The Electroencephalogram in Uniovular Twins Brought Up Apart

Juel-Nielsen, N; Harvald, B

doi:10.1159/000151054

Cited by 39 publications

(16 citation statements)

References 1 publication

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“…On the other hand, Gottlober (1938) failed to find an association between EEG patterns of parents and offspring, and assumed that the resemblance in EEG patterns, if any exist, are not marked. Convincing evidence to support genetic influences on the EEG was obtained by Juel-Nielsen and Harvald (1958) who studied eight MZ twins reared apart and who found that various LEG parameters were practically the same in all twins. Later studies used quantitative methods to measure the EEG, because of the lower reliability of visual inspection.…”

Section: Genetics Of Eeg Wave Formsmentioning

confidence: 99%

Genetics of the human electroencephalogram (EEG) and event-related brain potentials (ERPs): a review

1994

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Twin and family studies of normal variation in the human electroencephalogram (EEG) and event related potentials (ERPs) are reviewed. Most of these studies are characterized by small sample sizes. However, by summarizing these studies in one paper, we may be able to gain some insight into the genetic influences on individual differences in central nervous system functioning that may mediate genetically determined differences in behavior. It is clear that most EEG parameters are to a large extent genetically determined. The results for ERPs are based on a much smaller number of studies and suggest medium to large heritability.

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Section: Genetics Of Eeg Wave Formsmentioning

confidence: 99%

Genetics of the human electroencephalogram (EEG) and event-related brain potentials (ERPs): a review

1994

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“…Whereas the first quantitative sleep EEG studies in twins are published only now, it has long been reported that the waking EEG has much higher resemblance between MZ twins than between DZ twins and unrelated persons [33][34][35]. Subsequent findings confirmed that genetic factors underlie pronounced inter-individual differences and high intraindividual test-retest correlation in spontaneous waking EEG activity [36,37].…”

Section: Sleep and Waking Eegmentioning

confidence: 99%

Genotype-Dependent Differences in Sleep, Vigilance, and Response to Stimulants

Landolt

2008

CPD

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Abstract:To better understand the neurobiology of sleep disorders, detailed understanding of circadian and homeostatic sleep-wake regulation in healthy volunteers is mandatory. Sleep physiology and the repercussions of experimentallyinduced sleep deprivation on sleep and waking electroencephalogram (EEG), vigilance and subjective state are highly variable, even in healthy individuals. Accumulating evidence suggests that many aspects of normal sleep-wake regulation are at least in part genetically controlled. Current heritability estimates of sleep phenotypes vary between approximately 20-40 % for habitual sleep duration, to over 90 % for the spectral characteristics of the EEG in nonREM sleep. The molecular mechanisms underlying the trait-like, inter-individual variation are virtually unknown, and the human genetics of normal sleep is only at the beginning of being explored. The first studies identified distinct polymorphisms in genes contributing to the endogenous circadian clock and neurochemical systems previously implicated in sleep-wake regulation, to modulate sleep architecture and sleep EEG, vulnerability to sleep loss, and subjective and objective effects of caffeine on sleep. These insights are reviewed here. They disclose molecular mechanisms contributing to normal sleep-wake regulation in humans, and have potentially important implications for the neurobiology of sleep-wake disorders and their pharmacological treatment.

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“…Research aiming to extract genetic information from the human EEG began as early as 1938 [1] ; however, ® rst results became available only after 1955 [2,3]. More speci® cally, the research carried out was focused on three diå erent cases.…”

Section: Introductionmentioning

confidence: 99%

“…case, EEGs from members of the same family were investigated and compared [3,4,5]. In the second case, the common characteristics between the EEGs of monozygotic and of dizygotic twins were sought [2,6,7,8]. In the third case, diå erent EEGs from the same person were compared ; the objective being to extract more or less invariant characteristics that would characterize the individual [9].…”

Section: Introductionmentioning

confidence: 99%

On the use of EEG features towards person identification via neural networks

Poulos¹,

Rangoussi²,

Alexandris³

et al. 2001

Medical Informatics and the Internet in Medicine

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Person identification based on spectral information extracted from the EEG is addressed in this work a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experimentally investigate the connection between a person's EEG and genetically specific information. The proposed method, compared with previously proposed methods, has yielded encouraging correct classification scores in the range of 80% to 100% (case-dependent). These results are in agreement with previous research showing evidence that the EEG carries genetic information.

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The Electroencephalogram in Uniovular Twins Brought Up Apart

Cited by 39 publications

References 1 publication

Genetics of the human electroencephalogram (EEG) and event-related brain potentials (ERPs): a review

Genetics of the human electroencephalogram (EEG) and event-related brain potentials (ERPs): a review

Genotype-Dependent Differences in Sleep, Vigilance, and Response to Stimulants

On the use of EEG features towards person identification via neural networks

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