Partial event coincidence analysis for distinguishing direct and indirect coupling in functional network construction

Abstract

Correctly identifying interaction patterns from multivariate time series presents an important step in functional network construction. In this context, the widespread use of bivariate statistical association measures often results in a false identification of links because strong similarity between two time series can also emerge without the presence of a direct interaction due to intermediate mediators or common drivers. In order to properly distinguish such direct and indirect links for the special case of event-like data, we present here a new generalization of event coincidence analysis to a partial version thereof, which is aimed at excluding possible transitive effects of indirect couplings. Using coupled chaotic systems and stochastic processes on two generic coupling topologies (star and chain configuration), we demonstrate that the proposed methodology allows for the correct identification of indirect interactions. Subsequently, we apply our partial event coincidence analysis to multi-channel EEG recordings to investigate possible differences in coordinated alpha band activity among macroscopic brain regions in resting states with eyes open (EO) and closed (EC) conditions. Specifically, we find that direct connections typically correspond to close spatial neighbors while indirect ones often reflect longer-distance connections mediated via other brain regions. In the EC state, connections in the frontal parts of the brain are enhanced as compared to the EO state, while the opposite applies to the posterior regions. In general, our approach leads to a significant reduction in the number of indirect connections and thereby contributes to a better understanding of the alpha band desynchronization phenomenon in the EO state.

Ordinal methods for a characterization of evolving functional brain networks

“…Given this imbalance, many studies in the neurosciences (as well as in other disciplines) employed ordinal time-series-analysis techniques to either estimate the direction or the strength of interactions, or estimated both but by employing ordinal techniques that were derived from different concepts. In the latter case, it is important to note that the techniques' efficiency may be influenced differently by a number of confounding factors: volume conduction effects 136,137 , propagation delays and delayed couplings 70,138,139 , asymmetric signal-to-noise ratios [140][141][142] or eigenfrequency ratios 141,143 (in case of oscillating (sub)systems), peculiarities of the recording [144][145][146][147][148][149][150] , pre-processing steps such as filtering 151,152 , the techniques' capability to distinguish between (apparent) interdependencies due to common sources and (true) interdependencies due to interacting (sub)systems 141,147 , the techniques' capability to distinguish between direct and indirect interactions 80,[153][154][155] , or the techniques' different sensitivities for the various types of synchronization phenomena, to name just a few. Many confounding factors can be identified by investigating e.g.…”