Neural and genetic determinants of creativity

The Influence of Mating Context on Creativity: Insights from Simulated Dating Scenarios

Creativity offers both survival and reproductive benefits, being a desirable trait in potential mates and linked to fertility and sexuality. We investigated whether viewing attractive faces of potential short-term or long-term partners in a simulated dating portal enhances participants' creativity. We also explored possible mediators (arousal, mood, sexual arousal, motivation, and attraction) and moderators (relationship status, satisfaction, mate value, and sociosexual orientation). In Study 1, 483 participants (Mage = 30.06, SD = 6.37; 242 women, 241 men) viewed either four attractive or four unattractive opposite-sex potential partners and wrote self-promotional bios. No significant creativity differences were found between the attractive and unattractive groups. However, men were more flexible and produced more original ideas than women, while women showed greater fluency and self-creativity promotion. In Study 2, 494 participants (Mage = 30.84, SD = 6.06; 258 women, 236 men) viewed profiles of attractive potential partners for either short-term or long-term inclined relationships. Women's fluency and originality were higher in the long-term condition, but sexual arousal negatively impacted both fluency and originality when choosing an attractive partner for a long-term relationship, particularly when a real date desirability with the mate was high. Overall, the results suggest that creativity is influenced by the mating context, though the effects were modest. Future studies should increase sample sizes, geographic diversity, and experimental settings.

Dynamic switching between brain networks predicts creative ability

Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding to functional connectivity between the brain's Default Mode (DMN) and Executive Control (ECN) Networks. Here, we conduct a large-scale, multi-center examination of this hypothesis. Employing a meta-analytic network neuroscience approach, we analyze resting-state fMRI and creative task performance across 10 independent samples from Austria, Canada, China, Japan, and the United States (N = 2433)-constituting the largest and most ethnically diverse creativity neuroscience study to date. Using time-resolved network analysis, we investigate the relationship between creativity (i.e., divergent thinking ability) and dynamic switching between DMN and ECN. We find that creativity, but not general intelligence, can be reliably predicted by the number of DMN-ECN switches. Importantly, we identify an inverted-U relationship between creativity and the degree of balance between DMN-ECN switching, suggesting that optimal creative performance requires balanced brain network dynamics. Furthermore, an independent task-fMRI validation study (N = 31) demonstrates higher DMN-ECN switching during creative idea generation (compared to a control condition) and replicates the inverted-U relationship. Therefore, we provide robust evidence across multi-center datasets that creativity is tied to the capacity to dynamically switch between brain networks supporting spontaneous and controlled cognition.

Prediction of individual performance and verbal intelligence scores from resting-state fMRI in children and adolescents

The neuroimaging basis of intelligence remains elusive; however, there is a growing body of research employing connectome-based predictive modeling to estimate individual intelligence scores, aiming to identify the optimal set of neuroimaging features for accurately predicting an individual's cognitive abilities. Compared to adults, the disparities in cognitive performance among children and adolescents are more likely to captivate individuals' interest and attention. Limited research has been dedicated to exploring neuroimaging markers of intelligence specifically in the pediatric population. In this study, we utilized resting-state functional magnetic resonance imaging (fMRI) and intelligence quotient (IQ) scores of 170 healthy children and adolescents obtained from a public database to identify brain functional connectivity markers associated with individual intellectual behavior. Initially, we extracted and summarized relevant resting-state features from whole-brain or functional network connectivity that were most pertinent to IQ scores. Subsequently, these features were employed to establish prediction models for both performance and verbal IQ scores. Within a 10-fold cross-validation framework, our findings revealed that prediction models based on whole-brain functional connectivity effectively predicted performance IQ scores( R = 0.35 , P = 2.2 × 10 - 4 ) but not verbal IQ scores( R = 0.12 , P = 0.20 ). Results of prediction models based on brain functional network connectivity further demonstrated the exceptional predictive ability of the default mode network (DMN) and fronto-parietal task control network (FTPN) for performance IQ scores ( R = 0.71 , P = 2.2 × 10 - 18 ). The above findings have also been validated using an independent dataset. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity.

