Association between emotional intelligence and effective brain connectome: A large-scale spectral DCM study

Altered hippocampal effective connectivity predicts BMI and food approach behavior in children with obesity

Objective

The vicious circle model of obesity proposes that the hippocampus plays a crucial role in food reward processing and obesity. However, few studies focused on whether and how pediatric obesity influences the potential direction of information exchange between the hippocampus and key regions, as well as whether these alterations in neural interaction could predict future BMI and eating behaviors.

Methods

In this longitudinal study, a total of 39 children with excess weight (overweight/obesity) and 51 children with normal weight, aged 8 to 12, underwent resting-state fMRI. One year later, we conducted follow-up assessments of eating behaviors and BMI. Resting-state functional connectivity and spectral dynamic casual modeling (spDCM) technique were used to examine altered functional and effective connectivity (EC) of the hippocampus in children with overweight/obesity. Linear support vector regression, a machine learning method, was employed to further investigate whether these sensitive hippocampal connections at baseline could predict future BMI and eating behaviors.

Results

Compared to controls, children with excess weight displayed abnormal bidirectional inhibitory effects between the right hippocampus and left postcentral gyrus (PoCG), that is, stronger inhibitory hippocampus→PoCG EC but weaker inhibitory PoCG→hippocampus EC, which further predicted BMI and food approach behavior one year later.

Conclusion

These findings point to a particularly important role of abnormal information exchange between the hippocampus and somatosensory cortex in pediatric obesity and future food approach behavior, which provide novel insights into the neural hierarchical mechanisms underlying childhood obesity and further expand the spDCM model of adult obesity by identifying the directionality of abnormal influences between crucial circuits associated with appetitive regulation.

Altered connectivity patterns of medial and lateral orbitofrontal cortex underlie the severity of bulimic symptoms

Objective

Compared to clinical bulimia nervosa, sub-threshold bulimic symptoms are becoming more prevalent in non-clinical or general population, which is repeatedly linked with the connectivity in orbitofrontal cortex (OFC), including functionally heterogeneous the medial and lateral OFC (mOFC; lOFC). However, the specific connectivity patterns of the mOFC and lOFC in individuals with severe or mild bulimic symptoms (SB; MB) remain poorly understood.

Methods

We first utilized resting-state functional connectivity (FC) and spectral dynamic causal modeling (spDCM) to investigate abnormal functional and effective connectivity (EC) of OFC subregions in adults with different severity of bulimic. The SB group (n = 21), MB group (n = 114), and healthy controls (HC, n = 91) underwent rs-fMRI scans. A generalized linear model was applied to determine the OFC-seeded whole-brain FC across the three groups. Subsequently, spDCM was used to estimate differences in EC among the three groups based on the FC results.

Results

We observed a shared neural basis for SB and MB groups (i.e., weaker lOFC-superior parietal lobule connectivity), which may support the role of dysfunctional inhibitory control in general bulimic symptomatology. Whereas, SB group displayed greater lOFC-occipital pole connectivity than MB group, suggesting the specificity of the neural correlates of full-threshold/severe bulimia. The directional links from the mOFC to lOFC and amygdala could further explain the aberrant interactions of reward sensitivity with inhibitory control and homeostatic energy in sub-threshold/mild condition.

Conclusion

The current study provides novel evidence that divergent connectivity patterns of the lOFC and mOFC may contribute to different severities of bulimia, which will expands our understanding of the neurobiological substrates underlying bulimia across a spectrum from healthy to unhealthy.

Arousal modulates the amygdala-insula reciprocal connectivity during naturalistic emotional movie watching

Emotional arousal is a complex state recruiting distributed cortical and subcortical structures, in which the amygdala and insula play an important role. Although previous neuroimaging studies have showed that the amygdala and insula manifest reciprocal connectivity, the effective connectivities and modulatory patterns on the amygdala-insula interactions underpinning arousal are still largely unknown. One of the reasons may be attributed to static and discrete laboratory brain imaging paradigms used in most existing studies. In this study, by integrating naturalistic-paradigm (i.e., movie watching) functional magnetic resonance imaging (fMRI) with a computational affective model that predicts dynamic arousal for the movie stimuli, we investigated the effective amygdala-insula interactions and the modulatory effect of the input arousal on the effective connections. Specifically, the predicted dynamic arousal of the movie served as regressors in general linear model (GLM) analysis and brain activations were identified accordingly. The regions of interest (i.e., the bilateral amygdala and insula) were localized according to the GLM activation map. The effective connectivity and modulatory effect were then inferred by using dynamic causal modeling (DCM). Our experimental results demonstrated that amygdala was the site of driving arousal input and arousal had a modulatory effect on the reciprocal connections between amygdala and insula. Our study provides novel evidence to the underlying neural mechanisms of arousal in a dynamical naturalistic setting.

