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W S Tirsch

Publications and source records attributed to W S Tirsch.

9 recordsLinked to original sources
PubMed2004

Temporal order of nonlinear dynamics in human brain.

In previous spectral analysis investigations, we demonstrated that the spontaneous activity of the alpha EEG is not stationary but rather shows cyclic alterations with a circa 1-min periodicity. Following the conclusion that a power increase in the alpha band implies a neuronal synchronization, and vice versa, an associated decrease of the EEG complexity was postulated. Accordingly, a rhythmic variation, i.e., a temporal order of the nonlinear dynamics with similar period length, was expected. Bipolar 4-min EEG recordings were obtained from 20 awake subjects (mean age: 23.5+/-2.5 years) with eyes closed for the EEG leads C3, C4, Oz, and Fz according to the 10-20 system. For the automatic evaluation of spontaneous alterations of complexity, a sliding computation of the so-called correlation dimension, using an analysis window length of 20 s continuously shifted by 1 s, was performed. The time series of complexity exhibited an oscillatory behavior with a mean period length of 58.7 s; the Friedman test statistic revealed no significant topological differences. For the rejection of the null hypothesis that the observed periodicity is a random one, two-group t-tests and ANOVA with repeated measures were performed, comparing the corresponding amplitudes and period lengths with those derived from 20 pseudo-random signals (taken from a multivariate Gaussian normal distribution). The mean relative change of EEG complexity was highly significantly increased (P<0.0001) compared to that of random data. Likewise, the difference of mean period lengths was also significant (P<0.01). The results indicate that the coupling strength of the neural network of the brain changes periodically, with a cyclic alteration from a central to a parallel processing mode of information, reflecting state transitions from synchronized, low-complex EEG activity to desynchronized high-complex activity, and vice versa. Various neuronal control mechanisms that may be acting as pacemakers responsible for the temporal order of such transients are discussed. A disturbance of the temporal order may be of pathophysiological significance.

Action Potentials↗
PubMed2002

Correlation between spectral EEG parameters and intelligence test variables in school-age children.

OBJECTIVES: The objective of the study was to investigate the relationship between the degree of intelligence and spectral electroencephalographic (EEG) parameters and to find out which EEG variables are relevant for this correlation. METHODS: Bipolar 1 min EEG recordings were derived for the fronto-central and parieto-occipital leads from 155 clinically healthy 10-year-old children in vigilance state awake with eyes closed. Relative spectral power parameters were calculated by Fast Fourier Transform. Two-group t tests according to the score values of the 'WISC-R' intelligence test as well as univariate and multiple correlation analyses between spectral and intelligence test variables were carried out. RESULTS: A statistically significant positive correlation of the intelligence test variables with the spectral alpha power and a significant negative correlation with the spectral power of lower frequency bands was observed. Predicting the spectral parameters by means of intelligence test variables, significant multiple correlation coefficients were revealed in the frequency ranges: 0.5-1.5, 3.0-5.0 and 9.0-10.5 Hz. The highest significance (P<0.0001) was found for the spectral value at 9.0 Hz. CONCLUSIONS: The results of t tests and correlation analyses confirm the strong relationship between spectral EEG parameters and the degree of intelligence indicating that the 'EEG recordings do reflect intellectual abilities'. It may be supposed that the amount of the intelligence quotient is correlated with the degree of EEG maturation and thus reflects the active number of synapses and the degree of differentiation of the neuronal controlling system.

Alpha Rhythm↗
PubMed2000

Inverse covariation of spectral density and correlation dimension in cyclic EEG dynamics of the human brain.

The responsiveness or excitability of the central nervous system (CNS) to external or internal stimuli is systematically altered corresponding to transient changes of the EEG background activity, mainly in the alpha range. We hypothesise that a transient alpha power increase is due to an underlying increase in synchronisation or coupling strength between various neuronal elements or cortical networks. Consequently, the 'network' of the CNS may be more ordered and, hence, less complex in the case of high spectral density, and vice versa. The goals of the present paper are (1) to prove the inverse covariation between spectral density and correlation dimension for a set of human EEG data, (2) to falsify the null hypothesis that the observed relationship is a random one, and (3) to propose a neuronal approach which may explain the observed correlations. A sliding computation of the spectral density and correlation dimension [Grassberger P, Procaccia I (1983) Physica D 9:189-208] of mid-occipital EEG recordings derived from eight awake subjects with eyes closed was performed. The similarity between the two time courses was quantified by similarity measures and descriptive correlation coefficients. The temporal pattern of dimensional complexity showed an inverse relationship with simultaneously computed spectral power changes most pronounced in the alpha range. The group means of similarity measures and correlation coefficients were compared with the corresponding means of a sample set established by 20 Gaussian random signals. Statistically significant differences were obtained at the 0.1% level, rejecting the null hypothesis that the observed relationship is a random one. The results support the idea that the dynamics of the EEG signals investigated reflect a chaotic deterministic process with state transitions from 'high-dimensional' to 'low-dimensional' non-linear dynamics, and vice versa. Adequate neuronal models and approaches to interpret the disclosed transients and the inverse covariation between spectral density and dimensional complexity are proposed, giving additional insight into the integrative functioning of the CNS with respect to the strategy of information processing.

