Pair-wise Clustering of Large Scale Granger Causality Index Matrices for Revealing Communities

The analysis of large ensembles of time series is a fundamental challenge in different domains of biomedical image processing applications, specifically in the area of functional MRI data processing. An important aspect of such analysis is the ability to reconstruct community network structures based on interactive behavior between different nodes of the network which are captured in such time series. In this study, we start with a previously proposed novel approach that applies the linear Granger Causality concept to very high-dimensional time series. This approach is based on integrating dimensionality reduction into a multivariate time series model. If residuals of dimensionality reduced models can be transformed back into the original space, prediction errors in the high-dimensional space may be computed, and a large scale Granger Causality Index (lsGCI) is properly defined. The primary goal of this study was then to present an approach for recovering network structure from such lsGCI interactions through the application of pair-wise clustering. We specifically focus on a clustering approach based on topographic mapping of proximity data (TMP) for this purpose. We demonstrate our approach with a simulated network composed of five pair-wise different internal networks with varying strengths of community structure (based on the number of inter-network vertices). Our results suggest that such pair-wise clustering with TMP is capable of reconstructing the structure of the original network from lsGCI matrices that record the interactions between different nodes of the network when there is sufficient disparity between the intra- and inter-network vertices.