Oxytocin (OXT) modulates social behaviors. However, the administration of exogenous OXT in humans produces inconsistent behavioral changes, affecting future consideration of OXT as a treatment for autism and other disorders with social symptoms. Inter-individual variability in social functioning traits might play a key role in how OXT changes brain activity and, therefore, behavior. Here, we investigated if inter-individual variability might dictate how single-dose intranasal OXT administration (IN-OXT) changes spontaneous neural activity during the eyes-open resting state. We used a double-blinded, randomized, placebo-controlled, cross-over design on 30 typically developing young adult men to investigate the dynamics of EEG microstates corresponding to activity in defined neural networks. We confirmed previous reports that, at the group level, IN-OXT increases the representation of the attention and salience microstates. Furthermore, we identified a decreased representation of microstates associated with the default mode network. Using multivariate partial least square statistical analysis, we found that social functioning traits associated with IN-OXT-induced changes in microstate dynamics in specific spectral bands. Correlation analysis further revealed that the higher the social functioning, the more IN-OXT increased the appearance of the visual network-associated microstate, and suppressed the appearance of a default mode network-related microstate. The lower the social functioning, the more IN-OXT increases the appearance of the salience microstate. The effects we report on the salience microstate support the hypothesis that OXT regulates behavior by enhancing social salience. Moreover, our findings indicate that social functioning traits modulate responses to IN-OXT and could partially explain the inconsistent reports on IN-OXT effects.

Variability in brain activity that persists after accounting for overt behavioral and physiological states is often considered noise and controlled as a covariate in research. However, studying intra-individual variability in brain function can provide valuable insights into the dynamic nature of the brain. To explore this, we conducted a study on 43 participants analyzing the EEG microstate dynamics and self-reported spontaneous mental activity during five-minute resting-state recordings on two separate days with a twenty days average delay between recordings. Our results showed that the associations between EEG microstates and spontaneous cognition significantly changed from one day to another. Moreover, microstate changes were associated with changes in spontaneous cognition. Specifically, inter-day changes in Verbal thoughts about Others and future Planning were positively related to bottom-up sensory network-related microstate changes and negatively associated with top-down, attention, and salience network-related microstates. In addition, we find that personality traits are related to inter-day changes in microstates and spontaneous thoughts. Specifically, extraversion, neuroticism, agreeableness, and openness to experience moderated the relationship between inter-day changes in EEG microstates and spontaneous thoughts. Our study provides valuable information on the dynamic changes in the EEG microstate-spontaneous cognition organization, which could be essential for developing interventions and treatments for neuropsychiatric disorders.

AbstractWe spend a significant part of our lives navigating emotionally charged mind-wandering states by spontaneously imagining the past or the future, which predicts general well-being. We investigated brain self-generated affective states using EEG microstate analysis to identify the temporal dynamics of underlying brain networks that sustain endogenous affective state activity. With this aim, we compared the temporal dynamics of five distinct microstates between baseline resting-state, positive (e.g., awe, contentment), and negative (e.g., anger, fear) affective self-generated states. We found affect-related modulations of B, C, and D dynamics. Microstates B and D were increased, while microstate C was decreased during negative and positive valence self-generated affective states. In addition, we found valence-specific mechanisms of spontaneous affective regulation. Negative valence self-generated affective states specifically modulate the increased presence of D microstates and decreased occurrence of E microstates compared to baseline and positive valence affective states. The self-generated positive valence affective states are characterized by more prevalent B and les present A microstates compared to both baseline and negative valence affective states. These findings provide valuable insights into the neurodynamic patterns of affective regulation and implications for developing biomarkers for therapeutic interventions in mood and anxiety disorders.

To reduce the psycho-social burden increasing attention has focused on brain abnormalities in the most prevalent and highly co-occurring neuropsychiatric disorders, such as mood and anxiety. However, high inter-study variability in these patients results in inconsistent and contradictory alterations in the fast temporal dynamics of large-scale networks as measured by EEG microstates. Thus, in this meta-analysis, we aim to investigate the consistency of these changes to better understand possible common neuro-dynamical mechanisms of these disorders.In the systematic search, twelve studies investigating EEG microstate changes in participants with mood and anxiety disorders and individuals with subclinical depression were included in this meta-analysis, adding up to 787 participants.The results suggest that EEG microstates consistently discriminate mood and anxiety impairments from the general population in patients and subclinical states. Specifically, we found a small significant effect size for B microstates in patients compared to healthy controls, with larger effect sizes for increased B presence in unmedicated patients with comorbidity. In a subgroup meta-analysis of ten mood disorder studies, microstate D showed a significant effect size for decreased presence. When investigating only the two anxiety disorder studies, we found a significantly small effect size for the increased microstate A and a medium effect size for decreased microstate E (one study). However, more studies are needed to elucidate whether these findings are diagnostic-specific markers.Results are discussed in relation to the functional meaning of microstates and possible contribution to an explanatory mechanism of overlapping symptomatology of mood and anxiety disorders.

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

The objective of the present study is to evaluate the communication needs of the parents of children with cancer from the perspective of the semantic values of the morphological units used and integrated into sentence structures or sentence segments (with response status to open questions) and from the perspective of the factual analysis of the percentages of closed questions. We believe that the results we have reached, processed with interdisciplinary tools of psycholinguistics, morphology, syntax and semantics of the Romanian language and psychological analysis, can provide data that can contribute to the identification of the best strategies for ensuring emotional well-being and increasing the quality of life of families where there is a child with cancer.

The training of good motor coordination optimizes the motor skills, and these, over time, lead to the improvement of the motor skills specific to the practiced movement. The purpose of this study was to assess the capacity and speed of learning coordinated movements in the context of working in an online system. At the same time, we set out to evaluate the level of motor coordination achieved through training using the eLearning platform Hudl and the Teaching Games for Understanding approach. Thus, three groups of students were analyzed whose motor histories were different. The analysis of the three tested groups was performed in the first year of study and in the third year of study. The training programs have been adjusted/modified according to the COVID-19 pandemic situation, which has determined their application in the online system as well. The results of this study confirm that by assisting learning through computer programs, in the online system, these workouts can be effective. At the same time, the study shows us that, in addition to the natural evolution from childhood and adolescence, an advanced form of motor coordination appears, which is installed through the action of learning.

This chapter investigates how AR processes and user experience can be improved with Machine Learning (ML) techniques and proposes a solution for an intelligent AR mobile application named “FURNITURE”. The fusion of AI with AR is not a new initiative but at the same time, its potential is yet to be maximized. Currently, Artificial Intelligence (AI) is applied to almost every domain. As is the case with other technologies, Augmented Reality (AR) can take advantage of the current advancements in AI to allow the creation of a new class of AR applications. The FURNITURE application was designed with two complementary functionalities: (a) to use an ML trained model to decide if an image of a piece of furniture corresponds to a pre-defined style, i.e. is authentic; (b) to augment the piece of the furniture shown in a real context with a history of that furniture style and a 3D model representative for that furniture style, the augmentation being conditioned by a previous validation of authenticity. The “FURNITURE” application uses Google AutoML framework to train an ML model for image classification, and Wikitude AR framework for the development of the mobile AR application. The designed purpose of the AR application is to support students in the fields of art, interior design, architecture and art history, professionals, and the public. The application demonstrates the concept and is open for future extensions, by expanding the object list that can be classified as corresponding to a certain style.