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Novelty and appropriateness are two fundamental components of creativity. However, the way in which novelty and appropriateness are separated at behavioral and neural levels remains poorly understood. In the present study, we aim to distinguish behavioral and neural bases of novelty and appropriateness of creative idea generation. In alignment with two established theories of creative thinking, which respectively, emphasize semantic association and executive control, behavioral results indicate that novelty relies more on associative abilities, while appropriateness relies more on executive functions. Next, employing a connectome predictive modeling (CPM) approach in resting-state fMRI data, we define two functional network-based models—dominated by interactions within the default network and by interactions within the limbic network—that respectively, predict novelty and appropriateness (i.e., cross-brain prediction). Furthermore, the generalizability and specificity of the two functional connectivity patterns are verified in additional resting-state fMRI and task fMRI. Finally, the two functional connectivity patterns, respectively mediate the relationship between semantic association/executive control and novelty/appropriateness. These findings provide global and predictive distinctions between novelty and appropriateness in creative idea generation. Novelty and appropriateness are two fundamental components of creativity. This study distinguishes novelty and appropriateness of idea generation at the cognitive and neural levels. [ABSTRACT FROM AUTHOR]

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]

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. [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]

Creativity is imperative to the progression of human civilization, prosperity, and well-being. Past creative researches tends to emphasize the default mode network (DMN) or the frontoparietal network (FPN) somewhat exclusively. However, little is known about how these networks interact to contribute to creativity and whether common or distinct brain networks are responsible for visual and verbal creativity. Here, we use functional connectivity analysis of resting-state functional magnetic resonance imaging data to investigate visual and verbal creativity-related regions and networks in 282 healthy subjects. We found that functional connectivity within the bilateral superior parietal cortex of the FPN was negatively associated with visual and verbal creativity. The strength of connectivity between the DMN and FPN was positively related to both creative domains. Visual creativity was negatively correlated with functional connectivity within the precuneus of the pDMN and right middle frontal gyrus of the FPN, and verbal creativity was negatively correlated with functional connectivity within the medial prefrontal cortex of the aDMN. Critically, the FPN mediated the relationship between the aDMN and verbal creativity, and it also mediated the relationship between the pDMN and visual creativity. Taken together, decreased within-network connectivity of the FPN and DMN may allow for flexible between-network coupling in the highly creative brain. These findings provide indirect evidence for the cooperative role of the default and executive control networks in creativity, extending past research by revealing common and distinct brain systems underlying verbal and visual creative cognition. Hum Brain Mapp 38:2094-2111, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

• Few studies have evaluated the relationship between brain dynamics and intelligence. • Dorsal attention network temporal variability positively correlated with performance intelligence. • Salience network temporal variability positively correlated with performance intelligence. Intelligence is the ability to recognize and understand objective things, and use knowledge and experience to solve problems. Highly intelligent people show the ability to switch between different thought patterns and shift their mental focus. This suggests a link between intelligence and the dynamic interaction of brain networks. Thus, we investigated the relationships between resting-state dynamic brain network remodeling (temporal variability) and scores on the Wechsler Adult Intelligent Scale using a large dataset comprising 606 individuals. We found that performance intelligence was associated with greater temporal variability in the functional connectivity patterns of the dorsal attention network. High variability in these areas indicates flexible connectivity patterns, which may contribute to cognitive processes such as attention selection. In addition, performance intelligence was related to greater temporal variability in the functional connectivity patterns of the salience network. Thus, this study revealed a close relationship between performance intelligence and high variability in brain networks involved in attentional choice, spatial orientation, and cognitive control. [ABSTRACT FROM AUTHOR]

The loss of an only child is a negative life event and may potentially increase the risk of psychiatric disorders. However, the psychological consequences of the loss of an only child and the associated neural mechanisms remain largely unexplored. Degree centrality (DC), derived from resting-state functional magnetic resonance imaging (fMRI), was used to examine network communication in 22 older adults who lost their only child and 23 matched controls. The older adults who lost their only child exhibited an ineffective coping style. They also showed decreased distant and local DC in the precuneus and left inferior parietal lobule and decreased distant DC in the bilateral dorsolateral prefrontal cortex (DLPFC). Furthermore, the decreased local and distant DC of these regions and the decreased DLPFC-precuneus connectivity strength were negatively correlated with negative coping scores in the loss group but not in the controls. Overall, the results suggested a model that the impaired neural network communication of brain hubs within the default mode network (DMN) and central executive network (CEN) were associated with a negative coping style in older adults who lost their only child. The decreased connectivity of the hubs can be identified as a neural risk factor that is related to future psychopathology. [ABSTRACT FROM AUTHOR]

While a recent upsurge in the application of neuroimaging methods to creative cognition has yielded encouraging progress toward understanding the neural underpinnings of creativity, the neural basis of barriers to creativity are as yet unexplored. Here, we report the first investigation into the neural correlates of one such recently identified barrier to creativity: anxiety specific to creative thinking, or creativity anxiety (Daker et al., 2019). We employed a machine-learning technique for exploring relations between functional connectivity and behavior (connectome-based predictive modeling; CPM) to investigate the functional connections underlying creativity anxiety. Using whole-brain resting-state functional connectivity data, we identified a network of connections or "edges" that predicted individual differences in creativity anxiety, largely comprising connections within and between regions of the executive and default networks and the limbic system. We then found that the edges related to creativity anxiety identified in one sample generalize to predict creativity anxiety in an independent sample. We additionally found evidence that the network of edges related to creativity anxiety were largely distinct from those found in previous work to be related to divergent creative ability (Beaty et al., 2018). In addition to being the first work on the neural correlates of creativity anxiety, this research also included the development of a new Chinese-language version of the Creativity Anxiety Scale, and demonstrated that key behavioral findings from the initial work on creativity anxiety are replicable across cultures and languages. [ABSTRACT FROM AUTHOR]