Directed coupling in multi-brain networks underlies generalized synchrony during social exchange

Advances in social neuroscience have made neural signatures of social exchange measurable simultaneously across people. This has identified brain regions differentially active during social interaction between human dyads, but the underlying systems-level mechanisms are incompletely understood. This paper introduces dynamic causal modeling and Bayesian model comparison to assess the causal and directed connectivity between two brains in the context of hyperscanning (h-DCM). In this setting, correlated neuronal responses become the data features that have to be explained by models with and without between-brain (effective) connections. Connections between brains can be understood in the context of generalized synchrony, which explains how dynamical systems become synchronized when they are coupled to each another. Under generalized synchrony, each brain state can be predicted by the other brain or a mixture of both. Our results show that effective connectivity between brains is not a feature within dyads per se but emerges selectively during social exchange. We demonstrate a causal impact of the sender's brain activity on the receiver of information, which explains previous reports of two-brain synchrony. We discuss the implications of this work; in particular, how characterizing generalized synchrony enables the discovery of between-brain connections in any social contact, and the advantage of h-DCM in studying brain function on the subject level, dyadic level, and group level within a directed model of (between) brain function

A social inference model of idealization and devaluation

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Directed coupling in multi-brain networks underlies generalized synchrony during social exchange

Advances in social neuroscience have made neural signatures of social exchange measurable simultaneously. This has identified brain regions differentially active during social interaction between human dyads, but the underlying systems-level mechanisms are incompletely understood. This paper introduces dynamic causal modelling and Bayesian model comparison to assess the causal and directed connectivity between two brains in the context of hyperscanning (h-DCM). In this setting, correlated neuronal responses become the data features that have to be explained by models with and without between-brain (effective) connections. Connections between brains can be understood in the context of generalized synchrony, which explains how dynamical systems become synchronized when they are (even loosely) coupled to each another. Under generalized synchrony, each brain state can be predicted by the other brain or a mixture of both. Our results show that effective connectivity between brains is not a feature within dyads per se but emerges selectively during social exchange. We demonstrate a causal impact of the sender’s brain activity on the receiver of information, which explains previous reports of two-brain synchrony. We discuss the implications of this work; in particular, how characterizing generalized synchrony enables the discovery of between-brain connections in any social contact, and the advantage of h-DCM in studying brain function on the subject level, dyadic level, and group level within one directed and dynamic causal model of (between) brain function

Simulating the computational mechanisms of cognitive and behavioral psychotherapeutic interventions: Insights from active inference

Cognitive-behavioral therapy (CBT) leverages interactions between thoughts, feelings, and actions. In this paper, we use neurocomputational modelling to deepen our understanding of the mechanisms underlying these interactions and how therapeutic interventions can produce behavioral change through different mechanisms in different cases. We describe an active inference model that allows formal simulations of interactions between cognitive interventions (i.e., cognitive restructuring) and behavioral interventions (i.e., exposure) in producing adaptive behavior change (i.e., reducing maladaptive avoidance behavior). Using the example of Spider Phobia, we show simulations indicating that when conscious beliefs about safety/danger have strong interactions with affective/behavioral outcomes, behavioral change during exposure therapy is mediated by changes in these beliefs, preventing generalization. In contrast, when these interactions are weakened, behavior change leads to generalized learning (i.e., “over-writing” the implicit beliefs about action-outcome mappings that directly produce avoidance). The individual is therefore equipped to face any new context, safe or dangerous, remaining in a content state without the need for avoidance behavior – increasing resilience from a CBT perspective. However, this beneficial effect only held if cognitive restructuring induced uncertain beliefs about whether the spider is safe or not, but not when it first induced strong beliefs in safety. These results show how the same changes in behavior during CBT can be due to distinct underlying mechanisms, and predict lower rates of relapse when cognitive interventions focus on inducing uncertainty and on reducing the effects of automatic negative thoughts on behavior

Specificity, reliability and sensitivity of social brain responses during spontaneous mentalizing

The debilitating effects of social dysfunction in many psychiatric disorders prompt the need for systems-level biomarkers of social abilities that can be applied in clinical populations and longitudinal studies. A promising neuroimaging approach is the animated shapes paradigm based on so-called Frith-Happé animations (FHAs) which trigger spontaneous mentalizing with minimal cognitive demands. Here, we presented FHAs during functional magnetic resonance imaging to 46 subjects and examined the specificity and sensitivity of the elicited social brain responses. Test-retest reliability was additionally assessed in 28 subjects within a two-week interval. Specific responses to spontaneous mentalizing were observed in key areas of the social brain with high sensitivity and independently from the variant low-level kinematics of the FHAs. Mentalizing-specific responses were well replicable on the group level, suggesting good-to-excellent cross-sectional reliability [intraclass correlation coefficients (ICCs): 0.40-0.99; dice overlap at Puncorr0.40). These findings encourage the use of FHAs in neuroimaging research across developmental stages and psychiatric conditions, including the identification of biomarkers and pharmacological interventions

