States of dynamic connectivity flow in temporal multiplex networks: A case study in human epilepsy and postictal aphasia

Abstract We present a methodological framework for analyzing multifrequency dynamic functional connectivity (dFC) in electrophysiological recordings. The approach characterizes not only the magnitude of network reconfiguration over time but also whether these changes are spatially random or, instead, spatially organized in ways that drive a slower reconfiguration of modular structure. We define a generative null model of multiscale connectivity fluctuations that differ in their degree of spatiotemporal organization, and we describe dFC flows through the joint assessment of (a) instantaneous reconfiguration speed and (b) the extent and quality of ongoing modular reorganization. Different combinations of these features delineate distinct “flow styles,” ranging from more liquid to more frozen dynamics. As a case study, we apply this framework to SEEG recordings from epileptic patients. We identify transitions between dynamic “allegiance states,” whose flow styles closely mirror those of the null model. Seizure onset is associated with a pronounced slowing of dFC speed, while a specific postictal regime combines low speed with highly frozen allegiance and aligns most strongly with clinician-annotated aphasia. These pilot results suggest that temporal multiplex network analyses can reveal transient, frequency-specific network regimes linked to symptom expression and offers a generalizable tool for dissecting fast network dynamics in intracranial recordings.