WAG/Rij rats, a genetic animal model of absence epilepsy with comorbidity of depression, exhibit behavioral depression-like symptoms and spontaneous generalized spike-wave discharges (SWDs) in the EEG at the age of 6 to 8 months. The aim of the present study was to test the hypothesis that maternal care is an environmental factor which, along with genetic predisposition, may contribute to the expression of absence seizures and depression-like comorbidity later in life. To achieve this, a cross-fostering procedure was used. EEG and behavior in the forced swimming test were analyzed in WAG/Rij and Wistar offspring reared by their own mothers (non-cross-fostered), foster mothers of the same strain (in-fostered) or another strain (cross-fostered) at the age of 7 to 8 months. Maternal care and forced swimming test behavior were assessed in the dams. WAG/Rij mothers showed depression-like behavior and reduced maternal care irrespective of litter size and litter composition (own or foster pups) compared with Wistar dams. WAG/Rij offspring reared by Wistar dams with a high level of maternal care exhibited less and shorter SWDs and reduced depression-like comorbidity in adulthood compared with age-matched WAG/Rij offspring reared by their own or foster WAG/Rij mothers with a low level of maternal care. Moreover, rearing by Wistar mothers delayed the onset of absence epilepsy in WAG/Rij rats. Adoption by WAG/Rij dams did not change EEG and behavior in Wistar rats. Our study demonstrates that improvement of early care-giving environment can be used as a disease-modifying treatment to counteract epileptogenesis and behavioral comorbidities in genetic absence epilepsy.
A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time–frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time–frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time–frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson’s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson’s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.
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