# -----------------------------------------------------
labels = mne.read_labels_from_annot(subject,
'aparc_sub',
subjects_dir=subjects_dir)
epochs.apply_hilbert() # faster to apply in sensor space
stcs = apply_inverse_epochs(epochs,
inv,
lambda2=1. / 9.,
pick_ori='normal',
return_generator=True)
label_ts = mne.extract_label_time_course(stcs,
labels,
inv['src'],
return_generator=True)
corr = envelope_correlation(label_ts, verbose=True)
# let's plot this matrix
fig, ax = plt.subplots(figsize=(4, 4))
ax.imshow(corr, cmap='viridis', clim=np.percentile(corr, [5, 95]))
fig.tight_layout()
##############################################################################
# Compute the degree and plot it
# ------------------------------
# sphinx_gallery_thumbnail_number = 2
threshold_prop = 0.15 # percentage of strongest edges to keep in the graph
degree = mne.connectivity.degree(corr, threshold_prop=threshold_prop)
stc = mne.labels_to_stc(labels, degree)
stc = stc.in_label(
filters = make_lcmv(epochs.info,
fwd,
data_cov,
0.05,
cov,
pick_ori='max-power',
weight_norm='nai')
del fwd
##############################################################################
# Compute label time series and do envelope correlation
# -----------------------------------------------------
epochs.apply_hilbert() # faster to do in sensor space
stcs = apply_lcmv_epochs(epochs, filters, return_generator=True)
corr = envelope_correlation(stcs, verbose=True)
##############################################################################
# Compute the degree and plot it
# ------------------------------
degree = mne.connectivity.degree(corr, 0.15)
stc = mne.VolSourceEstimate(degree, src[0]['vertno'], 0, 1, 'bst_resting')
brain = stc.plot(src,
clim=dict(kind='percent', lims=[75, 85, 95]),
colormap='gnuplot',
subjects_dir=subjects_dir,
mode='glass_brain')
##############################################################################
# References
def test_envelope_correlation():
"""Test the envelope correlation function."""
rng = np.random.RandomState(0)
data = rng.randn(2, 4, 64)
data_hilbert = hilbert(data, axis=-1)
corr_orig = _compute_corrs_orig(data_hilbert)
assert (0 <= corr_orig).all()
assert (corr_orig < 1).all()
# using complex data
corr = envelope_correlation(data_hilbert)
assert_allclose(corr, corr_orig)
# using callable
corr = envelope_correlation(data_hilbert,
combine=lambda data: np.mean(data, axis=0))
assert_allclose(corr, corr_orig)
# do Hilbert internally, and don't combine
corr = envelope_correlation(data, combine=None)
assert corr.shape == (data.shape[0], ) + corr_orig.shape
corr = np.mean(corr, axis=0)
assert_allclose(corr, corr_orig)
# degenerate
with pytest.raises(ValueError, match='float'):
envelope_correlation(data.astype(int))
with pytest.raises(ValueError, match='entry in data must be 2D'):
envelope_correlation(data[np.newaxis])
with pytest.raises(ValueError, match='n_nodes mismatch'):
envelope_correlation([rng.randn(2, 8), rng.randn(3, 8)])
with pytest.raises(ValueError, match='mean or callable'):
envelope_correlation(data, 1.)
