Robust prediction of individual creative ability from brain functional connectivity

How Time Pressure Modulates Individual Differences in the Functional Connectivity of Chunk Memory in Chess Games

Previous studies on the chess game demonstrated that chess experts strongly rely on the activation of memory chunks to manifest accurate decision-making. Although the chunk memory might be affected by temporal constraints, it is unclear why the performance of chess experts is not significantly dropped under time pressure. In this study, our objective is to examine the variations in cognitive neural mechanisms between chess experts and novices under time pressure. The underlying cognitive neural mechanism was carefully inspected by accessing the chess game performance between 20 local experienced and 20 inexperienced chess players with 1-minute and 5-minute time constraints. In addition, functional near-infrared spectroscopy (fNIRS) recordings were carried out for each individual from the two groups while playing a 1-minute or 5-minute chess game. It was discovered that under temporal constraints, players exhibited different patterns of functional connectivity in frontal-parietal regions, suggesting that temporal stress can enhance segmentation processes in chess games. In particular, the experienced group exhibited significantly enhanced functional connectivity networks under time pressure including the dorsolateral prefrontal cortex, inferior frontal gyrus, supramarginal gyrus, and postcentral gyrus, which demonstrated the important role of the segmentation process for experienced players under time pressure. Our study found that experienced players were able to enhance recall, reorganize, and integrate chunks to improve chess performance under time pressure.

Assessing novelty, feasibility and value of creative ideas with an unsupervised approach using GPT-4

Creativity is defined by three key factors: novelty, feasibility and value. While many creativity tests focus primarily on novelty, they often neglect feasibility and value, thereby limiting their reflection of real-world creativity. In this study, we employ GPT-4, a large language model, to assess these three dimensions in a Japanese-language Alternative Uses Test (AUT). Using a crowdsourced evaluation method, we acquire ground truth data for 30 question items and test various GPT prompt designs. Our findings show that asking for multiple responses in a single prompt, using an 'explain first, rate later' design, is both cost-effective and accurate (r = .62, .59 and .33 for novelty, feasibility and value, respectively). Moreover, our method offers comparable accuracy to existing methods in assessing novelty, without the need for training data. We also evaluate additional models such as GPT-4 Turbo, GPT-4 Omni and Claude 3.5 Sonnet. Comparable performance across these models demonstrates the universal applicability of our prompt design. Our results contribute a straightforward platform for instant AUT evaluation and provide valuable ground truth data for future methodological research.

Utilizing connectome fingerprinting functional MRI models for motor activity prediction in presurgical planning: A feasibility study

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.

Connectome predictive modeling of trait mindfulness

Introduction

Trait mindfulness refers to one's disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved.

Methods

To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites.

Results

In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model.

Conclusions

We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

Multivariate analysis of multimodal brain structure predicts individual differences in risk and intertemporal preference

Large changes to brain structure (e.g., from damage or disease) can explain alterations in behavior. It is therefore plausible that smaller structural differences in healthy samples can be used to better understand and predict individual differences in behavior. Despite the brain's multivariate and distributed structure-to-function mapping, most studies have used univariate analyses of individual structural brain measures. Here we used a multivariate approach in a multimodal data set composed of volumetric, surface-based, diffusion-based, and functional resting-state MRI measures to predict reliable individual differences in risk and intertemporal preferences. We show that combining twelve brain structure measures led to better predictions across tasks than using any individual measure, and by examining model coefficients, we visualize the relative contribution of different brain measures from different brain regions. Using a multivariate approach to brain structure-to-function mapping that combines across many brain structure properties, along with reliably measured behavior phenotypes, may increase out-of-sample prediction accuracies and insight into neural underpinnings. Furthermore, this methodological approach may be useful to improve predictions and neural insight across basic, translational, and clinical research fields.

