ReviewOpportunities and methodological challenges in EEG and MEG resting state functional brain network research
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.