Literature DB >> 3169741

Genetic determination of the human EEG. Survey of recent results on twins reared together and apart.

H H Stassen1, D T Lykken, P Propping, G Bomben.   

Abstract

In this article, we have discussed recent progress in quantifying the genetically determined component of the resting EEG. This progress has been made possible in particular by the application of advanced information processing techniques such as "supervised learning," and the development of a problem-oriented "similarity" concept. Our work aimed at modeling previous findings regarding the distinct individuality of human brain-wave patterns, the high similarity between the EEGs of monozygotic twins, and the average within-pair similarity of dizygotic twins. Thus, we had three objectives: First, we wanted to improve the quantification of EEG characteristics with respect to reproducibility and specificity by means of adaptive procedures and repeated measurements. Second, we wanted to compare the "typical" within-subject EEG similarity with the "typical" within-pair EEG similarity of monozygotic and dizygotic twins brought up together. Finally, we were interested in the degree to which environmental factors affect the characteristics of human brain-wave patterns. Our investigations were based on the empirical data derived from five different populations: (1) 81 healthy subjects, (2) 24 pairs of monozygotic twins brought up together, (3) 25 pairs of dizygotic twins brought up together, (4) 28 pairs of monozygotic twins reared apart, and (5) 21 pairs of dizygotic twins reared apart. Following our similarity conception, repeated measurements on the set of 81 individuals were used as design samples, and new registrations from the same individuals taken 14 days later were referred to as test samples in order to develop the appropriate method and to determine all required calibration parameters. This specific approach allowed us to construct EEG spectral patterns which, with a specificity and reproducibility of greater than 90% each, largely met the requirements of genetic EEG studies. Hence, we were able systematically to investigate the within-pair EEG similarity of our twin samples.(ABSTRACT TRUNCATED AT 400 WORDS)

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Year:  1988        PMID: 3169741     DOI: 10.1007/bf00702862

Source DB:  PubMed          Journal:  Hum Genet        ISSN: 0340-6717            Impact factor:   4.132


  15 in total

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Journal:  Humangenetik       Date:  1967-09-06

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Authors:  P Propping
Journal:  Hum Genet       Date:  1977-03-14       Impact factor: 4.132

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Authors:  H H Stassen
Journal:  Methods Inf Med       Date:  1985-10       Impact factor: 2.176

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Authors:  D T Lykken
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Authors:  P Propping; J Kruger; A Janah
Journal:  Psychiatry Res       Date:  1980-03       Impact factor: 3.222

10.  The electroencephalogram (EEG) as a research tool in human behavior genetics: psychological examinations in healthy males with various inherited EEG variants. II. Results.

Authors:  F Vogel; E Schalt; J Krüger
Journal:  Hum Genet       Date:  1979-02-28       Impact factor: 4.132

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  10 in total

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Authors:  Andrey P Anokhin
Journal:  Int J Psychophysiol       Date:  2014-04-13       Impact factor: 2.997

2.  The Genetics of Sleep: Insight from Rodent Models.

Authors:  Keith C Summa; Fred W Turek
Journal:  Sleep Med Clin       Date:  2011-06-01

Review 3.  Genetics of the human electroencephalogram (EEG) and event-related brain potentials (ERPs): a review.

Authors:  C E van Beijsterveldt; D I Boomsma
Journal:  Hum Genet       Date:  1994-10       Impact factor: 4.132

4.  Short-term EEG spectral pattern as a single event in EEG phenomenology.

Authors:  Al A Fingelkurts; An A Fingelkurts
Journal:  Open Neuroimag J       Date:  2010-09-08

5.  Parental generalized EEG alpha activity predisposes to spike wave discharges in offspring.

Authors:  H Doose; E Castiglione; S Waltz
Journal:  Hum Genet       Date:  1995-12       Impact factor: 4.132

Review 6.  Uncovering genes for cognitive (dys)function and predisposition for alcoholism spectrum disorders: a review of human brain oscillations as effective endophenotypes.

Authors:  Madhavi Rangaswamy; Bernice Porjesz
Journal:  Brain Res       Date:  2008-06-24       Impact factor: 3.252

Review 7.  Understanding alcohol use disorders with neuroelectrophysiology.

Authors:  Madhavi Rangaswamy; Bernice Porjesz
Journal:  Handb Clin Neurol       Date:  2014

Review 8.  Electrophysiological markers of genetic risk for attention deficit hyperactivity disorder.

Authors:  Charlotte Tye; Gráinne McLoughlin; Jonna Kuntsi; Philip Asherson
Journal:  Expert Rev Mol Med       Date:  2011-03-23       Impact factor: 5.600

9.  On the Individuality of Sleep EEG Spectra.

Authors:  Achim Lewandowski; Roman Rosipal; Georg Dorffner
Journal:  J Psychophysiol       Date:  2013-07-22       Impact factor: 1.333

10.  Maturational Changes of Delta Waves in Monozygotic and Dizygotic Infant Twins.

Authors:  Mirjana Vucinovic; Goran Kardum; Jonatan Vukovic; Ana Vucinovic
Journal:  J Exp Neurosci       Date:  2018-08-27
  10 in total

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