with pytest.raises(ValueError, match='Combine option'):
envelope_correlation(data, 'foo')
with pytest.raises(ValueError, match='Invalid value.*orthogonalize.*'):
envelope_correlation(data, orthogonalize='foo')
corr_plain = envelope_correlation(data, combine=None, orthogonalize=False)
assert corr_plain.shape == (data.shape[0], ) + corr_orig.shape
assert np.min(corr_plain) < 0
corr_plain_mean = np.mean(corr_plain, axis=0)
assert_allclose(np.diag(corr_plain_mean), 1)
np_corr = np.array([np.corrcoef(np.abs(x)) for x in data_hilbert])
assert_allclose(corr_plain, np_corr)
# check against FieldTrip, which uses the square-log-norm version
# from scipy.io import savemat
# savemat('data.mat', dict(data_hilbert=data_hilbert))
# matlab
# load data
# ft_connectivity_powcorr_ortho(reshape(data_hilbert(1,:,:), [4, 64]))
# ft_connectivity_powcorr_ortho(reshape(data_hilbert(2,:,:), [4, 64]))
ft_vals = np.array([
[[np.nan, 0.196734553900236, 0.063173148355451, -0.242638384630448],
[0.196734553900236, np.nan, 0.041799775495150, -0.088205187548542],
[0.063173148355451, 0.041799775495150, np.nan, 0.090331428512317],
[-0.242638384630448, -0.088205187548542, 0.090331428512317, np.nan]],
[[np.nan, -0.013270857462890, 0.185200598081295, 0.140284351572544],
[-0.013270857462890, np.nan, 0.150981508043722, -0.000671809276372],
[0.185200598081295, 0.150981508043722, np.nan, 0.137460244313337],
[0.140284351572544, -0.000671809276372, 0.137460244313337, np.nan]],
], float)
ft_vals[np.isnan(ft_vals)] = 0
corr_log = envelope_correlation(data,
combine=None,
log=True,
absolute=False)
assert_allclose(corr_log, ft_vals)
epochs = mne.Epochs(raw,
events=events,
tmin=0,
tmax=.5,
baseline=None,
reject=dict(),
preload=True)
del raw
src = mne.read_source_spaces(src)
fwd = mne.make_forward_solution(epochs.info, trans, src, bem)
inv = make_inverse_operator(epochs.info, fwd, cov)
del fwd, src
labels = mne.read_labels_from_annot(subject,
'aparc',
subjects_dir=subjects_dir)
epochs.apply_hilbert()
stcs = apply_inverse_epochs(epochs,
inv,
lambda2=1. / 9.,
method='sLORETA',
pick_ori='normal',
return_generator=True)
label_ts = mne.extract_label_time_course(stcs,
labels,
inv['src'],
return_generator=True)
corr = envelope_correlation(label_ts)
np.fill_diagonal(corr, 0)
b = np.tril(corr, k=0)
np.save(os.path.join('path for result files to be saved', i + D), b)
def test_envelope_correlation():
"""Test the envelope correlation function."""
rng = np.random.RandomState(0)
data = rng.randn(2, 4, 64)
data_hilbert = hilbert(data, axis=-1)
corr_orig = _compute_corrs_orig(data_hilbert)
assert (0 < corr_orig).all()
assert (corr_orig < 1).all()
# using complex data
corr = envelope_correlation(data_hilbert)
assert_allclose(corr, corr_orig)
# do Hilbert internally, and don't combine
corr = envelope_correlation(data, combine=None)
assert corr.shape == (data.shape[0], ) + corr_orig.shape
corr = np.median(corr, axis=0)
assert_allclose(corr, corr_orig)
# degenerate
with pytest.raises(ValueError, match='float'):
envelope_correlation(data.astype(int))
with pytest.raises(ValueError, match='entry in data must be 2D'):
envelope_correlation(data[np.newaxis])
with pytest.raises(ValueError, match='n_nodes mismatch'):
envelope_correlation([rng.randn(2, 8), rng.randn(3, 8)])
with pytest.raises(TypeError, match='instance of str or None'):
envelope_correlation(data, 1.)
