Recognizing ideas generated in a creative task: the roles of the hippocampus and medial prefrontal cortex in facilitating self-generated learning.

Creative idea generation plays an important role in promoting successful memory formation. Yet, its underlying neural correlates remain unclear. We investigated the self-generated learning of creative ideas motivated by the schema-linked interactions between medial prefrontal and medial temporal regions framework. This was achieved by having participants generate ideas in the alternative uses task, self-evaluating their ideas based on novelty and source (i.e. new or old), and then later being tested on the recognition performance of the generated ideas. At the behavioral level, our results indicated superior performances in discriminating novel ideas, highlighting the novelty effect on memory. At the neural level, the regions-of-interest analyses revealed that successful recognition of novel ideas was associated with greater activations in the hippocampus (HPC) and medial prefrontal cortex (mPFC) during ideation. However, only activation in the right HPC was positively related to the successful recognition of novel ideas. Importantly, the weaker the connection between the right HPC and left mPFC, the higher the recognition accuracy of novel ideas. Moreover, activations in the right HPC and left mPFC were both effective predictors of successful recognition of novel ideas. These findings uniquely highlight the role of novelty in promoting self-generated learning of creative ideas.

Effect of Regional Brain Activity Following Repeat Transcranial Magnetic Stimulation in SCA3: A Secondary Analysis of a Randomized Clinical Trial.

Repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuroregulatory technique used to treat neurodegenerative diseases, holds promise for spinocerebellar ataxia type 3 (SCA3) treatment, although its efficacy and mechanisms remain unclear. This study aims to observe the short-term impact of cerebellar rTMS on motor function in SCA3 patients and utilize resting-state functional magnetic resonance imaging (RS-fMRI) to assess potential therapeutic mechanisms. Twenty-two SCA3 patients were randomly assigned to receive actual rTMS (AC group, n = 11, three men and eight women; age 32-55 years) or sham rTMS (SH group, n = 11, three men and eight women; age 26-58 years). Both groups underwent cerebellar rTMS or sham rTMS daily for 15 days. The primary outcome measured was the ICARS scores and parameters for regional brain activity. Compared to baseline, ICARS scores decreased more significantly in the AC group than in the SH group after the 15-day intervention. Imaging indicators revealed increased Amplitude of Low Frequency Fluctuation (ALFF) values in the posterior cerebellar lobe and cerebellar tonsil following AC stimulation. This study suggests that rTMS enhances motor functions in SCA3 patients by modulating the excitability of specific brain regions and associated pathways, reinforcing the potential clinical utility of rTMS in SCA3 treatment. The Chinese Clinical Trial Registry identifier is ChiCTR1800020133.

Psychological resilience-related functional connectomes predict creative personality.

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.

Diverse functional interaction driven by control-default network hubs supports creative thinking.

Complex cognitive processes, like creative thinking, rely on interactions among multiple neurocognitive processes to generate effective and innovative behaviors on demand, for which the brain's connector hubs play a crucial role. However, the unique contribution of specific hub sets to creative thinking is unknown. Employing three functional magnetic resonance imaging datasets (total N = 1,911), we demonstrate that connector hub sets are organized in a hierarchical manner based on diversity, with "control-default hubs"-which combine regions from the frontoparietal control and default mode networks-positioned at the apex. Specifically, control-default hubs exhibit the most diverse resting-state connectivity profiles and play the most substantial role in facilitating interactions between regions with dissimilar neurocognitive functions, a phenomenon we refer to as "diverse functional interaction". Critically, we found that the involvement of control-default hubs in facilitating diverse functional interaction robustly relates to creativity, explaining both task-induced functional connectivity changes and individual creative performance. Our findings suggest that control-default hubs drive diverse functional interaction in the brain, enabling complex cognition, including creative thinking. We thus uncover a biologically plausible explanation that further elucidates the widely reported contributions of certain frontoparietal control and default mode network regions in creativity studies.

A Convolutional Neural Network-Based Quantization Method for Block Compressed Sensing of Images.

Block compressed sensing (BCS) is a promising method for resource-constrained image/video coding applications. However, the quantization of BCS measurements has posed a challenge, leading to significant quantization errors and encoding redundancy. In this paper, we propose a quantization method for BCS measurements using convolutional neural networks (CNN). The quantization process maps measurements to quantized data that follow a uniform distribution based on the measurements' distribution, which aims to maximize the amount of information carried by the quantized data. The dequantization process restores the quantized data to data that conform to the measurements' distribution. The restored data are then modified by the correlation information of the measurements drawn from the quantized data, with the goal of minimizing the quantization errors. The proposed method uses CNNs to construct quantization and dequantization processes, and the networks are trained jointly. The distribution parameters of each block are used as side information, which is quantized with 1 bit by the same method. Extensive experiments on four public datasets showed that, compared with uniform quantization and entropy coding, the proposed method can improve the PSNR by an average of 0.48 dB without using entropy coding when the compression bit rate is 0.1 bpp.

