THE EVOLUTION OF COMPLEXITY IN HUMAN BRAIN-DEVELOPMENT - AN EEG STUDY

Analysis of the EEG as a signal from a deterministic non-linear system should, in principle, allow insights into the complexity of underlyin g brain activity. We examined the capability of this method to analyse the marked changes in brain activity during normal brain development. Resting EEGs of 54 healthy children (newborns to 14 years old) and of 12 normal adults were recorded digitally. The following parameters we re calculated: correlation dimension, a measure of the complexity of t he underlying system, and the first Lyapunov coefficient, indicating t he system's 'unpredictability'. Analysis of variance (ANOVA) was perfo rmed with probands grouped by age. The subgroups of children older tha n 1 year was further examined by regression analysis. In all analysed epochs, Lyapunov coefficients were significantly positive (P < 0.0001, t test). The presence of non-linear dynamics was asserted statistical ly in 64-76% of examined epochs. A highly significant increase in corr elation dimension with age was found in all examined leads (P < 0.0001 , ANOVA). In all age groups, marked differences in correlation dimensi on in different brain regions became evident (P < 0.01-0.0001, ANOVA). Evidence for the presence of non-linearity can be found even in newbo rns. Brain maturation was reflected in a marked and highly significant increase in correlation dimension (complexity). Our work indicates th at non-linear dynamics analysis is suitable for measuring complexity o f brain activity during maturation and provides age-dependent normal v alues as a basis for further study.