def test_pick_channels_csd():
"""Test selecting channels from a CrossSpectralDensity."""
csd = _make_csd()
csd = pick_channels_csd(csd, ['CH1', 'CH3'])
assert csd.ch_names == ['CH1', 'CH3']
assert_array_equal(csd._data,
[[0, 6, 12, 18], [2, 8, 14, 20], [5, 11, 17, 23]])
def test_pick_channels_csd():
"""Test selecting channels from a CrossSpectralDensity."""
csd = _make_csd()
csd = pick_channels_csd(csd, ['CH1', 'CH3'])
assert csd.ch_names == ['CH1', 'CH3']
assert_array_equal(csd._data, [[0, 6, 12, 18],
[2, 8, 14, 20],
[5, 11, 17, 23]])
fwd_tan = conpy.forward_to_tangential(fwd_r)
# get pairs for connectivity calculation
pairs = np.load("{}NEMO_ico4_connectivity_pairs.npy".format(meg_dir))
# pairs are defined in fsaverage space, map them to the source space of the current subject
fsaverage_to_subj = conpy.utils.get_morph_src_mapping(
fs_src, fwd_tan['src'], indices=True, subjects_dir=mri_dir)[0]
pairs = [[fsaverage_to_subj[v] for v in pairs[0]],
[fsaverage_to_subj[v] for v in pairs[1]]]
for cond in exp_conds:
print("Calculations for Condition: ", cond)
for freq, vals in freqs.items():
print("Calculations for Frequency: ", freq)
csd = read_csd("{dir}nc_{meg}-csd_{cond}_{freq}.h5".format(
dir=meg_dir, meg=meg, cond=cond, freq=freq))
csd = csd.mean()
csd = pick_channels_csd(csd, fwd_tan['info']['ch_names'])
con = conpy.dics_connectivity(vertex_pairs=pairs,
fwd=fwd_tan,
data_csd=csd,
reg=0.05,
n_jobs=8)
con.save("{dir}nc_{meg}_{cond}_{freq}-connectivity.h5".format(
dir=meg_dir, meg=meg, cond=cond, freq=freq))
# for the baseline conditions:
print("Running Baseline Conditions")
fwd_r = mne.read_forward_solution(
"{dir}nc_{meg}_from-fs_ico4_bas-r-fwd.fif".format(dir=meg_dir,
meg=meg))
# convert the forward model to one that defines two orthogonal dipoles at each source, that are tangential to a sphere
fwd_tan = conpy.forward_to_tangential(fwd_r)
def dics_connectivity(vertex_pairs,
fwd,
data_csd,
reg=0.05,
n_angles=50,
block_size=10000,
n_jobs=1,
verbose=None):
"""Compute spectral connectivity using a DICS beamformer.
Calculates the connectivity between the given vertex pairs using a DICS
beamformer [1]_ [2]_. Connectivity is defined in terms of coherence:
C = Sxy^2 [Sxx * Syy]^-1
Where Sxy is the cross-spectral density (CSD) between dipoles x and y, Sxx
is the power spectral density (PSD) at dipole x and Syy is the PSD at
dipole y.
Parameters
----------
vertex_pairs : pair of lists (vert_from_idx, vert_to_idx)
Vertex pairs between which connectivity is calculated. The pairs are
specified using two lists: the first list contains, for each pair, the
index of the first vertex. The second list contains, for each pair, the
index of the second vertex.
fwd : instance of Forward
Subject's forward solution, possibly restricted to only include
vertices that are close to the sensors. For 'canonical' mode, the
orientation needs to be tangential or free.
data_csd : instance of CrossSpectralDensity
The cross spectral density of the data.
reg : float
Tikhonov regularization parameter to control for trade-off between
spatial resolution and noise sensitivity. Defaults to 0.05.
n_angles : int
Number of angles to try when optimizing dipole orientations. Defaults
to 50.
block_size : int
Number of pairs to process in a single batch. Beware of memory
requirements, which are ``n_jobs * block_size``. Defaults to 10000.
n_jobs : int
Number of blocks to process simultaneously. Defaults to 1.
verbose : bool | str | int | None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Returns
-------
connectivity : instance of Connectivity
The adjacency matrix.