A Social Inference Model of Idealization and Devaluation

People often form polarized beliefs about others. In a clinical setting this is referred to as a dichotomous or ‘split’ representation of others, whereby others are not imbued with possessing mixtures of opposing properties. Here, we formalise these accounts as an oversimplified categorical model of others’ internal, intentional, states. We show how a resulting idealization and devaluation of others can be stabilized by attributing unexpected behaviour to fictive external factors. For example, under idealization, less-than-perfect behaviour is attributed to unfavourable external conditions, thereby maintaining belief in the other’s goodness. This feature of the model accounts for how extreme beliefs are buffered against counter-evidence, while at the same time being prone to precipitous changes of polarity. Equivalent inference applied to the self creates an oscillation between self-aggrandizement and self-deprecation, capturing oscillatory relational and affective dynamics. Notably, such oscillatory dynamics arise out of the Bayesian nature of the model, wherein a subject arrives at the most plausible explanation for their observations, given their current expectations. Thus, the model we present accounts for aspects of splitting that appear ‘defensive’, without the need to postulate a specific defensive intention. By contrast, we associate psychological health with a fine-grained representation of internal states, constrained by an integrated prior, corresponding to notions of ‘character’. Finally, the model predicts that extreme appraisals of self or other are associated with causal attribution errors

No association between cardiometabolic risk and neural reactivity to acute psychosocial stress

Background: Exaggerated reactivity to acute psychosocial stress is associated with an increased risk of cardiovascular and metabolic disease. A dysfunction of the cortico-limbic network coordinating the peripheral adaptation to acute stress exposure may constitute a brain mechanism underlying this association. We opted to characterize the changes of this network associated with acute psychosocial stress exposure in individuals with low and high cardiometabolic risk (CMR). Methods: In 57 subjects without overt cardiac or cerebral disease, the Framingham risk score and presence/absence of type 2 diabetes or metabolic syndrome defined CMR. Psychosocial stress was induced during functional magnetic resonance imaging (fMRI) of brain activity by an established social threat paradigm. Measurements of heart rate, blood pressure and saliva cortisol quantified the peripheral stress reaction. Regression analyses for the anterior cingulate cortex, hippocampus, amygdala, insula and regulatory prefrontal regions evaluated the association of stress-associated brain activation and CMR. Results: Psychosocial stress exposure was associated with an increased activity of a brain network including anterior and posterior cingulate cortex, putamen, insula, parahippocampus and right hippocampus. Psychosocial stress-associated brain activation did neither covary with Framingham risk score nor differ between groups with low or high CMR. Conclusion: Exposure to acute psychosocial stress induces the activation of a well-defined cortico-limbic network. However, we did not find an association between CMR and this network's stress reactivity. Keywords: Psychosocial stress, fMRT, Cortico-limbic, Cardiovascular, Type 2 diabetes, Framingham risk scor

Brain structural correlates of upward social mobility in ethnic minority individuals

Purpose!#!Perigenual anterior cingulate cortex (pACC) is a neural convergence site for social stress-related risk factors for mental health, including ethnic minority status. Current social status, a strong predictor of mental and somatic health, has been related to gray matter volume in this region, but the effects of social mobility over the lifespan are unknown and may differ in minorities. Recent studies suggest a diminished health return of upward social mobility for ethnic minority individuals, potentially due to sustained stress-associated experiences and subsequent activation of the neural stress response system.!##!Methods!#!To address this issue, we studied an ethnic minority sample with strong upward social mobility. In a cross-sectional design, we examined 64 young adult native German and 76 ethnic minority individuals with comparable sociodemographic attributes using whole-brain structural magnetic resonance imaging.!##!Results!#!Results showed a significant group-dependent interaction between perceived upward social mobility and pACC gray matter volume, with a significant negative association in the ethnic minority individuals. Post-hoc analysis showed a significant mediation of the relationship between perceived upward social mobility and pACC volume by perceived chronic stress, a variable that was significantly correlated with perceived discrimination in our ethnic minority group.!##!Conclusion!#!Our findings extend prior work by pointing to a biological signature of the 'allostatic costs' of socioeconomic attainment in socially disadvantaged upwardly mobile individuals in a key neural node implicated in the regulation of stress and negative affect