EEG microstates

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Dietrich Lehmann et al. (2009), Scholarpedia, 4(3):7632. doi:10.4249/scholarpedia.7632 revision #88985 [link to/cite this article]
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A complex system such as the brain that comprises many local functional states can be said to be in one particular global functional state at each moment in time (Ashby, 1960). Brain states change in a non-continuous manner: brain functional state over time shows extended periods during which there is small variance of state; these periods of quasi-stability are concatenated by rapid and major changes of state. An example is wakeful consciousness and its sudden disappearance with sleep onset. Such state changes are associated with major changes in brain electric activity as recorded from the scalp of the intact human head as electroencephalogram ("EEG"). In the sub-second time range which is relevant for human conscious mentation and for useful interaction with the environment, brain electric activity can be parsed into brief split second microstates characterized by quasi-stable spatial distributions (landscapes) of electric potential that are connected by quick changes in landscapes. As different electric potential landscapes must have been generated by different distributions of neuronal electric activity in the brain, it is reasonable to assume that different microstates embody different functions of the brain. The experimental results suggest that the seemingly continual stream of consciousness is incorporated by successive steps of brain operations, reminiscent of the flight-perch-sequences of subjective experience (James, 1890). Microstate analysis has begun to develop a dictionary of functions of these sub-second brain microstates and to explore their syntax.

Contents

Brain electric fields

Brain electric field data (EEG and event-related potentials [ERP]) recorded simultaneously from many electrodes (locations) on the human head surface can be viewed as series of maps of the momentary spatial distributions of electric potential, as 'potential landscapes' (Lehmann, 1971, 1972). Typically, 128 to 512 maps per second are used.

The historical and unfortunate discussions in the EEG community about the choice of a presumable 'inactive' electric reference location are not an issue here, because a given landscape cannot be changed by the location of the point from which it is measured; this choice merely determines the value labels of the isopotential lines - quite like the rising or receding water level of a lake in a mountainous area changes the location of the zero water level mark, but not the landscape (Lehmann, 1987; Geselowitz, 1998).

Over time, the potential landscapes vary in electric strength. Map Hilliness (Lehmann, 1971) assesses map strength; it is defined as the sum of the absolute microvolt values measured at all electrodes divided by the number of electrodes; the assessment must be done after the values in each map have been expressed as deviations from the mean of all momentary values (spatial DC offset removal, 'average reference'). Global Field Power is a related, parametric assessment of map strength, computed as standard deviation of the momentary potential values (Lehmann and Skrandies, 1980).

Over time, the potential landscapes vary also in configuration. For numerical comparisons of map landscapes, Global Map Dissimilarity is computed (Lehmann and Skrandies, 1980): The two maps to be compared are average-referenced and scaled to unity Global Field Power; then, one map is subtracted form the other one. The value of Global Field Power of the resulting difference map is the magnitude of Global Map Dissimilarity.

Statistical comparison of potential landscapes between experimental conditions or between different groups of subjects uses as dependent measure Global Map Dissimilarity, or extracted parameters such as the location of the two centroid locations of the map's positive and negative potential areas (Wackermann et al., 1993) or the electric gravity center (the mean of the two centroid locations); all are strength-independent measures. Such analyses determine whether different neuronal generators have been involved in the different conditions or groups at a given time. Typically, non-parametric randomization tests are used (Karniski et al. 1994; Kondakor et al., 1995; Strik et al., 1998; see Murray et al., 2008). Statistical assessment of the specificity of the microstates for different experimental conditions has been achieved by spatial fitting procedures using Global Map Dissimilarity as metric (Brandeis et al., 1992; Pegna et al., 1997; Michel et al., 1999; Michel et al., 2001; Murray et al., 2008).


Parsing the series of momentary potential maps into microstates

In continually recorded human EEG, series of momentary maps of electric potential landscapes during task-free resting show discontinuous changes of landscapes (Lehmann, 1971, 1972). The movie (Fig. 1) visualizes this: it shows the sequence of EEG landscapes recorded from 19 electrodes during a 2 second epoch from a healthy young man who was asked to relax with closed eyes (128 maps per second; the head is seen from above, nose up; red are positive, blue are negative potential regions referenced to the mean of all momentary potentials).

Figure 1: Scalp field potential distribution maps during 2 seconds (time-stretched into about 25 sec, endless loop). Head seen from above, nose up; red positive, blue negative potential areas.

Map strength in general is irrelevant for landscape comparisons: only the spatial configuration of the potential distribution is considered when assessing map similarity. In the case of EEG where there is oscillatory activity of the generator processes, polarity also is irrelevant. In the case of event-related potential (ERP) maps, map polarity is important; polarity was used to label the conventional 'components', the peaks and troughs of ERP waveshapes.

