Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, there has been a growing interest to examine the temporal dynamics of the brain's network activity. While different approaches to capture fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. Temporal network theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences and engineering. The objective of this paper is twofold: (i) to present a detailed description of the central tenets and outline measures from temporal network theory; (ii) apply these measures to a resting-state fMRI dataset to illustrate their utility. Further, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this paper are freely available as a python package Teneto. (7,8,9), yielding knowledge about functional network properties (10,11,12,13) which has been applied to clinical populations (14,15).In parallel to research on the brain's connectome, there has been a focus on studying the dynamics of brain activity. When the brain is modeled as a dynamic system, a diverse range of properties can be explored, prominent examples of this are metastability (16,17,18,19,20) and oscillations (21,22,23). Brain oscillations, inherently dynamic, have become a vital ingredient in proposed mechanisms ranging from psychological processes such as memory (24, 25, 26), attention (27, 28), to basic neural communication of a top-down and bottom-up type of information transfer (29,30,31,32,33,34,35,36).Recently, approaches to study brain connectomics and the dynamics of neuronal communication have started to merge. A significant amount of work has recently been carried out that aims to quantify dynamic fluctuations of network activity in the brain using fMRI (37,38,39,40,41,42) as well as MEG (7,43,9,44,35). This research area to unify brain connectommics with the dynamic properties of neuronal communication has been called the "dynome" (45) and the "chronnectome" (46). As the brain can quickly fluctuate between different tasks, the overarching aim of this area of research is to understand the dynamic interplay of the brain's networks. The intent of this research is that it will yield insight about the complex and dynamic cognitive human abilities.