with pytest.raises(ValueError, match='Unknown combine option'):
envelope_correlation(data, 'foo')
def run_correlation(subjects_dir, subject, volume_spacing, freq, ortho_bool):
num_threads(8)
ortho_flag = str(ortho_bool)
frequency = str(freq)
DATA_DIR = Path(f'{subjects_dir}', f'{subject}', 'mne_files')
eye_proj1 = f'{DATA_DIR}/{subject}_eyes1-proj.fif.gz'
eye_proj2 = f'{DATA_DIR}/{subject}_eyes2-proj.fif.gz'
fname_meg = f'{DATA_DIR}/{subject}_ses-rest_task-rest.fif'
t1_fname = os.path.join(subjects_dir, subject, 'mri', 'T1.mgz')
heartbeat_proj = f'{DATA_DIR}/{subject}_heartbeat-proj.fif.gz'
fwd_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-fwd.fif.gz'
src_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-src.fif.gz'
cov_fname = f'{DATA_DIR}/{subject}-cov_{volume_spacing}.fif.gz'
raw_cov_fname = f'{DATA_DIR}/{subject}-rawcov_{volume_spacing}.fif.gz'
raw_proj = f'{DATA_DIR}/{subject}_ses-rest_task-rest_proj.fif.gz'
source_voxel_coords = f'{DATA_DIR}/{subject}_coords_{volume_spacing}.pkl'
corr_file_acLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_acLeft.npy'
corr_file_scLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_scLeft.npy'
corr_file_vcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_vcLeft.npy'
corr_file_mtLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtLeft.npy'
corr_file_mtlLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtlLeft.npy'
corr_file_smcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_smcLeft.npy'
corr_file_lpcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_lpcLeft.npy'
corr_file_dpfcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_dpfcLeft.npy'
corr_file_tmpcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_tmpcLeft.npy'
corr_file_acRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_acRight.npy'
corr_file_scRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_scRight.npy'
corr_file_vcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_vcRight.npy'
corr_file_mtRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtRight.npy'
corr_file_mtlRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtlRight.npy'
corr_file_smcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_smcRight.npy'
corr_file_lpcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_lpcRight.npy'
corr_file_dpfcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_dpfcRight.npy'
corr_file_tmpcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_tmpcRight.npy'
corr_file_mpfc = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mpfc.npy'
corr_file_sma = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_sma.npy'
check_for_files = []
check_for_files.append(corr_file_acLeft)
check_for_files.append(corr_file_scLeft)
check_for_files.append(corr_file_vcLeft)
check_for_files.append(corr_file_mtLeft)
check_for_files.append(corr_file_mtlLeft)
check_for_files.append(corr_file_smcLeft)
check_for_files.append(corr_file_lpcLeft)
check_for_files.append(corr_file_dpfcLeft)
check_for_files.append(corr_file_tmpcLeft)
check_for_files.append(corr_file_acRight)
check_for_files.append(corr_file_scRight)
check_for_files.append(corr_file_vcRight)
check_for_files.append(corr_file_mtRight)
check_for_files.append(corr_file_mtlRight)
check_for_files.append(corr_file_smcRight)
check_for_files.append(corr_file_lpcRight)
check_for_files.append(corr_file_dpfcRight)
check_for_files.append(corr_file_tmpcRight)
check_for_files.append(corr_file_mpfc)
check_for_files.append(corr_file_sma)
file_exist = [f for f in check_for_files if os.path.isfile(f)]
file_not_exist = list(set(file_exist) ^ set(check_for_files))
if not file_not_exist:
print('SC, AC, VC correlation files exists...')
else:
trans = f'/home/senthilp/caesar/camcan/cc700/camcan_coreg-master/trans/{subject}-trans.fif' # The transformation file obtained by coregistration
file_trans = pathlib.Path(trans)
file_ss = pathlib.Path(src_fname)
file_fm = pathlib.Path(fwd_fname)
file_proj = pathlib.Path(raw_proj)
file_cov = pathlib.Path(cov_fname)
file_rawcov = pathlib.Path(raw_cov_fname)
t1 = nib.load(t1_fname)
if not file_trans.exists():
print (f'{trans} File doesnt exist...')