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.

Regional grey matter volume in the posterior cingulate cortex predicts positive risk-taking: A moderation model.

Positive risk-taking is a crucial element of individual creativity and social development. However, little is known regarding the relation between individual neural differences and positive risk-taking. In addition, critical thinking (CT) and gender have been proven to be two important individual-specific factors associated with risk-taking behaviour, and different levels of CT and gender may have diverse effects on the relationship between brain structure and positive risk-taking. The present study examined the relationship between positive risk-taking and regional grey matter volume (rGMV) in 292 healthy participants. The results showed that positive risk-taking was significantly positively associated with the rGMV of the posterior cingulate cortex (PCC). In addition, this study investigated individual differences in critical thinking and found that it moderated the relationship between rGMV and positive risk-taking. Individuals with lower CT had a stronger association between rGMV and positive risk-taking. Further analysis showed that for males, a greater rGMV was significantly linked to higher positive risk-taking tendency. These findings suggest that PCC evaluates risk and serves as a behavioural adaptation hub for positive risk-taking. This study thereby contributes to the literature on individual differences in brain structure and risk-taking by elucidating the moderating effects of CT and gender in healthy adults.

The bright side and dark side of daydreaming predict creativity together through brain functional connectivity.

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.

Memory Suppression Ability can be Robustly Predicted by the Internetwork Communication of Frontoparietal Control Network.

Memory suppression (MS) is essential for mental well-being. However, no studies have explored how intrinsic resting-state functional connectivity (rs-FC) predicts this ability. Here, we adopted the connectome-based predictive modeling (CPM) based on the resting-state fMRI data to investigate whether and how rs-FC profiles in predefined brain networks (the frontoparietal control networks or FPCN) can predict MS in healthy individuals with 497 participants. The MS ability was assessed by MS-induced forgetting during the think/no-think paradigm. The results showed that FPCN network was especially informative for generating the prediction model for MS. Some regions of FPCN, such as middle frontal gyrus, superior frontal gyrus and inferior parietal lobe were critical in predicting MS. Moreover, functional interplay between FPCN and multiple networks, such as dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), the limbic system and subcortical regions, enabled prediction of MS. Crucially, the predictive FPCN networks were stable and specific to MS. These results indicated that FPCN flexibility interacts with other networks to underpin the ability of MS. These would also be beneficial for understanding how compromises in these functional networks may have led to the intrusive thoughts and memories characterized in some mental disorders.

Structural Covariance of the Ipsilesional Primary Motor Cortex in Subcortical Stroke Patients with Motor Deficits.

The analysis of structural covariance has emerged as a powerful tool to explore the morphometric correlations among broadly distributed brain regions. However, little is known about the interactions between the damaged primary motor cortex (M1) and other brain regions in stroke patients with motor deficits. This study is aimed at investigating the structural covariance pattern of the ipsilesional M1 in chronic subcortical stroke patients with motor deficits. High-resolution T1-weighted brain images were acquired from 58 chronic subcortical stroke patients with motor deficits (29 with left-sided lesions and 29 with right-sided lesions) and 50 healthy controls. Structural covariance patterns were identified by a seed-based structural covariance method based on gray matter (GM) volume. Group comparisons between stroke patients (left-sided or right-sided groups) and healthy controls were determined by a permutation test. The association between alterations in the regional GM volume and motor recovery after stroke was investigated by a multivariate regression approach. Structural covariance analysis revealed an extensive increase in the structural interactions between the ipsilesional M1 and other brain regions in stroke patients, involving not only motor-related brain regions but also non-motor-related brain regions. We also identified a slightly different pattern of structural covariance between the left-sided stroke group and the right-sided stroke group, thus indicating a lesion-side effect of cortical reorganization after stroke. Moreover, alterations in the GM volume of structural covariance brain regions were significantly correlated to the motor function scores in stroke patients. These findings indicated that the structural covariance patterns of the ipsilesional M1 in chronic subcortical stroke patients were induced by motor-related plasticity. Our findings may help us to better understand the neurobiological mechanisms of motor impairment and recovery in patients with subcortical stroke from different perspectives.

