Abstract Objective: This paper presents an in-depth analysis of the recent literature on dynamic functional connectivity (dFC) analysis. This represents a paradigm shift in the analysis of neural data to overcome the inherent limitations of static assumptions about functional brain connectivity. By exploiting the information provided by high temporal resolution neuroimaging techniques, such as magnetoencephalography (MEG) and electroencephalography (EEG), the possibility of tracking functional network organization and reconfiguration that support brain functions at different temporal scales has been extensively explored. Approach: This review examines the current state-of-the-art of the methodological approaches for dFC analysis in biomedical science, focusing on literature from 2018 to 2024 and on the analysis of EEG and MEG data. The review primarily concentrates on methods for estimating the time-resolved functional connectivity matrix, also providing an overview of approaches for summarising and inferring dynamic information. Main Results: An insight into the available methodological approaches for tracking dFC at different temporal scales is offered. Besides the classical sliding window method, advances in instantaneous dFC algorithms are described and two novel approaches are introduced: Microstate-based dFC (micro-dFC) and data-driven dFC methods. For each approach, specific features are detailed, and the dataset characteristics to ensure applicability are discussed. In addition, possible post-processing procedures for extracting the dynamic properties and information of interest are presented. Significance: The undoubted potential of dFC analysis for the study of brain dynamics is highlighted, providing a guide for its application, also taking into consideration the study protocol, the nature of the data and the temporal resolution of interest. Current limitations and open challenges are also critically addressed.
Coelli et al. (Mon,) studied this question.