Elsevier

Clinical Neurophysiology

Volume 126, Issue 8, August 2015, Pages 1468-1481
Clinical Neurophysiology

Review
Opportunities and methodological challenges in EEG and MEG resting state functional brain network research

https://doi.org/10.1016/j.clinph.2014.11.018Get rights and content

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.

Abstract

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.

Section snippets

Introduction and rationale

In recent years, there has been a growing interest in characterizing the functional network of the brain ‘at rest’. This so-called ‘resting state’ paradigm is believed to reflect intrinsic activity of the brain, which may reveal valuable information on how different brain areas communicate (Greicius et al., 2003, Deco et al., 2011, Birn, 2012). It has linked spontaneous – task independent – fluctuations in neural activity to diseases, cognitive decline, and disturbances in consciousness (

What is ‘resting state’ and how does it affect the recording?

Resting state is the state in which a subject is awake and not performing an explicit mental or physical task. Traditionally, the ‘resting state’ condition was commonly used in EEG research – besides event-related potential studies – to study patterns of brain activity, whereas fMRI research was mainly focused on alterations in activity during task performance. Early EEG studies, including the first EEG recordings performed by Berger (Berger, 1929), already provided evidence for patterns of

Choice of reference

In contrast to MEG, the electric potentials measured by EEG electrodes are defined with respect to a reference. Besides bipolar recordings, in which EEG activity is defined by the electric potential difference between two electrodes, EEG recordings often use a single common reference such as auricular, mastoid or central electrode as reference. These conventional reference montages are confounded by brain activity that will eventually affect further analysis. As a result, recordings are often

Connectivity measures

To investigate functional interactions between brain regions, EEG and MEG studies have used different connectivity measures, for an overview see (Pereda et al., 2005, Stam, 2005, Bonita et al., 2014). The quantification of interacting brain regions can be subdivided into functional and effective connectivity measures (Friston, 1994, Friston, 2011). Connectivity measures are based on statistical interdependencies between signals (Aertsen et al., 1989). The extent to which brain regions are

Functional networks

Resting state EEG and MEG data can be used to construct connectivity matrices and, consequently, functional networks by using network analysis (Sporns et al., 2004, Bullmore and Sporns, 2009, Stam, 2010). In contrast to connectivity measures, which only provide information on how pairs of different brain regions are (functionally) connected, network analysis characterizes the organization of networks (Stam and van Straaten, 2012b). Complex network analysis, a branch of graph theory, reduces the

Conclusions and suggestions for future research

We have summarized several problems and challenges by reviewing current practice in resting state functional connectivity EEG and MEG research. First, performing a resting state recording might not be as straightforward as it seems; behavior during, and perception of, a stimulus independent condition may vary greatly between subjects despite similar instructions (Diaz et al., 2013). In our overview, we differentiated subject-related from measurement-related methodological issues. For future

Acknowledgments

Eric van Diessen was financially supported by the Dutch National Epilepsy Fund (NEF 09-93). We are thankful for the constructive comments of the anonymous reviewers.

Conflict of interest: None.

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    These authors contributed equally.

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