Serotonergic psychedelics are recently gaining a lot of attention as a potential treatment of several neuropsychiatric disorders. Broadband desynchronization of EEG activity and disconnection in humans have been repeatedly shown; however, translational data from animals are completely lacking. Therefore, the main aim of our study was to assess the effects of tryptamine and phenethylamine psychedelics (psilocin 4 mg/kg, LSD 0.2 mg/kg, mescaline 100 mg/kg, and DOB 5 mg/kg) on EEG in freely moving rats. A system consisting of 14 cortical EEG electrodes, co-registration of behavioral activity of animals with subsequent analysis only in segments corresponding to behavioral inactivity (resting-state-like EEG) was used in order to reach a high level of translational validity. Analyses of the mean power, topographic brain-mapping, and functional connectivity revealed that all of the psychedelics irrespective of the structural family induced overall and time-dependent global decrease/desynchronization of EEG activity and disconnection within 1–40 Hz. Major changes in activity were localized on the large areas of the frontal and sensorimotor cortex showing some subtle spatial patterns characterizing each substance. A rebound of occipital theta (4–8 Hz) activity was detected at later stages after treatment with mescaline and LSD. Connectivity analyses showed an overall decrease in global connectivity for both the components of cross-spectral and phase-lagged coherence. Since our results show almost identical effects to those known from human EEG/MEG studies, we conclude that our method has robust translational validity.

Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO2 channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively.

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