Investigating the neural substrate variations between easy and challenging creative association tasks during product design within an fMRI scanner

In practice, individuals strive to develop highly original and valuable creative products within specific limitations. However, previous functional Magnetic Resonance Imaging (fMRI) studies focused on divergent-thinking tasks without considering the "valuableness" of an idea. Additionally, different types of creative tasks (e.g., the easier association vs. the harder association task) may engage distinct cognitive processes. This study aimed to investigate the underlying neural mechanisms associated with different types of creative thinking, specifically focusing on the generation of the most original and valuable creative product within an fMRI scanner. Twenty-one college students participated in a block design study. During each trial, participants were instructed to draw the most original and valuable product inspired by a given figure. The findings revealed that, in comparison to the harder association task, the easier association task led to broader activation across multiple brain regions. However, this broader activation resulted in inefficient thinking and poorer creative performance. Notably, the orbitofrontal cortex exhibited activation across various creativity tasks and displayed connectivity with several seed brain regions, highlighting the importance of decision-making when only one original and valuable product design is allowed. Furthermore, the complex functional connectivity observed between different brain networks reflects the intricate nature of creative thinking. To conclude, widespread activation of brain regions does not necessarily indicate superior creativity. Instead, optimal creative performance within constraints is achieved through an efficient utilization of association for generating innovative ideas, inhibition for suppressing unoriginal ideas, and decision-making to select the most creative idea.

Navigating the multiple dimensions of the creativity-mental disorder link: a Convergence Mental Health perspective

BACKGROUND

This paper discusses a paradox in mental health. It manifests as a relationship between adverse "bad" effects (suffering, clinical costs, loss of productivity) in individuals and populations and advantageous "good" aspects of mental disorders. These beneficial aspects (scientific, artistic and political accomplishments) emanate at the societal level through the frequently unprecedented creativity of people suffering from mental disorders and their relatives. Such gains can contribute to societal innovation and problem-solving. Especially in times of accelerated social-ecological change, approaches are needed that facilitate best-possible mental health care but also recognize creative ideas conducive to beneficial clinical and social-ecological innovations as soon as possible.

DISCUSSION

This paper emphasizes the need to account for creativity as a crucial component in evolving mental health systems and societies. It highlights the need for wide-ranging approaches and discusses how research targeting multiple facets (e.g., brain level, cognitive neuroscience, psychiatry, neurology, socio-cultural, economic and other factors) might further our understanding of the creativity-mental disorder link and its importance for innovating mental health systems and societies.

CONCLUSION

Our discussion clarifies that considerable research will be needed to obtain a better understanding of how creativity associated with mental disorders may help to create more sustainable societies on a fast-changing planet through innovative ideas. Given the current-state-of-the-art of research and healthcare management, our discussion is currently speculative. However, it provides a basis for how pros and cons might be studied in the future through transdisciplinary research and collaborations across sectors of society.

The Individualized Prediction of Neurocognitive Function in People Living With HIV Based on Clinical and Multimodal Connectome Data

Neurocognitive impairment continues to be common comorbidity for people living with HIV (PLWH). Given the chronic nature of HIV disease, identifying reliable biomarkers of these impairments is essential to advance our understanding of the underlying neural foundation and facilitate screening and diagnosis in clinical care. While neuroimaging provides immense potential for such biomarkers, to date, investigations in PLWH have been mostly limited to either univariate mass techniques or a single neuroimaging modality. In the present study, connectome-based predictive modeling (CPM) was proposed to predict individual differences of cognitive functioning in PLWH, using resting-state functional connectivity (FC), white matter structural connectivity (SC), and clinical relevant measures. We also adopted an efficient feature selection approach to identify the most predictive features, which achieved an optimal prediction accuracy of r = 0.61 in the discovery dataset (n = 102) and r = 0.45 in an independent validation HIV cohort (n = 88). Two brain templates and nine distinct prediction models were also tested for better modeling generalizability. Results show that combining multimodal FC and SC features enabled higher prediction accuracy of cognitive scores in PLWH, while adding clinical and demographic metrics may further improve the prediction by introducing complementary information, which may help better evaluate the individual-level cognitive performance in PLWH.

