Transcript-based estimators for characterizing interactions

TL;DR

This paper revisits transcript-based estimators to analyze interactions in complex systems, demonstrating their application to human brain dynamics and revealing new insights into spatial-temporal interactions during different vigilance states.

Contribution

It introduces the application of transcript-based estimators to real-world brain data, showcasing their potential to uncover intricate interactions in multichannel recordings.

Findings

Transcript-based estimators effectively characterize brain interactions.

Methods reveal differences in brain dynamics across vigilance states.

Application to real data demonstrates practical utility.

Abstract

The concept of transcripts was introduced in 2009 as a means to characterize various aspects of the functional relationship between time series of interacting systems. Based on this concept that utilizes algebraic relations between ordinal patterns derived from time series, estimators for the strength, direction, and complexity of interactions have been introduced. These estimators, however, have not yet found widespread application in studies of interactions between real-world systems. Here, we revisit the concept of transcripts and showcase the usage of transcript-based estimators for a time-series-based investigation of interactions between coupled paradigmatic dynamical systems of varying complexity. At the example of a time-resolved analysis of multichannel and multiday recordings of ongoing human brain dynamics, we demonstrate the potential of the methods to provide novel insights into the intricate spatial-temporal interactions in the human brain underlying different vigilance states.