Brain↗
PubMed1997

Correlation of developmental neurological findings with spectral analytical EEG evaluations in pre-school age children.

For the differentiation of developmental neurological disorders in pre-school age children, the relationship between automatically derived EEG parameters and developmental neurological findings was investigated. Within the scope of the Munich Pediatric Longitudinal Study, the sample sets of 4- and 5-year-old children (according to the frontal and parieto-occipital EEG derivations) with selected abnormal findings categorized by special items were compared with the corresponding control groups. This was carried out by means of one-sided t tests and relative frequency band-related as well as single-step spectral power parameters in the alpha range of the EEG. Automatic analysis using single-step power values was superior to that using band-related parameters. This led to the conclusion that use of age-specific single-step parameters for a quantitative EEG analysis and ignoring the classical frequency bands will yield statistically greatly improved results. For 4- and 5-year-old children, the best separation of the neurologically abnormal groups from the normal control groups was obtained using relative spectral values in the frequency range of 9.0-9.8 Hz with a maximum at 9.4 Hz. At the same time, the topographical conditions of brain immaturation should be taken into account. The results for the children examined in this study differ in a stronger distinction over the frontocentral brain region of 4- and 5-year-olds (P < 0.01) and through an additional distinction over the parietooccipital region of the 5-year-olds (P < 0.001). It still must be tested whether the spectral parameter at 9.4 Hz is age-specific for 4- and 5-year-old children or whether in other age groups different spectral parameters are of use. As an examiner-independent method, the automatic EEG analysis should become an integral component of developmental neurological diagnostics.

Attention Deficit Disorder with Hyperactivity↗
PubMed1992

Comparative coherence studies in healthy volunteers and Down's syndrome patients from childhood to adult age.

Within the scope of the Munich Pediatric Longitudinal Study, EEG coherence was studied in 212 Down's syndrome patients and 342 healthy controls aged from 6 months up to 30 years. The digitalized EEG records were subjected to spectral analysis. Frequency band-related coherences were calculated to reveal age-specific differences in the functional relationship between two brain areas in Down's syndrome patients and controls. The results show that in the "eyes-open" state the intra-hemispheric coherence in the alpha band was significantly lower (P less than 0.05) in the Down's syndrome patients than in the controls whereas that in the delta bands it was generally higher. The intra-hemispheric coherence in the "eyes-closed" state was generally higher in the Down's syndrome groups than in the controls; however, significant differences could be detected only in some age groups. The age-specific development of coherence in the inter-hemispheric parieto-occipital region was almost identical in Down's syndrome children as in controls, both with open and closed eyes. The most distinct differences were found in the fronto-central inter-hemispheric coherence (P less than 0.01), while the coherence deficiencies in the Down's syndrome group became more prominent with increasing age from school age onwards. These electrophysiological results are compared with the results of neuropathological and neurophysiological studies of other authors. It can be suggested that there are correlations with a significantly small number of dendritic spines in Down's syndrome patients, which was determined in neuropathological examinations. A neuronal model of interpretation is presented which explains the increasing developmental deficit with age in Down's syndrome children.

Adolescent↗
PubMed1985

Z-transformed EEG power spectra of children with Down syndrome vs a control group.

Standardized EEG records of eighty-eight children with Down syndrome aged between six months and five years were analysed. The EEGs were digitalized, evaluated by spectral analysis, and subjected to z-transformation, a method which allows a direct comparison of the Down syndrome children with a normal control group (Munich Developmental Longitudinal Study). The results show a significant increase in absolute power, especially theta power; the absolute alpha power is less markedly increased or even decreased. The most significant differences, however, are observed when calculating the relative alpha power. Children with Down syndrome show a reduced relative alpha power already at the age of six months, and this reduction becomes even more prominent with growing age.

Child↗
PubMed1980

[Automatic recognition of age - specific development of the EEG in infancy and early childhood - a five year follow-up study (author's transl)].

This study was initiated with a view to early detection of dysfunctions of the central nervous system (CNS) and to observe the physiological development of the CNS by means of EEG obtained in the age-groups: new-born, half, 1, 2, 3, 4 and 5 years. This was supported by standardized clinical examinations for neurological and somatic findings and by standardized tests of psychomotoric and intellectual development. In this investigation the data as used for automatic processing consisted of 110 EEGs in the age-groups: half, 1, 2, 3 and 4 years. Bipolar EEGs were recorded using "ten-twenty" method with the following leads: F4--C4, P4--O2, F3--C3 and P3--O1. For feature extraction three methods of data reduction were applied, i.e. interval-amplitude, spectral analysis and the autoregressive model. From the features thus obtained, age-specific frequency parameters were selected by statistical methods; further evaluation was performed by cluster and discriminant analysis. The former showed an unequivocal case grouping for each age-group. Using unmatched samples of only clinically healthy children from the three age-groups 1/2, 1 and 2 years, linear discriminant analysis were applied to the parameters of the three methods of data reduction. This procedure yields in mean recognition rates of 95%, 98% resp. 98% in hold-one-out classification. Similar results are obtained with samples from four or five age groups (1/2 to 4 years). The results of this type of automatic EEG analysis show that discriminant analysis can be used to allocate EEGs to specific age-groups; hence, it may readily be ascertained whether differences between chronological age and development age as evidenced by the EEG exist.

Age Factors↗