sys.exit(0)
#info = mne.io.read_info(fname_meg)
# plot_registration(info, trans, subject, subjects_dir)
if not file_ss.exists():
src = compute_SourceSpace(subject, subjects_dir, src_fname, source_voxel_coords, plot=True, ss='volume',
volume_spacing=volume_spacing)
seed_l_sc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SSC_Left'])
seed_r_sc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SSC_Right'])
seed_l_ac = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['AC_Left'])
seed_r_ac = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['AC_Right'])
seed_l_vc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['VC_Left'])
seed_r_vc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['VC_Right'])
seed_l_mt = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MT+_Left'])
seed_r_mt = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MT+_Right'])
seed_l_mtl = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MTL_Left'])
seed_r_mtl = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MTL_Right'])
seed_l_smc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SMC_Left'])
seed_r_smc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SMC_Right'])
seed_l_lpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['LPC_Left'])
seed_r_lpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['LPC_Right'])
seed_l_dpfc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['DPFC_Left'])
seed_r_dpfc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['DPFC_Right'])
seed_l_tmpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['TMPC_Left'])
seed_r_tmpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['TMPC_Right'])
seed_mpfc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MPFC_MidBrain'])
seed_sma = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SMA_MidBrain'])
src_inuse = np.where(src[0]['inuse'] == 1)
loc_l_sc = src_inuse[0][0]
loc_r_sc = src_inuse[0][1]
loc_l_ac = src_inuse[0][2]
loc_r_ac = src_inuse[0][3]
loc_l_vc = src_inuse[0][4]
loc_r_vc = src_inuse[0][5]
loc_l_mt = src_inuse[0][6]
loc_r_mt = src_inuse[0][7]
loc_l_mtl = src_inuse[0][8]
loc_r_mtl = src_inuse[0][9]
loc_l_smc = src_inuse[0][10]
loc_r_smc = src_inuse[0][11]
loc_l_lpc = src_inuse[0][12]
loc_r_lpc = src_inuse[0][13]
loc_l_dpfc = src_inuse[0][14]
loc_r_dpfc = src_inuse[0][15]
loc_l_tmpc = src_inuse[0][16]
loc_r_tmpc = src_inuse[0][17]
loc_mpfc = src_inuse[0][18]
loc_sma = src_inuse[0][19]
src[0]['rr'][loc_l_sc] = seed_l_sc
src[0]['rr'][loc_r_sc] = seed_r_sc
src[0]['rr'][loc_l_ac] = seed_l_ac
src[0]['rr'][loc_r_ac] = seed_r_ac
src[0]['rr'][loc_l_vc] = seed_l_vc
src[0]['rr'][loc_r_vc] = seed_r_vc
src[0]['rr'][loc_l_mt] = seed_l_mt
src[0]['rr'][loc_r_mt] = seed_r_mt
src[0]['rr'][loc_l_mtl] = seed_l_mtl
src[0]['rr'][loc_r_mtl] = seed_r_mtl
src[0]['rr'][loc_l_smc] = seed_l_smc
src[0]['rr'][loc_r_smc] = seed_r_smc
src[0]['rr'][loc_l_lpc] = seed_l_lpc
src[0]['rr'][loc_r_lpc] = seed_r_lpc
src[0]['rr'][loc_l_dpfc] = seed_l_dpfc
src[0]['rr'][loc_r_dpfc] = seed_r_dpfc
src[0]['rr'][loc_l_tmpc] = seed_l_tmpc
src[0]['rr'][loc_r_tmpc] = seed_r_tmpc
src[0]['rr'][loc_mpfc] = seed_mpfc
src[0]['rr'][loc_sma] = seed_sma
src.save(src_fname, overwrite=True)
src = mne.read_source_spaces(src_fname)
#view_SS_brain(subject, subjects_dir, src)
if not file_fm.exists():
forward_model(subject, subjects_dir, fname_meg, trans, src, fwd_fname)
fwd = mne.read_forward_solution(fwd_fname)
# sensitivty_plot(subject, subjects_dir, fwd)
raw = mne.io.read_raw_fif(fname_meg, verbose='error', preload=True)
srate = raw.info['sfreq']
n_time_samps = raw.n_times
time_secs = raw.times
ch_names = raw.ch_names
n_chan = len(ch_names)
freq_res = srate/n_time_samps
print('\n')
print('-------------------------- Data summary-------------------------------')
print(f'Subject {subject}')
print(f"Frequency resolution {freq_res} Hz")
print(f"The first few channel names are {ch_names[:3]}")
print(f"The last time sample at {time_secs[-1]} seconds.")
print(f"Sampling Frequency (No of time points/sec) {srate} Hz")
print(f"Miscellaneous acquisition info {raw.info['description']}")
print(f"Bad channels marked during data acquisition {raw.info['bads']}")
print(f"Convert time in sec ( 60s ) to ingeter index {raw.time_as_index(60)}") # Convert time to indices
print(f"The raw data object has {n_time_samps} time samples and {n_chan} channels.")
