Cortical oscillations in different frequency bands have been shown to be intimately involved in exploration of environment and cognition. Here, the local field potentials in the hippocampus, the medial prefrontal cortex (mPFC), and the medial entorhinal cortex (mEC) were recorded simultaneously in rats during the execution of the episodic-like memory task. The power of theta (~4-10 Hz), slow gamma (~25-50 Hz), and fast gamma oscillations (~55-100 Hz) was analyzed in all structures examined. Particular attention was paid to the theta coherence between three mentioned structures. The modulation of the power of gamma rhythms by the phase of theta cycle during the execution of the episodic-like memory test by rats was also closely studied. Healthy rats and rats one month after kainate-induced status epilepticus (SE) were examined. Paroxysmal activity in the hippocampus (high amplitude interictal spikes), excessive excitability of animals, and the death of hippocampal and dentate granular cells in rats with kainate-evoked SE were observed, which indicated the development of seizure focus in the hippocampus (epileptogenesis). One month after SE, the rats exhibited a specific impairment of episodic memory for the what-where-when triad: unlike healthy rats, epileptogenic SE animals did not identify the objects during the test. This impairment was associated with the changes in the characteristics of theta and gamma rhythms and specific violation of theta coherence and theta/gamma coupling in these structures in comparison with the healthy animals. We believe that these disturbances in the cortical areas play a role in episodic memory dysfunction in kainate-treated animals. These findings can shed light on the mechanisms of cognitive deficit during epileptogenesis.

Using a model of Parkinson’s disease (PD) induced by the bilateral injection of neurotoxin 6-hydroxydopamine (6-OHDA) into rat brain substantia nigra (SN), we showed uridine to exert a protective effect associated with activation of the mitochondrial ATP-dependent potassium (mitoK-ATP) channel. Injection of 4 µg neurotoxin evoked a 70% decrease in the time the experimental animal spent on the rod in the RotaRod test, an increase in the amount of lipid peroxides in blood serum and cerebral-cortex mitochondria and the rate of reactive oxygen species formation, and a decrease in Ca²⁺ retention in mitochondria. Herewith, lymphocytes featured an increase in the activity of lactate dehydrogenase, a cytosolic enzyme of glycolysis, without changes in succinate-dehydrogenase activity. Structural changes occurring in the SN and striatum manifested themselves in the destruction of mitochondria, degeneration of neurons and synapses, and stratification of myelin sheaths in them. Subcutaneous injections of 30 µg/kg uridine for 22 days restored the neurotoxin-induced changes in these parameters to levels close to the control. 5-Hydroxydecanoate (5 mg/kg), a specific mitoK-ATP channel inhibitor, eliminated the beneficial effect of uridine for almost all characteristics tested, indicating the involvement of the mitoK-ATP channel in the protective effect of uridine. The mechanism of the protective effect of uridine and its therapeutic applications for the prevention and treatment of PD are discussed.

Functional changes in the septal GABAergic system of animals with a model of temporal lobe epilepsy

Sleep spindles, together with K-complexes, are the distinctive patterns of neuronal activity in EEG recordings during stage 2 sleep. When the mechanisms of sleep spindle generation are impaired, e.g., in epilepsy, their quantitative parameters change. The analysis of these changes can provide valuable insights into the formation of epileptiform activity patterns and help to develop an additional tool for more accurate medical diagnosis. Despite the central role of EEG in the diagnosis of epilepsy, disorders of consciousness, and neurological research, resources specifically dedicated to large-scale EEG data analysis are under-represented. In our study, we collect a specialized database of clinical EEG recordings from epilepsy patients and controls during N2 sleep, characterized by rhythmic spindle activity in frontocentral and vertex regions, and manually annotate them. We then quantify four key sleep spindle characteristics using a comparison of manual annotation by a clinician and artificial intelligence technologies. A thorough evaluation of state-of-the-art deep learning architectures for detecting and characterizing sleep spindles in EEG recordings from epilepsy patients is conducted. The results show that the 1D U-Net and SEED architectures achieve competitive overall performance, but their precision-to-recall ratios differ markedly in clinical settings. This suggests that different approaches may be appropriate for each clinical situation. Furthermore, our results demonstrate that epilepsy is associated with significant and quantifiable changes in sleep spindle morphology and frequency. Automated analysis of these characteristics using artificial intelligence provides a reliable biomarker that provides a detailed picture of thalamocortical dysfunction in epilepsy. This approach has great potential for accelerated diagnosis and the development of targeted therapeutic strategies for epilepsy.

Sleep spindles, together with K-complexes, are hallmark oscillatory events observed in electroencephalographic (EEG) recordings during stage N2 sleep. Alterations in spindle characteristics, including frequency, amplitude, duration, and density, are frequently reported in epilepsy and may reflect underlying disturbances in thalamocortical network function. Quantitative analysis of these alterations has the potential to improve our understanding of epileptiform activity and support the development of clinically useful biomarkers. In this work, we present an automated framework for sleep spindle analysis in EEG recordings from both healthy subjects and patients with epilepsy. The framework integrates deep learning architectures (1D U-Net, SlumberNet, and SEED) with statistical evaluation methods to address two complementary tasks: (i) spindle segmentation and (ii) direct regression-based prediction of spindle characteristics. The proposed approach was validated on two datasets: the open-access Montreal Archive of Sleep Studies (MASS) and a custom clinical database of pediatric epilepsy patients acquired at the Video-EEG Laboratory “Genomed” (Moscow, Russia). Our results demonstrate that while both convolutional and hybrid recurrent–convolutional architectures achieve comparable overall F1-scores, their precision–recall profiles differ substantially. This enables a principled, context-specific selection of models, with U-Net favoring high sensitivity and SEED favoring high precision. Moreover, we show that segmentation-based pipelines consistently outperform direct regression (segmentation-free) approaches for characteristic prediction. These findings provide methodological guidance for the optimal deployment of deep learning models in sleep spindle analysis and establish a foundation for robust, automated, and clinician-independent EEG biomarkers in epilepsy.