The molecular genetic basis of creativity: a mini review and perspectives

Although creativity is one of the defining features of human species, it is just the beginning of an ambitious attempt for psychologists to understand its genetic basis. With ongoing efforts, great progress has been achieved in molecular genetic studies of creativity. In this mini review, we highlighted recent molecular genetic findings for both domain-general and domain-specific creativity, and provided some perspectives for future studies. It is expected that this work will provide an update on the knowledge regarding the molecular genetic basis of creativity, and contribute to the further development of this field.

Linking functional connectome gradient to individual creativity

INTRODUCTION

Human brain network is organized as a hierarchical organization, exhibiting various connectome gradients. The principal gradient is anchored by the modality-specific primary areas and the transmodal regions. Previous studies have suggested that the unimodal-transmodal gradient in the functional connectome may offer an overarching framework for high-order cognitions of human brain. However, there is still a lacking of direct evidence to associate these two.

OBJECTIVES

Therefore, we aim to explore the association between creativity, a typical human high-order cognitive function, and unimodal-transmodal gradient, using two independent datasets of young adults.

METHODS

For each individual, we identified the unimodal-transmodal gradient in functional connectome and calculated its global measures. Then we correlated the individual creativity score with measures of unimodal-transmodal gradient at global-brain, subsystem, and regional level.

RESULTS

The results suggested that better creative performance was associated with greater distance between primary areas and transmodal regions in gradient axes, and less distance between ventral attention network and default mode network. Individual creativity was also found positively correlated with regional gradients in ventral attention network, and negatively correlated with gradients of regions in visual cortex.

CONCLUSION

Together, these findings directly link the unimodal-transmodal gradient to individual creativity, providing empirical evidence for the cognitive implications of functional connectome gradient.

Creative problem solving in knowledge-rich contexts

Creative problem solving (CPS) in real-world contexts often relies on reorganization of existing knowledge to serve new, problem-relevant functions. However, classic creativity paradigms that minimize knowledge content are generally used to investigate creativity, including CPS. We argue that CPS research should expand consideration of knowledge-rich problem contexts, both in novices and experts within specific domains. In particular, paradigms focusing on creative analogical transfer of knowledge may reflect CPS skills that are applicable to real-world problem solving. Such paradigms have begun to provide process-level insights into cognitive and neural characteristics of knowledge-rich CPS and point to multiple avenues for fruitfully expanding inquiry into the role of crystalized knowledge in creativity.

Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders

Multi-modal magnetic resonance imaging (MRI) is widely used for diagnosing brain disease in clinical practice. However, the high-dimensionality of MRI images is challenging when training a convolution neural network. In addition, utilizing multiple MRI modalities jointly is even more challenging. We developed a method using decomposition-based correlation learning (DCL). To overcome the above challenges, we used a strategy to capture the complex relationship between structural MRI and functional MRI data. Under the guidance of matrix decomposition, DCL takes into account the spike magnitude of leading eigenvalues, the number of samples, and the dimensionality of the matrix. A canonical correlation analysis (CCA) was used to analyze the correlation and construct matrices. We evaluated DCL in the classification of multiple neuropsychiatric disorders listed in the Consortium for Neuropsychiatric Phenomics (CNP) dataset. In experiments, our method had a higher accuracy than several existing methods. Moreover, we found interesting feature connections from brain matrices based on DCL that can differentiate disease and normal cases and different subtypes of the disease. Furthermore, we extended experiments on a large sample size dataset and a small sample size dataset, compared with several other well-established methods that were designed for the multi neuropsychiatric disorder classification; our proposed method achieved state-of-the-art performance on all three datasets.

