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We report here a study of 45 students (all men) of different ethnicities (mainly Tajik and Kyrgyz) aged 17–28 years, who moved to the North (Yakutsk, Republic of Sakha (Yakutia)). The study addressed the balance of connectivity of resting state networks in study condition 1, when participants entered new and unfamiliar living conditions, and in study condition 2, when subjects had lived in these conditions for one year. The 128-channel EEG was recorded at rest. Connectivity between nodes of the resting state networks and the rest of the brain was calculated. Connectivity contrasts the attention networks vs default mode network in conditions 1 and 2 were performed. Condition 1 was characterized by a predominance of attention networks over the default mode network of the brain, which may be associated with an increase in attention focused on the perception of new stimuli and tasks in the new conditions. In condition 2, after living in these environmental conditions for one year, the balance of the resting state networks shifted towards a predominance of the brain's default mode network over the attention networks. [ABSTRACT FROM AUTHOR]

Studies of cerebral activity during the processing of self-referential information, in comparison with the processing of information related to other people, are based on use of mass univariate analysis with the assumption that activity in one region does not depend on activity in other regions. Recent times have seen an increase in interest in the use of neuroimaging in studies of spatially distributed information using multidimensional approaches such as multi-voxel pattern analysis (MVPA). We report here the use of MVPA to analyze fMRI data recorded during a task involving assessment of the self and other people of different degrees of closeness. Testing of the patterns identified by machine learning showed that these brain activity patterns predicted what the subject was assessing self or other in 75–88% of cases. Prognostically significant structures were widely distributed in different areas of the brain and, in addition to the cortical median structures making the greatest contribution, included areas of the visual, lateral prefrontal, and many other cortical areas. The most informative areas for the selection of the Self variant on classifying self/other were the ventral regions of the medial prefrontal and cingulate cortex, while for selection of Other the most informative were the parietal and occipital median areas. Principal components analysis revealed a combination of brain structures, including the anterior cingulate gyrus and the bilateral amygdalas, whose factor scores correlated positively with the psychometric reward sensitivity scale and negatively with the neuroticism scale. Activity in this combination of structures can be regarded as a protective factor against affective disorders. In general, the results obtained here demonstrate the productivity of using machine learning methods for analysis of data from experiments of this type. [ABSTRACT FROM AUTHOR]

Since the 1950s, the dominant paradigm in the cognitive sciences has been cognitivism, which arose as an alternative to behaviorism and regards cognitive processes predominantly as various kinds of "computations" similar to those performed by a computer; there is no fundamental difference from a universal Turing machine (TM). Despite significant progress made in the last quarter of the 20th century within the framework of this paradigm, many remain dissatisfied as it cannot adequately explain some features of cognitive processes. Connectionism, which emerged later, although recognizing the role of computational processes, regards them as based not on a TM, but on a neural network, as this provides a better model of brain functionality than TM-type computations. Neural networks, unlike classical computers, show resilience and flexibility in the face of problems arising in the real world, such as increasing input noise or blockade of part of the network. They are also well suited for tasks requiring parallel resolution of many conflicting constraints. Despite this, the analogy between the functioning of the human brain and artificial neural networks is still limited, because of radical differences in the design and associated capabilities of such systems. In parallel with the paradigms of cognitivism and connectionism, further ideas developed, whereby cognition is the result of strictly biological processes involved in the interaction of an organism with the external environment. These concepts, which have become increasingly popular in recent years, have taken shape in various strands of so-called enactivism. This review compares the theoretical postulates of cognitivism, connectionism, and enactivism, along with paradigms of predictive coding and the principle of free energy. [ABSTRACT FROM AUTHOR]

