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.

Fetal autonomic brain age scores, segmented heart rate variability analysis, and traditional short term variability.

Disturbances of fetal autonomic brain development can be evaluated from fetal heart rate patterns (HRP) reflecting the activity of the autonomic nervous system. Although HRP analysis from cardiotocographic (CTG) recordings is established for fetal surveillance, temporal resolution is low. Fetal magnetocardiography (MCG), however, provides stable continuous recordings at a higher temporal resolution combined with a more precise heart rate variability (HRV) analysis. A direct comparison of CTG and MCG based HRV analysis is pending. The aims of the present study are: (i) to compare the fetal maturation age predicting value of the MCG based fetal Autonomic Brain Age Score (fABAS) approach with that of CTG based Dawes-Redman methodology; and (ii) to elaborate fABAS methodology by segmentation according to fetal behavioral states and HRP. We investigated MCG recordings from 418 normal fetuses, aged between 21 and 40 weeks of gestation. In linear regression models we obtained an age predicting value of CTG compatible short term variability (STV) of R (2) = 0.200 (coefficient of determination) in contrast to MCG/fABAS related multivariate models with R (2) = 0.648 in 30 min recordings, R (2) = 0.610 in active sleep segments of 10 min, and R (2) = 0.626 in quiet sleep segments of 10 min. Additionally segmented analysis under particular exclusion of accelerations (AC) and decelerations (DC) in quiet sleep resulted in a novel multivariate model with R (2) = 0.706. According to our results, fMCG based fABAS may provide a promising tool for the estimation of fetal autonomic brain age. Beside other traditional and novel HRV indices as possible indicators of developmental disturbances, the establishment of a fABAS score normogram may represent a specific reference. The present results are intended to contribute to further exploration and validation using independent data sets and multicenter research structures.