print('------------------------------------------------------------------------')
print('\n')
# raw.plot(n_channels=10, scalings='auto', title='Data from arrays', show=True, block=True)
if not file_proj.exists():
projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2, ch_name='ECG063')
projs_eog1, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG061')
projs_eog2, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG062')
if projs_ecg is not None:
mne.write_proj(heartbeat_proj, projs_ecg) # Saving projectors
raw.info['projs'] += projs_ecg
if projs_eog1 is not None:
mne.write_proj(eye_proj1, projs_eog1)
raw.info['projs'] += projs_eog1
if projs_eog2 is not None:
mne.write_proj(eye_proj2, projs_eog2)
raw.info['projs'] += projs_eog2
raw.apply_proj()
raw.save(raw_proj, proj=True, overwrite=True)
print(raw_proj)
raw_proj_applied = mne.io.read_raw_fif(raw_proj, verbose='error', preload=True)
print(f'High-pass filtering data at 0.5 Hz')
raw_proj_applied.filter(l_freq=0.5, h_freq=None, method='iir')
if not file_cov.exists():
cov = mne.compute_raw_covariance(raw_proj_applied) # compute before band-pass of interest
mne.write_cov(cov_fname, cov)
cov = mne.read_cov(cov_fname)
# cov.plot(raw.info, proj=True, exclude='bads', show_svd=False
# raw_proj_applied.crop(tmax=10)
do_epochs = False
l_freq = freq-2.0
h_freq = freq+2.0
print(f'Band pass filter data [{l_freq}, {h_freq}]')
raw_proj_filtered = raw_proj_applied.filter(l_freq=l_freq, h_freq=h_freq)
if do_epochs:
print('Segmenting raw data...')
events = mne.make_fixed_length_events(raw_proj_filtered, duration=5.)
raw_proj_filtered = mne.Epochs(raw_proj_filtered, events=events, tmin=0, tmax=5.,
baseline=None, preload=True)
data_cov = mne.compute_covariance(raw_proj_filtered)
else:
if not file_rawcov.exists():
data_cov = mne.compute_raw_covariance(raw_proj_filtered)
mne.write_cov(raw_cov_fname, data_cov)
else:
data_cov = mne.read_cov(file_rawcov)
filters = make_lcmv(raw_proj_filtered.info, fwd, data_cov, 0.05, cov,
pick_ori='max-power', weight_norm='nai')
raw_proj_filtered_comp = raw_proj_filtered.apply_hilbert()
if do_epochs:
stcs = apply_lcmv_epochs(raw_proj_filtered_comp, filters, return_generator=False)
else:
stcs = apply_lcmv_raw(raw_proj_filtered_comp, filters, verbose=True)
stcs = [stcs]
# Power Envelope Correlation
print(f'Computing Power Envelope Correlation for {subject}....Orthogonalize {ortho_flag}')
all_corr = envelope_correlation(stcs, combine=None, orthogonalize=False,
log=False, absolute=True, verbose=None)
np.save(corr_file_scLeft, all_corr[seed_left_sc])
np.save(corr_file_acLeft, all_corr[seed_left_ac])
np.save(corr_file_vcLeft, all_corr[seed_left_vc])
np.save(corr_file_mtLeft, all_corr[seed_left_mt])
np.save(corr_file_mtlLeft, all_corr[seed_left_mtl])
np.save(corr_file_smcLeft, all_corr[seed_left_smc])
np.save(corr_file_lpcLeft, all_corr[seed_left_lpc])
np.save(corr_file_dpfcLeft, all_corr[seed_left_dpfc])
np.save(corr_file_tmpcLeft, all_corr[seed_left_tmpc])
np.save(corr_file_scRight, all_corr[seed_right_sc])
np.save(corr_file_acRight, all_corr[seed_right_ac])
np.save(corr_file_vcRight, all_corr[seed_right_vc])
np.save(corr_file_mtRight, all_corr[seed_right_mt])
np.save(corr_file_mtlRight, all_corr[seed_right_mtl])
np.save(corr_file_smcRight, all_corr[seed_right_smc])
np.save(corr_file_lpcRight, all_corr[seed_right_lpc])
np.save(corr_file_dpfcRight, all_corr[seed_right_dpfc])
np.save(corr_file_tmpcRight, all_corr[seed_right_tmpc])
np.save(corr_file_mpfc, all_corr[seed_mpfc_index])
np.save(corr_file_sma, all_corr[seed_sma_index])
del stcs
pos = 15. # 1.5 cm is very broad, done here for speed!