The question of the nature of the relationship between mental processes and activity in the brain is not the prerogative of philosophers. All professional scientists involved in studying the human brain must, one way or another, decide this question for themselves. The dominant approach in the modern scientific worldview is reductionism, which holds that mental states can in principle be reduced to activity in the brain. Here I review some of the data obtained in recent years that shed light on the nature of the relationship between the content of mental processes and cerebral activity. These data on the encoding of sensory and semantic linguistic information, as well as more complex information related to the content of abstract concepts, show that the cerebral activity accompanying the extraction of meaning is widely distributed and probabilistic in nature and does not correspond to the nature of mental content, which is generally certain and holistic. Thus, currently available empirical data indicate that reductionism cannot reasonably be seen as a viable option and the only variant possible within the framework of materialism is that of recognizing mentality as an emergent entity. [ABSTRACT FROM AUTHOR]

The information content of EEG recordings, which are widely used and important for assessing the functional activity of the brain, is significantly increased by the use of mathematical analysis, where an important place is occupied by the spatial synchronization characteristic, i.e., the functional connectivity of biopotentials (based on correlation and coherence analysis, phase synchronization, etc.). The success of neuroimaging methods in recent years has not only confirmed the significance of this indicator, but has also contributed to improvements in approaches to its statistical evaluation and visualization. Graph theory (GT) is a promising method for analyzing the neural network organization of the brain. Its advantages are that it provides a visual description of the entire structure of the network and its individual components, as well as defining the relationships between them. The purpose of this review is to present approaches to the application of graph theory and the potentials of this method based on the analysis of published data. We present general information on the areas of application of GT, address the most common and informative metrics, and provide recommendations for selecting software. Modifications of GT analysis of the EEG are described: without primary localization of the generation sources of EEG components and with their localization. Examples of the effective use of graph theory analysis of the electroencephalogram of the healthy and diseased brain are given. [ABSTRACT FROM AUTHOR]

Data in the literature suggest that the presence of the S allele of the serotonin transporter gene (5-HTTLPR) is associated with an increased risk of developing depression in individuals who have experienced the effects of stress in the recent past. However, there are many reports, including meta-analyses, which do not confirm this association. This inconsistency in results can be partly explained by the fact that the association between 5-HTTLPR polymorphisms, stressful life events, and symptoms of mental disorders may not be so direct and obvious. Here we report our analysis of the mechanisms of interaction of the 5-HTTLPR polymorphism with the level of stressful life events in terms of their influence on the onset of symptoms of mental disorders using intermediate variables regarded as indicators of resting state network connectivity and means of regulating the emotions. Cerebral responses to increased levels of stress were found not to differ in carriers of different alleles of the serotonin transporter gene, though these reactions could reduce the likelihood of depression in carriers of the LL genotype, and, conversely, provoke depression in carriers of the S allele. This difference is explained by the fact that carriers of the LL genotype tend to rethink stressful situations in a less negative light, which prevents the emergence of psychopathology, while stress reactions in carriers of the S allele are accompanied by the appearance of repetitive obsessive thoughts and futile attempts to suppress them, which can ultimately lead to depression. [ABSTRACT FROM AUTHOR]

Polymorphism of the 5-HTTLPR serotonin transporter gene is one of the best studied markers of the serotoninergic system. Scientific interest in investigating 5-HTTLPR polymorphism arises because many studies have demonstrated an association between the short allele (S) and the development of a number of mental disorders. Several studies have shown that different 5-HTTLPR alleles are associated with different patterns of brain bioelectrical activity in the resting state and on performance of test tasks. However, despite a quite large number of studies of this marker, the results often contradict each other. Possible causes of disagreement between results may be that the complex structure of the SLC6A4 gene is ignored, heterogeneity in cohorts, and the use of different methods. The aim of the present work was to conduct a literature review and use this to carry out a complex analysis of psychological, behavioral, genetic, and neurophysiological data to summarize the effects associated with 5-HTTLPR polymorphism and at least partly to explain the variability in the results obtained by different authors. [ABSTRACT FROM AUTHOR]

