| Innovation is not only the origin of the technological progress and economic development for a country but the base of problem solving and social adaptation for an individual.Thus it is imperative for social development to assess,predict and promote individual creative ability in an effective way.Creativity as one of the most advanced cognitive abilities of the human,its essence and origin have been plagued by many researchers in several fields.In the field of creativity,the two core questions are what the nature of creativity is and how to evaluate creativity.In recent years,many researchers have used neuroimaging techniques to explore the cognitive and neural mechanism of creativity,largely improving our understanding of the brain mechanism of creativity and individual differences.Previous studies have shown that individual innovation processes,creative performance are associated with extensive brain regional activities,brain structures,and functional networks.However,due to the complexity of creativit y,researchers seems to fail to achieve a deep understanding of the nature of creativity,in contrast,the findings made the brain structure,function and creativity of the relations hip more confusing.The complexity of creativity is mainly reflected in two aspects: mult ip le processing and multiple compositions.From the point view of processing,creative thinking is not a single processing process.From the point view of composition,creativit y is not only dependent on creative thinking but also subject to the individual's cognitive ability,experience,personality and other factors.The existing research often considered a single dimension of the creative process or creative performance of individ ua l differences in the neural mechanism,so that ignored the multidimensional nature of creativity.Based on this,we define creativity as a function of multiple cognitive components and personality traits,with the ability to produce novel,unique and valuable products.Under the framework of innovation quality,we implement behavioral network analysis to explore the composition of innovative quality and the relationship between the internal and external components,and then investigate the brain structural and functio na l basis of creativity using a mind-brain association method.Last,we used the brain features associated with the innovative qualities to predict the creative potential and creative achievements,which is to verify the validity of the brain characteristics in the evaluat io n and prediction of the creativity and the feasibility of individual creativity based on the brain imaging technology.Study 1 explored the profile of innovative qualities using behavioral data.In experiment 1,clustering analysis was applied to be divided all varieties into 4 components: creative thinking,creative cognition,positive traits and negative traits.And then subjects were divided into two groups by hierarchical clustering analysis.The nonparametric test revealed that there were significant differences in creative thinking,positive traits and negative traits between two groups.Moreover,student's test or nonparametric test was performed to investigate the difference between high creativit y group(HC)and low creativity group(LC)in each variable score.For creative thinking,results showed significant differences in the questioning ability,divergent thinking abilit y,imagination,with HC scoring significantly higher than LC;For positive traits,significant differences in openness,imagination,curiosity,adventure and so on,with HC scoring significantly higher than LC;However,neuroticism was higher in LC than HC.Behavioral network analysis found that high node degree variants included divergent thinking,imagination,associative ability,extroversion,challenge,openness,curiosit y,imagination,and neuroticism,which indicating that these ability or traits play a hub role in the innovation-quality network.In addition,divergent thinking,association abilit y,openness,neuroticism have more betweenness in creativity network,which indicat ing these attributes are important relay points in the innovation-quality network.What's more,the components of creative thinking have the largest mean node degree than other components,which indicating that creative thinking is the core component of the creative network.Study 2 investigated the structural and functional profile of creativity.Firstly,we explored the brain structural basis of creativity using canonical correlation analysis in Experiment 2.Results showed that creativity was divided into three components : creative thinking,positive-negative traits and creative cognition.Creative thinking mainly includes divergent thinking,imagination,questioning ability,prototypical inspiration and other variables;positive-negative characteristics mainly include Gough,openness,curiosity,imagination,adventure,neuroticism,trait anxiety and other variables;Cognitive components include convergent thinking,reasoning,working memory,cognitive flexibility,and inhibition ability.Corresponding structural features,the creative thinking mainly involved in SFG,DLPFC,mPFC,STG,posterior parietal cortex(PPC),insular(INS),subcortex and several fiber connections involving in lateral temporal cortex(LTC)to PFC,LTC and PPC,and fronto-parietal connection;Positive-nega t ive characteristics was mainly involved in a wide range of prefrontal areas,STG,ITG,SPL,Hippocampus(Hipp),thalamus,amygdala and basal ganglia,and several fiber connections involving in subcortex to PFC and temporal cortex;Creative cognitive mainly involved in IFG,precentral gyrus(PCG),IPL,ITG,INS,and white matter connection between temporal cortex and subcortex,lateral PFC and PPC.Experiment 3 investigated the brain functional basis of the creative quality.The results showed that the innovative qualities mainly included three components: creative thinking,positive-negative trait and creative cognition.Creative thinking was mainly related to functio na l connection within the default mode network(DMN)and the executive control network(ECN),and functional connectivity between two networks.Positive-negative traits mainly related to functional connection between IFG and subcortex,and functio na l connection between Hipp and PPC.Creative cognition mainly related to functio na l connection within salience network(SN)and cingulo-opercular