src = mne.setup_volume_source_space('bst_resting', pos, bem=bem,
subjects_dir=subjects_dir, verbose=True)
fwd = mne.make_forward_solution(epochs.info, trans, src, bem)
data_cov = mne.compute_covariance(epochs)
filters = make_lcmv(epochs.info, fwd, data_cov, 0.05, cov,
pick_ori='max-power', weight_norm='nai')
del fwd
##############################################################################
# Compute label time series and do envelope correlation
# -----------------------------------------------------
epochs.apply_hilbert() # faster to do in sensor space
stcs = apply_lcmv_epochs(epochs, filters, return_generator=True)
corr = envelope_correlation(stcs, verbose=True)
##############################################################################
# Compute the degree and plot it
# ------------------------------
degree = mne.connectivity.degree(corr, 0.15)
stc = mne.VolSourceEstimate(degree, src[0]['vertno'], 0, 1, 'bst_resting')
brain = stc.plot(
src, clim=dict(kind='percent', lims=[75, 85, 95]), colormap='gnuplot',
subjects_dir=subjects_dir, mode='glass_brain')
##############################################################################
# References
# ----------
# .. [1] Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK (2012)
def test_envelope_correlation():
"""Test the envelope correlation function."""
rng = np.random.RandomState(0)
data = rng.randn(2, 4, 64)
data_hilbert = hilbert(data, axis=-1)
corr_orig = _compute_corrs_orig(data_hilbert)
assert (0 < corr_orig).all()
assert (corr_orig < 1).all()
# using complex data
corr = envelope_correlation(data_hilbert)
assert_allclose(corr, corr_orig)
# do Hilbert internally, and don't combine
corr = envelope_correlation(data, combine=None)
assert corr.shape == (data.shape[0],) + corr_orig.shape
corr = np.median(corr, axis=0)
assert_allclose(corr, corr_orig)
# degenerate
with pytest.raises(ValueError, match='float'):
envelope_correlation(data.astype(int))
with pytest.raises(ValueError, match='entry in data must be 2D'):
envelope_correlation(data[np.newaxis])
with pytest.raises(ValueError, match='n_nodes mismatch'):
envelope_correlation([rng.randn(2, 8), rng.randn(3, 8)])
with pytest.raises(TypeError, match='instance of str or None'):
envelope_correlation(data, 1.)
with pytest.raises(ValueError, match='Unknown combine option'):
envelope_correlation(data, 'foo')
def test_envelope_correlation():
"""Test the envelope correlation function."""
rng = np.random.RandomState(0)
data = rng.randn(2, 4, 64)
data_hilbert = hilbert(data, axis=-1)
corr_orig = _compute_corrs_orig(data_hilbert)
assert (0 < corr_orig).all()
assert (corr_orig < 1).all()
# using complex data
corr = envelope_correlation(data_hilbert)
assert_allclose(corr, corr_orig)
# using callable
corr = envelope_correlation(data_hilbert,
combine=lambda data: np.mean(data, axis=0))
assert_allclose(corr, corr_orig)
# do Hilbert internally, and don't combine
corr = envelope_correlation(data, combine=None)
assert corr.shape == (data.shape[0], ) + corr_orig.shape
corr = np.mean(corr, axis=0)
assert_allclose(corr, corr_orig)
# degenerate
with pytest.raises(ValueError, match='float'):
envelope_correlation(data.astype(int))
with pytest.raises(ValueError, match='entry in data must be 2D'):
envelope_correlation(data[np.newaxis])
with pytest.raises(ValueError, match='n_nodes mismatch'):
envelope_correlation([rng.randn(2, 8), rng.randn(3, 8)])
with pytest.raises(ValueError, match='mean or callable'):
envelope_correlation(data, 1.)
