Masking Artifacts in EEG Time Series

Nongenetic pathologic developments of brain-wave patterns in monozygotic twins discordant and concordant for schizophrenia

Evidence from previous studies has suggested that the inter-individual differences in human brain-wave patterns (EEG) are predominantly determined by genetic factors. In particular, the within-pair EEG concordance of monozygotic (mz) twins was found to be typically as high as r = 0.81 across channels and frequency bands, thus being comparable to that between repeated assessments on the same individual with typically r = 0.83. Yet our investigations into mz twins discordant and concordant for schizophrenia yielded a significantly reduced within-pair EEG concordance for both, the pairs discordant for schizophrenia and the pairs concordant for schizophrenia (with concordance for schizophrenia assessed through a syndrome-oriented approach). A multivariate discriminant function of EEG parameters distinguished in a reproducible way between affected and unaffected subjects at an overall performance of >75% correctly classified subjects, while the severity of illness, as derived from EEG-differences between affected and unaffected subjects, was closely related to the severity of illness as provided by psychopathology syndrome scores. Consequently, EEG anomalies associated with schizophrenia and manifested differently in the mz co-twins concordant for schizophrenia are likely the effect of nongenetic, pathologic processes that evolved independently in the co-twins' genetically identical brains once the illness began to progress. The existence of such nongenetic processes would suggest a modification of the standard phenotype-to-genotype search strategies of molecular-genetic studies that aim to link the schizophrenia phenotype to genetic vulnerability factors.

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

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)