Creative connections: the neural correlates of semantic relatedness are associated with creativity

The associative theory of creativity proposes that creative ideas result from connecting remotely related concepts in memory. Previous research found that higher creative individuals exhibit a more flexible organization of semantic memory, generate more uncommon word associations, and judge remote concepts as more related. In this study (N = 93), we used fMRI to investigate brain regions involved in judging the relatedness of concepts that vary in their semantic distance, and how such neural involvement relates to individual differences in creativity. Brain regions where activity increased with semantic relatedness mainly overlapped with default, control, salience, semantic control, and multiple demand networks. The default and semantic control networks exhibited increased involvement when evaluating more remote associations. Finally, higher creative people, who provided higher relatedness judgements on average, exhibited lower activity in those regions, possibly reflecting higher neural efficiency. We discuss these findings in the context of the neurocognitive processing underlying creativity. Overall, our findings indicate that judging remote concepts as related reflects a cognitive mechanism underlying creativity and shed light on the neural correlates of this mechanism.

Predicting creative behavior using resting-state electroencephalography

Neuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model's predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity.

Functional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking

Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults. We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses are associated with alcohol use severity, quantified by Semi-Structured Assessment for the Genetics of Alcoholism, in 981 young adults. We combined the CPM findings and the JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity. The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all features, including medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling. To conclude, a distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These "neural fingerprints" elucidate how alcohol misuse impacts the brain and provide evidence of new targets for future intervention.

Semantic associative abilities and executive control functions predict novelty and appropriateness of idea generation

Novelty and appropriateness are two fundamental components of creativity. However, the way in which novelty and appropriateness are separated at behavioral and neural levels remains poorly understood. In the present study, we aim to distinguish behavioral and neural bases of novelty and appropriateness of creative idea generation. In alignment with two established theories of creative thinking, which respectively, emphasize semantic association and executive control, behavioral results indicate that novelty relies more on associative abilities, while appropriateness relies more on executive functions. Next, employing a connectome predictive modeling (CPM) approach in resting-state fMRI data, we define two functional network-based models-dominated by interactions within the default network and by interactions within the limbic network-that respectively, predict novelty and appropriateness (i.e., cross-brain prediction). Furthermore, the generalizability and specificity of the two functional connectivity patterns are verified in additional resting-state fMRI and task fMRI. Finally, the two functional connectivity patterns, respectively mediate the relationship between semantic association/executive control and novelty/appropriateness. These findings provide global and predictive distinctions between novelty and appropriateness in creative idea generation.

In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants "rest" matters

Resting-state fMRI (rs-fMRI) scans-namely those lacking experimentally-controlled stimuli or cognitive demands-are often used to identify aberrant patterns of functional connectivity (FC) in clinical populations. To minimize interpretational uncertainty, researchers control for across-cohort disparities in age, gender, co-morbidities, and head motion. Yet, studies rarely, if ever, consider the possibility that systematic differences in inner experience (i.e., what subjects think and feel during the scan) may directly affect FC measures. Here we demonstrate that is the case using a rs-fMRI dataset comprising 471 scans annotated with experiential data. Wide-spread significant differences in FC are observed between scans that systematically differ in terms of reported in-scanner experience. Additionally, we show that FC can successfully predict specific aspects of in-scanner experience in a manner similar to how it predicts demographics, cognitive abilities, clinical outcomes and labels. Together, these results highlight the key role of in-scanner experience in shaping rs-fMRI estimates of FC.

Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. First, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes' predictions. Moreover, few of the current graph learning models are interpretable, which may not be capable of providing biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning (HSGPL) model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. To further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using data from human connectome project (HCP) and open access series of imaging studies (OASIS). Our results from extensive experiments demonstrate the superiority of the proposed model compared with several state-of-the-art techniques. In addition, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.

Cross-cohort replicable resting-state functional connectivity in predicting symptoms and cognition of schizophrenia

Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions.

