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Ambiguity tolerance (AT) is the way one perceives and processes ambiguous environmental stimulus information. By using resting-state functional magnetic resonance imaging (fMRI) and the Multiple Stimulus Types Ambiguity Tolerance Scale-II (MSTAT-II), the present study investigated the association between AT and functional connectivity of the brain in 315 healthy young participants. Based on previous studies, the middle temporal gyrus (MTG), inferior parietal lobe (IPL), orbitofrontal gyrus (OFC), middle frontal gyrus (MFG), anterior insula (AI), anterior cingulate cortex (ACC) and amygdala were selected as the seed regions. We investigated the association between whole-brain functional connectivity and AT scores. The results showed a significant positive correlation between the AT scores and functional connectivity between the left IPL and middle cingulate cortex (MCC), left MFG, and left MTG, reflecting the neural basis of AT, which represents how people perceive ambiguous stimuli and assess possible risks underlying ambiguity. And the OFC was found a negative correlation between the AT scores and functional connectivity between the ACC, which revealed the underlying mechanism of ambiguity intolerance., Author(s): Di Liu [sup.1] , Jiangzhou Sun [sup.2] [sup.3] , Zhiting Ren [sup.2] [sup.3] , Jiahui Yang [sup.1] , Baoguo Shi [sup.1] , Jiang Qiu [sup.2] [sup.3] Author Affiliations: (1) [...]

Both psychological resilience and creativity are complex concepts that have positive effects on individual adaptation. Previous studies have shown overlaps between the key brain regions or brain functional networks related to psychological resilience and creativity. However, no direct experimental evidence has been provided to support the assumption that psychological resilience and creativity share a common brain basis. Therefore, the present study investigated the relationship between psychological resilience and creativity using neural imaging method with a machine learning approach. At the behavioral level, we found that psychological resilience was positively related to creative personality. Predictive analysis based on static functional connectivity (FC) and dynamic FC demonstrated that FCs related to psychological resilience could effectively predict an individual's creative personality score. Both the static FC and dynamic FC were mainly located in the default mode network. These results prove that psychological resilience and creativity share a common brain functional basis. These findings also provide insights into the possibility of promoting individual positive adaptation from negative events or situations in a creative way. This study combined neural imaging with a machine learning approach to investigate the relationship between psychological resilience and creativity. Predictive analysis demonstrated that functional connectivity combined with psychological resilience could effectively predict an individual's creative personality score. These findings suggest that psychological resilience and creativity share some common cognitive neural mechanisms. [ABSTRACT FROM AUTHOR]

The long-lasting global pandemic of Coronavirus disease 2019 (COVID-19) has changed our daily life in many ways and put heavy burden on our mental health. Having a predictive model of negative emotions during COVID-19 is of great importance for identifying potential risky population. To establish a neural predictive model achieving both good interpretability and predictivity, we have utilized a large-scale (n = 542) longitudinal dataset, alongside two independent samples for external validation. We built a predictive model based on psychologically meaningful resting state neural activities. The whole-brain resting-state neural activity and social-psychological profile of the subjects were obtained from Sept. to Dec. 2019 (Time 1). Their negative emotions were tracked and re-assessed twice, on Feb 22 (Time 2) and Apr 24 (Time 3), 2020, respectively. We first applied canonical correlation analysis on both the neural profiles and psychological profiles collected on Time 1, this step selects only the psychological meaningful neural patterns for later model construction. We then trained the neural predictive model using those identified features on data obtained on Time 2. It achieved a good prediction performance (r = 0.44, p = 8.13 × 10-27). The two most important neural predictors are associated with self-control and social interaction. This study established an effective neural prediction model of negative emotions, achieving good interpretability and predictivity. It will be useful for identifying potential risky population of emotional disorders related to COVID-19. [ABSTRACT FROM AUTHOR]