Task-residual effective connectivity of motor network in transient ischemic attack

Transient ischemic attack (TIA) is a temporary episode of neurological dysfunction that results from focal brain ischemia. Although TIA symptoms are quickly resolved, patients with TIA have a high risk of stroke and persistent impairments in multiple domains of cognitive and motor functions. In this study, using spectral dynamic causal modeling, we investigate the changes in task-residual effective connectivity of patients with TIA during fist-closing movements. 28 healthy participants and 15 age-matched patients with TIA undergo functional magnetic resonance imaging at 7T. Here we show that during visually cued motor movement, patients with TIA have significantly higher effective connectivity toward the ipsilateral primary motor cortex and lower connectivity to the supplementary motor area than healthy controls. Our results imply that TIA patients have aberrant connections among motor regions, and these changes may reflect the decreased efficiency of primary motor function and disrupted control of voluntary movement in patients with TIA.

Altered effective connectivity between reward and inhibitory control networks in people with binge eating episodes: A spectral dynamic causal modeling study

BACKGROUND

Converging evidence points to the crucial role of brain connectivity involved in aberrant behavioral control and reward reactivity in the onset and maintenance of binge eating. However, the directional interaction pattern between brain's reward and inhibitory control systems in people with binge eating episodes is largely unknown.

METHODS

Resting-state fMRI data were collected from 36 adults with binge eating episodes (age: 19.05 ± 0.90) and 36 well-matched controls (age: 18.88 ± 0.78). We applied spectral dynamic causal modeling approach to estimate effective connectivity of the executive control network (ECN) and reward network (RN) with 15 predefined regions of interest, and investigate the between-group differences in directional connectivity.

RESULTS

Compared with controls, the positive connections within the ECN were significantly strengthened in individuals with binge eating episodes, while the negative connections from the ECN to RN and from the RN to ECN were significantly weakened. In adults with binge eating episodes, the RN→ECN connectivity was positively related to binge frequency even controlling for age, sex, and body mass index.

CONCLUSION

This study represents an important first step in addressing the role of directional integration between reward and inhibitory control networks in binge eating, and provides novel evidence that the ability of people with binge eating episodes to maintain a balance between inhibitory control and reward reactivity is decreased, as reflected by diminished bidirectional negative effects of prefrontal-subcortical circuitry at rest.

How past trauma impacts emotional intelligence: Examining the connection

Backed by both research and practice, the organizational psychology field has come to value emotional intelligence (EI) as being vital for leader and employee effectiveness. While this field values EI, it has paid little attention to the antecedents of emotional intelligence, leaving the EI domain without clarity on (1) why EI might vary across individuals, and (2) how to best develop EI. In this article, we rely on neuroscience and psychology research to make the case that past psychological trauma impacts later EI capabilities. Specifically, we present evidence that psychological trauma impairs the brain areas and functions that support EI. Establishing psychological trauma has valuable theoretical and practical implications that include providing an explanation of why EI might vary across individuals and providing a focus for improving EI: healing from past trauma. Further theoretical and practical implications for the field of organizational psychology are provided.

Neuroimaging alterations of the suicidal brain and its relevance to practice: an updated review of MRI studies

Suicide is a leading cause of death in the United States. Historically, scientific inquiry has focused on psychological theory. However, more recent studies have started to shed light on complex biosignatures using MRI techniques, including task-based and resting-state functional MRI, brain morphometry, and diffusion tensor imaging. Here, we review recent research across these modalities, with a focus on participants with depression and Suicidal Thoughts and Behavior (STB). A PubMed search identified 149 articles specific to our population of study, and this was further refined to rule out more diffuse pathologies such as psychotic disorders and organic brain injury and illness. This left 69 articles which are reviewed in the current study. The collated articles reviewed point to a complex impairment showing atypical functional activation in areas associated with perception of reward, social/affective stimuli, top-down control, and reward-based learning. This is broadly supported by the atypical morphometric and diffusion-weighted alterations and, most significantly, in the network-based resting-state functional connectivity data that extrapolates network functions from well validated psychological paradigms using functional MRI analysis. We see an emerging picture of cognitive dysfunction evident in task-based and resting state fMRI and network neuroscience studies, likely preceded by structural changes best demonstrated in morphometric and diffusion-weighted studies. We propose a clinically-oriented chronology of the diathesis-stress model of suicide and link other areas of research that may be useful to the practicing clinician, while helping to advance the translational study of the neurobiology of suicide.

