conpy.connectivity.dics_connectivity

conpy.connectivity.dics_connectivity(vertex_pairs, fwd, data_csd, reg=0.05, coh_metric='absolute', 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_pairspair 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.

fwdinstance 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_csdinstance of CrossSpectralDensity

The cross spectral density of the data.

regfloat

Tikhonov regularization parameter to control for trade-off between spatial resolution and noise sensitivity. Defaults to 0.05.

coh_metric‘absolute’ | ‘imaginary’

The coherence metric to use. Either the square of absolute coherence (‘absolute’) or the square of the imaginary part of the coherence (‘imaginary’). Defaults to ‘absolute’.

n_anglesint

Number of angles to try when optimizing dipole orientations. Defaults to 50.

block_sizeint

Number of pairs to process in a single batch. Beware of memory requirements, which are n_jobs * block_size. Defaults to 10000.

n_jobsint

Number of blocks to process simultaneously. Defaults to 1.

verbosebool | str | int | None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns
connectivityinstance 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.