Using network analysis of EEG data, changes in the centrality of second-order cortical networks were examined under cognitive load and during rest. The results show a reorganization of the brain's inter-network organization depending on the nature of activity, which may further allow studying higher-order properties of phenomena such as consciousness, inaccessible to other analytical approaches.

The study employed EEG network analysis to investigate the relationship between the brain's functional organization, visual working memory and fluid intelligence. Network metrics were assessed at the global, meso-, and local levels. A machine learning model reliably estimated fluid intelligence, demonstrating the predictive value of the brain's network architecture.

Altruism is an enigmatic form of prosocial behavior, characterized by diverse motivations and significant interindividual differences. Studying neural mechanisms of altruism is crucial to identify objective markers of pro- and antisocial tendencies in behavior. This study was designed to delve into the mechanisms of altruism by analyzing EEG-based functional connectivity patterns within the framework of the network approach. To experimentally induce a situation of altruistic decision-making, we employed the Pain versus Gain (PvsG) task, which implies making choices concerning financial self-benefit and pain of the other. Our results reveal that the behavioral measure of altruism in the experiment correlated with emotional empathy, which is in line with the “empathy-altruism” hypothesis. Applying the network approach to EEG functional connectivity analysis, we discovered that the very process of decision-making in the PvsG is characterized by the synchronous activity of structures in the right hemisphere, which are involved in empathy for pain. The prosociality of decisions was reflected in functional connectivity between the rostral ACC and orbital IFG in the left hemisphere and the overall network centrality of the caudal ACC. This finding additionally points to the distinct functional roles of the ACC subregions in altruistic decision-making. The proposed neural mechanisms of altruism can further be used to identify neurophysiological markers of prosociality in behavior.

Introduction The brain mechanisms of altruism cannot be strictly localized; therefore, the analysis of brain functional connectivity (FC) can reveal intrinsic mechanisms of altruism or, conversely, anti-social tendencies in behavior. Objectives The objective was to investigate local FC patterns of altruistic decision-making using the “Pain versus Gain” (PvsG) paradigm. Methods The sample included 38 participants (18 females), 21.2±2.1 y.o. who signed the informed consent form and filled in the Interpersonal Reactivity Index questionnaire (IRI). The study protocol was approved by the local ethical committee. The PvsG task consisted of the control (CC) and experimental condition (EC) with 20 trials, each with 6 possible decisions. In the CC, participants had to decide which finger the second fake participant (FP) to move (one of five fingers or no move). In the EC, they were given money (1000 rubles) and had to choose in every trial between self-benefit (to keep 50, 40, 30, 20, 10 or 0 rubles) or FP’s pain induced by the medical electromyostimulator (with 6 levels of intensity, from “highest” to absence), e.g., when a participant keeps no money, the FP receives no stimulation. The FP was not present, and his finger moves and hand reactions to the stimulation were pre-recorded and presented as feedback. 62-channel EEG was recorded simultaneously, and the time intervals for decision-making were used for the weighted Phase Lag Index (wPLI) computation between the reconstructed cortical sources. Spearman coefficients with p-values correction via permutations were calculated between FC difference (EC vs. CC) and the sum of money given out. Results The money given out correlates positively with the Empathic Concern (R=0.38, p=0.01), Perspective-taking (R=0.42, p=0.01), Fantasy scale (R=0.4, p=0.01), and does not correlate with the Personal Distress scale (R=0.17, p=0.28) of the IRI. Significant correlations were found between the money given out and the FC between the right lingual gyrus (lg) and caudal ACC in 4-30 Hz band (R=0.54, p<0.001) and FC between the caudal ACC and left insula in 8-13 Hz band (R=0.58, p<0.001). Conclusions The PvsG task is a valid paradigm for the investigation of brain mechanisms of altruistic decision-making. We described local FC correlates of prosociality formalized in the money given out which is associated with the measures of empathic concern and cognitive empathy. The ACC and insula are involved in salience network and pain matrix with the lg as an afferent, and their activity is modulated by empathy towards others. Thus, we claim that altruism depends on empathic motivation, which is associated with FC between these regions. Disclosure of Interest None Declared

IntroductionDepression is characterized by a pattern of specific changes in the network organization of brain functioning.ObjectivesWe researched a graph structure specificity in a depressive student sample by analyzing resting-state EEG. All possible combinations of graph metrics, frequency bands, and sensors/sources levels of networks were examined.MethodsWe recorded resting-state EEG in fourteen participants with high Beck Depression Inventory score (24.4 ± 9.7; 20.4 ± 1.5 y.o.; 14 females; 1 left-handed) and fourteen participants with a low score (6.8 ± 3.7; 21.3 ± 2.0 y.o.; 8 females; 1 left-handed). We applied weighted phase-lag index (wPLI) to construct functional networks at sensors and sources levels and computed characteristic path length (CPL), clustering coefficient (CC), index of modularity (Q), small-world index (SWI) in 4-8, 8-13, 13-30, and 4-30 Hz frequency bands. We used Mann-Whitney U-test (p < 0.05) to investigate between-group differences in the graph metrics.ResultsThe depressive sample was characterized by increased CC and Q in the 4-30 Hz band networks and decreased CPL in the beta-band network (sensors-level for CPL and CC, and sources-level for Q).ConclusionsElevated CC and Q may relate to an increase of intramodular connectivity, and CPL reduction reflects the global connectivity increasing. We hypothesize that intramodular hyperconnectivity could explain the rise of global functional connectivity in participants with depressive symptoms. Funding: This research has been supported by the Interdisciplinary Scientific and Educational School of Lomonosov Moscow State University ‘Brain, Cognitive Systems, Artificial Intelligence’.DisclosureNo significant relationships.