No Evidence of Creative Benefit Accompanying Dyslexia: A Meta-Analysis

Research on the question of creative benefit accompanying dyslexia has produced conflicting findings. In this meta-analysis, we determined summary effects of mean and variance differences in creativity between groups with and without dyslexia. Twenty studies were included (n = 770 individuals with dyslexia, n = 1,671 controls). A random-effects robust variance estimation (RVE) analysis indicated no mean (g = -0.02, p = .84) or variance (g = -0.0004, p = .99) differences in creativity between groups. The mean summary effect was moderated by age, gender, and creativity domain. Compared with adolescents, adults with dyslexia showed an advantage over nondyslexic adults in creativity. In addition, a higher proportion of males in the dyslexia group was associated with poorer performance compared with the controls. Finally, the dyslexia group showed a significant performance disadvantage in verbal versus figural creativity. Regarding variance differences, they varied across age and creativity domains. Compared with adults, adolescents showed smaller variability in the dyslexia group. If the creativity task measured verbal versus figural or combined creativity, the dyslexia group exhibited smaller variability. Altogether, our results suggest that individuals with dyslexia as a group are no more creative or show greater variability in creativity than peers without dyslexia.

The neurobiological basis of divergent thinking: Insight from gene co-expression network-based analysis

Although many efforts have been made to explore the genetic basis of divergent thinking (DT), there is still a gap in the understanding of how these findings relate to the neurobiology of DT. In a combined sample of 1,682 Chinese participants, by integrating GWAS with previously identified brain-specific gene co-expression network modules, this study explored for the first time the functional brain-specific gene co-expression networks underlying DT. The results showed that gene co-expression network modules in anterior cingulate cortex, caudate, amygdala and substantia nigra were enriched with DT association signals. Further functional enrichment analysis showed that these DT-related gene co-expression network modules were enriched for key biological process and cellular component related to myelination, suggesting that cortical and sub-cortical grey matter myelination may serve as important neurobiological basis of DT. Although the underlying mechanisms need to be further refined, this exploratory study may provide new insight into the neurobiology of DT.

Spatiospectral brain networks reflective of improvisational experience

Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical chord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.

Cortical Networks of Creative Ability Trace Gene Expression Profiles of Synaptic Plasticity in the Human Brain

The ability to produce novel ideas is central to societal progress and innovation; however, little is known about the biological basis of creativity. Here, we investigate the organization of brain networks that support creativity by combining functional neuroimaging data with gene expression information. Given the multifaceted nature of creative thinking, we hypothesized that distributed connectivity would not only be related to individual differences in creative ability, but also delineate the cortical distributions of genes involved in synaptic plasticity. We defined neuroimaging phenotypes using a graph theory approach that detects local and distributed network circuits, then characterized the spatial associations between functional connectivity and cortical gene expression distributions. Our findings reveal strong spatial correlations between connectivity maps and sets of genes devoted to synaptic assembly and signaling. This connectomic-transcriptome approach thus identifies gene expression profiles associated with high creative ability, linking cognitive flexibility to neural plasticity in the human brain.

Mind Causality: A Computational Neuroscience Approach

A neuroscience-based approach has recently been proposed for the relation between the mind and the brain. The proposal is that events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. Here, some implications are developed. It is proposed that causality, at least as it applies to the brain, should satisfy three conditions. First, interventionist tests for causality must be satisfied. Second, the causally related events should be at the same level of explanation. Third, a temporal order condition must be satisfied, with a suitable time scale in the order of 10 ms (to exclude application to quantum physics; and a cause cannot follow an effect). Next, although it may be useful for different purposes to describe causality involving the mind and brain at the mental level, or at the brain level, it is argued that the brain level may sometimes be more accurate, for sometimes causal accounts at the mental level may arise from confabulation by the mentalee, whereas understanding exactly what computations have occurred in the brain that result in a choice or action will provide the correct causal account for why a choice or action was made. Next, it is argued that possible cases of "downward causation" can be accounted for by a within-levels-of-explanation account of causality. This computational neuroscience approach provides an opportunity to proceed beyond Cartesian dualism and physical reductionism in considering the relations between the mind and the brain.

Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning

Creativity is part of human nature and is commonly understood as a phenomenon whereby something original and worthwhile is formed. Owing to this ability, humans can produce innovative information that often facilitates growth in our society. Creativity also contributes to esthetic and artistic productions, such as music and art. However, the mechanism by which creativity emerges in the brain remains debatable. Recently, a growing body of evidence has suggested that statistical learning contributes to creativity. Statistical learning is an innate and implicit function of the human brain and is considered essential for brain development. Through statistical learning, humans can produce and comprehend structured information, such as music. It is thought that creativity is linked to acquired knowledge, but so-called "eureka" moments often occur unexpectedly under subconscious conditions, without the intention to use the acquired knowledge. Given that a creative moment is intrinsically implicit, we postulate that some types of creativity can be linked to implicit statistical knowledge in the brain. This article reviews neural and computational studies on how creativity emerges within the framework of statistical learning in the brain (i.e., statistical creativity). Here, we propose a hierarchical model of statistical learning: statistically chunking into a unit (hereafter and shallow statistical learning) and combining several units (hereafter and deep statistical learning). We suggest that deep statistical learning contributes dominantly to statistical creativity in music. Furthermore, the temporal dynamics of perceptual uncertainty can be another potential causal factor in statistical creativity. Considering that statistical learning is fundamental to brain development, we also discuss how typical versus atypical brain development modulates hierarchical statistical learning and statistical creativity. We believe that this review will shed light on the key roles of statistical learning in musical creativity and facilitate further investigation of how creativity emerges in the brain.

Multimodal data revealed different neurobiological correlates of intelligence between males and females

Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

A Positive Role of Negative Mood on Creativity: The Opportunity in the Crisis of the COVID-19 Epidemic

The COVID-19 epidemic is associated with negative mood, which has the potential to be a powerful driver of creativity. However, the influence of negative mood on cognitive creativity and emotional creativity remains elusive. Previous research has indicated that self-focused attention is likely to be related to both negative mood and creativity. The current study introduced two self-focused attention variables (i.e., rumination, reflection) to explore how negative mood might contribute to cognitive creativity and emotional creativity. Based on a sample of 351 participants, our study found that (1) negative mood during the outbreak of COVID-19 was associated with cognitive creativity and emotional creativity. Meanwhile, there were significant serial mediation effects of rumination and reflection in the relationship between negative mood and creativity and (2) the psychological impact after exposure to the COVID-19 epidemic was positively correlated with emotional creativity but not with cognitive creativity. These results suggested that individuals, in real life and work, could achieve better creative performance through moderate self-focus. Moreover, individuals with different mood states can be induced to enhance their creativity in times of crisis through intervention training to promote reflection.

Connectome-based evidence for creative thinking as an emergent property of ordinary cognitive operations

Creative thinking is a hallmark of human cognition, which enables us to generate novel and useful ideas. Nevertheless, its emergence within the macro-scale neurocognitive circuitry remains largely unknown. Using resting-state fMRI data from two large population samples (SWU: n = 931; HCP: n = 1001) and a novel "travelling pattern prediction analysis", here we identified the modularized functional connectivity patterns linked to creative thinking ability, which concurrently explained individual variability across ordinary cognitive abilities such as episodic memory, working memory and relational processing. Further interrogation of this neural pattern with graph theoretical tools revealed both hub-like brain structures and globally-efficient information transfer paths that together may facilitate higher creative thinking ability through the convergence of distinct cognitive operations. Collectively, our results provide reliable evidence for the hypothesized emergence of creative thinking from core cognitive components through neural integration, and thus allude to a significant theoretical advancement in the study of creativity.

