The identification of temporal communities through trajectory clustering correlates with single-trial behavioural fluctuations in neuroimaging data

Abstract

Interacting sets of nodes and fluctuations in their interaction are important properties of a dynamic network system. In some cases the edges reflecting these interactions are directly quantifiable from the data collected. However, in many cases (such as functional magnetic resonance imaging (fMRI) data), the edges must be inferred from statistical relations between the nodes. Here we present a new method, Temporal Communities through Trajectory Clustering (TCTC), that derives time-varying communities directly from time-series data collected from the nodes in a network. First, we verify TCTC on resting and task fMRI data by showing that time-averaged results correspond with expected static connectivity results. We then show that the time-varying communities correlate and predict single-trial behaviour. This new perspective on temporal community detection of node-collected data identifies robust communities revealing ongoing spatiotemporal community configurations during task performance.