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EEG Microstates is the term used to describe transient, patterned, and quasi-stable, global topographies of one's EEG. These microstates tend to last anywhere from milliseconds to seconds. These transient periods are hypothesized to be the most basic initializations of human neurologic tasks, and are thus nicknamed "the atoms of thought"[1]. Most microstate data is often obtained from one's Alpha Wave of their EEG signal. [2] This idea of these microstates being "quasi-stable" means that the "global [EEG] topography is fixed, but strength might vary and polarity invert." [3]
Drs. Thomas Koenig (University Hospital of Psychiatry, Switzerland) and Dietricht Lehmann (KEY Institute for Brain-Mind Research, Switzerland) are often credited as the pioneers of EEG Microstate analysis. [2]. In their 1999 paper in the "European Archives of Psychiatry and Clinical Neuroscience"[1], Koenig and Lehmann had been analyzing the EEGs of schizophrenic patients in order to investigate the potential basic cognitive roots of the disorder. They began to turn their attention to the EEGs on a millisecond scale. They determined both normal subjects and schizophrenic patient shared these microstates, but they varied in characteristics between the two groups, and concluded,
" Momentary brain electric field configurations are manifestations of momentary global functional state of the brain. Field configurations tend to persist over some time in the sub-second range ("microstates") and concentrate within few classes of configurations. Accordingly, brain field data can be reduced efficiently into sequences of re-occuring classes of brain microstates, not overlapping in time. Different configurations must have been caused by different active neural ensembles, and thus different microstates assumedly implement different functions."[1]
Isolating and analyzing one's EEG microstate sequence is a post-hoc operation that typically utilizes several averaging and filtering steps. When Koenig and Lehman ran their experiment in 1999 they constructed these sequences by starting from a subject's eyes-closed resting state EEG. The first several event-free minutes were isolated, then epochs of around 2 seconds each were refiltered (bandpass ≈ 2-20 Hz). Once the epochs were filtered, these microstates were clustered into mean ' 'classes' ' via K-Means Clustering, post hoc. [1]
"Similarity of EEG spatial configuration of each prototype map with each of the 10 maps is computed using the coefficient of determination to omit the maps' polarities. ..."Separately for each class the prototype maps are updated combining all assigned maps by computing the first spatial principal component( Principal Component Analysis ) of the maps and thereby maximizing the common variance while disregarding the map polarity." This process is repeated several times using different randomly selected prototype maps from among the collected data.[1]
"The optimal number of classes is determined by the minimum of the cross validation index which considers both the number of used classes and the percent variance explained by the class mean maps [4] A mean map of each class can be computed by arbitrarily selecting maps as prototypes, then "testing, in each subject, all possible permutations of the...individual microstate maps for the best fit with the...prototype maps and updating the protoypes by averaging the best-fit permutated individual microstate maps." [1] This process is repeated with new randomly chosen prototype maps until the assignments no longer change. The resulting map with the smallest inter-subject variance is chosen as the mean map for that certain class. Then for each subject their 2-second epochs are assigned to a the class of "minimal average dissimilarity" (ANOVA). [1] There are most commonly 4 microstate classes determined by this method.[3]
In most studies[1][5][6][7][8][9][10] there turns out to be the same 4 classes of microstates topographies:
Typical 4-class microstate topography sequence
A: right-frontal to left-posterior
B: left-frontal to right-posterior
C: frontal to occipital
D: mostly frontal and medial to slightly less occipital activity than class C
It is the current hypothesis that EEG Microstates represent the basic steps of cognition and neural information processing in the brain, but here's still much research that needs to be done to cement this theory.