with pytest.raises(ValueError, match='Combine option'):
envelope_correlation(data, 'foo')
with pytest.raises(ValueError, match='Invalid value.*orthogonalize.*'):
envelope_correlation(data, orthogonalize='foo')
corr_plain = envelope_correlation(data, combine=None, orthogonalize=False)
assert corr_plain.shape == (data.shape[0], ) + corr_orig.shape
assert np.min(corr_plain) < 0
corr_plain_mean = np.mean(corr_plain, axis=0)
assert_allclose(np.diag(corr_plain_mean), 1)
np_corr = np.array([np.corrcoef(np.abs(x)) for x in data_hilbert])
assert_allclose(corr_plain, np_corr)
# epoch raw into 5 sec trials
print(' \nLoading ...%s' %
op.relpath(eps_fname, defaults.megdata))
for ix, (kk, vv) in enumerate(defaults.bands.items()):
hp, lp = vv
label_ts = funcs.extract_labels_timeseries(subject,
raw,
hp,
lp,
cov,
fwd,
defaults.subjects_dir,
eps_fname,
fslabels,
return_generator=True)
corr_mats[si, ix] = envelope_correlation(label_ts)
# Compute pairwise degree source connectivity amongst labels
degrees[si, ix] = mne.connectivity.degree(corr_mats[si, ix])
fout_ = op.join(eps_dir, '%s_%s_fcDs.h5' % (subject, kk))
write_hdf5(fname=fout_,
data={
'corr': corr_mats[si, ix],
'deg': degrees[si, ix]
},
overwrite=True)
plt.close('all')
for ix, (kk, vv) in enumerate(defaults.bands.items()):
corr_ = corr_mats[:, ix].mean(0)
# Plot group narrow-band corr matrix
fig, ax = plt.subplots(figsize=(4, 4))
img = ax.imshow(corr_,
def run_correlation(subjects_dir, subject, volume_spacing, freq):
num_threads(8)
frequency = str(freq)
DATA_DIR = Path(f'{subjects_dir}', f'{subject}', 'mne_files')
eye_proj1 = f'{DATA_DIR}/{subject}_eyes1-proj.fif.gz'
eye_proj2 = f'{DATA_DIR}/{subject}_eyes2-proj.fif.gz'
fname_meg = f'{DATA_DIR}/{subject}_ses-rest_task-rest.fif'
t1_fname = os.path.join(subjects_dir, subject, 'mri', 'T1.mgz')
heartbeat_proj = f'{DATA_DIR}/{subject}_heartbeat-proj.fif.gz'
fwd_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-fwd-label.fif.gz'
src_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-src-label.fif.gz'
cov_fname = f'{DATA_DIR}/{subject}-cov_{volume_spacing}-label.fif.gz'
raw_cov_fname = f'{DATA_DIR}/{subject}-rawcov_{volume_spacing}-label.fif.gz'
raw_proj = f'{DATA_DIR}/{subject}_ses-rest_task-rest_proj-label.fif.gz'
source_voxel_coords = f'{DATA_DIR}/{subject}_coords_{volume_spacing}.pkl'
freesurfer_label = f'{DATA_DIR}/{subject}_freesurferlabel_{volume_spacing}-label.pkl'
corr_true_file_label = f'{DATA_DIR}/{subject}_corr_ortho_true_{volume_spacing}_{frequency}_label.npy'
check_for_files = []
check_for_files.append(corr_true_file_label)
file_exist = [f for f in check_for_files if os.path.isfile(f)]
file_not_exist = list(set(file_exist) ^ set(check_for_files))
if not file_not_exist:
print('correlation files exists...')
else:
trans = f'/home/senthilp/caesar/camcan/cc700/camcan_coreg-master/trans/{subject}-trans.fif' # The transformation file obtained by coregistration
file_trans = pathlib.Path(trans)
file_ss = pathlib.Path(src_fname)
file_fm = pathlib.Path(fwd_fname)
file_proj = pathlib.Path(raw_proj)
file_cov = pathlib.Path(cov_fname)
file_rawcov = pathlib.Path(raw_cov_fname)
t1 = nib.load(t1_fname)
if not file_trans.exists():
print (f'{trans} File doesnt exist...')
sys.exit(0)
info = mne.io.read_info(fname_meg)
# plot_registration(info, trans, subject, subjects_dir)
print(file_ss)
if not file_ss.exists():
src = compute_SourceSpace(subject, subjects_dir, src_fname, source_voxel_coords, plot=True, ss='volume',
volume_spacing=volume_spacing)
src.save(src_fname, overwrite=True)
src = mne.read_source_spaces(src_fname)
#view_SS_brain(subject, subjects_dir, src)
if not file_fm.exists():
forward_model(subject, subjects_dir, fname_meg, trans, src, fwd_fname)
fwd = mne.read_forward_solution(fwd_fname)
# sensitivty_plot(subject, subjects_dir, fwd)
raw = mne.io.read_raw_fif(fname_meg, verbose='error', preload=True)
srate = raw.info['sfreq']
n_time_samps = raw.n_times
time_secs = raw.times
ch_names = raw.ch_names
n_chan = len(ch_names)
freq_res = srate/n_time_samps
print('\n')
print('-------------------------- Data summary-------------------------------')
print(f'Subject {subject}')
print(f"Frequency resolution {freq_res} Hz")
print(f"The first few channel names are {ch_names[:3]}")
print(f"The last time sample at {time_secs[-1]} seconds.")
