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

   https://doi.org/10.1162/jocn_a_01658  

   Orwig, William; Diez, Ibai; Vannini, Patrizia; Beaty, Roger; Sepulcre, Jorge (March 2021, Journal of Cognitive Neuroscience)

   null (Ed.)

   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. 

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2. Functional Realignment of Frontoparietal Subnetworks during Divergent Creative Thinking

   https://doi.org/10.1093/cercor/bhab100  

   Beaty, Roger E; Cortes, Robert A; Zeitlen, Daniel C; Weinberger, Adam B; Green, Adam E (April 2021, Cerebral Cortex)

   null (Ed.)

   Abstract Creative cognition has been consistently associated with functional connectivity between frontoparietal control and default networks. However, recent research identified distinct connectivity dynamics for subnetworks within the larger frontoparietal system—one subnetwork (FPCNa) shows positive coupling with the default network and another subnetwork (FPCNb) shows negative default coupling—raising questions about how these networks interact during creative cognition. Here we examine frontoparietal subnetwork functional connectivity in a large sample of participants (n = 171) who completed a divergent creative thinking task and a resting-state scan during fMRI. We replicated recent findings on functional connectivity of frontoparietal subnetworks at rest: FPCNa positively correlated with the default network and FPCNb negatively correlated with the default network. Critically, we found that divergent thinking evoked functional connectivity between both frontoparietal subnetworks and the default network, but in different ways. Using community detection, we found that FPCNa regions showed greater coassignment to a default network community. However, FPCNb showed overall stronger functional connectivity with the default network—reflecting a reversal of negative connectivity at rest—and the strength of FPCNb-default network connectivity correlated with individual creative ability. These findings provide novel evidence of a behavioral benefit to the cooperation of typically anticorrelated brain networks. 

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3. Connectome-Based Predictive Modeling of Creativity Anxiety

   https://doi.org/10.1016/j.neuroimage.2020.117469  

   Ren, Zhiting; Daker, Richard J; Shi, Liang; Sun, Jiangzhou; Beaty, Roger E; Wu, Xinran; Chen, Qunlin; Yang, Wenjing; Lyons, Ian M; Green, Adam E; et al (January 2021, NeuroImage)

   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). Wee mployed 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. 

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4. Uncovering hidden patterns of design ideation using hidden Markov modeling and neuroimaging

   https://doi.org/10.1017/S0890060423000021  

   Hu, Mo; McComb, Christopher; Goucher-Lambert, Kosa (January 2023, Artificial Intelligence for Engineering Design, Analysis and Manufacturing)

   Abstract The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design. 

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5. Classification of Adolescent Major Depressive Disorder via Static and Dynamic Connectivity

   https://doi.org/10.1109/JBHI.2020.3043427  

   Sen, Bhaskar; Cullen, Kathryn R.; Parhi, Keshab K. (December 2020, IEEE Journal of Biomedical and Health Informatics)

   null (Ed.)

   Prior papers have explored the functional connectivity changes for patients suffering from major depressive disorder (MDD). This paper introduces an approach for classifying adolescents suffering from MDD using resting-state fMRI. Accurate diagnosis of MDD involves interviews with adolescent patients and their parents, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), behavioral observation as well as the experience of a clinician. Discovering predictive biomarkers for diagnosing MDD patients using functional magnetic resonance imaging (fMRI) scans can assist the clinicians in their diagnostic assessments. This paper investigates various static and dynamic connectivity measures extracted from resting-state fMRI for assisting with MDD diagnosis. First, absolute Pearson correlation matrices from 85 brain regions are computed and they are used to calculate static features for predicting MDD. A predictive sub-network extracted using sub-graph entropy classifies adolescent MDD vs. typical healthy controls with high accuracy, sensitivity and specificity. Next, approaches utilizing dynamic connectivity are employed to extract tensor based, independent component based and principal component based subject specific attributes. Finally, features from static and dynamic approaches are combined to create a feature vector for classification. A leave-one-out cross-validation method is used for the final predictor performance. Out of 49 adolescents with MDD and 33 matched healthy controls, a support vector machine (SVM) classifier using a radial basis function (RBF) kernel using differential sub-graph entropy combined with dynamic connectivity features classifies MDD vs. healthy controls with an accuracy of 0.82 for leave-one-out cross-validation. This classifier has specificity and sensitivity of 0.79 and 0.84, respectively. This performance demonstrates the utility of MRI based diagnosis of psychiatric disorders like MDD using a combination of static and dynamic functional connectivity features of the brain. 

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