Pre-acquired Functional Connectivity Predicts Choice Inconsistency

Economic choice theories usually assume that humans maximize utility in their choices. However, studies have shown that humans make inconsistent choices, leading to suboptimal behavior, even without context-dependent manipulations. Previous studies showed that activation in value and motor networks are associated with inconsistent choices at the moment of choice. Here, we investigated if the neural predispositions, measured before a choice task, can predict choice inconsistency in a later risky choice task. Using functional connectivity (FC) measures from resting-state functional magnetic resonance imaging (rsfMRI), derived before any choice was made, we aimed to predict subjects' inconsistency levels in a later-performed choice task. We hypothesized that rsfMRI FC measures extracted from value and motor brain areas would predict inconsistency. Forty subjects (21 females) completed a rsfMRI scan before performing a risky choice task. We compared models that were trained on FC that included only hypothesized value and motor regions with models trained on whole-brain FC. We found that both model types significantly predicted inconsistency levels. Moreover, even the whole-brain models relied mostly on FC between value and motor areas. For external validation, we used a neural network pretrained on FC matrices of 37,000 subjects and fine-tuned it on our data and again showed significant predictions. Together, this shows that the tendency for choice inconsistency is predicted by predispositions of the nervous system and that synchrony between the motor and value networks plays a crucial role in this tendency.

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.

Behaviorally meaningful functional networks mediate the effect of Alzheimer's pathology on cognition

Tau pathology is associated with cognitive impairment in both aging and Alzheimer's disease, but the functional and structural bases of this relationship remain unclear. We hypothesized that the integrity of behaviorally meaningful functional networks would help explain the relationship between tau and cognitive performance. Using resting state fMRI, we identified unique networks related to episodic memory and executive function cognitive domains. The episodic memory network was particularly related to tau pathology measured with positron emission tomography in the entorhinal and temporal cortices. Further, episodic memory network strength mediated the relationship between tau pathology and cognitive performance above and beyond neurodegeneration. We replicated the association between these networks and tau pathology in a separate cohort of older adults, including both cognitively unimpaired and mildly impaired individuals. Together, these results suggest that behaviorally meaningful functional brain networks represent a functional mechanism linking tau pathology and cognition.

Diverse adolescents' transcendent thinking predicts young adult psychosocial outcomes via brain network development

Developmental scientists have long described mid-adolescents' emerging capacities to make deep meaning about the social world and self, here called transcendent thinking, as a hallmark developmental stage. In this 5-years longitudinal study, sixty-five 14-18 years-old youths' proclivities to grapple psychologically with the ethical, systems-level and personal implications of social stories, predicted future increases in the coordination of two key brain networks: the default-mode network, involved in reflective, autobiographical and free-form thinking, and the executive control network, involved in effortful, focused thinking; findings were independent of IQ, ethnicity, and socioeconomic background. This neural development predicted late-adolescent identity development, which predicted young-adult self-liking and relationship satisfaction, in a developmental cascade. The findings reveal a novel predictor of mid-adolescents' neural development, and suggest the importance of attending to adolescents' proclivities to engage agentically with complex perspectives and emotions on the social and personal relevance of issues, such as through civically minded educational approaches.

Intrinsic functional connectivity among memory networks does not predict individual differences in narrative recall

Individuals differ greatly in their ability to remember the details of past events, yet little is known about the brain processes that explain such individual differences in a healthy young population. Previous research suggests that episodic memory relies on functional communication among ventral regions of the default mode network ("DMN-C") that are strongly interconnected with the medial temporal lobes. In this study, we investigated whether the intrinsic functional connectivity of the DMN-C subnetwork is related to individual differences in memory ability, examining this relationship across 243 individuals (ages 18-50 years) from the openly available Cambridge Center for Aging and Neuroscience (Cam-CAN) dataset. We first estimated each participant's whole-brain intrinsic functional brain connectivity by combining data from resting-state, movie-watching, and sensorimotor task scans to increase statistical power. We then examined whether intrinsic functional connectivity predicted performance on a narrative recall task. We found no evidence that functional connectivity of the DMN-C, with itself, with other related DMN subnetworks, or with the rest of the brain, was related to narrative recall. Exploratory connectome-based predictive modeling (CBPM) analyses of the entire connectome revealed a whole-brain multivariate pattern that predicted performance, although these changes were largely outside of known memory networks. These results add to emerging evidence suggesting that individual differences in memory cannot be easily explained by brain differences in areas typically associated with episodic memory function.