See Also
--------
all_to_all_connectivity_pairs : Obtain pairs for all-to-all connectivity.
one_to_all_connectivity_pairs : Obtain pairs for one-to-all connectivity.
References
----------
.. [1] Gross, J., Kujala, J., Hamalainen, M., Timmermann, L., Schnitzler,
A., & Salmelin, R. (2001). Dynamic imaging of coherent sources:
Studying neural interactions in the human brain. Proceedings of the
National Academy of Sciences, 98(2), 694–699.
.. [2] Kujala, J., Gross, J., & Salmelin, R. (2008). Localization of
correlated network activity at the cortical level with MEG.
NeuroImage, 39(4), 1706–1720.
"""
fwd = pick_channels_forward(fwd, data_csd.ch_names)
data_csd = pick_channels_csd(data_csd, fwd['info']['ch_names'])
vertex_from, vertex_to = vertex_pairs
if len(vertex_from) != len(vertex_to):
raise ValueError('Lengths of the two lists of vertices do not match.')
n_pairs = len(vertex_from)
G = fwd['sol']['data'].copy()
n_orient = G.shape[1] // fwd['nsource']
if n_orient == 1:
raise ValueError('A forward operator with free or tangential '
'orientation must be used.')
elif n_orient == 3:
# Convert forward to tangential orientation for more speed.
fwd = forward_to_tangential(fwd)
G = fwd['sol']['data']
n_orient = 2
G = G.reshape(G.shape[0], fwd['nsource'], n_orient)
# Normalize the lead field
G /= np.linalg.norm(G, axis=0)
Cm = data_csd.get_data()
Cm_inv, alpha = _reg_pinv(Cm, reg)
del Cm
W = np.dot(G.T, Cm_inv)
# Pre-compute spectral power at each unique vertex
unique_verts, vertex_map = np.unique(np.r_[vertex_from, vertex_to],
return_inverse=True)
spec_power_inv = np.array(
[np.dot(W[:, vert, :], G[:, vert, :]) for vert in unique_verts])
# Map vertex indices to unique indices, so the pre-computed spectral power
# can be retrieved
vertex_from_map = vertex_map[:len(vertex_from)]
vertex_to_map = vertex_map[len(vertex_from):]
coherence = np.zeros((len(vertex_from)))
# Define a search space for dipole orientations
angles = np.arange(n_angles) * np.pi / n_angles
orientations = np.vstack((np.sin(angles), np.cos(angles)))
# Create chunks of pairs to evaluate at once
n_blocks = int(np.ceil(n_pairs / float(block_size)))
blocks = [
slice(i * block_size, min((i + 1) * block_size, n_pairs))
for i in range(n_blocks)
]
parallel, my_compute_dics_coherence, _ = parallel_func(
_compute_dics_coherence, n_jobs, verbose)
logger.info('Computing coherence between %d source pairs in %d blocks...' %
(n_pairs, n_blocks))
if numba_enabled:
logger.info('Using numba optimized code path.')
coherence = np.hstack(
parallel(
my_compute_dics_coherence(W, G, vertex_from_map[block],
vertex_to_map[block], spec_power_inv,
orientations) for block in blocks))
logger.info('[done]')
return VertexConnectivity(
data=coherence,
pairs=[v[:len(coherence)] for v in vertex_pairs],
vertices=[s['vertno'] for s in fwd['src']],
vertex_degree=None, # Compute this in the constructor
subject=fwd['src'][0]['subject_his_id'],
)
from matplotlib import pyplot as plt
import mne
from mne.time_frequency import read_csd, pick_channels_csd
from config import fname, subjects, freq_bands
info = mne.io.read_info(fname.epo(subject=subjects[0]))
grads = [info['ch_names'][ch] for ch in mne.pick_types(info, meg='grad')]
csd = read_csd(fname.csd(subject=subjects[0], condition='face'))
csd = pick_channels_csd(csd, grads)
csd = csd.mean([f[0] for f in freq_bands], [f[1] for f in freq_bands])
# Plot theta, alpha, low beta
csd[:3].plot(info, n_cols=3, show=False)
plt.savefig('../paper/figures/csd1.pdf', bbox_inches='tight')
# Plot high beta 1, high beta 2 and low gamma
csd[3:].plot(info, n_cols=3, show=False)
plt.savefig('../paper/figures/csd2.pdf', bbox_inches='tight')
