld = zip(seed_ts_ld, stc_LD)
con_F, freqs, times, n_epochs, n_tapers = spectral_connectivity(
comb_ts_f,
method=method,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
faverage=True,
n_jobs=10)
con_O, freqs, times, n_epochs, n_tapers = spectral_connectivity(
comb_ts_o,
method=method,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
faverage=True,
n_jobs=10)
con_M, freqs, times, n_epochs, n_tapers = spectral_connectivity(
comb_ts_m,
method=method,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
faverage=True,
n_jobs=10)
con_LD, freqs, times, n_epochs, n_tapers = spectral_connectivity(
comb_ts_ld,
method=method,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
faverage=True,
n_jobs=10)
con_SD = (con_F + con_O + con_M) / 3
con_stc_F = mne.SourceEstimate(con_F,
vertices=vertices_SD,
tmin=t_min * 1e-3,
tstep=2e-3,
subject=sub_to)
con_stc_O = mne.SourceEstimate(con_O,
vertices=vertices_SD,
tmin=t_min * 1e-3,
tstep=2e-3,
subject=sub_to)
con_stc_M = mne.SourceEstimate(con_M,
vertices=vertices_SD,
tmin=t_min * 1e-3,
tstep=2e-3,
subject=sub_to)
con_stc_SD = mne.SourceEstimate(con_SD,
vertices=vertices_SD,
tmin=t_min * 1e-3,
tstep=2e-3,
subject=sub_to)
con_stc_LD = mne.SourceEstimate(con_LD,
vertices=vertices_SD,
tmin=t_min * 1e-3,
tstep=2e-3,
subject=sub_to)
stc_total_F[win][k][f] = morph_SD.apply(con_stc_F)
stc_total_O[win][k][f] = morph_SD.apply(con_stc_O)
stc_total_M[win][k][f] = morph_SD.apply(con_stc_M)
stc_total_SD[win][k][f] = morph_SD.apply(con_stc_SD)
stc_total_LD[win][k][f] = morph_LD.apply(con_stc_LD)
# with open(stc_F_file_name, "wb") as fp: #Pickling
# pickle.dump(stc_total_F, fp)
# with open(stc_O_file_name, "wb") as fp: #Pickling
# pickle.dump(stc_total_O, fp)
# with open(stc_M_file_name, "wb") as fp: #Pickling
# pickle.dump(stc_total_M, fp)
# with open(stc_SD_file_name, "wb") as fp: #Pickling
# pickle.dump(stc_total_SD, fp)
with open(stc_LD_file_name, "wb") as fp: # Pickling
pickle.dump(stc_total_LD, fp)
e = time.time()
print(e - s)
def SN_effective_connectivity_bands(i, method):
n_subjects = len(subjects)
meg = subjects[i]
sub_to = MRI_sub[i][1:15]
print('Participant : ', i)
stc_SD_file_name = os.path.expanduser(
'~'
) + '/my_semnet/json_files/connectivity/stc_' + method + '200_bands_SD_sub' + str(
i) + '.json'
stc_LD_file_name = os.path.expanduser(
'~'
) + '/my_semnet/json_files/connectivity/stc_' + method + '200_bands_LD_sub' + str(
i) + '.json'
morphed_labels = mne.morph_labels(SN_ROI,subject_to=data_path+sub_to,\
subject_from='fsaverage',subjects_dir=data_path)
# Reading epochs
epo_name_SD = data_path + meg + 'block_SD_words_epochs-epo.fif'
epo_name_LD = data_path + meg + 'block_LD_words_epochs-epo.fif'
epochs_sd = mne.read_epochs(epo_name_SD, preload=True)
epochs_ld = mne.read_epochs(epo_name_LD, preload=True)
epochs_SD = epochs_sd['words'].copy().resample(500)
epochs_LD = epochs_ld['words'].copy().resample(500)
# Equalize trial counts to eliminate bias (which would otherwise be
# introduced by the abs() performed below)
equalize_epoch_counts([epochs_SD, epochs_LD])
# Reading inverse operator
inv_fname_SD = data_path + meg + 'InvOp_SD_EMEG-inv.fif'
inv_fname_LD = data_path + meg + 'InvOp_LD_EMEG-inv.fif'
inv_op_SD = read_inverse_operator(inv_fname_SD)
inv_op_LD = read_inverse_operator(inv_fname_LD)
stc_sd = apply_inverse_epochs(epochs_SD,
inv_op_SD,
C.lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
stc_ld = apply_inverse_epochs(epochs_LD,
inv_op_LD,
C.lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
times = epochs_SD.times
stc_SD_t = []
stc_LD_t = []
src_SD = inv_op_SD['src']
src_LD = inv_op_LD['src']
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices_SD = [src_SD[j]['vertno'] for j in range(2)]
n_signals_tot = 1 + len(vertices_SD[0]) + len(vertices_SD[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
morph_SD = mne.compute_source_morph(src=inv_op_SD['src'],\
subject_from=sub_to, subject_to=C.subject_to,\
spacing=C.spacing_morph, subjects_dir=C.data_path)
morph_LD = mne.compute_source_morph(src= inv_op_LD['src'],\
subject_from=sub_to, subject_to=C.subject_to,\
spacing=C.spacing_morph, subjects_dir=C.data_path)
for n in np.arange(0, len(stc_sd)):
stc_SD_t.append(stc_baseline_correction(stc_sd[n], times))
stc_LD_t.append(stc_baseline_correction(stc_ld[n], times))
for win in np.arange(0, len(C.con_time_window)):
t_min = C.con_time_window[win]
t_max = C.con_time_window[win] + C.con_time_window_len
stc_SD = []
stc_LD = []
for n in np.arange(0, len(stc_sd)):
stc_SD.append(stc_SD_t[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
stc_LD.append(stc_LD_t[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
for k in np.arange(0, 6):
print('Participant : ', i, '/ ROI: ', k)
morphed_labels[k].name = C.rois_labels[k]
seed_ts_sd = mne.extract_label_time_course(stc_SD, morphed_labels[k], \
src_SD, mode='mean_flip',return_generator=False)
seed_ts_ld = mne.extract_label_time_course(stc_LD, morphed_labels[k], \
src_LD, mode='mean_flip',return_generator=False)
for f in np.arange(0, len(C.con_freq_band_psi) - 1):
f_min = C.con_freq_band_psi[f]
f_max = C.con_freq_band_psi[f + 1]
print('Participant : ' , i, '/ ROI: ',k,' win:', win,\
' freq: ',f)
comb_ts_sd = zip(seed_ts_sd, stc_SD)
comb_ts_ld = zip(seed_ts_ld, stc_LD)
psi_SD, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts_sd,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
n_jobs=15)
psi_LD, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts_ld,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
n_jobs=15)
psi_stc_SD = mne.SourceEstimate(psi_SD, vertices=vertices_SD,\
tmin=t_min*1e-3, tstep=2e-3,subject=sub_to)
psi_stc_LD = mne.SourceEstimate(psi_LD, vertices=vertices_SD,\
tmin=t_min*1e-3, tstep=2e-3,subject=sub_to)
stc_total_SD[win][k][f] = morph_SD.apply(psi_stc_SD)
stc_total_LD[win][k][f] = morph_LD.apply(psi_stc_LD)
with open(stc_SD_file_name, "wb") as fp: #Pickling
pickle.dump(stc_total_SD, fp)
with open(stc_LD_file_name, "wb") as fp: #Pickling
pickle.dump(stc_total_LD, fp)
e = time.time()
print(e - s)
