Multivariate pattern analysis of cooperation and competition in constructive action.

The neural underpinning of cooperative and competitive constructive activity has been investigated using mass-univariate approaches. In this study, we sought to compare the results of these approaches with the results of multivariate pattern analysis (MVPA). In particular, we wanted to test whether MVPA supports the claim made in previous studies that cooperation is associated with the activity of reward-related brain circuits. Participants were required to construct a pattern on the screen either individually or in cooperation or competition with another person during an fMRI scan. Both the MVPA classification methods and the representational similarity analysis indicated the involvement of orbitofrontal and ventromedial prefrontal areas in processes that distinguish between cooperation and competition, and activation analysis showed that these areas are more active during cooperation than during competition. However, a single trial analysis showed that the effect was reversed when only winning trials were considered. In these trials, activation of reward-related areas was higher during competition than during cooperation. Moreover, the contrast between won and lost trials in terms of reward circuits involvement was sharper under competition than under cooperation. Thus, although cooperation can be generally more rewarding than competition, it is associated with smaller difference between trials lost and trials won in terms of reward circuits activation. One may speculate that in cooperation, victory and defeat are shared with the partner and, contrary to competition, are not experienced as personal achievement or failure.

Individual differences in the neural representation of cooperation and competition.

Much evidence links the Big Five's agreeableness to a propensity for cooperation and aggressiveness to a propensity for competition. However, the neural basis for these associations is unknown. In this functional magnetic resonance imaging study, using multivariate pattern analysis of data recorded during a computer game in which participants were required to construct target patterns either in cooperation or in competition with another person, we sought to determine how individual differences in neural representations of cooperative and competitive behavior relate to individual differences in agreeableness and aggressiveness. During cooperation, agreeableness was positively correlated with the consistency of spatial patterns of neural activation in the left temporoparietal junction (TPJ) and showed positive correlations with inter-subject similarity in the dynamics of neural responses in the posterior default mode network hub and areas involved in the regulation of attention, movement planning, and visual perception. During competition, aggressiveness was positively correlated with the consistency of spatial patterns in the left and right TPJ and showed positive correlations with neural dynamics in visual processing and movement regulation areas. These results are consistent with the assumption that agreeable individuals are more involved in cooperative interactions with others, whereas aggression-prone individuals are more involved in competitive interactions.

Convolutional neural networks for classifying healthy individuals practicing or not practicing meditation according to the EEG data.

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.

Validation of a face image assessment technology to study the dynamics of human functional states in the EEG resting-state paradigm.

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. In the experiment, the subjects were sitting in front of a computer screen and performed the following task: sequentially closed their eyes (three trials of 2 minutes each) and opened them (three trials of the same duration between periods of closed eyes) when the screen was either empty or when it was showing a video recording of their own face or the face of an unfamiliar person of the same gender as the participant. EEG, ECG and a video of the face were recorded for all subjects. In the work a separate subtask of the study was also addressed: validating a technique for assessing the dynamics of the subjects' facial muscle activity using the recorded videos of the "eyes open" trials to obtain covariates that can be included in subsequent processing along with EEG correlates in neurocognitive experiments with a paradigm that does not involve the performance of active cognitive tasks ("resting-state conditions"). It was shown that the subject's gender, stimulus type (screen empty or showing own/other face), trial number are accompanied by differences in facial activity and can be used as study-specific covariates. It was concluded that the analysis of the dynamics of facial activity based on video recording of "eyes open" trials can be used as an additional method in neurocognitive research to study implicit cognitive processes associated with the perception of oneself and other, in the functional rest paradigm.

Development of a neural network for diagnosing the risk of depression according to the experimental data of the stop signal paradigm.

These days, the ability to predict the result of the development of the system is the guarantee of the successful functioning of the system. Improving the quality and volume of information, complicating its presentation, the need to detect hidden connections makes it ineffective, and most often impossible, to use classical statistical forecasting methods. Among the various forecasting methods, methods based on the use of artificial neural networks occupy a special place. The main objective of our work is to create a neural network that predicts the risk of depression in a person using data obtained using a motor control performance testing system. The stop-signal paradigm (SSP) is an experimental technique to assess a person's ability to activate deliberate movements or inhibit movements that have become inadequate to external conditions. In modern medicine, the SSP is most commonly used to diagnose movement disorders such as Parkinson's disease or the effects of stroke. We hypothesized that SSP could serve as a basis for detecting the risk of affective diseases, including depression. The neural network we are developing is supposed to combine such behavioral indicators as: the amount of missed responses, amount of correct responses, average time, the amount of correct inhibition of movements after stop-signal onset. Such a combination of indicators will provide increased accuracy in predicting the presence of depression in a person. The artificial neural network implemented in the work allows diagnosing the risk of depression on the basis of the data obtained in the stop-signal task. An architecture was developed and a system was implemented for testing motor control indicators in humans, then it was tested in real experiments. A comparison of neural network technologies and methods of mathematical statistics was carried out. A neural network was implemented to diagnose the risk of depression using stop-signal paradigm data. The efficiency of the neural network (in terms of accuracy) was demonstrated on data with an expert assessment for the presence of depression and data from the motor control testing system.

Comprehensive analysis of the 5-HTTLPR allelic polymorphism effect on behavioral and neurophysiological indicators of executive control in people from different ethnic groups in Siberia.

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.

[Severity of depressive symptoms and oscillatory responses to emotional facial expressions].

We studies the differences in oscillatory responses to emotional facial expressions in 40 subjects with different severity of depressive symptoms (19 men and 21 women) aged 18-30 years. In subjects with low severity of depressive symptoms (group 2), the perception of happy facial expressions was marked by lower delta synchronization as compared with angry and neutral expressions; in subjects with high severity of depressive chronization as compared with angry and neutral expression; in subjects with high severity of depressive symptoms (group 1), the perception of happy faces was marked by higher delta synchronization. Since an increase in delta oscillations is usually observed in aversive conditions, we suggest that happy facial expressions are perceived as negative stimuli by the subjects of group 1. The perception of angry facial expressions was accompanied by alpha band desynchronization in Group 2 and by alpha synchronization in Group 1. Basing on Klimesch's theory, this effect suggests that the subjects of group 1 are initially set up for the perception of negative emotional information. The effect of emotional stimulus category was significant in group 2, but not significant in group 1, which is an evidence of disorders in the recognition of emotional information in depression-prone individuals.

[Vulnerability to Depression and Oscillatory Resting-State Networks].

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.

Dynamics of acoustical evoked potentials on a background of habituation to verbal stimulus in persons with different individual anxiety levels.

Changes in the amplitude and the latencies of peak P300 of acoustical evoked potential on a background of the verbal stimulus novelty loss were used for determination of the brain activity specifity in individuals with various anxiety levels. The increase of the peak P300 latency of acoustical evoked potential on a background of trace processes arising after habituation was determined. This parameter did not depend on an anxiety level. However, the latency change depended on the attentional level to a submitted signal. The peak P300 amplitude in the right temporal cortex field was connected with the anxiety level--there was a growth of amplitude on a habituation background with the low anxious subjects, and with the highly anxious people the changes of this parameter were not observed. The anxiety level has rendered influence to dehabituation reaction. Dehabituation is observed with the low-anxiety subjects.