Creative therapy in health and disease: Inner vision

Can we better understand the unique mechanisms of de novo abilities in light of our current knowledge of the psychological and neuroscientific literature on creativity? This review outlines the state-of-the-art in the neuroscience of creativity and points out crucial aspects that still demand further exploration, such as brain plasticity. The progressive development of current neuroscience research on creativity presents a multitude of prospects and potentials for furnishing efficacious therapy in the context of health and illness. Therefore, we discuss directions for future studies, identifying a focus on pinpointing the neglected beneficial practices for creative therapy. We emphasize the neglected neuroscience perspective of creativity on health and disease and how creative therapy could offer limitless possibilities to improve our well-being and give hope to patients with neurodegenerative diseases to compensate for their brain injuries and cognitive impairments by expressing their hidden creativity.

Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia

INTRODUCTION

Verbal fluency tasks are common in Alzheimer's disease (AD) assessments. Yet, standard valid response counts fail to reveal disease-specific semantic memory patterns. Here, we leveraged automated word-property analysis to capture neurocognitive markers of AD vis-à-vis behavioral variant frontotemporal dementia (bvFTD).

METHODS

Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word's frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group-level discrimination, patient-level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns.

RESULTS

Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group- and subject-level discrimination only in AD, also predicting executive outcomes. Disease-specific cortical thickness patterns were predicted by frequency in both disorders. Default-mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD.

DISCUSSION

Word-property analysis of fluency can boost AD characterization and diagnosis.

HIGHLIGHTS

We report novel word-property analyses of verbal fluency in AD and bvFTD. Standard valid response counts captured deficits and brain patterns in both groups. Specific word properties (e.g., frequency, granularity) were altered only in AD. Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD. Word-property analysis of fluency can boost AD characterization and diagnosis.

Finger motor representation supports the autonomy in arithmetic: neuroimaging evidence from abacus training

Researches have reported the close association between fingers and arithmetic. However, it remains unclear whether and how finger training can benefit arithmetic. To address this issue, we used the abacus-based mental calculation (AMC), which combines finger training and mental arithmetic learning, to explore the neural correlates underlying finger-related arithmetic training. A total of 147 Chinese children (75 M/72 F, mean age, 6.89 ± 0.46) were recruited and randomly assigned into AMC and control groups at primary school entry. The AMC group received 5 years of AMC training, and arithmetic abilities and resting-state functional magnetic resonance images data were collected from both groups at year 1/3/5. The connectome-based predictive modeling was used to find the arithmetic-related networks of each group. Compared to controls, the AMC's positively arithmetic-related network was less located in the control module, and the inter-module connections between somatomotor-default and somatomotor-control modules shifted to somatomotor-visual and somatomotor-dorsal attention modules. Furthermore, the positive network of the AMC group exhibited a segregated connectivity pattern, with more intra-module connections than the control group. Overall, our results suggested that finger motor representation with motor module involvement facilitated arithmetic-related network segregation, reflecting increased autonomy of AMC, thus reducing the dependency of arithmetic on higher-order cognitive functions.