Introduction In anxiety disorders with a lot of research on the effectiveness of treatment procedure it is important to consider patients’ implicit attitude towards mental health services, especially psychological help. Objectives To investigate the attitude towards psychological help in anxiety disorders. Methods In order to reconstruct an implicit attitude towards psychological help the method of color-emotional semantic associations (Kiselnikov et al., 2014) was used. Ten patients with anxiety disorders and 25 subjects from control group with no history of attending mental health services evaluated subjective differences between 15 semantic objects, 10 basic emotions and 10 colors. Factor analysis was used. Results The analysis revealed the two-factor structure: “Valence” and “Arousal”. The semantic object “Psychological help” got 0.92 and 0.72 as first factor loadings and 0.26 and -0.65 as second factor loadings in anxiety disorders and in control group, respectively. The comparison showed a more intense and positive attitude towards psychological help in anxiety disorders. Contrariwise, the data for other semantic objects showed the tendency of more intense and negative evaluations in the clinical group. Conclusions In anxiety disorders a shift in the categorical structure of consciousness to more negative and intense attitudes could be associated to anxiety and threat readiness. However, the attitude towards psychological help was an exception as more intense and positive which could be considered as an important factor of the effectiveness of the treatment in anxiety disorders. The research was supported by Russian Foundation for Basic Research with the Grant 17-29-02506.

The brain at wakefulness is active even in the absence of goal-directed behavior or salient stimuli. However, patterns of this resting-state (RS) activity can undergo long-term alterations following exposure to preceding meaningful stimuli. This study was aimed to develop an unbiased method to detect such changes in the RS activity after exposure to emotionally meaningful stimuli. For this purpose, we used functional magnetic resonance imaging (fMRI) of RS brain activity before and after acquisition and extinction of experimental conditioned fear. A group of healthy volunteers participated in three fMRI sessions: a RS before fear conditioning, a fear extinction session, and a RS immediately after fear extinction. The fear-conditioning paradigm consisted of three neutral visual stimuli paired with a partial reinforcement by a mild electric current. We used both linear and non-linear dimensionality reduction approaches to distinguish between the initial RS and the RS after stimuli exposure. The principal component analysis (PCA) as a linear dimensionality reduction method showed significantly worse results than non-linear methods (Isomap, LLE, Laplacian eigenmaps). Using the Laplacian eigenmaps manifold learning method, we were able to show significant differences between the two RSs at the level of individual participants. This detection was further improved by smoothing the BOLD signal with the wavelet multiresolution analysis. The developed method can improve the discrimination of functional states collected in longitudinal fMRI studies.

This review examines the hypothesis that one of the links in the pathogenesis of schizophrenia may be a violation of the folding mechanism of the convolutions and furrows of the brain in the prenatal period with a change in the index of gyrification and thickness of the cortex in certain brain areas. According to available, this indicator data remains unchanged throughout life, so it can be considered as a potential biomarker for the diagnosis of schizophrenia. Testing of this hypothesis became possible due to the development of neuroimaging methods and computer processing of the results of high resolution MRI scans. A systematic review of the published data was carried out; it was possible to conduct a meta-analysis with preliminary data on areas of interest in the brain for 6 articles, the gyrification of which is impaired in schizophrenia.Hypogyrification by the GI calculation method has been identified in areas of the brain responsible for cognitive functions, language, and the quality of social interactions.

IntroductionRecent studies mostly focus on the links between measures of alpha-band EEG networks and intelligence. However, associations between wide frequency range EEG networks and general intelligence level remain underresearched.ObjectivesIn this study in a student sample we aimed to correlate the intelligence level and graph metrics of the sensors/sources-level networks constructed in different frequency EEG bands.MethodsWe recorded eyes-closed resting-state EEG in 28 healthy participants (21.4±2.1 y.o., 18 females, 1 left-handed). The Raven’s Standard Progressive Matrices Plus (‘SPM Plus’, 60 figures) was used as an intelligence measure. We constructed networks for all possible combinations of sensors/sources-level and 4-8, 8-13, 13-30, or 4-30 Hz frequency bands using Weighted Phase-Lag Index (wPLI), and calculated four graph metrics (Characteristic Path Length, Clustering Coefficient, Modularity, and Small World Index) for each network. Spearman correlation (with Holm-Sidak correction) was applied to characterize the relations between the SPM Plus scores and all the network metrics.ResultsSPM Plus scores varied from 35 to 57 (mean 45.3±4.2), and the intelligence level negatively correlated with Modularity in beta-band (r = -0.63, pcorr = 0.0253).ConclusionsHigh modularity may reflect relatively high segregation, but not integration, of networks (Girn, Mills, Christoff, 2019). Accordingly, our findings may shed light on the neural mechanisms of the general inefficiency of global cognitive processing in the case of intellectual decline related to different mental disorders. Funding: This research has been supported by the Interdisciplinary Scientific and Educational School of Lomonosov Moscow State University ‘Brain, Cognitive Systems, Artificial Intelligence’.DisclosureNo significant relationships.