At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

Emotional exhaustion in the context of vulnerability to burnout is a part of the universal narrative of high stress and systematically reported in healthcare professionals. The sense of coherence (SOC) is a salutogenic construct, operationalized by A. Antonovsky as a generalized resistance resource (GRR) to stress in three dimensions: meaningfulness (Me), the desire of a person to be motivated to cope; comprehensibility (C), the belief that the challenge is understood; and manageability (Ma), the belief that coping resources are available. The relation between SOC and the dimensions of burnout—Emotional Exhaustion (EE), Depersonalization (D), and Personal Accomplishment (PA)—is a part of salutogenic functioning, which reveals the inner motivation and self-organization of the psycho-emotional energetic resource. This study traces the salutogenic functioning of Bulgarian healthcare professionals during the pandemic. A general psychological background of coherence and exhaustion has been identified. All components of the SOC were positively correlated to Personal Accomplishment. Emotional Exhaustion and Depersonalization correlated negatively with coherence. SOC was validated as a possible determinant to predict the reduction of exhaustion and depersonalization as well as high levels of professional performance. The dimension of Meaningfulness in the coherence phenomenon was demonstrated to have the highest predictive value for professional burnout.

Introduction: In the current study, we used the Stroop Color and Word Test (SCWT) combined with an n-back component in functional magnetic resonance imaging (fMRI) in order to activate the working memory and cognitive interference in patients with Major Depressive Disorder (MDD) as compared to healthy controls. Our hypothesis was that there would be significant alterations in the selective visual attention processing regions of the brain which may identify mechanisms underlying major depression. Materials and Methods: Fifty participants, of which 24 were patients with depression and 26 healthy controls were recruited. Results: The first major finding of the current study was hypoactivation in the lingual gyrus during the condition with instructions to track the sequence of the words (word>color) of the Stroop n-back task and hyperactivation of the same structure in the opposite (color>word) condition where subjects had to focus on the order of the word color in depressed patients as compared to healthy controls. Conclusions: Changes in these regions have been consistently reported across studies with different fMRI techniques in both adolescent and adult patients with MDD reinforcing the role of the region in the pathophysiology of depression. Further studies are needed to examine possible longitudinal changes in the region and its activity in remission.

This mini-review examines functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) among opioid users. The goal is to summarize existing research data and clarify the implications of altered brain connectivity in this population. The first part of the review addresses the critical question of how opioid addiction influences the functional connectivity of key brain networks, such as the default mode network (DMN), salience network (SN), and executive control network (ECN). It examines the neurological basis of opioid addiction, the principles of rs-fMRI, different methodologies employed in this type of research, and inconsistencies and methodological challenges that complicate the interpretation of findings. The second part of the article presents findings derived from our ongoing research in the field. We tested 42 participants of whom 23 healthy controls and 19 patients with opioid use disorder. Each participant underwent an MRI scanning procedure comprised of structural, resting-state and task sequences. The neuroimaging data was processed using the CONN Toolbox running on MATLAB. Our preliminary rs-fMRI findings reveal significant disruptions in functional connectivity in individuals with opioid addiction within DMN and SN networks involved in cognitive functions such as decision-making and impulse control. The review concludes by emphasizing the importance of standardizing research practices, conducting longitudinal randomized studies, and developing a more holistic approach to understanding the effects of heroin addiction. These efforts would contribute to the development of personalized and effective intervention strategies.

There have been studies previously the neurobiological underpinnings of personality traits in various paradigms such as psychobiological theory and Eysenck’s model as well as five-factor model. However, there are limited results in terms of co-clustering of the functional connectivity as measured by functional MRI, and personality profiles. In the present study, we have analyzed resting-state connectivity networks and character type with the Lowen bioenergetic test in 66 healthy subjects. There have been identified direct correspondences between network metrics such as eigenvector centrality (EC), clustering coefficient (CC), node strength (NS) and specific personality characteristics. Specifically, N Acc L and OFCmed were associated with oral and masochistic traits in terms of EC and CC, while Insula R is associated with oral traits in terms of NS and EC. It is noteworthy that we observed significant correlations between individual items and node measures in specific regions, suggesting a more targeted relationship. However, the more relevant finding is the correlation between metrics (NS, CC, and EC) and overall traits. A hierarchical clustering algorithm (agglomerative clustering, an unsupervised machine learning technique) and principal component analysis were applied, where we identified three prominent principal components that cumulatively explain 76% of the psychometric data. Furthermore, we managed to cluster the network metrics (by unsupervised clustering) to explore whether neural connectivity patterns could be grouped based on combined average network metrics and psychometric data (global and local efficiencies, node strength, eigenvector centrality, and node strength). We identified three principal components, where the cumulative amount of explained data reaches 99%. The correspondence between network measures (CC and NS) and predictors (responses to Lowen’s items) is 62% predicted with a precision of 90%.