The Association between Working Memory and Divergent Thinking: The Moderating Role of Formal Musical Background

Divergent thinking (DT) is widely considered an essential cognitive dimension of creativity, which involves goal-oriented processes, including working memory (WM), which allows for retrieving and loading of information into the attentional stream and, consequently, enhancing divergence of thinking. Despite the critical role of WM in DT, little work has been done on the mechanism affecting this interplay. The current study addressed the involvement of a formal musical background in the relationship between WM and DT and was conducted with 83 healthy young adults (M = 19.64 years; SD = 0.52 years; 33 females). The participants were requested to indicate if they had a formal background in music in the conservatory (M = 4.78 years; SD = 5.50 years) as well as perform the digit span forward test (DSFT) and the alternative uses task-AUT from the Torrance test of creative thinking (TTCT). The results indicated that years of formal musical background moderated the association between WM and DT. These findings suggest that music enhances the positive effect of high-order cognitive processes, such as WM, on the ability to think divergently. Theoretical and practical implications as well as limitations were discussed.

Time course of EEG power during creative problem-solving with insight or remote thinking

Problem-solving often requires creativity and is critical in everyday life. However, the neurocognitive mechanisms underlying creative problem-solving remain poorly understood. Two mechanisms have been highlighted: the formation of new connections among problem elements and insight solving, characterized by sudden realization of a solution. In this study, we investigated EEG activity during a modified version of the remote associates test, a classical insight problem task that requires finding a word connecting three unrelated words. This allowed us to explore the brain correlates associated with the semantic remoteness of connections (by varying the remoteness of the solution word across trials) and with insight solving (identified as a Eurêka moment reported by the participants). Semantic remoteness was associated with power increase in the alpha band (8-12 Hz) in a left parieto-temporal cluster, the beta band (13-30 Hz) in a right fronto-temporal cluster in the early phase of the task, and the theta band (3-7 Hz) in a bilateral frontal cluster just prior to participants' responses. Insight solving was associated with power increase preceding participants' responses in the alpha and gamma (31-60 Hz) bands in a left temporal cluster and the theta band in a frontal cluster. Source reconstructions revealed the brain regions associated with these clusters. Overall, our findings shed new light on some of the mechanisms involved in creative problem-solving.

Individual differences and creative ideation: neuromodulatory signatures of mindset and response inhibition

This study addresses the modulatory role of individual mindset in explaining the relationship between response inhibition (RI) and divergent thinking (DT) using transcranial direct current stimulation (tDCS). Forty undergraduate students (22 male and 18 female), aged between 18 and 23 years (average age = 19 years, SD = 1.48), were recruited. Participants received either anodal tDCS of the right IFG coupled with cathodal tDCS of the left IFG (R + L-; N = 19) or the opposite coupling (R-L+; N = 21). We tested DT performance using the alternative uses task (AUT), measuring participants' fluency, originality, and flexibility in the response production, as well as participants' mindsets. Furthermore, we applied a go-no-go task to examine the role of RI before and after stimulating the inferior frontal gyrus (IFG) using tDCS. The results showed that the mindset levels acted as moderators on stimulation conditions and enhanced RI on AUT fluency and flexibility but not originality. Intriguingly, growth mindsets have opposite moderating effects on the change in DT, resulting from the tDCS stimulation of the left and the right IFG, with reduced fluency but enhanced flexibility. Our findings imply that understanding neural modulatory signatures of ideational processes with tDCS strongly benefits from evaluating cognitive status and control functions.

Oxytocin enhances the triangular association among behavior, resting-state, and task-state functional connectivity

Considerable advances in the role of oxytocin (OT) effect on behavior and the brain network have been made, but the effect of OT on the association between inter-individual differences in functional connectivity (FC) and behavior is elusive. Here, by using a face-perception task and multiple connectome-based predictive models, we aimed to (1) determine whether OT could enhance the association among behavioral performance, resting-state FC (rsFC), and task-state FC (tsFC) and (2) if so, explore the role of OT in enhancing this triangular association. We found that in the OT group, the prediction performance of using rsFC or tsFC to predict task behavior was higher than that of the PL group. Additionally, the correlation coefficient between rsFC and tsFC was substantially higher in the OT group than in the PL group. The strength of these associations could be partly explained by OT altering the brain's FCs related to social cognition and face perception in both the resting and task states, mainly in brain regions such as the limbic system, prefrontal cortex, temporal poles, and temporoparietal junction. Taken together, these results provide novel evidence and a corresponding mechanism for how neuropeptides cause increased associations among inter-individual differences across different levels (e.g., behavior and large-scale brain networks in both resting and task-state), and may inspire future research on the role of neuropeptides in the cross levels association of both clinical and nonclinical use.