Creative Connections: Computational Semantic Distance Captures Individual Creativity and Resting-State Functional Connectivity

Recent studies of creative cognition have revealed interactions between functional brain networks involved in the generation of novel ideas; however, the neural basis of creativity is highly complex and presents a great challenge in the field of cognitive neuroscience, partly because of ambiguity around how to assess creativity. We applied a novel computational method of verbal creativity assessment-semantic distance-and performed weighted degree functional connectivity analyses to explore how individual differences in assembly of resting-state networks are associated with this objective creativity assessment. To measure creative performance, a sample of healthy adults (n = 175) completed a battery of divergent thinking (DT) tasks, in which they were asked to think of unusual uses for everyday objects. Computational semantic models were applied to calculate the semantic distance between objects and responses to obtain an objective measure of DT performance. All participants underwent resting-state imaging, from which we computed voxel-wise connectivity matrices between all gray matter voxels. A linear regression analysis was applied between DT and weighted degree of the connectivity matrices. Our analysis revealed a significant connectivity decrease in the visual-temporal and parietal regions, in relation to increased levels of DT. Link-level analyses showed higher local connectivity within visual regions was associated with lower DT, whereas projections from the precuneus to the right inferior occipital and temporal cortex were positively associated with DT. Our results demonstrate differential patterns of resting-state connectivity associated with individual creative thinking ability, extending past work using a new application to automatically assess creativity via semantic distance.

Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises

The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.

Creativity and ADHD: A review of behavioral studies, the effect of psychostimulants and neural underpinnings

Attention deficit/hyperactivity disorder (ADHD) is a debilitating disorder and most research therefore focuses on its deficits and its treatment. Research on the potential positive sides of ADHD is limited, and although a comprehensive overview of empirical studies on this subject is missing, it has been suggested that ADHD is associated with enhanced creativity. To identify important relations, trends and gaps in the literature, we review 31 behavioral studies on creativity and ADHD, distinguishing different research designs, age groups, creativity measurements and effects of psychostimulants, as well as reflecting the potential underlying neural mechanisms of creativity and ADHD. Most studies find evidence for increased divergent thinking for those with high ADHD scores (subclinical) but not for those with the disorder (clinical). The rates of creative abilities/achievements were high among both clinical and subclinical groups. We found no evidence for increased convergent thinking abilities in ADHD, nor did we find an overall negative effect of psychostimulants on creativity. Neuroscientific findings suggest candidate regions as well as mechanisms that should be studied further to increase our understanding of the relationship between creativity and ADHD. We propose research opportunities to boost the knowledge needed to better understand the potential positive side of ADHD.

Genome-wide Association Study of Creativity Reveals Genetic Overlap With Psychiatric Disorders, Risk Tolerance, and Risky Behaviors

Creativity represents one of the most important and partially heritable human characteristics, yet little is known about its genetic basis. Epidemiological studies reveal associations between creativity and psychiatric disorders as well as multiple personality and behavioral traits. To test whether creativity and these disorders or traits share genetic basis, we performed genome-wide association studies (GWAS) followed by polygenic risk score (PRS) analyses. Two cohorts of Han Chinese subjects (4,834 individuals in total) aged 18-45 were recruited for creativity measurement using typical performance test. After exclusion of the outliers with significantly deviated creativity scores and low-quality genotyping results, 4,664 participants were proceeded for GWAS. We conducted PRS analyses using both the classical pruning and thresholding (P+T) method and the LDpred method. The extent of polygenic risk was estimated through linear regression adjusting for the top 3 genotyping principal components. R2 was used as a measurement of the explained variance. PRS analyses demonstrated significantly positive genetic overlap, respectively, between creativity with schizophrenia ((P+T) method: R2(max) ~ .196%, P = .00245; LDpred method: R2(max) ~ .226%, P = .00114), depression ((P+T) method: R2(max) ~ .178%, P = .00389; LDpred method: R2(max) ~ .093%, P = .03675), general risk tolerance ((P+T) method: R2(max) ~ .177%, P = .00399; LDpred method: R2(max) ~ .305%, P = .00016), and risky behaviors ((P+T) method: R2(max) ~ .187%, P = .00307; LDpred method: R2(max) ~ .155%, P = .00715). Our results suggest that human creativity is probably a polygenic trait affected by numerous variations with tiny effects. Genetic variations that predispose to psychiatric disorders and risky behaviors may underlie part of the genetic basis of creativity, confirming the epidemiological associations between creativity and these traits.