In EEG as well as ERP map series, for brief, sub-second time periods, map landscapes typically remain quasi-stable, then change very quickly into different landscapes. A sequential microstate analysis approach first showed the feature of non-continuity of landscape changes in spontaneous EEG, using plots of the electrode locations of extreme (maximum or minimum) potential values over time. Fig. 2 shows such plots for the movie sequence of Fig. 1
Figure 2: Plots of the % time that a maximum (left) or minimum (right) potential value was observed at the 19 electrode positions in the movie sequence of Fig. 1. On brown: >4% time.
; Fig. 3 illustrates mean results from 5 subjects.
Figure 3: after Fig. 9 in Lehmann, 1971.
These plots demonstrated that extreme potentials occur in restricted scalp regions, residing in a given region for several successive maps, then jumping to another region. Thus typically, step-wise changes and not continual 'travelling' of the extreme locations are observed (Lehmann, 1971). Using the entire information in the maps, curves of Global Map Dissimilarity for pairs of successive maps over time determined microstate changes that are represented by peaks in the curve of ERP data (Lehmann and Skrandies 1980, 1984; Brandeis and Lehmann, 1986; Michel et al., 1992; Michel and Lehmann, 1993) and EEG data (Lehmann et al., 1987). Post-hoc, microstates can be clustered into a limited number of landscape classes (Wackermann et al., 1993; Strik and Lehmann, 1993). The global microstate analysis approach clusters all maps that are to be analyzed into a preselected or self-determined (via cross-validation), finite number of landscape classes applying Global Map Dissimilarity (Pascual-Marqui et al., 1995; Koenig et al., 1999; Michel et al., 1999) as illustrated in Fig. 4 for spontaneous EEG and Fig. 5 for ERP data.
Figure 4: Microstate Segmentation of 4 seconds of spontaneous EEG using a cluster analysis. The waveshapes represent eyes-closed EEG recorded from 42 electrodes. For each time point, the potential distribution map was calculated and all maps of the 4 seconds were subjected to a k-means cluster analysis. A cross-validation criterion identified four dominant maps. Fitting these maps back to the original data revealed that each map appeared repeatedly and dominated during certain time segments, the 'microstates'. These microstates are color-coded in the curve of Global Field Power at the bottom, and marked by numbers under this curve; they were then classified (illustrated in the 3rd row) according to the standard microstate classes of Fig. 6.
Figure 5: Microstate analysis of event-related potentials (ERP). Subjects were listening to series of tones of two different frequencies presented in random sequence, one frequently (75%), the other rarely (25%). Subjects were instructed to count the rare tones. ERP were calculated separately for rare and frequent tones. The overlaid traces of all 128 recording channels are shown in the middle, with black traces for rare tones and red traces for frequent tones. Both ERP were conjointly subjected to a cluster analysis that identified 9 maps best representing the whole dataset. Fitting these maps to the ERP revealed that each map was present during a certain time period (a microstate). These periods are illustrated under the Global Field Power curve of the two conditions. Same color indicates same maps. The initial microstates and the final microstates were the same in the two conditions, but three microstates during 150-650 ms differed. One of them represents the "P300 component". Thus, microstate segmentation of ERP defines components as periods of time with stable map topography that only increase and decrease in strength.


Functional significance of EEG microstates

In spontaneous EEG, four standard classes of microstate landscapes were distinguished (Fig. 6), whose parameters (e.g. duration, occurrences per second, covered percentage of analysis time) change as function of age (Koenig et al., 2002).
Figure 6: The four microstate classes (standard classes) as identified in the spontaneous EEG of 496 healthy people (6 to 80 year-olds). The mean duration of the microstates is around 80-100 ms and varies with age. Head seen from above, nose up; red positive, blue negative potential areas (after Koenig et al., 2002).
EEG microstates in medication-naïve, first-episode, productive schizophrenics (Koenig et al., 1999; Lehmann et al., 2005; Irisawa et al., 2006) were shortened in two of the four standard classes, and showed aberrant sequencing of the microstate classes (abnormal microstate 'syntax') compared to healthy controls (Lehmann et al., 2005). Chronic schizophrenics with positive symptomatology also exhibited shortened microstate duration (Strelets et al., 2003). The shortening of microstates of certain classes was interpreted as abortive termination of specific steps of information processing that result in the schizophrenic symptomatology of loosened associations. Neuroleptic medication increased microstate duration in schizophrenics (Yoshimura et al 2007). Shortening of microstate duration has also been observed in depressive patients along with increased topographical variance (Strik et al., 1995). In healthy people, microstate durations were found to depend on wakefulness and sleep stage (Cantero et al., 1999, 2002), to decrease in deep hypnosis (Katayama et al., 2007), and to increase in meditation (Faber et al., 2005). Cognition-enhancing medication affected microstate topography in a dose-dependent way (Lehmann et al., 1993). Spontaneous thoughts which are high or low on a visual imagery scale are associated with two different EEG microstate classes immediately before the prompted reports (Lehmann et al., 1998); these spontaneous microstates and event-related microstates 286-354 ms post-stimulus while reading abstract or imagery words (Koenig et al., 1998) when analyzed with tomographic imaging ('LORETA', Pascual-Marqui et al., (1994) showed common activated intracerebral brain areas: left anterior brain areas for abstract, right posterior for imagery (Lehmann et al., 2004).