network(CON).Experiment 4 investigated the brain structural and functional basis of creativity by integrating three brain modalities.The results showed that brain features related to creative thinking mainly involved in semantic and memory-related systems,including the posterior areas of DMN and functional connections within DMN.Creative cognitio n mainly involved in ECN,including functional connections between mPFC and lateral PFC,and morphological features of lateral PFC,such as IFG,DLPFC.Positive-nega t ive traits mainly related to medial part of DMN,TPJ and subcortex area related to motivat io n and reward.Experiment 5 investigated whether brain multimodal could be used to effectively assess individual creative ability by discriminant analysis.The results revealed that the multimodal features can be able to distinguish between HC and LC.From the integrating modal indicators,the multimodel features is the best way to predict groups' label,followed by the brain structural features.For singlemode,we found that the characteristic of white matter connection is the best for the prediction of group's label,followed by gray matter volume and the worst is functional connection characterist ics.The optimal evaluation model is tested using the new data set.Results revealed that the accuracy rate of the integrating three brain modalities for predicting creative ability is 73%,but the rate for predicting intelligence only is random level.These results suggested that these brain features selected in this study are effective and that it is feasible to assess individual creative levels using brain imaging features.Study 3 explored the effect of multimodal features of the brain used to predict individual creative potential and creative achievement.Experiment 6 examined longitudinal alterations of brain structure and their relation to creative potential in a sample of 159 healthy young adults who were scanned using MRI 2-3 times over the course of 3 years.The most robust predictor of future creative ability was the right DLPFC,which in conjunction with baseline creative capacity showed a 31% prediction rate.Longitudinal analysis revealed that slower decreases in gray matter density within left frontoparietal and right frontotemporal clusters predicted enhanced creative abilit y.Moreover,white matter connections between the lateral PFC and the mPFC,within the temporal area could effectively predict the individual future creative potential.We conclude that continuous goal-directed planning and accumulated knowledge are implemented in the right DLPFC and temporal areas,respectively,which in turn support longitudinal gains in creative cognitive ability.Experiment 7a used rest-state functio na l connections to predict individual creative potential after 3 years.Our results revealed that the functional connections within DMN and ECN can effectively predict individ ua l creative potential.Especially,functional connectivity between mSFG and ITG,IFG and STG,DLPFC and IPL can be negatively related to creative performance.Experiment 7b examined whether the cooperation between DMN and ECN could predict individ ua l creative potential in the task period.The results showed that the activation of areas within DMN including IPL,PCC,PCUN significantly predicted creative performance of 3 months later.These results suggested that posterior areas within DMN play a key role in the creative thinking.Experiment 8 tested the relationship between brain features combination behavioral measures related to creativity and real-life creative achieve me nt obtained 4 years later.Our results revealed gray matter volume in bilateral DLPFC,functional connections within DMN and between DMN and ECN,white matter connections within IFG and bilateral mSFG were significantly associated with creative achievement.In addition,behavioral features including divergent thinking,working memory,imagination and curiosity were also associated with individual creative achievement.The predictive rate of combine model is 84%,and the predictive effect of the brain features is better than the behavioral features.In sum,creativity is an interaction system involving in creative thinking,cognitive,personality.Creative thinking is the core of creativity,which mainly includes divergent thinking and imagination.Creative personality is the basis of creativity,which mainly includes openness,curiosity and other positive personality traits,but also contains neuroticism and other negative traits.Creative cognition is a necessary condition for creativity,which is necessary cognitive function during creative problems solving,such as working memory,reasoning and cognitive flexibility.The brain basis associated with creativity includes episodic memory and semantic memory system involving in the lateral and medial temporal gyrus,and semantic retrieval and control system involving in the lateral parietal lobe and DLPFC,lateral vmPFC,and execution of the control network responsible for working memory and inhibition,and the DMN and subcortex system responsible for emotions,personality and motivation.Effective prediction indicators of individual creativity potential and achievements mainly included the functio na l connection between DLPFC and lateral temporal cortex,as well as DLPFC and IPL.In addition,GMV in the DLPFC,white matter connections in the IFG and between lateral mSFG showed a significant predictive effect.Taken together,our results suggest that morphological feature of the DLPFC and the white matter and functional connections across DLPFC reflect the critical characteristics of high creativity individual-the abilit y of originality semantic combination but with limitation,which is a key feature for predicting future creative potential and creative achievement.The results of this study will not only help us to have a deeper understanding of the structure and composition of creativity but also provide a robust support for understanding the nature of creativit y.Moreover,these results provide evidence for supporting the assessment and prediction of individual creative ability based on brain imaging technology.We believe this way will make a far-reaching and brilliant contribution by using brain features for assessing and predicting creativity ability in creativity education and talent selection. |
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