derive_temporalnetwork¶

derive_temporalnetwork(data, params)[source]¶

Derives connectivity from the data.

A lot of data is inherently built with edges: (e.g. communication between two individuals).
However other networks are derived from the covariance of time series: (e.g. brain networks between two regions).
Covariance based metrics deriving time-resolved networks can be done in multiple ways.: There are other methods apart from covariance based.
Derive a weight vector for each time point and then the corrrelation coefficient: for each time point.

data : array: Time series data to perform connectivity derivation on. (Default dimensions are: (time as rows, nodes as columns). Change params{‘dimord’} if you want it the other way (see below).
params : dict: Parameters for each method (see below).

method : str: method: “distance”,”slidingwindow”, “taperedslidingwindow”,

“jackknife”, “multiplytemporalderivative”. Alternatively, method can be a weight matrix of size time x time.

Different methods have method specific paramaters (see below)

postpro : “no” (default). Other alternatives are: “fisher”, “boxcox”, “standardize”

and any combination seperated by a + (e,g, “fisher+boxcox”).

See postpro_pipeline for more information.

dimord : str: Dimension order: ‘node,time’ (default) or ‘time,node’. People like to represent their data differently and this is an easy way to be sure that you are inputing the data in the correct way.
analysis_id : str or int: add to identify specfic analysis. Generated report will be placed in ‘./report/’ + analysis_id + ‘/derivation_report.html
report : bool: False by default. If true, A report is saved in ./report/[analysis_id]/derivation_report.html if “yes”
report_path : str: String where the report is saved. Default is ./report/[analysis_id]/derivation_report.html

Distance metric calculates 1/Distance metric weights, and scales between 0 and 1. W[t,t] is excluded from the scaling and then set to 1.

params[‘distance’]: str: Distance metric (e.g. ‘euclidean’). See teneto.utils.get_distance_function for more info

params[‘windowsize’] : int: Size of window.

params[‘windowsize’] : int

Size of window.

params[‘distribution’] : str

Scipy distribution (e.g. ‘norm’,’expon’). Any distribution here: https://docs.scipy.org/doc/scipy/reference/stats.html

params[‘distribution_params’] : dict

Dictionary of distribution parameter, excluding the data “x” to generate pdf.

The data x should be considered to be centered at 0 and have a length of window size.: (i.e. a window size of 5 entails x is [-2, -1, 0, 1, 2] a window size of 6 entails [-2.5, -1.5, 0.5, 0.5, 1.5, 2.5])

Given x params[‘distribution_params’] contains the remaining parameters.

e.g. normal distribution requires pdf(x, loc, scale) where loc=mean and scale=std.

Say we have a gaussian distribution, a window size of 21 and params[‘distribution_params’] = {‘loc’: 0, ‘scale’: 5}.: This will lead to a gaussian with its peak at in the middle of each window with a standard deviation of 5.

params[‘windowsize’] : int: Size of window.

No parameters are necessary.

Optional parameters:

params[‘weight-var’] : array, (optional): NxN array to weight the JC estimates (standerdized-JC*W). If weightby is selected, do not standerdize in postpro.
params[‘weight-mean’] : array, (optional): NxN array to weight the JC estimates (standerdized-JC+W). If weightby is selected, do not standerdize in postpro.

No parameters are necessary.

Returns:	G – Connectivity estimates (nodes x nodes x time)
Return type:	array

About the general weighted pearson approach used for most methods, see: Thompson & Fransson (2019) A common framework for the problem of deriving estimates of dynamic functional brain connectivity. Neuroimage. (https://doi.org/10.1016/j.neuroimage.2017.12.057)