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

We present a methodological framework for analysing multi-frequency dynamic functional connectivity (dFC) in electrophysiological recordings. The approach characterises not only the magnitude of network reconfiguration over time, but also whether these changes are spatially random or, instead, spatially organised in ways that drive a slower reconfiguration of modular structure. We define a generative null model of multi-scale connectivity fluctuations that differ in their degree of spatiotemporal organisation, and we describe dFC flows through the joint assessment of (i) instantaneous reconfiguration speed and (ii) the extent and quality of ongoing modular reorganisation. 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 post-ictal 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 generalisable tool for dissecting fast network dynamics in intracranial recordings. Author SummaryCognitive functions rely on the brains capacity to continually reorganize interactions among neuronal populations. Capturing this flexibility requires describing not only average functional network structure but also how it evolves over time. Using time- and frequency-resolved coherence from intracranial recordings in epilepsy patients, we model dynamic functional connectivity as a temporal multiplex network spanning multiple frequency bands. We introduce a frame-work that distinguishes different "styles" of network reconfiguration--faster or slower, and more structured or more random.