Sensation-seeking is related to functional connectivities of the medial orbitofrontal cortex with the anterior cingulate cortex

Sensation-seeking is a multifaceted personality trait with components that include experience-seeking, thrill and adventure seeking, disinhibition, and susceptibility to boredom, and is an aspect of impulsiveness. We analysed brain regions involved in sensation-seeking in a large-scale study with 414 participants and showed that the sensation-seeking score could be optimally predicted from the functional connectivity with typically (in different participants) 18 links between brain areas (measured in the resting state with fMRI) with correlation r ​= ​0.34 (p ​= ​7.3 ​× ​10^-13) between the predicted and actual sensation-seeking score across all participants. Interestingly, 8 of the 11 links that were common for all participants were between the medial orbitofrontal cortex and the anterior cingulate cortex and yielded a prediction accuracy r ​= ​0.30 (p ​= ​4.8 ​× ​10^-10). We propose that this important aspect of personality, sensation-seeking, reflects a strong effect of reward (in which the medial orbitofrontal cortex is implicated) on promoting actions to obtain rewards (in which the anterior cingulate cortex is implicated). Risk-taking was found to have a moderate correlation with sensation-seeking (r ​= ​0.49, p ​= ​3.9 ​× ​10^-26), and three of these functional connectivities were significantly correlated (p ​< ​0.05) with the overall risk-taking score. This discovery helps to show how the medial orbitofrontal and anterior cingulate cortices influence behaviour and personality, and indicate that sensation-seeking can involve in part the medial orbitofrontal cortex reward system, which can thereby become associated with risk-taking and a type of impulsiveness.

Gender Differences in Connectome-based Predictions of Individualized Intelligence Quotient and Sub-domain Scores

Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions-particularly prefrontal-parietal and basal ganglia-whereas the network pattern differs between genders.

Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships

Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene et al. (2018) predicted the fluid intelligence scores using FCs derived from rest and multiple task conditions, suggesting that task-induced brain state manipulation improved prediction of individual traits. Here, using a large dataset incorporating fMRI data from rest and 7 distinct task conditions, we replicated the original study by employing a different machine learning approach, and applying the method to predict two reading comprehension-related cognitive measures. Consistent with their findings, we found that task-based machine learning models often outperformed rest-based models. We also observed that combining multi-task fMRI improved prediction performance, yet, integrating the more fMRI conditions can not necessarily ensure better predictions. Compared with rest, the predictive FCs derived from language and working memory tasks were highlighted with more predictive power in predominantly default mode and frontoparietal networks. Moreover, prediction models demonstrated high stability to be generalizable across distinct cognitive states. Together, this replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits, providing strong evidence for the validity and robustness of the original findings.

Different role of the supplementary motor area and the insula between musicians and non-musicians in a controlled musical creativity task

The ability to compose creative musical ideas depends on the cooperation of brain mechanisms involved in multiple processes, including controlled creative cognition, which is a type of creativity that has so far been poorly researched. Therefore, the objective of this study was to examine the brain evoked activations by using fMRI, in both musicians and non-musicians, during a general task of controlled musical creativity and its relationship with general creativity. Results revealed that during a rhythmic improvisation task, musicians show greater activation of the motor supplementary area, the anterior cingulate cortex, the dorsolateral prefrontal cortex, and the insula, along with greater deactivation of the default mode network in comparison with non-musicians. For the group of musicians, we also found a positive correlation between the time improvising and the activation of the supplementary motor area, whilst in the non-musicians group improvisation time correlated with the activation of the insula. The results found for the musicians support the notion that the supplementary motor area plays a role in the representation and execution of musical behaviour, while the results in non-musicians reveal the role of the insula in the processing of novel musical information.

