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)

译文

:在本文中,我们讨论了量化静息EEG的遗传确定成分的最新进展。特别是通过应用高级信息处理技术(例如“监督学习”)和面向问题的“相似性”概念的发展,使这一进步成为可能。我们的工作旨在对先前的发现进行建模,这些发现涉及人类脑电波模式的独特个性,单卵双胞胎的EEG之间的高度相似性以及双卵双胞胎的平均对内相似性。因此,我们有三个目标:首先,我们想通过适应性程序和重复测量来改善关于可重复性和特异性的脑电图特征的量化。其次,我们想比较单卵双胎和双卵双胎的“典型”个体内脑电图相似性与“典型”个体对内脑电图相似性。最后,我们对环境因素在多大程度上影响人类脑电波模式特征感兴趣。我们的调查基于来自五个不同人群的经验数据:(1)81位健康受试者,(2)24对单卵双生在一起,(3)25对双卵双生在一起,(4)28对的单卵双胞胎成双胞胎分开,和(5)21对的双卵双胞胎成双胞胎分开。遵循我们的相似性概念,将对81位个体进行的重复测量用作设计样本,并将14天后从同一个体获得的新注册信息称为测试样本,以开发适当的方法并确定所有必需的校准参数。这种特定的方法使我们能够构建脑电图谱模式,每个模式的特异性和可重复性均大于90%,在很大程度上满足了遗传性脑电图研究的要求。因此,我们能够系统地研究我们双胞胎样本的配对内脑电图相似性。(摘要截短为400字)

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