Verbal Creativity Is Correlated With the Dynamic Reconfiguration of Brain Networks in the Resting State

Neural Dynamics During the Generation and Evaluation of Creative and Non-Creative Ideas

What are the neural dynamics that drive creative thinking? Recent studies have provided much insight into the neural mechanisms of creative thought. Specifically, the interaction between the executive control, default mode, and salience brain networks has been shown to be an important marker of individual differences in creative ability. However, how these different brain systems might be recruited dynamically during the two key components of the creative process-generation and evaluation of ideas-remains far from understood. In the current study we applied state-of-the-art network neuroscience methodologies to examine the neural dynamics related to the generation and evaluation of creative and non-creative ideas using a novel within-subjects design. Participants completed two functional magnetic resonance imaging sessions, taking place a week apart. In the first imaging session, participants generated either creative (alternative uses) or non-creative (common characteristics) responses to common objects. In the second imaging session, participants evaluated their own creative and non-creative responses to the same objects. Network neuroscience methods were applied to examine and directly compare reconfiguration, integration, and recruitment of brain networks during these four conditions. We found that generating creative ideas led to significantly higher network reconfiguration than generating non-creative ideas, whereas evaluating creative and non-creative ideas led to similar levels of network integration. Furthermore, we found that these differences were attributable to different dynamic patterns of neural activity across the executive control, default mode, and salience networks. This study is the first to show within-subject differences in neural dynamics related to generating and evaluating creative and non-creative ideas.

Interactions between functional networks in Parkinson's disease mild cognitive impairment

The study of mild cognitive impairment (MCI) is critical to understand the underlying processes of cognitive decline in Parkinson's disease (PD). Functional connectivity (FC) disruptions in PD-MCI patients have been observed in several networks. However, the functional and cognitive changes associated with the disruptions observed in these networks are still unclear. Using a data-driven methodology based on independent component analysis, we examined differences in FC RSNs among PD-MCI, PD cognitively normal patients (PD-CN) and healthy controls (HC) and studied their associations with cognitive and motor variables. A significant difference was found between PD-MCI vs PD-CN and HC in a FC-trait comprising sensorimotor (SMN), dorsal attention (DAN), ventral attention (VAN) and frontoparietal (FPN) networks. This FC-trait was associated with working memory, memory and the UPDRS motor scale. SMN involvement in verbal memory recall may be related with the FC-trait correlation with memory deficits. Meanwhile, working memory impairment may be reflected in the DAN, VAN and FPN interconnectivity disruptions with the SMN. Furthermore, interactions between the SMN and the DAN, VAN and FPN network reflect the intertwined decline of motor and cognitive abilities in PD-MCI. Our findings suggest that the memory impairments observed in PD-MCI are associated with reduced FC within the SMN and between SMN and attention networks.

Brain structures associated with individual differences in decisional and emotional forgiveness

In responding to interpersonal conflicts, forgiveness goes a long way. Past brain imaging studies have examined the activation patterns of forgiving responses. However, the individual differences in brain structures associated with decisional forgiveness and emotional forgiveness are not well understood. In this voxel-based morphometry study, participants (85 men, 210 women) completed the Decisional Forgiveness Scale (DFS) and the Emotional Forgiveness Scale (EFS) and underwent an anatomical magnetic resonance imaging scan. Higher DFS scores were associated with larger GM volumes in a cluster that included regions in the orbitofrontal cortex (OFC). Higher EFS scores were associated with larger GM volumes in a cluster that included regions in the medial prefrontal cortex (mPFC) and the superior frontal gyrus (SFG), which were also associated with smaller GM volumes in a cluster that included regions in the left inferior parietal lobule (IPL). The associations between the identified regions and DFS scores and EFS scores were supported by the cross-validation test. In addition, the GMV of OFC, mPFC and SFG partially mediated the relationship between DFS and EFS. These results provide direct neuroanatomical evidence for an association between decisional and emotional forgiveness and brain regions which are critical for cognitive control, theory of mind and moral judgment.

Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction

OBJECTIVE

Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks.

METHODS

To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating both fMRI time series and functional connectivity (FC) between each pair of brain regions. Specifically, our model learns a graph embedding from individual brain networks derived from multi-modal data. A manifold-based regularization term is enforced to consider the relationships of subjects both within and between modalities. Furthermore, we propose the gradient-weighted regression activation mapping (Grad-RAM) and the edge mask learning to interpret the model, which is then used to identify significant cognition-related biomarkers.