Microstates as atoms of thought and consciousness

Durations of microstates during spontaneous task-free resting EEG on average are in the range of 70 to 125 milliseconds (Lehmann et al., 1987, 1998, 2005; Koenig et al., 2002). The type of momentary thought (e.g. visual versus abstract thinking) is incorporated in different microstates (Lehmann et al., 1998, 2004). The observations on microstates in spontaneous brain electric activity suggest that the apparent continual "stream of consciousness" consists of concatenated identifiable brief packets in the time range of fractions of seconds, in a time range postulated for ‘elementary deliberations’ (Newell, 1992), for visual and auditory perceptions (Efron, 1970), and as needed or available for changing or bridging perceptual input organization or attention (Michaels and Turvey, 1979; DiLollo, 1980; Reeves and Sperling, 1986; Posner et al., 1987; Motter, 1994). Entry of content chunks into consciousness (e.g., Baars' Global Workspace, Baars, 2007) apparently requires such minimum durations. In sum, the evidence suggests that brain electric microstates qualify for basic building blocks of mentation, as candidates for conscious or non-conscious 'atoms of thought and emotion' (Lehmann 1990; Lehmann et al., 1998, 2004, 2005; Changeux and Michel, 2004).


Event-related microstates

Numerous studies on ERP microstates contribute to a microstate dictionary of different brain functions. For example, subjective contour perception and attention were incorporated in specific ERP microstates (Brandeis and Lehmann, 1989). Specific microstates distinguish visual depth from contour perception (Michel et al., 1992) and perception of color in motion as compared to achromatic moving stimuli (Morand et al., 2000). A microstate has been identified that systematically increased in duration with the angle of rotation of a letter that had to be rotated mentally (Pegna et al., 1997). Similar mental rotation microstates were found for body parts (Overnay et al., 2005; Petit et al., 2006; Arzy et al., 2006). In schizotypy, perceptual aberration of body image correlated with increased duration of the microstate 310-390 ms after task onset that asked to report the orientation of the displayed body image (Arzy et al., 2007). Reading abstract and visual imaginable (concrete) words evoked two different microstate classes around 300 ms after word onset (Koenig et al., 1998; Sysoeva et al., 2007) and during a 40–100 ms microstate (Sysoeva et al., 2007). Priming differently affected ERP microstates to abstract and concrete words (Wirth et al., 2008). An early distinct microstate also was identified for emotional words (Ortigue et al., 2004). When reading emotional words, their emotional valence is represented in an earlier microstate than their arousing strength (Gianotti et al., 2008). Correct rejection of irrelevant visual information is reflected in a specific microstate very early after stimulus presentation (Schnider et al., 2002). Unique microstates have been described for auditory and somatosensory what and where perception (Ducommun et al., 2002; Spierer et al., 2007) as well as for multisensory information processing (Murray et al., 2004). Also reported were pharmacological effects on specific ERP microstates (e.g., Michel et al., 1993).


Microstate-dependent information processing

The general rule that information processing by the brain depends on the brain's momentary functional state also holds at the microstate level: The microstate just before stimulus onset determines how the stimulus is going to be processed. When evoked potentials are separately averaged for different pre-stimulus microstate classes, they drastically differ, despite physically identical stimuli (Kondakor et al., 1997; Lehmann et al., 1994). Different pre-stimulus microstates also change the perception of physically identical stimuli: Specific microstates precede the change of illusory motion perception (Müller et al., 2005) as well as the switch in perception of a Necker-cube (Britz et al., 2008). Perception of emoitional words presented to the left visual field (right hemisphere) is facilitated when a specific microstate is present just before word presentation (Mohr et al., 2005). Together these studies demonstrate the state-dependency of brain information processing in the subsecond time range.


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Internal references

  • Valentino Braitenberg (2007) Brain. Scholarpedia, 2(11):2918.
  • Rodolfo Llinas (2008) Neuron. Scholarpedia, 3(8):1490.
  • Philip Holmes and Eric T. Shea-Brown (2006) Stability. Scholarpedia, 1(10):1838.


See also

Event related potentials, EEG

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