This study investigated EEG Microstate variance across normal humans of varying age. It showed a "lawful, complex evolution with age" [11]with spikes in mean microstate duration around ages 12, 16, 18, and 40-60 years, suggesting that there is significant cerebral evolution occurring at those ages. [11]. As for the cause of this, they hypothesized that it was due to the growth and restructure of neural pathways,
"In studies on the micro-architecture of developing brain tissue, it has been observed that after an initial excess of relatively unorganized synaptic connections, the number of synapses gradually decreased, while the degree of organization of the connections increased (Huttenlocher, 1979; Rakic et al., 1986). It is thus more likely that the observed changes in microstate profile result form the elimination of non-functional connections rather than from the formation of new ones. Another possible relation of the present results with neurobiological processes comes form the observation that with increasing age, asymmetric microstates diminish, while symmetric microstates increase. Assuming that asymmetric mi- crostates result from predominantly unilateral brain activity, while symmetric microstates indicate predominantly bilateral activity, the observed effects may be related to the growth of the corpus callosum, which continues until late adolescence (e.g., Giedd et al., 1999)."[11]
In a more recent and groundbreaking study done by researchers in Geneva, the temporal dynamics and possible fractal properties of EEG microstates were analyzed in normal human subjects. Since microstates are a global topography, but occur on such small time scales and change so rapidly, Van De Ville, Britz, and Michel hypothesized that these "atoms of thoughts" are fractal like in the temporal dimension. That is, whether scaled up or scaled down, an EEG is itself a composition of microstates. This hypothesis was initially illuminated by the strong correlation between the rapid time scale and transience of EEG microstates and the much slower signals of a resting state fMRI.
"The connection between EEG microstates and fMRI resting state networks (RSNs) was established by convolving the time courses of the occurrence of the different EEG microstates with the hemodynamic response function (HRF) and then using these as regressors in a general linear model for conventional fMRI analysis. Because the HRF acts as a strong temporal smoothing filter on the rapid EEG-based signal, it is remarkable that statistically significant correlations can be found. The fact that this smoothing did not remove any information-carrying signal from the microstate sequence and that furthermore the original microstate sequences and the regressors show the same relative behavior at temporal scales about two orders of magnitude apart suggests that the time courses of the EEG microstates are scale invariant."
This scale-invariant dynamic is the strongest characteristic of a fractal, and since microstates are indicative of global neuronal networks, it is justifiable to conclude that these microstates exhibit temporally monofractal (one-dimentsional fractal) behavior. From here we can see the possibility that fMRI, which is also a global topography measure, is possibly just a scaled-up manifestation of its microstates, and thus further supports the hypothesis that EEG microstates are the fundamental unit of one's global cognitive processing.
Comparing normal humans' EEG Microstate classes to those of psychologic patients have yielded important results suggesting that the basic resting-state condition of these patients' brains are irregular. This implies that before any information is processed or created, it is bound to the dynamics of the irregular microstate sequencing. [1][12][13][14][15][16][17]. Although microstate analysis has great potential to help understand the basic mechanisms of some neurological diseases, there is still much work and understanding that needs to be developed before it can be a widely accepted diagnostic.[2]
Koenig & Lehmann, in their breakthrough 1999 study, looked at microstates of schizophrenics versus control patients. Schizophrenia is one of the main disorders studied with EEG Microstate analysis. The resting state EEGs were found to have aberrant microstates classes that lasted either too long or too short in comparison to analogous microstate classes of normal humans. [1] Schizophrenics' brains spend too much time in a topographically right-anterior to left-posterior (referred to as "Class A") microstate orientation, which maps to unfocused, frontal lobe-quiescent states, and too little time in focused frontal lobe-strong attentional states. Schizophrenic patients spent an average of 24.7% of the observed time in this Class A microstate, versus the controls who spent an average of 19% of observed time in Class A.[1] These inappropriate microstate durations occur intermittently in an otherwise normal microstate sequence. This further supports the theory that microstates are the basic steps of cognition, for if such a small scale irregularity causes such a dramatic disorder it must be a precursor for both basic and higher level brain function. [1]. It is also noteworthy that schizophrenic-like behavior can be induced by manipulating a normal human's alpha wave frequency. [2]
In July of 2011, Dr. Koenig collaborated with researchers from Kanazawa University in Japan, and from the University of Bern in Switzerland to do a microstate analysis on patients with Panic Disorder (PD). They found that these patients spent too much time in the same right-anterior to left-posterior microstate as in the schizophrenic studies. .[18]. This would suggest a temporal lobe malfunction, which has been reported in fMRI studies of patient with PD. The patients spent an average of 9.26 milliseconds longer in this microstate than did control subjects. These aberrant microstate sequences are very similar to those in the Schizophrenia study, and as anxiety is the most prevalent symptom of schizophrenia, we may be seeing a strong correlation between different severities of neurological pathologies and a patient's microstate sequence.
