Connectivity#

frites.conn:

Information-based connectivity metrics and utility functions.

This submodule contains two types of functions :

  1. Connectivity metrics : methods to estimate either the undirected or directed connectivity. Some methods are performed within-trials and others across-trials. In the case of within-trials metrics, it is then possible to estimate if the connectivity is modulated by the task by passing the connectivity arrays to frites.workflow.WfMi

  2. Connectivity related utility functions : small utility functions that work on connectivity arrays

Connectivity metrics#

conn_dfc(data[, win_sample, times, roi, ...])

Single trial Dynamic Functional Connectivity.

conn_covgc(data, dt, lag, t0[, step, roi, ...])

Single-trial covariance-based Granger Causality for gaussian variables.

conn_ccf(data[, times, roi, normalized, ...])

Single trial Cross-Correlation Function.

conn_spec(data[, freqs, metric, roi, times, ...])

Wavelet-based single-trial time-resolved spectral connectivity.

conn_te(data[, times, roi, min_delay, ...])

Compute the across-trials transfer entropy (TE).

conn_ii(data, y[, roi, times, mi_type, ...])

Interaction Information on connectivity pairs and behavioral variable.

conn_pid(data, y[, roi, times, mi_type, ...])

Compute the Partial Information Decomposition on connectivity pairs.

conn_fit(data, y[, roi, times, mi_type, ...])

Feature-specific information transfer.

Utility functions#

Reshaping connectivity outputs#

conn_reshape_undirected(da[, sep, order, ...])

Reshape a raveled undirected array of connectivity.

conn_reshape_directed(da[, net, sep, order, ...])

Reshape a raveled directed array of connectivity.

conn_ravel_directed(da[, sep, drop_within])

Ravel a directed array.

conn_net(da[, roi, order, sep, invert, verbose])

Compute the net on directed connectivity.

conn_links(roi[, directed, net, ...])

Construct pairwise links for functional connectivity.

conn_get_pairs(roi[, directed, nb_min_suj, ...])

Get possible connectivity pairs for multiple subjects.

Metrics to apply on FC#

conn_fcd_corr(conn[, roi, times, tskip, ...])

Compute the correlation on dynamic network.

Define sliding windows#

define_windows(times[, windows, slwin_len, ...])

Define temporal windows.

plot_windows(times, win_sample[, x, title, ...])

Simple plotting function for representing windows.