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Address correspondence to: Thomas Koenig, Translational Research Center, University Hospital of Psychiatry, University of Bern, Bolligenstrasse 111, 3000 Bern 60, Switzerland, E-mail: [email protected]
Dear Editor,
It was with great interest that we read the recently published article by Hatz and colleagues (2016) in Brain Connectivity entitled "Reliability of functional connectivity of EEG applying microstates-segmented versus classical calculation of phase lag index." The article rightfully argues that measures of brain connectivity based on frequency domain indices of connectivity may be problematic, because these measures assume the signals to be stationary during the usually arbitrarily selected analysis windows. For the analysis of connectivity, it may thus be more appropriate to apply data-driven parcellation procedures that identify time periods that assumingly consist of singular and transiently stable patterns of connectivity before these patterns of connectivity are further quantified. This point is well taken, and is certainly worthwhile and timely to address. At the same time, any decomposition of the electroencephalography (EEG), and thus also any analysis of connectivity among EEG subcomponents, requires specific a priori models that define what constitutes a component, and how different components can be uniquely isolated. Departing from very different choices of how such separations may be obtained and justified, the currently available methodology offers several methods to quantify brain functional connectivity based on resting-state EEG data. When combining methods that assess brain connectivity, it is thus essential that we are aware of these a priori choices, because they imply very different definitions of what constitutes "being connected."
The article we are commenting on used a combination of two methods to investigate brain connectivity, namely the so-called microstate analysis (Pascual-Marqui et al., 1995) and the phase-locking index (PLI) (Stam et al., 2007). The authors reported that when EEG data were parcellated into time periods that correspond to the presence of particular microstates, that is, time periods of quasi-stable scalp field configuration, the test-retest reliability of connectivity patterns as obtained by the analysis of lagged coherence...