Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: A vulnerability marker of schizophrenia?

Tomescu, Miralena I.; Rihs, Tonia A.; Becker, Robert; Britz, Juliane; Custo, Anna; Grouiller, Frédéric; Schneider, Maude; Debbané, Martin; Eliez, Stéphan; Michel, Christoph M.

doi:10.1016/j.schres.2014.05.036

Cited by 133 publications

(131 citation statements)

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“…Such an interpretation was also put forward in a study describing negative correlations of microstate C occurrence and fluid intelligence (Santarnecchi et al, 2017). Tomescu and colleagues (Tomescu et al, 2015;Tomescu et al, 2014) examined patients with 22q11 deletion syndrome, who have a 30% increase in the risk of developing schizophrenia relative to healthy controls. Similar alterations were observed for microstates C and D, indicating that these microstates may represent early markers for the risk of developing schizophrenia.…”

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

confidence: 97%

“…The amount of global variance that these four cluster maps explain varies between different reports, ranging from 65 to 84% (see figure 3). Seitzmann and colleagues (Seitzman et al, 2017) On the other hand, Tomescu et al (Tomescu et al, 2014) found that only four cluster maps explained 80% of the variance. In our recent study that included 164 subjects recorded with 204-channel EEG during 3-7 minutes eyes-closed, a meta-criterion consisting of 11 individual optimization criteria was used to define the number of clusters.…”

Section: Defining the Number Of Clustersmentioning

confidence: 99%

“…Two studies have also investigated patients at high risk for developing schizophrenia (Andreou et al, 2014;Tomescu et al, 2014), while an additional study examined differences in EEG microstates in patients with schizophrenia who reported hallucinations (Kindler et al, 2011) (discussed in (Lehmann and Michel, 2011). Seven of these studies were included in a recent meta-analysis by Rieger and colleagues .…”

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

confidence: 99%

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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: 97%

Section: Defining the Number Of Clustersmentioning

confidence: 99%

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

confidence: 99%

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

2018

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“…We completely agree that the traditional microstate methodology could systematically overestimate mean state duration due to the sparse sampling at GFP peaks. However, mean durations of approximately 80 msec were identified in recent EEG papers where microstates have been fitted to the entire data set (Tomescu et al, 2014).…”

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confidence: 99%

“…Such an adapted approach should not only use valid assumptions, but also attempt to validate hypothetical background states that are only "unobservable" under sparse sampling by taking the actual EEG data between GFP peaks into account. This may be achieved by integrating the empirically available relationship between topographic changes and GFP into the model, and fitting microstate topographies across the entire data (Tomescu et al, 2014). In addition, the application of some reasonably justified filters would reduce the problem of noisy assignments during polarity reversals (Pascual-Marqui et al, 1995;Van de Ville et al, 2010).…”

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confidence: 99%

Inappropriate assumptions about EEG state changes and their impact on the quantification of EEG state dynamics

Koenig

Brandeis

2016

NeuroImage

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It was with great interest that we read the recently published paper by Gärtner and colleagues (Gärtner et al., 2015) in Neuroimage regarding EEG microstates and background processes. The parcellation of multichannel continuous EEG data into periods of quasi-stable spatial configuration of oscillatory activity (microstate analysis) is becoming increasingly recognized as an innovative analysis for the spontaneous organization of brain function, and has frequently been covered by this journal Brodbeck et al., 2012;Custo et al., 2014;Gärtner et al., 2015;Katayama et al., 2007;Koenig et al., 2002;Musso et al., 2010;Yuan et al., 2012). This interest is timely, because conceptually very similar approaches (i.e., focusing on temporally coherent network activity) have become a powerful tool to analyze fMRI resting state data.The paper by Gärtner and colleagues addresses an important technical issue in the analysis of transiently stable EEG microstates. Since there is predominantly oscillatory activity in EEG, the signal is small and noisy around the moments of polarity reversal of the EEG field, which complicates the attribution of those EEG spatial configurations to a particular microstate. Many EEG studies have dealt with this problem by analyzing and attributing only time-points where the EEG field amplitude has a momentary maximum (the so-called Global Field Power (GFP) peak), and interpolating the assignment of these periods in between those maxima, thereby addressing the topographic stability across GFP peaks. These studies have quite consistently yielded microstate durations of approximately 60-100 msec, assuming 4 classes of microstates (Koenig et al., 2002).Gärtner and colleagues rightfully criticize the above described methodology for being incomplete, since a considerable part of the results is based on interpolation. Given that an EEG is dominated by alphaband activity, one would expect around 20 GFP peaks per second, which at a standard sampling rate of 250Hz, amounts to less than 10% of the data points. They propose a theoretical model that assumes that the sequence of EEG microstates may be explained as the result of a sparse sampling of an unobservable underlying background process. Based on the assumptions discussed in more detail below, but still relying only on observations made at GFP peaks, they estimate the duration of the states of this background process to be around 10 msec, which is much shorter than what the literature has reported on microstates. Although their model still reproduces longer microstate durations when considering only the stability across GFP peaks, it implies that the observation that microstate durations are much longer than state durations of the background process is a result of the sparse sampling of the background process.

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Understanding the pediatric psychiatric phenotype of 22q11.2 deletion syndrome

Fiksinski

Schneider

Murphy

et al. 2018

American J of Med Genetics Pt A

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The purpose of this article is to provide an overview of current insights into the neurodevelopmental and psychiatric manifestations of 22q11.2 deletion syndrome (22q11DS) in children and adolescents. The pediatric neuropsychiatric expression of 22q11DS is characterized by high variability, both interindividual and intraindividual (different expressions over the lifespan). Besides varying levels of intellectual disability, the prevalence of autism spectrum disorders, attention deficit disorders, anxiety disorders, and psychotic disorders in young individuals with 22q11DS is significantly higher than in the general population, or in individuals with idiopathic intellectual disability. Possible explanations for this observed phenotypic variability will be discussed, including genetic pleiotropy, gene-environment interactions, the age-dependency of phenotypes, but also the impact of assessment and ascertainment bias as well as the limitations of our current diagnostic classification system. The implications inferred by these observations aforementioned bear direct relevance to both scientists and clinicians. Observations regarding the neuropsychiatric manifestations in individuals with 22q11DS exemplify the need for a dimensional approach to neuropsychiatric assessment, in addition to our current categorical diagnostic classification system. The potential usefulness of 22q11DS as a genetic model to study the early phases of schizophrenia as well as the phenomenon of neuropsychiatric pleiotropy observed in many CNV's will be delineated. From a clinical perspective, the importance of regular neuropsychiatric evaluations with attention to symptoms not always captured in diagnostic categories and of maintaining equilibrium between individual difficulties and competencies and environmental demands will be discussed.

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Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: A vulnerability marker of schizophrenia?

Cited by 133 publications

References 36 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

Inappropriate assumptions about EEG state changes and their impact on the quantification of EEG state dynamics

Understanding the pediatric psychiatric phenotype of 22q11.2 deletion syndrome

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