Universal characteristics of evolution and development are inherent in fetal autonomic brain maturation.

Adverse prenatal environmental influences to the developing fetus are associated with mental and cardiovascular disease in later life. Universal developmental characteristics such as self-organization, pattern formation, and adaptation in the growing information processing system have not yet been sufficiently analyzed with respect to description of normal fetal development and identification of developmental disturbances. Fetal heart rate patterns are the only non-invasive order parameter of the developing autonomic brain available with respect to the developing complex organ system. The objective of the present study was to investigate whether universal indices, known from evolution and phylogeny, describe the ontogenetic fetal development from 20 weeks of gestation onwards. By means of a 10-fold cross-validated data-driven multivariate regression modeling procedure, relevant indices of heart rate variability (HRV) were explored using 552 fetal heart rate recordings, each lasting over 30 min. We found that models which included HRV indices of increasing fluctuation amplitude, complexity and fractal long-range dependencies largely estimated the maturation age (coefficients of determination 0.61-0.66). Consideration of these characteristics in prenatal care may not only have implications for early identification of developmental disturbances, but also for the development of system-theory-based therapeutic strategies.

Low HRV entropy is strongly associated with myocardial infarction.

Heart rate variability (HRV) is a marker of autonomous activity in the heart. An important application of HRV measures is the stratification of mortality risk after myocardial infarction. Our hypothesis is that the information entropy of HRV, a non-linear approach, is a suitable measure for this assessment. As a first step, to evaluate the effect of myocardial infarction on the entropy, we compared the entropy to standard HRV parameters. The entropy was estimated by compressing the tachogram with Bzip2. For univariate comparison, statistical tests were used. Multivariate analysis was carried out using automatically generated decision trees. The classification rate and the simplicity of the decision trees were the two evaluation criteria. The findings support our hypothesis. The meanNN-normalized entropy is reduced in patients with myocardial infarction with very high significance. One entropy parameter alone exceeds the discrimination strength of multivariate standards-based trees.

Reconstruction of multiple neuromagnetic sources using augmented evolution strategies-- a comparative study.

The localization of dipolar sources in the brain based on electroencephalography (EEG) or magnetoencephalography (MEG) data is a frequent problem in the neurosciences. Deterministic standard approaches such as the Levenberg-Marquardt (LM) method often have problems in finding the global optimum of the associated nonlinear optimization function, when two or more dipoles are to be reconstructed. In such cases, probabilistic approaches turned out to be superior, but their applicability in neuromagnetic source localizations is not yet satisfactory. The objective of this study was to find probabilistic optimization strategies that perform better in such applications. Thus, hybrid and nested evolution strategies (NES) which both realize a combination of global and local search by means of multilevel optimizations were newly designed. The new methods were benchmarked and compared to the established evolution strategies (ES), to fast evolution strategies (FES), and to the deterministic LM method by conducting a two-dipole fit with MEG data sets from neuropsychological experiments. The best results were achieved with NES.