RESULTS

We validate our MGCN model on the Philadelphia Neurodevelopmental Cohort to predict individual wide range achievement test (WRAT) score. Our model obtains superior predictive performance over GCN with a single modality and other competing approaches. The identified biomarkers are cross-validated from different approaches.

CONCLUSION AND SIGNIFICANCE

This paper develops a new interpretable graph deep learning framework for cognition prediction, with the potential to overcome the limitations of several current data-fusion models. The results demonstrate the power of MGCN in analyzing multi-modal fMRI and discovering significant biomarkers for human brain studies.

A comprehensive approach to study the resting-state brain network related to creative potential

Studies related to creativity generally investigate cognition and brain functioning linked to creative achievement. However, this approach does not allow characterization of creative potential. To better define creative potential, we studied cognitive function related to creative processes and the associated brain resting functional connectivity. Therefore, in this pilot study, we constructed a cognitive functioning model via structural equation modeling assuming an influence of working memory (WM) and analytical thinking on creativity assessed by the Torrance Tests of Creative Thinking. On the basis of this model, we differentiated two groups with different functioning levels on the basis of their creative score. We identified one group as the high-creative potential group, with a positive correlation between WM and creativity and a negative correlation between analytical thinking and creativity. The other group was the low-creative potential group, with no correlation between WM and creativity and a negative correlation between analytical thinking and creativity. Then, we examined brain functional connectivity at rest and found that the high-creative potential group had increased connectivity in the attentional network (AN) and default-mode network (DMN) and decreased connectivity in the salience network (SN). Our findings highlight the involvement of the AN. We, therefore, linked this network to creative potential, which is consistent with cognitive theories suggesting that creativity is underpinned by attentional processes.

Healthy Aging Alters the Functional Connectivity of Creative Cognition in the Default Mode Network and Cerebellar Network

Creativity is a higher-order neurocognitive process that produces unusual and unique thoughts. Behavioral and neuroimaging studies of younger adults have revealed that creative performance is the product of dynamic and spontaneous processes involving multiple cognitive functions and interactions between large-scale brain networks, including the default mode network (DMN), fronto-parietal executive control network (ECN), and salience network (SN). In this resting-state functional magnetic resonance imaging (rs-fMRI) study, group independent component analysis (group-ICA) and resting state functional connectivity (RSFC) measures were applied to examine whether and how various functional connected networks of the creative brain, particularly the default-executive and cerebro-cerebellar networks, are altered with advancing age. The group-ICA approach identified 11 major brain networks across age groups that reflected age-invariant resting-state networks. Compared with older adults, younger adults exhibited more specific and widespread dorsal network and sensorimotor network connectivity within and between the DMN, fronto-parietal ECN, and visual, auditory, and cerebellar networks associated with creativity. This outcome suggests age-specific changes in the functional connected network, particularly in the default-executive and cerebro-cerebellar networks. Our connectivity data further elucidate the critical roles of the cerebellum and cerebro-cerebellar connectivity in creativity in older adults. Furthermore, our findings provide evidence supporting the default-executive coupling hypothesis of aging and novel insights into the interactions of cerebro-cerebellar networks with creative cognition in older adults, which suggest alterations in the cognitive processes of the creative aging brain.

Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations

Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization.

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.

The dynamics of resting-state alpha oscillations predict individual differences in creativity

The neuronal mechanisms underlying creativity are poorly understood. Arguably, the brain's ability to switch states would contribute to achieving novel ideas, and thus to creativity. Faster brain-state switching is reflected in the temporal dynamics of functional brain activity. Stronger autocorrelations in brain activity measures can make a brain stay in a certain state for longer periods, whereas low temporal autocorrelations reflect faster state switching. We established the brain's inherent tendency to switch or stay in a resting, no-task condition using 128 channel electroencephalography (EEG). We assessed temporal autocorrelations of the amplitude modulation of the dominant alpha oscillations (8-13 Hz). Creativity was measured by a self-rating, an examiner-rating and the alternative uses task in 40 healthy young adults, which was scored on dimensions of verbal fluency, originality, elaboration, usefulness, and flexibility. For each dimension, the total number of subject-reported alternative uses that matched the criterion was noted (the quantity measure), as well as the proportion of uses that matched the dimensional criterion. A principal components analysis confirmed the two-component structure of quantity and quality. Partial correlation analysis was used controlling for gender and age, and a cluster permutation test was performed to correct for multiple testing. A significant cluster over right central/temporal brain areas was found with a negative correlation between creativity and temporal autocorrelations were observed (p = 0.028). To our knowledge, this is the first demonstration that individual variation in the dynamically changing activity in the brain may offer a neuronal explanation for individual variation in creative ideation.