Variability in Resting-State Functional Magnetic Resonance Imaging: The Effect of Body Mass, Blood Pressure, Hematocrit, and Glycated Hemoglobin on Hemodynamic and Neuronal Parameters

Introduction: Replicability has become an increasing focus within the scientific communities with the ongoing "replication crisis." One area that appears to struggle with unreliable results is resting-state functional magnetic resonance imaging (rs-fMRI). Therefore, the current study aimed at improving the knowledge of endogenous factors that contribute to inter-individual variability. Methods: Arterial blood pressure (BP), body mass, hematocrit, and glycated hemoglobin were investigated as potential sources of between-subject variability in rs-fMRI, in healthy individuals. Whether changes in resting-state networks (rs-networks) could be attributed to variability in the blood-oxygen-level-dependent (BOLD)-signal, changes in neuronal activity, or both was of special interest. Within-subject parameters were estimated by utilizing dynamic-causal modeling, as it allows to make inferences on the estimated hemodynamic (BOLD-signal dynamics) and neuronal parameters (effective connectivity) separately. Results: The results of the analyses imply that BP and body mass can cause between-subject and between-group variability in the BOLD-signal and that all the included factors can affect the underlying connectivity. Discussion: Given the results of the current and previous studies, rs-fMRI results appear to be susceptible to a range of factors, which is likely to contribute to the low degree of replicability of these studies. Interestingly, the highest degree of variability seems to appear within the much-studied default mode network and its connections to other networks. Impact statement We believe that thanks to the evidence that we have collected by analyzing the well-controlled data of the Human Connectome Project with dynamic-causal modeling (DCM) and by focusing not only on the effective connectivity, which is the typical way of using DCM, but also by analyzing the underlying hemodynamic parameters, we were able to explore the underlying vascular dependencies in a much broader perspective. Our results challenge the premise for studying changes in the default mode network as a clinical marker of disease, and we add to the growing list of factors that contribute to resting-state network variability.

Hypothalamic effective connectivity at rest is associated with body weight and energy homeostasis

Hunger and satiety drive eating behaviours via changes in brain function. The hypothalamus is a central component of the brain networks that regulate food intake. Animal research parsed the roles of the lateral hypothalamus (LH) and medial hypothalamus (MH) in hunger and satiety, respectively. Here, we examined how hunger and satiety change information flow between human LH and MH brain networks, and how these interactions are influenced by body mass index (BMI). Forty participants (16 overweight/obese) underwent two resting-state functional MRI scans while being fasted and sated. The excitatory/inhibitory influence of information flow between the MH and LH was modelled using spectral dynamic causal modelling. Our results revealed two core networks interacting across homeostatic state and weight: subcortical bidirectional connections between the LH, MH and the substantia nigra pars compacta (prSN), and cortical top-down inhibition from fronto-parietal and temporal areas. During fasting, we found higher inhibition between the LH and prSN, whereas the prSN received greater top-down inhibition from across the cortex. Individuals with higher BMI showed that these network dynamics occur irrespective of homeostatic state. Our findings reveal fasting affects brain dynamics over a distributed hypothalamic-midbrain-cortical network. This network is less sensitive to state-related fluctuations among people with obesity.

A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis

Dynamic causal modeling (DCM) is a tool used for effective connectivity (EC) estimation in neuroimage analysis. But it is a model-driven analysis method, and the structure of the EC network needs to be determined in advance based on a large amount of prior knowledge. This characteristic makes it difficult to apply DCM to the exploratory brain network analysis. The exploratory analysis of DCM can be realized from two perspectives: one is to reduce the computational cost of the model; the other is to reduce the model space. From the perspective of model space reduction, a model space exploration strategy is proposed, including two algorithms. One algorithm, named GreedyEC, starts with reducing EC from full model, and the other, named GreedyROI, start with adding EC from one node model. Then the two algorithms were applied to the task state functional magnetic resonance imaging (fMRI) data of visual object recognition and selected the best DCM model from the perspective of model comparison based on Bayesian model compare method. Results show that combining the results of the two algorithms can further improve the effect of DCM exploratory analysis. For convenience in application, the algorithms were encapsulated into MATLAB function based on SPM to help neuroscience researchers to analyze the brain causal information flow network. The strategy provides a model space exploration tool that may obtain the best model from the perspective of model comparison and lower the threshold of DCM analysis.

