Publications by authors named "Saprygin A"

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.

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Article Synopsis
  • The study focuses on creating an artificial neural network to predict depression risk based on a motor control testing system, particularly using the stop-signal paradigm (SSP).
  • The SSP is commonly used in medical diagnostics for movement disorders but is hypothesized to help detect affective disorders like depression through behavioral metrics.
  • The research highlights how the neural network outperforms traditional statistical methods by integrating various performance indicators, aiming for more accurate predictions of depression.
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The article presents the results of a study aimed at finding covariates to account for the activity of implicit cognitive processes in conditions of functional rest of the subjects and during them being presented their own or someone else's face in a joint analysis of EEG experiment data. The proposed approach is based on the analysis of the dynamics of the facial muscles of the subject recorded on video. The pilot study involved 18 healthy volunteers.

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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.

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The aim was to investigate behavioral reactions and event-related potential (ERP) responses in healthy participants under conditions of personalized attribution of emotional appraisal vocabulary to one-self or to other people. One hundred and fifty emotionally neutral, positive and negative words describing people's traits were used. Subjects were asked to attribute each word to four types of people: one-self, loved, unpleasant and neutral person.

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The main goal of this study was to examine the effect of long-term meditation practice on behavioral indicators and ERP peak characteristics during an error-recognition task, where participants were presented with emotionally negative (evoking anxiety or aggression) written sentences describing self-related or non-self-related emotional state and personality traits. In total, 200 sentences written in Russian with varying emotional coloring were presented during the task, with half of the sentences containing a grammatical error that the participants were asked to identify. The EEG was recorded in age-matched control individuals ( = 17) and two groups of Samatha meditators with relatively short- (3-5 years' experience, = 18) and long-term (10-30 years' experience, = 18) practice experience.

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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.

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