Decreased EEG microstate duration and anteriorisation of the brain electrical fields in mild and moderate dementia of the Alzheimer type

Strik, Werner; Chiaramonti, R.; Muscas, Giovanni; Paganini, Marco; Mueller, Thomas; Fallgatter, Andreas J.; Versari, A.; Zappoli, R

doi:10.1016/s0925-4927(97)00054-1

Cited by 90 publications

(58 citation statements)

References 22 publications

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“…In the present review, we focus on those studies that used k-mean cluster analysis to determine the number of microstates and those that fixed the number of states to the canonical four maps. Earlier studies using different approaches for analysis (Dierks et al, 1997;Kinoshita et al, 1995;Strik et al, 1997;Strik et al, 1995) are not listed. The most prominent pathology studied using this approach is schizophrenia.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

2018

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SummaryThe present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of largescale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales.The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.

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Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

2018

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“…The dynamics of these MS may be the basis for rapid reorganization and adaptation of functional brain networks . Additionally, differences in MS occurrence and duration may be important to altered brain function in psychiatric conditions such as panic disorder (Kikuchi et al 2011), dementia (Nishida et al 2013;Strik et al 1997), schizophrenia (Andreou et al 2014;Kindler et al 2011;Koenig et al 1999;Lehmann et al 2005), and Tourette syndrome (Stevens et al 1996). Therefore, in this study, we employ MS topography specific frequency decomposition of the EEG oscillations, since both the different standard EEG frequency bands as well as scalp field distribution, such as MS, are relevant for functional network analyses.…”

Section: Introductionmentioning

confidence: 99%

Discovering frequency sensitive thalamic nuclei from EEG microstate informed resting state fMRI

et al. 2015

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Microstates (MS), the fingerprints of the momentarily and time-varying states of the brain derived from electroencephalography (EEG), are associated with the resting state networks (RSNs). However, using MS fluctuations along different EEG frequency bands to model the functional MRI (fMRI) signal has not been investigated so far, or elucidated the role of the thalamus as a fundamental gateway and a putative key structure in cortical functional networks. Therefore, in the current study, we used MS predictors in standard frequency bands to predict blood oxygenation level dependent (BOLD) signal fluctuations. We discovered that multivariate modeling of BOLD-fMRI using six EEG-MS classes in eight frequency bands strongly correlated with thalamic areas and large-scale cortical networks. Thalamic nuclei exhibited distinct patterns of correlations for individual MS that were associated with specific EEG frequency bands. Anterior and ventral thalamic nuclei were sensitive to the beta frequency band, medial nuclei were sensitive to both alpha and beta frequency bands, and posterior nuclei such as the pulvinar were sensitive to delta and theta frequency bands. These results demonstrate that EEG-MS informed fMRI can elucidate thalamic activity not directly observable by EEG, which may be highly relevant to understand the rapid formation of thalamocortical networks.

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“…They reproduce well across subjects and can be identified across the entire life span (15), indicating that they might be mediated by predetermined anatomical connections. Alterations of microstates have been reported in schizophrenia (16,17), depression (18), and Alzheimer's disease (19,20) and as a function of drug administration and hypnosis (21-23).Recent work (24, 25) revealed a link between the rapid changes in the time courses of EEG microstate sequences on the one hand and slow coherent changes in the blood oxygen-level-dependent (BOLD) signal obtained with fMRI during rest on the other hand. More precisely, we identified the four prototypical EEG microstates during rest that each could explain one large-scale restingstate network (RSN) obtained from BOLD fMRI (25).…”

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EEG microstate sequences in healthy humans at rest reveal scale-free dynamics

Ville

Britz

Michel

2010

Proc. Natl. Acad. Sci. U.S.A.

491

422

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Recent findings identified electroencephalography (EEG) microstates as the electrophysiological correlates of fMRI resting-state networks. Microstates are defined as short periods (100 ms) during which the EEG scalp topography remains quasi-stable; that is, the global topography is fixed but strength might vary and polarity invert. Microstates represent the subsecond coherent activation within global functional brain networks. Surprisingly, these rapidly changing EEG microstates correlate significantly with activity in fMRI resting-state networks after convolution with the hemodynamic response function that constitutes a strong temporal smoothing filter. We postulate here that microstate sequences should reveal scale-free, self-similar dynamics to explain this remarkable effect and thus that microstate time series show dependencies over long time ranges. To that aim, we deploy wavelet-based fractal analysis that allows determining scale-free behavior. We find strong statistical evidence that microstate sequences are scale free over six dyadic scales covering the 256-ms to 16-s range. The degree of long-range dependency is maintained when shuffling the local microstate labels but becomes indistinguishable from white noise when equalizing microstate durations, which indicates that temporal dynamics are their key characteristic. These results advance the understanding of temporal dynamics of brain-scale neuronal network models such as the global workspace model. Whereas microstates can be considered the "atoms of thoughts," the shortest constituting elements of cognition, they carry a dynamic signature that is reminiscent at characteristic timescales up to multiple seconds. The scale-free dynamics of the microstates might be the basis for the rapid reorganization and adaptation of the functional networks of the brain.critical state | microstates | resting-state networks | self-similar processes | wavelet fractal analysis T he human brain is intrinsically organized into interconnected neuronal clusters that form large-scale neurocognitive networks (1, 2). These networks have to dynamically and rapidly reorganize and coordinate on subsecond temporal scales to allow the execution of mental processes in a timely fashion (3, 4). Precise timing is crucial for the government of the continuous information flow from multiple sources to ensure perception, cognition, and action and ultimately consciousness. The anatomical architecture of several large-scale networks is well known and has been studied with different methods ranging from tracer studies to resting-state fMRI (5, 6). However, much less is known about their underlying temporal dynamics.Multichannel electroencephalography (EEG) is a key method to access real-time information about the function of large-scale neuronal networks with high temporal resolution. Traditionally, spontaneous EEG analysis relies mainly on the power variation in different frequency bands at a subset of electrodes; however, observing this variation inherently sacrifices temporal accuracy due t...

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Decreased EEG microstate duration and anteriorisation of the brain electrical fields in mild and moderate dementia of the Alzheimer type

Cited by 90 publications

References 22 publications

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

Discovering frequency sensitive thalamic nuclei from EEG microstate informed resting state fMRI

EEG microstate sequences in healthy humans at rest reveal scale-free dynamics

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