Low-Complexity Adaptive Sampling of Block Compressed Sensing Based on Distortion Minimization.

Block compressed sensing (BCS) is suitable for image sampling and compression in resource-constrained applications. Adaptive sampling methods can effectively improve the rate-distortion performance of BCS. However, adaptive sampling methods bring high computational complexity to the encoder, which loses the superiority of BCS. In this paper, we focus on improving the adaptive sampling performance at the cost of low computational complexity. Firstly, we analyze the additional computational complexity of the existing adaptive sampling methods for BCS. Secondly, the adaptive sampling problem of BCS is modeled as a distortion minimization problem. We present three distortion models to reveal the relationship between block sampling rate and block distortion and use a simple neural network to predict the model parameters from several measurements. Finally, a fast estimation method is proposed to allocate block sampling rates based on distortion minimization. The results demonstrate that the proposed estimation method of block sampling rates is effective. Two of the three proposed distortion models can make the proposed estimation method have better performance than the existing adaptive sampling methods of BCS. Compared with the calculation of BCS at the sampling rate of 0.1, the additional calculation of the proposed adaptive sampling method is less than 1.9%.

Connectome-Based Predictive Modeling of Creativity Anxiety.

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.

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.

Morphometry of the Hippocampus Across the Adult Life-Span in Patients with Depressive Disorders: Association with Neuroticism.

Neuroticism is one of the main endophenotypes of major depressive disorder (MDD) and is closely related to the negative effect systems of Research Domain Criteria (RDoC) domains. The relationship between neuroticism and aging is dynamic and complex. Moreover, reduced hippocampal volumes are probably the most frequently reported structural neuroimaging finding associated with MDD. However, it remains unclear to what extent hippocampal abnormalities are linked with age and neuroticism changes in people with depression through the adult life span. This study aimed to examine the interplay between aging and neuroticism on hippocampal morphometric across the adult life-span in a relative large sample of patients with depressive disorders (114 patients, 73 females, age range: 18-74 years) and healthy control (HC) subjects (112 healthy controls, 72 females, age range: 19-72 years). MDD patients showed reduced bilateral hippocampal volumes. The effect of aging on the left hippocampal showed linear and the right hippocampal volume non-linear trajectories throughout the adult life span in healthy groups and MDD groups respectively. The hippocampal atrophy was dynamically impacted by depression at the early stages of adult life. Furthermore, we observed that right hippocampal volume reduction was associated with higher neuroticism in depressive patients younger than 30.65 years old. Our results suggest that the age-related atrophy in the right hippocampal volume was more affected by individual differences in neuroticism among younger depressive patients. Hippocampal volume reduction as a vulnerability factor for early-onset and major geriatric depression may have a distinct endophenotype.

Brain Entropy is Associated with Divergent Thinking.

Creativity is the ability to generate original and useful products, and it is considered central to the progression of human civilization. As a noninherited emerging process, creativity may stem from temporally dynamic brain activity, which, however, has not been well studied. The purpose of this study was to measure brain dynamics using entropy and to examine the associations between brain entropy (BEN) and divergent thinking in a large healthy sample. The results showed that divergent thinking was consistently positively correlated with regional BEN in the left dorsal anterior cingulate cortex/pre-supplementary motor area and left dorsolateral prefrontal cortex, suggesting that creativity is closely related to the functional dynamics of the control networks involved in cognitive flexibility and inhibitory control. Importantly, our main results were cross-validated in two independent cohorts from two different cultures. Additionally, three dimensions of divergent thinking (fluency, flexibility, and originality) were positively correlated with regional BEN in the left inferior frontal gyrus and left middle temporal gyrus, suggesting that more highly creative individuals possess more flexible semantic associative networks. Taken together, our findings provide the first evidence of the associations of regional BEN with individual variations in divergent thinking and show that BEN is sensitive to detecting variations in important cognitive abilities in healthy subjects.

Robust prediction of individual creative ability from brain functional connectivity.

People's ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis-connectome-based predictive modeling-to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences (r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems-intrinsic functional networks that tend to work in opposition-suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

A General Rate-Distortion Optimization Method for Block Compressed Sensing of Images.

