Neonatal exposure to estradiol in female rats influences neuroactive steroid concentrations and behaviour in the adult rat

The gonadal steroids, in particular estradiol, exert an important action during perinatal period in the regulation of sexual dimorphism and neuronal plasticity, and in the growth and development of nervous system. Exposure of the developing female to estrogens during perinatal period may have long-lasting effects that are now regarded as “programming” the female neuroendocrine axis to malfunction in adulthood. The purpose of this study was to describe the effect of a single administration of a low dose (10 μg) of β-estradiol 3-benzoate (EB) to female rats on the day of birth on brain and plasma concentrations of the neuroactive steroid allopregnanolone, general behaviours and behavioral sensitivity to benzodiazepines. Neonatal administration of EB induces a dramatic reduction in the cerebrocortical and plasma levels of allopregnanolone and progesterone that were apparent in both juvenile (21 days) and adult (60 days). In contrast, this treatment did not affect 17β-estradiol levels. Female rats treated with β-estradiol 3-benzoate showed a delay in vaginal opening, aciclicity characterized by prolonged estrus, and ovarian failure. Given that allopregnanolone elicits anxiolytic, antidepressive, anticonvulsant, sedative-hypnotic effects and facilitates social behaviour, we assessed whether this treatment might modify different emotional, cognitive and social behaviours. This treatment did not affect locomotor activity, anxiety- and mood-related behaviours, seizures sensitivity and spatial memory. In contrast, neonatal β-estradiol 3-benzoate-treated rats showed a dominant, but not aggressive, behaviour and an increase in body investigation, especially anogenital investigation, characteristic of male appetitive behaviour. On the contrary, neonatal administration of β-estradiol 3-benzoate to female rats increases sensitivity to the anxiolytic, sedative, and amnesic effects of diazepam in adulthood. These results indicate that the marked and persistent reduction in the cerebrocortical and peripheral concentration of the neuroactive steroid allopregnanolone induced by neonatal treatment with β-estradiol 3-benzoate does not change baseline behaviours in adult rats. On the contrary, the low levels of allopregnanolone seems to be associated to changes in the behavioural sensitivity to the positive allosteric modulator of the GABAA receptor, diazepam. These effects of estradiol suggest that it plays a major role in pharmacological regulation both of GABAergic transmission and of the abundance of endogenous modulators of such transmission during development of the central nervous system.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.