Brain hemispheric involvement in visuospatial and verbal divergent thinking

Hemispheric lateralization for creative thinking remains a controversial topic. Early behavioral and neuroimaging research supported right hemisphere dominance in creative thinking, but more recent evidence suggests the left hemisphere plays an equally important role. In addition, the extent to which hemispheric lateralization in specific brain regions relates to individual creative ability, and whether hemispheric dominance relates to distinct task performance, remain poorly understood. Here, using multivariate predictive modeling of resting-state functional MRI data in a large sample of adults (N = 502), we estimated hemispheric segregation and integration for each brain region and investigated these lateralization indices with respect to individual differences in visuospatial and verbal divergent thinking. Our analyses revealed that individual visuospatial divergent thinking performance could be predicted by right-hemispheric segregation within the visual network, sensorimotor network, and some regions within the default mode network. High visuospatial divergent thinking was related to stronger functional connectivity between the visual network, fronto-parietal network, and default mode network within the right hemisphere. In contrast, high verbal divergent thinking performance could be predicted by inter-hemispheric balance within regions mainly involved in complex semantic processing (e.g., lateral temporal cortex and inferior frontal gyrus) and cognitive control processing (e.g., inferior frontal gyrus, middle frontal cortex, and superior parietal lobule). The current study suggests that two distinct forms of functional lateralization support individual differences in visuospatial and verbal divergent thinking. These findings have important implications for our understanding of hemispheric interaction mechanisms of creative thinking.

Improvement in creativity after transcranial random noise stimulation (tRNS) over the left dorsolateral prefrontal cortex

Creativity has previously been shown to improve after the application of direct and alternating current transcranial stimulation over the dorsolateral prefrontal cortex (DLPFC). However, previous studies have not tested whether transcranial random noise stimulation (tRNS) was efficient for this purpose. The aim of this randomized, double-blind, placebo-controlled study was to investigate the effect of tRNS on both verbal convergent and (verbal and visual) divergent thinking during left DLPFC tRNS stimulation. Thirty healthy participants were randomly allocated to either a tRNS active group or a sham group. Each session lasted 20 min and the current was set to 1.5 mA (100-500 Hz). Participants' verbal convergent thinking was assessed with the Remote Associates Test (RAT). Verbal and visual divergent thinking were respectively measured by using the Unusual Uses and Picture Completion subtests from the Torrance Tests of Creative Thinking. Bootstrapped analysis of variance showed significant differences in the mean change scores between the active tRNS group and the sham group in RAT scores (d = 1.68); unusual uses: fluency (d = 2.29) and originality (d = 1.43); and general creativity (d = 1.45). Visual divergent thinking, in contrast, did not show any significant improvement. Our results suggested that tRNS over the left DLPFC is effective for increasing verbal divergent and convergent thinking.

The Interaction of TPH1 A779C Polymorphism and Maternal Authoritarianism on Creative Potential

Exploring the possible mechanisms through which gene may interact with environment to influence creativity has been one of the leading issues in creativity research. In a sample of four hundred and twenty-one Chinese undergraduate students, the present study investigated for the first time the interaction of TPH1 A779C polymorphism and maternal parenting styles on creative potential. The results showed that there was a significant interaction of TPH1 A779C polymorphism and maternal authoritarianism on creative potential. Moreover, the analysis of regions of significance (Ros) provided supporting evidences for both the diathesis-stress model (flexibility) and the differential susceptibility model (originality). These findings extend our understanding concerning the mechanisms by which gene and environment may act in coordination to contribute to creativity.

Network Neuroscience of Creative Cognition: Mapping Cognitive Mechanisms and Individual Differences in the Creative Brain

Network neuroscience research is providing increasing specificity on the contribution of large-scale brain networks to creative cognition. Here, we summarize recent experimental work examining cognitive mechanisms of network interactions and correlational studies assessing network dynamics associated with individual creative abilities. Our review identifies three cognitive processes related to network interactions during creative performance: goal-directed memory retrieval, prepotent-response inhibition, and internally-focused attention. Correlational work using prediction modeling indicates that functional connectivity between networks-particularly the executive control and default networks-can reliably predict an individual's creative thinking ability. We discuss potential directions for future network neuroscience, including assessing creative performance in specific domains and using brain stimulation to test causal hypotheses regarding network interactions and cognitive mechanisms of creative thought.