frites.conn.conn_pid#

frites.conn.conn_pid(data, y, roi=None, times=None, mi_type='cc', gcrn=True, dt=1, verbose=None, **kw_links)[source]#

Compute the Partial Information Decomposition on connectivity pairs.

This function can be used to untangle how the information about a stimulus is carried inside a brain network.

Parameters
datanumpy:array_like

Electrophysiological data. Several input types are supported :

  • Standard NumPy arrays of shape (n_epochs, n_roi, n_times)

  • mne.Epochs

  • xarray.DataArray of shape (n_epochs, n_roi, n_times)

ynumpy:array_like

The feature of shape (n_trials,). This feature vector can either be categorical and in that case, the mutual information type has to ‘cd’ or y can also be a continuous regressor and in that case the mutual information type has to be ‘cc’

roinumpy:array_like | python:None

Array of region of interest name of shape (n_roi,)

timesnumpy:array_like | python:None

Array of time points of shape (n_times,)

mi_type{‘cc’, ‘cd’}
Mutual information type. Switch between :

  • ‘cc’ : if the y input is a continuous regressor

  • ‘cd’ : if the y input is a discret vector with categorical integers inside

gcrnbool | python:True

Specify if the Gaussian Copula Rank Normalization should be applied. Default is True.

dtpython:int | 1

Number of successive time points to consider when computing MI. Increasing this number increase the smoothness of the results but will also increase computing time.

kw_linkspython:dict | {}

Additional arguments for selecting links to compute are passed to the function frites.conn.conn_links()

Returns
mi_nodenumpy:array_like

The array of mutual infromation estimated on each node of shape (n_roi, n_times)

uniquenumpy:array_like

The unique contribution of each node of shape (n_roi, n_times)

infototnumpy:array_like

The total information in the network of shape (n_pairs, n_times)

redundancynumpy:array_like

The redundancy in the network of shape (n_pairs, n_times)

synergynumpy:array_like

The synergy in the network of shape (n_pairs, n_times)

See also

conn_links, conn_ii

References

Williams and Beer, 2010: [21]

Examples using frites.conn.conn_pid#

PID: Decomposing the information carried by pairs of brain regions

PID: Decomposing the information carried by pairs of brain regions