Opportunities and methodological challenges in EEG and MEG resting state functional brain network research
•Resting state EEG and MEG recordings are increasingly used for functional connectivity and functional brain network analysis.•We highlight advantages and disadvantages of methodological choices throughout the recording and analysis pipeline and how this may affect construction of functional connect...
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| Vydáno v: | Clinical neurophysiology Ročník 126; číslo 8; s. 1468 - 1481 |
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| Hlavní autoři: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Netherlands
Elsevier Ireland Ltd
01.08.2015
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| Témata: | |
| ISSN: | 1388-2457, 1872-8952, 1872-8952 |
| On-line přístup: | Získat plný text |
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| Abstract | •Resting state EEG and MEG recordings are increasingly used for functional connectivity and functional brain network analysis.•We highlight advantages and disadvantages of methodological choices throughout the recording and analysis pipeline and how this may affect construction of functional connectivity and networks.•We give several recommendations for subject instructions and data acquisition for resting state neurophysiological research.
Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies. |
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| AbstractList | Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies.Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies. Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies. Highlights • Resting state EEG and MEG recordings are increasingly used for functional connectivity and functional brain network analysis. • We highlight advantages and disadvantages of methodological choices throughout the recording and analysis pipeline and how this may affect construction of functional connectivity and networks. • We give several recommendations for subject instructions and data acquisition for resting state neurophysiological research. •Resting state EEG and MEG recordings are increasingly used for functional connectivity and functional brain network analysis.•We highlight advantages and disadvantages of methodological choices throughout the recording and analysis pipeline and how this may affect construction of functional connectivity and networks.•We give several recommendations for subject instructions and data acquisition for resting state neurophysiological research. Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies. |
| Author | Hillebrand, A. van der Kooi, A.W. Numan, T. Hofman, D. Stam, C.J. van Diessen, E. van Dijk, B.W. van Lutterveld, R. van Dellen, E. van Straaten, E.C.W. Boersma, M. |
| Author_xml | – sequence: 1 givenname: E. surname: van Diessen fullname: van Diessen, E. email: E.vanDiessen-3@umcutrecht.nl organization: Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands – sequence: 2 givenname: T. surname: Numan fullname: Numan, T. organization: Department of Intensive Care, University Medical Center Utrecht, The Netherlands – sequence: 3 givenname: E. surname: van Dellen fullname: van Dellen, E. organization: Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands – sequence: 4 givenname: A.W. surname: van der Kooi fullname: van der Kooi, A.W. organization: Department of Intensive Care, University Medical Center Utrecht, The Netherlands – sequence: 5 givenname: M. surname: Boersma fullname: Boersma, M. organization: Department of Experimental Psychology, Utrecht University, The Netherlands – sequence: 6 givenname: D. surname: Hofman fullname: Hofman, D. organization: Department of Experimental Psychology, Utrecht University, The Netherlands – sequence: 7 givenname: R. surname: van Lutterveld fullname: van Lutterveld, R. organization: Center for Mindfulness, University of Massachusetts School of Medicine, Worcester, Massachusetts, USA – sequence: 8 givenname: B.W. surname: van Dijk fullname: van Dijk, B.W. organization: Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands – sequence: 9 givenname: E.C.W. surname: van Straaten fullname: van Straaten, E.C.W. organization: Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands – sequence: 10 givenname: A. surname: Hillebrand fullname: Hillebrand, A. organization: Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands – sequence: 11 givenname: C.J. surname: Stam fullname: Stam, C.J. organization: Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25511636$$D View this record in MEDLINE/PubMed |
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| Snippet | •Resting state EEG and MEG recordings are increasingly used for functional connectivity and functional brain network analysis.•We highlight advantages and... Highlights • Resting state EEG and MEG recordings are increasingly used for functional connectivity and functional brain network analysis. • We highlight... Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network... |
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| SubjectTerms | Brain - physiology Brain Mapping EEG Electroencephalography - methods Functional connectivity Functional networks Functional Neuroimaging - methods Graph analysis Humans Magnetoencephalography - methods MEG Minimum spanning tree Nerve Net - physiology Neurology Neurons - physiology Resting state |
| Title | Opportunities and methodological challenges in EEG and MEG resting state functional brain network research |
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