Noninvasive Techniques to Assess the Development of the Fetal Brain and Nervous System

Fetal brain and neural development is a highly ordered and complex process. Investigating the anatomical and functional characteristics at different gestational ages helps understand the process and helps detect pathogenic interventions/abnormalities and further treatments at an early stage. Changes in maternal physiology/stress may cause fetal impairment. Healthy and fetuses at risk show different fetal behavioral patterns. Fetal autonomic nervous system maturation can be assessed using fetal heart rate and heart rate variability. Fetal heart rate patterns (HRPs) give information on vagal and sympathetic activation, fetal movement, and fetal behavioral states. Fetal facial expressions and auditory and visual stimuli responses can also be related to brain maturation. Researchers are using different techniques to assess fetal brain/neural maturation noninvasively. Fetal face expressions/emotions captured by 4D ultrasound help in determining fetal brain maturation using a scoring system named Kurjak Antenatal Neurological Test (KANET). As gestation progresses, the difference in frequencies of facial expressions and emotions can be observed. Different facial expressions represent the brain’s maturation, like spontaneous eye blinking represents central dopamine maturation, while yawning reflects brain stem and peripheral neuromuscular maturation. The fetus also responds to visual or auditory stimuli, which can be detected using fMRI/MEG systems. This response reflects the maturation of the brain and neural networks. Also, the fMRI system detects fetal functional connectivity/networks. fMRI detects this response based on the blood oxygen-dependent level (BOLD) effect. The MEG system analyzes the fetal response based on auditory and visual stimuli. As the gestation progresses, a decrease in latency could be observed. The MEG system could also analyze spontaneous fetal patterns and their relation with GA and behavioral state. Autonomic nervous system maturation can analyze based on the relationship between fetal heart rate variability (fHRV) and gestational age (GA), where increased fHRV can be observed as GA advances. This fHRV detection can be done using CTG/NI-fECG/MCG techniques. Innovative HRV indices such as recurrence quantification analysis (RQA), fetal autonomic brain age score (FABAS), entropy, phase-rectified signal averaging analysis (PRSA), and binary symbolic dynamics remarkably improved the predictive value of the traditional HRV parameters like long-term variability (LTV) and short-term variability (STV), frequency- and time-domain indices, and complexity measures on different time scales. Maternal–fetal coupling is also an important parameter in studying fetal well-being. Based on studies, it was concluded that the multivariate approach provides specific and sensitive scores as a different aspect of autonomic control is reflected in various HRV parameters. fMCG is highly accurate than CTG and NI-fECG. But fMCG is very expensive, making it difficult for ordinary people. On the other hand, CTG is less costly and readily available. But it has low accuracy, and interpretation of short-term variability is difficult. But the different technique, NI-fECG, is a low-cost and easily accessible system than fMCG and more accurate than CTG; further exploration of this technique could improve the obstetricians’ judgment, especially during prematurity.