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The development of objective methods for assessing stress levels is an important task of applied neuroscience. Analysis of EEG recorded as part of a behavioral self-control program can serve as the basis for the development of test methods that allow classifying people by stress level. It is well known that participation in meditation practices leads to the development of skills of voluntary self-control over the individual's mental state due to an increased concentration of attention to themselves. As a consequence of meditation practices, participants can reduce overall anxiety and stress levels. The aim of our study was to develop, train and test a convolutional neural network capable of classifying individuals into groups of practitioners and non-practitioners of meditation by analysis of eventrelated brain potentials recorded during stop-signal paradigm. Four non-deep convolutional network architectures were developed, trained and tested on samples of 100 people (51 meditators and 49 non-meditators). Subsequently, all structures were additionally tested on an independent sample of 25 people. It was found that a structure using a one-dimensional convolutional layer combining the layer and a two-layer fully connected network showed the best performance in simulation tests. However, this model was often subject to overfitting due to the limitation of the display size of the data set. The phenomenon of overfitting was mitigated by changing the structure and scale of the model, initialization network parameters, regularization, random deactivation (dropout) and hyperparameters of cross-validation screening. The resulting model showed 82 % accuracy in classifying people into subgroups. The use of such models can be expected to be effective in assessing stress levels and inclination to anxiety and depression disorders in other groups of subjects., Competing Interests: The authors declare no conflict of interest., (Copyright © AUTHORS.)

The allelic polymorphism of the serotonin transporter's gene 5-HTTLPR is considered as one of the factors determining an individual genetic predisposition to the development of a wide range of affective disorders, including depression. Many studies have shown that the climatic and social conditions of people's life can have a significant impact on the connections of 5-HTTLPR with the risk of depression. The stop-signal paradigm (SSP) is an experimental method allowing evaluating an individual ability to the self-control of behavior in a changing environment. In the SSP experiment, a subject should either press one of several buttons quickly after the appearance of the target stimuli or suppress the already started movement if an inhibitory signal follows the target stimulus. The aim of this study is a research of associations between the allelic the 5-HTTLPR polymorphism and the individual scores of the personal anxiety level, as well as the behavioral and neurophysiological indicators of the ability to self-control over motor reactions in the SSP. The study was conducted among people from three ethno-regional groups: healthy Caucasoids from Novosibirsk, the Mongoloid groups of the indigenous population of the Tuva Republic and Sakha Republic (Yakutia). Genetic, ethnographic, and psychological influences on an individual's ability to control motor responses were compared. The amplitude of the premotor peak of the evoked brain potential was used as a neurophysiological marker of the person's readiness to the execution of target-directed activity. It was revealed that the frequency of the S-allele polymorphism 5-HTTLPR was significantly higher for both mongoloid groups compared to the Caucasoids. The S/S genotype was associated with an increased level of personal anxiety and at the same time with a better ability to the self-control of behavior in the SSP experiment. Anxiety level, participants' sex, ethnicity, and allelic polymorphism 5-HTTLPR had a statistically significant effect on the amplitude of the premotor readiness potential recorded under the SSP conditions in the frontal and parietal-occipital cortical regions. Our data support the hypothesis that the S/S genotype of the 5-HTTLPR polymorphism may be associated with more success in adapting to the climatic conditions connected with high life risk in comparison to L/L and L/S genotypes., (Copyright © AUTHORS.)

Depression is the most commonly observed mood disorder, which is accompanied by changes in emotional processes and the default mode network (DMN) activity. In this study, we aimed to investigate how predisposition to depression shows up in the emotional coloring of spontaneous thoughts and the activity of oscillatory resting-state networks, as revealed by source localization and independent component analysis techniques. Depressive symptoms correlated positively with the prevalence of negative emotion during EEG registration and with delta and theta activity in the orbitofrontal cortex and negatively with theta activity in the DMN. Since an increase of low-frequency oscillations in the orbitofrontal cortex is observed in aversive states, whereas their decrease in the DMN reflects an activation of this network, which is related to self-referenced processing, our results are consistent with the notion that vulnerability to depression is associated with general negative emotional disposition and excessive focus on the self.