The time-varying brain: a comprehensive review of dynamic functional connectivity analysis in EEG and MEG.
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| Title: | The time-varying brain: a comprehensive review of dynamic functional connectivity analysis in EEG and MEG. |
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| Authors: | Coelli S; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano. Piazza Leonardo da Vinci 32, 20133 Milano, Italy., Corda M; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano. Piazza Leonardo da Vinci 32, 20133 Milano, Italy., Bianchi AM; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano. Piazza Leonardo da Vinci 32, 20133 Milano, Italy.; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy. |
| Source: | Journal of neural engineering [J Neural Eng] 2025 Oct 30; Vol. 22 (5). Date of Electronic Publication: 2025 Oct 30. |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004- |
| MeSH Terms: | Magnetoencephalography*/methods , Magnetoencephalography*/trends , Electroencephalography*/methods , Electroencephalography*/trends , Brain*/physiology , Nerve Net*/physiology, Humans ; Time Factors ; Connectome/methods |
| 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. (Creative Commons Attribution license.) |
| Contributed Indexing: | Keywords: dynamic functional connectivity; electroencephalography; magnetoencephalography; sliding windows; time-varying modelling |
| Entry Date(s): | Date Created: 20251013 Date Completed: 20251030 Latest Revision: 20251030 |
| Update Code: | 20251030 |
| DOI: | 10.1088/1741-2552/ae1258 |
| PMID: | 41082873 |
| Database: | MEDLINE |
Nájsť tento článok vo Web of Science | 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.<br /> (Creative Commons Attribution license.) |
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| ISSN: | 1741-2552 |
| DOI: | 10.1088/1741-2552/ae1258 |