Self‐control is a core psychological construct for human beings and it plays a crucial role in the adaptation to society and achievement of success and happiness for individuals. Although progress has been made in behavioral studies examining self‐control, its neural mechanisms remain unclear. In this study, we employed a machine‐learning approach—relevance vector regression (RVR) to explore the potential predictive power of intrinsic functional connections to trait self‐control in a large sample (N = 390). We used resting‐state functional MRI (fMRI) to explore whole‐brain functional connectivity patterns characteristic of 390 healthy adults and to confirm the effectiveness of RVR in predicting individual trait self‐control scores. A set of connections across multiple neural networks that significantly predicted individual differences were identified, including the classic control network (e.g., fronto‐parietal network (FPN), salience network (SAL)), the sensorimotor network (Mot), and the medial frontal network (MF). Key nodes that contributed to the predictive model included the dorsolateral prefrontal cortex (dlPFC), middle frontal gyrus (MFG), anterior cingulate and paracingulate gyri, inferior temporal gyrus (ITG) that have been associated with trait self‐control. Our findings further assert that self‐control is a multidimensional construct rooted in the interactions between multiple neural networks. Our research employed a machine‐learning framework (Relevance Vector Regression) during a functional MRI scan to investigate the functional connections underlying trait self‐control. A set of connections that significantly predicted trait self‐control were identified, including classic control network (fronto‐parietal network and salience network), sensorimotor network and medial frontal network. We provide the evidence to demonstrate that self‐control is a multidimensional construct rooted in the interactions between multiple neural networks. [ABSTRACT FROM AUTHOR]

Background: Many emotional experiences such as anxiety and depression are influenced by negative affect (NA). NA has both trait and state features, which play different roles in physiological and mental health. Attending to NA common to various emotional experiences and their trait-state features might help deepen the understanding of the shared foundation of related emotional disorders. Methods: The principal component of five measures was calculated to indicate individuals' NA level. Applying the connectivity-based correlation analysis, we first identified resting-state functional connectives (FCs) relating to NA in sample 1 (n = 367), which were validated through an independent sample (n = 232; sample 2). Next, based on the variability of FCs across large timescale, we further divided the NA-related FCs into high- and low-variability groups. Finally, FCs in different variability groups were separately applied to predict individuals' neuroticism level (which is assumed to be the core trait-related factor underlying NA), and the change of NA level (which represents the state-related fluctuation of NA). Results: The low-variability FCs were primarily within the default mode network (DMN) and between the DMN and dorsal attention network/sensory system and significantly predicted trait rather than state NA. The high-variability FCs were primarily between the DMN and ventral attention network, the fronto-parietal network and DMN/sensory system, and significantly predicted the change of NA level. Conclusions: The trait and state NA can be separately predicted by stable and variable spontaneous FCs with different attentional processes and emotion regulatory mechanisms, which could deepen our understanding of NA. [ABSTRACT FROM AUTHOR]

Daydreaming and creativity have similar cognitive processes and neural basis. However, few empirical studies have examined the relationship between daydreaming and creativity using cognitive neuroscience methods. The present study explored the relationship between different types of daydreaming and creativity and their common neural basis. The behavioral results revealed that positive constructive daydreaming is positively related to creativity, while poor attentional control is negatively related to it. Machine learning framework was adopted to examine the predictive effect of daydreaming‐related brain functional connectivity (FC) on creativity. The results demonstrated that task FCs related to positive constructive daydreaming and task FCs related to poor attentional control both predicted an individual's creativity score successfully. In addition, task FCs combining the positive constructive daydreaming and poor attentional control also had significant predictive effect on creativity score. Furthermore, predictive analysis based on resting‐state FCs showed similar patterns. Both of the subscale‐related FCs and combined FCs had significant predictive effect on creativity score. Further analysis showed the task and the resting‐state FCs both mainly located in the default mode network, central executive network, salience network, and attention network. These results showed that daydreaming was closely related to creativity, as they shared common FC basis. [ABSTRACT FROM AUTHOR]