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.

Background: Cognitive resources are allocated based on task difficulty, duration and immersion level. The development of virtual reality (VR) technology, based on head-mounted displays (HMDs), has led to increased immersive user experiences, affecting mental load and effort, as measured by Electroencephalography (EEG). However, brain dynamics under different difficulty and immersive levels have not been fully described. Methods: EEG recordings and microstate (MS) analysis were used in classical desktop (DSK) and HMD-based stimulation environments to explore the effects of cognitive demand and immersion level on brain dynamics and their relationship with attention domains. Twenty-seven healthy participants performed a two-level N-back task in both environments, while EEG signals were acquired. Brain dynamics were evaluated through MS-based dynamic parameters and power-based indices were computed to estimate attention and engagement. Results: The increased task difficulty modulates EEG microstate dynamics, regardless of the immersion level. Specifically, the highest mental demand was associated with an increased presence of MS D, related to the task active network, confirming its involvement in cognitive tasks. Conclusions: Although immersion level did not significantly influence MS dynamics, it affected the relationship with attention level, with a stronger correlation observed in the DSK condition and a weaker one in the HMD scenarios.