Block compressed sensing (BCS) is a promising technology for image sampling and compression for resource-constrained applications, but it needs to balance the sampling rate and quantization bit-depth for a bit-rate constraint. In this paper, we summarize the commonly used CS quantization frameworks into a unified framework, and a new bit-rate model and a model of the optimal bit-depth are proposed for the unified CS framework. The proposed bit-rate model reveals the relationship between the bit-rate, sampling rate, and bit-depth based on the information entropy of generalized Gaussian distribution. The optimal bit-depth model can predict the optimal bit-depth of CS measurements at a given bit-rate. Then, we propose a general algorithm for choosing sampling rate and bit-depth based on the proposed models. Experimental results show that the proposed algorithm achieves near-optimal rate-distortion performance for the uniform quantization framework and predictive quantization framework in BCS.

Default network contributions to episodic and semantic processing during divergent creative thinking: A representational similarity analysis.

Cognitive and neuroimaging evidence suggests that episodic and semantic memory-memory for autobiographical events and conceptual knowledge, respectively-support different aspects of creative thinking, with a growing number of studies reporting activation of brain regions within the default network during performance on creative thinking tasks. The present research sought to dissociate neural contributions of these memory processes by inducing episodic or semantic retrieval orientations prior to performance on a divergent thinking task during fMRI. We conducted a representational similarity analysis (RSA) to identify multivoxel patterns of neural activity that were similar across induction (episodic and semantic) and idea generation. At the behavioral level, we found that semantic induction was associated with increased idea originality, assessed via computational estimates of semantic distance between concepts. RSA revealed that multivoxel patterns during semantic induction and subsequent idea generation were more similar (compared to episodic induction) within the left angular gyrus (AG), posterior cingulate cortex (PCC), and left anterior inferior parietal lobe (IPL). Conversely, activity patterns during episodic induction and subsequent generation were more similar within left parahippocampal gyrus and right anterior IPL. Together, the findings point to dissociable contributions of episodic and semantic memory processes to creative cognition and suggest that distinct regions within the default network support specific memory-related processes during divergent thinking.

Mapping the artistic brain: Common and distinct neural activations associated with musical, drawing, and literary creativity.

Whether creativity is a domain-general or domain-specific ability has been a topic of intense speculation. Although previous studies have examined domain-specific mechanisms of creative performance, little is known about commonalities and distinctions in neural correlates across different domains. We applied activation likelihood estimation (ALE) meta-analysis to identify the brain activation of domain-mechanisms by synthesizing functional neuroimaging studies across three forms of artistic creativity: music improvisation, drawing, and literary creativity. ALE meta-analysis yielded a domain-general pattern across three artistic forms, with overlapping clusters in the presupplementary motor area (pre-SMA), left dorsolateral prefrontal cortex, and right inferior frontal gyrus (IFG). Regarding domain-specificity, musical creativity was associated with recruitment of the SMA-proper, bilateral IFG, left precentral gyrus, and left middle frontal gyrus (MFG) compared to the other two artistic forms; drawing creativity recruited the left fusiform gyrus, left precuneus, right parahippocampal gyrus, and right MFG compared to musical creativity; and literary creativity recruited the left angular gyrus and right lingual gyrus compared to musical creativity. Contrasting drawing and literary creativity revealed no significant differences in neural activation, suggesting that these domains may rely on a common neurocognitive system. Overall, these findings reveal a central, domain-general system for artistic creativity, but with each domain relying to some degree on domain-specific neural circuits.

The reductions in the subcallosal region cortical volume and surface area in major depressive disorder across the adult life span.

BACKGROUND: Imaging studies have shown that the subcallosal region (SCR) volume was decreased in patients with major depressive disorder (MDD). However, whether the volumetric reductions in the SCR are due to thinning of the cortex or a loss of surface area (SA) remains unclear. In addition, the relationship between cortical measurements of the SCR and age through the adult life span in MDD remains unclear. METHODS: We used a cross-sectional design from 114 individuals with MDD and 112 matched healthy control (HC) individuals across the adult life span (range: 18-74 years). The mean cortical volume (CV), SA and cortical thickness (CT) of the SCR were computed using cortical parcellation based on FreeSurfer software. Multivariate analyses of covariance models were performed to compare differences between the MDD and HC groups on cortical measurements of the SCR. Multiple linear regression models were used to test age-by-group interaction effects on these cortical measurements of the SCR. RESULTS: The MDD had significant reductions in the CV and SA of the left SCR compared with HC individuals after controlling of other variables. The left SCR CV and SA reductions compared with matched controls were observed only in early adulthood patients. We also found a significant age-related CT reduction in the SCR both in the MDD and HC participants. CONCLUSIONS: The SCR volume reduction was mainly driven by SA in MDD. The different trajectories between the CT and SA of the SCR with age may provide valuable information to distinguish pathological processes and normal ageing in MDD.