The aim of the present work was to study the relationships between the individual characteristics of emotional control and changes in brain bioelectrical activity during the implicit perception of emotional coloration of verbal information. The study, which included 43 volunteers (26 women) aged 18–34 years evaluated individual features of emotional control strategies using the Gross questionnaire and total brain bioelectrical activity (the EEG) was measured during presentation of negative, positive, and neutral sentences. Attention was distracted from the emotional content of the sentences by giving study participants a task consisting of finding syntax errors. The search for syntax errors had to be done as quickly as possible, the presence of the emotional component in the sentence not being emphasized. The study identified a positive correlation between cognitive reappraisal of emotions and spectral power in the θ range in the period between 100 msec and 300 msec during presentation of negative sentences not containing syntax errors. A positive correlation was also found between expressive suppression and spectral power in the β range in the period from 100 to 1300 msec during presentation of positive sentences containing syntax errors. [ABSTRACT FROM AUTHOR]

For many decades in psychology and the history of science the phenomenon of incubation has been known but not fully understood; this is the preparation of a solution to a problem during a period of relative rest. The aim of the present work was to use EEG data to seek patterns of resting state network connectivity during incubation associated with successful postincubation solution of tasks. Subjects carried out a remote associations tests in three stages: a fi rst attempt, incubation (listening to an audio recording irrelevant to solving the task), and a second attempt to solve the unsolved tasks. Subjects who after incubation solved at least one additional task showed a more marked association of the left sensorimotor cortex with the insula and a less marked association with the left dorsolateral frontal cortex during the period of incubation. These data are consistent with the view that at the stage of seeking a solution, the optimum situation is a combination of decreased activity in the executive control network, leading to defocusing of attention, and increased activity in the motivational salience network. [ABSTRACT FROM AUTHOR]

The aim of the present work was to study brain activity using fMRI data during assessment of the self and other people. Subjects were presented with adjectives describing positive, neutral, or negative characteristics and were asked to assess the extent to which these characteristics applied to the subject him- or herself, or to a friend, a stranger, or an unpleasant person. The main effect of the Object of assessment factor was apparent in the right upper parietal lobe, which is part of the default network of the brain and overlaps with the temporoparietal junction area. Activation in this area was maximal on self-evaluation. The main effect of the Type of adjective factor was also seen in structures in the posterior center of the default network. As these areas are involved in constructing perspectives from the point of view of other people, it can be suggested that the "I" is perceived in continuous relation to the social environment and is assessed in terms of the presumed views of other people. [ABSTRACT FROM AUTHOR]

Analysis of fMRI in the resting state (RS) is a suitable methodological approach to studying basal levels of functional brain activity in humans in health and disease. The inadequate development of this direction in Russia is partly due to the small number of Russian publications describing approaches to data processing. This study uses an algorithm for analysis of fMRI signals in the RS based on independent components analysis (ICA) run in the FSL environment and used for studies of typical functional resting state networks (RSN) in health. Averaging observation data by group, which is applicable for studies of healthy people, is often not appropriate for studies of different forms of cerebral pathology, which are characterized by significantly greater levels of variation in hemodynamics. Thus, studies of 17 healthy subjects included comparative evaluation of the topography and a number of quantitative measures of typical RSN identified by group and individual analysis of fMRI signals. These networks were comparable with RSN described in the literature as main and were also reproducible in group and individual analysis, which confirms the suitability, reliability, and effectiveness of using this algorithm. Individual analysis of RSN identified variability linked with a number of psychophysiological characteristics of healthy subjects (sex, motor asymmetry profile, EEG pattern), partly explaining the different levels of compliance with the patterns of the group networks. Results obtained from individual fMRI and EEG comparisons showed the potential of analysis of the topography of the sources of individual rhythms as EEG markers for RSN. The lowest levels of variability of fMRI characteristics of resting networks in health (such as maximum network activation intensity, mean frequency of the active zone of the spectrum, frequency of dominant peak) may have diagnostic value for studies of RSN in pathology. [ABSTRACT FROM AUTHOR]