The Functional Val158Met Polymorphism of<i>COMT</i>Predicts Interindividual Differences in Brain α Oscillations in Young Men

Bodenmann, Sereina; Rusterholz, Thomas; Dürr, Roland; Stoll, Claudia; Bachmann, Valérie; Geissler, Eva; Jaggi-Schwarz, Karin; Landolt, Hans‐Peter

doi:10.1523/jneurosci.1427-09.2009

Cited by 106 publications

(79 citation statements)

References 42 publications

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“…We didn't find the laterality influence on the IAPF mean. Moreover the same results that genotype-dependent difference in IAPF was independent of EEG location received by Bodenmann and coauthors [40]. These data reflect the universal corticothalamic origin for alpha generation in healthy men.…”

Section: So the Casupporting

confidence: 80%

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Comments for Current Interpretation EEG Alpha Activity: A Review and Analysis

Bazanova¹

2012

JBBS

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Introduction: Exploring the EEG alpha oscillations generates considerable interest because there is well known its role in cognitive and psycho emotional aspects of human life. However, till now there isn't well determined definition what is alpha activity phenomena and which indices are characterize it. Method: This article focuses on the attempt to determine EEG alpha-activity phenomena, its physical, molecular and morphological nature, to highlight its indices and their role in optimal functioning. Results: Specifically, this article examines individual alpha activity indices: 1) the individual alpha peak frequency; 2) activation magnitude measured by estimation the amount of alpha amplitude suppression in response to eyes open and individual alpha band width; 3) three alpha "autorhythmicity" indices: intra-spindle amplitude variability, spindle length and steepness. Conclusions: Throughout, the article provides a number of suggestions which alpha activity indices and in which conditions could be applied in psycho physiological investigations, what is their role in optimal functioning and what are possible directions for future research.

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Section: So the Casupporting

confidence: 80%

“…The same results were received by the functional Val158Met polymorphism of COMT study, where LAF-Val/VAL homozygote subjects demonstrated lower score in counting task than HAF-Met/MET homozygote young men [40]. So, rise the question: are smarter brains running faster?…”

Section: So the Casupporting

confidence: 60%

Comments for Current Interpretation EEG Alpha Activity: A Review and Analysis

Bazanova¹

2012

JBBS

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“…By contrast, the variation of the COMT gene is associated with consistently lower EEG activity in the upper alpha (11-13 Hz) range in NREM sleep, REM sleep, and wakefulness (Bodenmann et al, 2009a). The difference in NREM sleep (Fig.…”

Section: G>a Polymorphism Of Catechol-o-methyltransferase (Comt) Genementioning

confidence: 91%

“…Similarly, the heritability of the peak frequency in the alpha range equals roughly 80 % (van Beijsterveldt and van Baal, 2002). Bodenmann et al (2009a) …”

Section: Heritability Of Waking Eegmentioning

confidence: 99%

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Genetic determination of sleep EEG profiles in healthy humans

Landolt

2011

Progress in Brain Research

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The contribution of slow brain oscillations including delta, theta, alpha, and sigma frequencies to the sleep electroencephalography (EEG) is finely regulated by circadian and homeostatic influences, and reflects functional aspects of wakefulness and sleep. Accumulating evidence demonstrates that individual sleep EEG patterns in non-rapid-eye-movement (NREM) sleep and rapid-eye-movement (REM) sleep are heritable traits. More specifically, multiple recordings in the same individuals, as well as studies in monozygotic and dizygotic twins suggest that a very high percentage of the robust interindividual variation and the high intraindividual stability of sleep EEG profiles can be explained by genetic factors (> 90% in distinct frequency bands). Still little is known about which genes contribute to different sleep EEG phenotypes in healthy humans. The genetic variations that have been identified to date include functional polymorphisms of the clock gene PER3 and of genes contributing to signal transduction pathways involving adenosine (ADA, ADORA2A), brain-derived neurotrophic factor (BDNF), dopamine (COMT), and prion protein (PRNP). Some of these polymorphisms profoundly modulate sleep EEG profiles; their effects are reviewed here. It is concluded that the search for genetic contributions to slow sleep EEG oscillations constitutes a promising avenue to identify molecular mechanisms underlying sleep-wake regulation in humans.

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Genetics of Human Sleep and Sleep Disorders

Kornum

2017

Handbook of Neurobehavioral Genetics and Phenotyping

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The Functional Val158Met Polymorphism ofCOMTPredicts Interindividual Differences in Brain α Oscillations in Young Men

Cited by 106 publications

References 42 publications

Comments for Current Interpretation EEG Alpha Activity: A Review and Analysis

Comments for Current Interpretation EEG Alpha Activity: A Review and Analysis

Genetic determination of sleep EEG profiles in healthy humans

Genetics of Human Sleep and Sleep Disorders

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