Ordinal methods for a characterization of evolving functional brain networks

TL;DR

This paper reviews ordinal time series analysis methods, emphasizing their robustness and potential to improve understanding of the dynamic interactions within evolving functional brain networks.

Contribution

It summarizes current techniques and highlights limitations, proposing directions for advancing the analysis of brain network dynamics using ordinal methods.

Findings

Ordinal analysis captures meaningful temporal information despite data simplification.

Robustness against noise makes ordinal methods suitable for brain data.

Current limitations suggest need for methodological advancements.

Abstract

Ordinal time series analysis is based on the idea to map time series to ordinal patterns, i.e., order relations between the values of a time series and not the values themselves, as introduced in 2002 by C. Bandt and B. Pompe. Despite a resulting loss of information, this approach captures meaningful information about the temporal structure of the underlying system dynamics as well as about properties of interactions between coupled systems. This - together with its conceptual simplicity and robustness against measurement noise - makes ordinal time series analysis well suited to improve characterization of the still poorly understood spatial-temporal dynamics of the human brain. This minireview briefly summarizes the state-of-the-art of uni- and bivariate ordinal time-series-analysis techniques together with applications in the neurosciences. It will highlight current limitations to stimulate further developments which would be necessary to advance characterization of evolving functional brain networks.