In 2004, Cantero, Atienza, Salas, and Gómez studied alpha rhythms in normal human subjects during 3 different drowsy/sleep states: eyes closed/relaxing, drowsiness at sleep onset, and REM sleep. They found that the mean determined microstate classes were different amongst consciousness states on 3 different parameters.[19]
Mean microstate duration was longer during eyes closed relaxation than the other 2 states
Total number of microstates per second was greatest during drowsiness at sleep onset
The number of classes determined was also greatest during drowsiness at sleep onset [19]
This study yet again illuminates the complexity of brain activity and EEG dynamics. The data suggest that "alpha (wave) activity could be indexing different brain information in each arousal state."[19] Furthermore, they suggest that the alpha rhythm could be the "natural resonance frequency of the visual cortex during the waking state, whereas the alpha activity that appears in the drowsiness period at sleep onset could be indexing the hypnagogic imagery self-generated by the sleeping brain, and a phasic event in the case of REM sleep." [19]. Another claim is that longer periods of stable brain activity may handling smaller amounts of information processing, and thus few changes in microstates, while shorter, less stable brain activity may reflect large amounts of different information to process, and thus more microstate changes.
^ abcdefghijklmKoenig, T., Lehmann, D., Merlo, M. C. G., Kochi, K., Hell, D., & Koukkou, M. (1999). A deviant EEG brain microstate in acute, neuroleptic-naive schizophrenics at rest. [Article]. European Archives of Psychiatry & Clinical Neuroscience, 249(4), 205.
^ abcd Isenhart, Robert. "The State of EEG Microstates." Online interview. 26 Sept. 2011.
^ abcVan De Ville, Dimitri, Juliane Britz, and Christoph Michel. "EEG Microstate Sequences in Healthy Humans at Rest Reveal Scale-free Dynamics." PNAS 107.42 (2010): 18179-8184. PNAS, 19 Oct. 2010. Web. http://www.pnas.org/content/early/2010/09/30/1007841107
^ Kikuchi, M., Koenig, T., Munesue, T., Hanaoka, A., Strik, W., Dierks, T., . . . Minabe, Y. (2011). EEG Microstate Analysis in Drug-Naive Patients with Panic Disorder. Plos One, 6(7). doi: e22912
^ Kindler, J., Hubl, D., Strik, W. K., Dierks, T., & Koenig, T. (2011). Resting-state EEG in schizophrenia: Auditory verbal hallucinations are related to shortening of specific microstates. Clinical Neurophysiology, 122(6), 1179-1182. doi: 10.1016/j.clinph.2010.10.042
^ Lehmann, D., Faber, P. L., Galderisi, S., Herrmann, W. M., Kinoshita, T., Koukkou, M., . . . Koenig, T. (2005). EEG microstate duration and syntax in acute, medication-naïve, first-episode schizophrenia: a multi-center study. Psychiatry Research: Neuroimaging, 138(2), 141-156. doi: 10.1016/j.pscychresns.2004.05.007
^ Stevens, A., Lutzenberger, W., Bartels, D. M., Strik, W., & Lindner, K. (1997). Increased duration and altered topography of EEG microstates during cognitive tasks in chronic schizophrenia. Psychiatry Research, 66(1), 45-57. doi: 10.1016/s0165-1781(96)02938-1
