Coherence-based Time Series Clustering for Brain Connectivity Visualization

Abstract

We develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure for characterizing connectivity in a network by clustering nodes or groups of channels that display high level of coordination as measured by "cluster-coherence". While the most common approach to measuring dependence between clusters is through pairs of single time series, our method proposes cluster coherence which measures dependence between whole clusters rather than between single elements. Thus it takes into account both the dependence between clusters and within channels in a cluster. Using our method, the identified clusters contain time series that exhibit high cross-dependence in the spectral domain. That is, these clusters correspond to connected brain regions with synchronized oscillatory activity. In the simulation studies, we show that the proposed HCC outperforms commonly used clustering algorithms, such as average coherence and minimum coherence based methods. To study clustering in a network of multichannel electroencephalograms (EEG) during an epileptic seizure, we applied the HCC method and identified connectivity in alpha (8-12) Hertz and beta (16-30) Hertz bands at different phases of the recording: before an epileptic seizure, during the early and middle phases of the seizure episode. To increase the potential impact of this work in neuroscience, we also developed the HCC-Vis, an R-Shiny app (RStudio), which can be downloaded from this https://carolinaeuan.shinyapps.io/hcc-vis/.