Influence of periaqueductal gray on other salience network nodes predicts social sensitivity

The intrinsic connectivity of the salience network (SN) plays an important role in social behavior, however the directional influence that individual nodes have on each other has not yet been fully determined. In this study, we used spectral dynamic causal modeling to characterize the effective connectivity patterns in the SN for 44 healthy older adults and for 44 patients with behavioral variant frontotemporal dementia (bvFTD) who have focal SN dysfunction. We examined the relationship of SN effective connections with individuals' socioemotional sensitivity, using the revised self-monitoring scale, an informant-facing questionnaire that assesses sensitivity to expressive behavior. Overall, average SN effective connectivity for bvFTD patients differs from healthy older adults in cortical, hypothalamic, and thalamic nodes. For the majority of healthy individuals, strong periaqueductal gray (PAG) output to right cortical (p < .01) and thalamic nodes (p < .05), but not PAG output to other central pattern generators contributed to sensitivity to socioemotional cues. This effect did not exist for the majority of bvFTD patients; PAG output toward other SN nodes was weak, and this lack of output negatively influenced socioemotional sensitivity. Instead, input to the left vAI from other SN nodes supported patients' sensitivity to others' socioemotional behavior (p < .05), though less effectively. The key role of PAG output to cortical and thalamic nodes for socioemotional sensitivity suggests that its core functions, that is, generating autonomic changes in the body, and moreover representing the internal state of the body, is necessary for optimal social responsiveness, and its breakdown is central to bvFTD patients' social behavior deficits.

Sleep quality and duration are associated with greater trait emotional intelligence

OBJECTIVES

Prior work suggests that short sleep and total sleep deprivation are associated with reduced trait Emotional Intelligence (trait EI) but not reduced ability Emotional Intelligence (ability EI). To expand this knowledge base, we investigated the role of habitual sleep quality on trait and ability EI above and beyond the known effects of recent sleep duration.

METHODS

A large sample, comprising 477 healthy adults completed the Pittsburgh Sleep Quality Index (PSQI), Trait Emotional Intelligence Questionnaire (TEIQue; trait EI), and Mayer-Salovey-Caruso Emotional Intelligence Scale (MSCEIT; ability EI).

RESULTS

Bivariate correlation and multiple linear regression showed that recent sleep duration and PSQI sleep quality each independently predicted higher trait EI scores, including Emotionality, Self-Control, Sociability, and Well-being, but were unrelated to ability EI scores.

CONCLUSIONS

In this large community sample, recent sleep duration and habitual sleep quality both independently associated with self-perceived dispositional aspects of EI (ie, trait EI). In contrast, recent sleep duration and PSQI score were unrelated to more crystalized aspects of EI performance, which encompass the general fund of emotional information and the ability to understand and reason about emotional concepts (ie, ability EI). In sum, greater self-perceived sleep duration and quality was associated with subjective perceptions of better emotional functioning, but was unrelated to performance-based metrics of emotional reasoning.

Development of functional connectivity within and among the resting-state networks in anesthetized rhesus monkeys

OBJECTIVE

The age-related changes in the resting-state networks (RSNs) exhibited temporally specific patterns in humans, and humans and rhesus monkeys have similar RSNs. We hypothesized that the RSNs in rhesus monkeys experienced similar developmental patterns as humans.

METHODS

We acquired resting-state fMRI data from 62 rhesus monkeys, which were divided into childhood, adolescence, and early adulthood groups. Group independent component analysis (ICA) was used to identify monkey RSNs. We detected the between-group differences in the RSNs and static, dynamic, and effective functional network connections (FNCs) using one-way variance analysis (ANOVA) and post-hoc analysis.

RESULTS

Eight rhesus RSNs were identified, including cerebellum (CN), left and right lateral visual (LVN and RVN), posterior default mode (pDMN), visuospatial (VSN), frontal (FN), salience (SN), and sensorimotor networks (SMN). In internal connections, the CN, SN, FN, and SMN mainly matured in early adulthood. The static FNCs associated with FN, SN, pDMN primarily experienced fast descending slow ascending type (U-shaped) developmental patterns for maturation, and the dynamic FNCs related to pDMN (RVN, CN, and SMN) and SMN (CN) were mature in early adulthood. The effective FNC results showed that the pDMN and VSN (stimulated), SN (inhibited), and FN (first inhibited then stimulated) chiefly matured in early adulthood.

CONCLUSION

We identified eight monkey RSNs, which exhibited similar development patterns as humans. All the RSNs and FNCs in monkeys were not widely changed but fine-tuned. Our study clarified that the progressive synchronization, exploration, and regulation of cognitive RSNs within the pDMN, FN, SN, and VSN denoted potential maturation of the RSNs throughout development. We confirmed the development patterns of RSNs and FNCs would support the use of monkeys as a best animal model for human brain function.