^ Strelets, V., Faber, P. L., Golikova, J., Novototsky-Vlasov, V., Koenig, T., Gianotti, L. R. R., . . . Lehmann, D. (2003). Chronic schizophrenics with positive symptomatology have shortened EEG microstate durations. Clinical Neurophysiology, 114(11), 2043-2051. doi: 10.1016/s1388-2457(03)00211-6
^ Strik, W. K., Chiaramonti, R., Muscas, G. C., Paganini, M., Mueller, T. J., Fallgatter, A. J., . . . Zappoli, R. (1997). Decreased EEG microstate duration and anteriorisation of the brain electrical fields in mild and moderate dementia of the Alzheimer type. Psychiatry Research: Neuroimaging, 75(3), 183-191. doi: 10.1016/s0925-4927(97)00054-1
^ abcd Koenig, T., Prichep, L., Lehmann, D., Sosa, P. V., Braeker, E., Kleinlogel, H., . . . John, E. R. (2002). Millisecond by Millisecond, Year by Year: Normative EEG Microstates and Developmental Stages. NeuroImage, 16(1), 41-48. doi: 10.1006/nimg.2002.1070
^ Kikuchi, M., Koenig, T., Munesue, T., Hanaoka, A., Strik, W., Dierks, T., . . . Minabe, Y. (2011). EEG Microstate Analysis in Drug-Naive Patients with Panic Disorder. Plos One, 6(7). doi: e22912
^ Kindler, J., Hubl, D., Strik, W. K., Dierks, T., & Koenig, T. (2011). Resting-state EEG in schizophrenia: Auditory verbal hallucinations are related to shortening of specific microstates. Clinical Neurophysiology, 122(6), 1179-1182. doi: 10.1016/j.clinph.2010.10.042
^ Lehmann, D., Faber, P. L., Galderisi, S., Herrmann, W. M., Kinoshita, T., Koukkou, M., . . . Koenig, T. (2005). EEG microstate duration and syntax in acute, medication-naïve, first-episode schizophrenia: a multi-center study. Psychiatry Research: Neuroimaging, 138(2), 141-156. doi: 10.1016/j.pscychresns.2004.05.007
^ Stevens, A., Lutzenberger, W., Bartels, D. M., Strik, W., & Lindner, K. (1997). Increased duration and altered topography of EEG microstates during cognitive tasks in chronic schizophrenia. Psychiatry Research, 66(1), 45-57. doi: 10.1016/s0165-1781(96)02938-1
^ Strelets, V., Faber, P. L., Golikova, J., Novototsky-Vlasov, V., Koenig, T., Gianotti, L. R. R., . . . Lehmann, D. (2003). Chronic schizophrenics with positive symptomatology have shortened EEG microstate durations. Clinical Neurophysiology, 114(11), 2043-2051. doi: 10.1016/s1388-2457(03)00211-6
^ Strik, W. K., Chiaramonti, R., Muscas, G. C., Paganini, M., Mueller, T. J., Fallgatter, A. J., . . . Zappoli, R. (1997). Decreased EEG microstate duration and anteriorisation of the brain electrical fields in mild and moderate dementia of the Alzheimer type. Psychiatry Research: Neuroimaging, 75(3), 183-191. doi: 10.1016/s0925-4927(97)00054-1
^ Kikuchi, M., Koenig, T., Munesue, T., Hanaoka, A., Strik, W., Dierks, T., . . . Minabe, Y. (2011). EEG Microstate Analysis in Drug-Naive Patients with Panic Disorder. Plos One, 6(7). doi: e22912
^ abcd Cantero, José L., Mercedes Atienza, Rosa M. Salas, and Carlos M. Gómez. "Brain Spatial Microstates of Human Spontaneous Alpha Activity in Relaxed Wakefulness, Drowsiness Period, and REM Sleep." Brain Topography 11.4 (199): 257-63. SpringerLink, 27 Aug. 2004. Web. 6 Nov. 2011.