print(f"Sampling Frequency (No of time points/sec) {srate} Hz")
print(f"Miscellaneous acquisition info {raw.info['description']}")
print(f"Bad channels marked during data acquisition {raw.info['bads']}")
print(f"Convert time in sec ( 60s ) to ingeter index {raw.time_as_index(60)}") # Convert time to indices
print(f"The raw data object has {n_time_samps} time samples and {n_chan} channels.")
print('------------------------------------------------------------------------')
print('\n')
# raw.plot(n_channels=10, scalings='auto', title='Data from arrays', show=True, block=True)
if not file_proj.exists():
projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2, ch_name='ECG063')
projs_eog1, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG061')
projs_eog2, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG062')
if projs_ecg is not None:
mne.write_proj(heartbeat_proj, projs_ecg) # Saving projectors
raw.info['projs'] += projs_ecg
if projs_eog1 is not None:
mne.write_proj(eye_proj1, projs_eog1)
raw.info['projs'] += projs_eog1
if projs_eog2 is not None:
mne.write_proj(eye_proj2, projs_eog2)
raw.info['projs'] += projs_eog2
raw.apply_proj()
raw.save(raw_proj, proj=True, overwrite=True)
print(raw_proj)
raw_proj_applied = mne.io.read_raw_fif(raw_proj, verbose='error', preload=True)
print(f'High-pass filtering data at 0.5 Hz')
raw_proj_applied.filter(l_freq=0.5, h_freq=None, method='iir')
if not file_cov.exists():
cov = mne.compute_raw_covariance(raw_proj_applied) # compute before band-pass of interest
mne.write_cov(cov_fname, cov)
cov = mne.read_cov(cov_fname)
# cov.plot(raw.info, proj=True, exclude='bads', show_svd=False
# raw_proj_applied.crop(tmax=10)
do_epochs = False
l_freq = freq-2.0
h_freq = freq+2.0
print(f'Band pass filter data [{l_freq}, {h_freq}]')
raw_proj_filtered = raw_proj_applied.filter(l_freq=l_freq, h_freq=h_freq)
if do_epochs:
print('Segmenting raw data...')
events = mne.make_fixed_length_events(raw_proj_filtered, duration=5.)
raw_proj_filtered = mne.Epochs(raw_proj_filtered, events=events, tmin=0, tmax=5.,
baseline=None, preload=True)
data_cov = mne.compute_covariance(raw_proj_filtered)
else:
if not file_rawcov.exists():
data_cov = mne.compute_raw_covariance(raw_proj_filtered)
mne.write_cov(raw_cov_fname, data_cov)
else:
data_cov = mne.read_cov(file_rawcov)
filters = make_lcmv(raw_proj_filtered.info, fwd, data_cov, 0.05, cov,
pick_ori='max-power', weight_norm='nai')
raw_proj_filtered_comp = raw_proj_filtered.apply_hilbert()
if do_epochs:
stcs = apply_lcmv_epochs(raw_proj_filtered_comp, filters, return_generator=False)
else:
stcs = apply_lcmv_raw(raw_proj_filtered_comp, filters, verbose=True)
print('Extracting label time course...')
atlas = f'{subjects_dir}/{subject}/mri/aparc.a2009s+aseg.mgz'
label_ts = mne.extract_label_time_course(stcs, atlas, fwd['src'], return_generator=False)
label_ts = [label_ts]
# Power Envelope Correlation
print(f'Computing Power Envelope Correlation for {subject}....Orthogonalize True')
all_corr = envelope_correlation(label_ts, combine=None, orthogonalize="pairwise",
log=True, absolute=True, verbose=None)
print(f'Correlation saved to {corr_true_file_label}')
np.save(corr_true_file_label, all_corr)
del stcs
