frites.conn.conn_dfc#

frites.conn.conn_dfc(data, win_sample=None, times=None, roi=None, agg_ch=False, estimator=None, gcrn=False, n_jobs=1, verbose=None, **kw_links)[source]#

Single trial Dynamic Functional Connectivity.

This function computes the pairwise Dynamic Functional Connectivity (DFC) by estimating the statistical dependencies between time-series (possibly on sliding windows) and at the single-trial level using a measure of information. By default, if no estimator are provided the information shared between the time-series of two brain regions is estimated using the Gaussian-Copula Mutual Information (GCMI). However, other estimators can be provided (e.g correlation, distance correlation etc.)

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)

win_samplenumpy:array_like | python:None

Array of shape (n_windows, 2) describing where each window start and finish. You can use the function frites.conn.define_windows() to define either manually either sliding windows. If None, the entire time window is used instead.

timesnumpy:array_like | python:None

Time vector array of shape (n_times,). If the input is an xarray, the name of the time dimension can be provided

roinumpy:array_like | python:None

ROI names of a single subject. If the input is an xarray, the name of the ROI dimension can be provided

agg_chbool | python:False

In case there are multiple electrodes, channels, contacts or sources inside a brain region, specify how the data has to be aggregated. Use either :

agg_ch=False : compute the pairwise DFC aross all possible pairs

agg_ch=True : compute the multivariate information

Note that feature is only available for measures of information supporting multivariate computations.

estimatorfrites.estimator | python:None

Estimator in order to measure the amount of information shared between two time-series coming from two distinct brain regions. Note that if you want to privide an estimator, be sure that it is made for continuous variables (mi_type=’cc’). By default the Gaussian-Copula mutual-information is used.

n_jobspython:int | 1