Memory and creativity: A meta-analytic examination of the relationship between memory systems and creative cognition

Increasing evidence suggests that specific memory systems (e.g., semantic vs. episodic) may support specific creative thought processes. However, there are a number of inconsistencies in the literature regarding the strength, direction, and influence of different memory (semantic, episodic, working, and short-term) and creativity (divergent and convergent thinking) types, as well as the influence of external factors (age, stimuli modality) on this purported relationship. In this meta-analysis, we examined 525 correlations from 79 published studies and unpublished datasets, representing data from 12,846 individual participants. We found a small but significant (r = .19) correlation between memory and creative cognition. Among semantic, episodic, working, and short-term memory, all correlations were significant, but semantic memory - particularly verbal fluency, the ability to strategically retrieve information from long-term memory - was found to drive this relationship. Further, working memory capacity was found to be more strongly related to convergent than divergent creative thinking. We also found that within visual creativity, the relationship with visual memory was greater than that of verbal memory, but within verbal creativity, the relationship with verbal memory was greater than that of visual memory. Finally, the memory-creativity correlation was larger for children compared to young adults despite no impact of age on the overall effect size. These results yield three key conclusions: (1) semantic memory supports both verbal and nonverbal creative thinking, (2) working memory supports convergent creative thinking, and (3) the cognitive control of memory is central to performance on creative thinking tasks.

Multi-level and joint attention networks on brain functional connectivity for cross-cognitive prediction

Deep learning on resting-state functional MRI (rs-fMRI) has shown great success in predicting a single cognition or mental disease. Nevertheless, cognitive functions or mental diseases may share neural mechanisms that can benefit their prediction/classification. We propose a multi-level and joint attention (ML-Joint-Att) network to learn high-order representations of brain functional connectivities that are specific and shared across multiple tasks. We design the ML-Joint-Att network with edge and node convolutional operators, an adaptive inception module, and three attention modules, including network-wise, region-wise, and region-wise joint attention modules. The adaptive inception learns brain functional connectivity at multiple spatial scales. The network-wise and region-wise attention modules take the multi-scale functional connectivities as input and learn features at the network and regional levels for individual tasks. Moreover, the joint attention module is designed as region-wise joint attention to learn shared brain features that contribute to and compensate for the prediction of multiple tasks. We employed the Adolescent Brain Cognitive Development (ABCD) dataset (n =9092) to evaluate the ML-Joint-Att network for the prediction of cognitive flexibility and inhibition. Our experiments demonstrated the usefulness of the three attention modules and identified brain functional connectivities and regions specific and common between cognitive flexibility and inhibition. In particular, the joint attention module can significantly improve the prediction of both cognitive functions. Moreover, leave-one-site cross-validation showed that the ML-Joint-Att network is robust to independent samples obtained from different sites of the ABCD study. Our network outperformed existing machine learning techniques, including Brain Bias Set (BBS), spatio-temporal graph convolution network (ST-GCN), and BrainNetCNN. We demonstrated the generalization of our method to other applications, such as the prediction of fluid intelligence and crystallized intelligence, which also outperformed the ST-GCN and BrainNetCNN.

Social support and overeating in young women: The role of altering functional network connectivity patterns and negative emotions

Research suggests that social support has a protective effect on emotional health and emotionally induced overeating. Women are especially more sensitive to benefits from social support when facing eating problems. Although it has been demonstrated that social support can affect the neural processes of emotion regulation and reward perception, it is unclear how social support alters synergistic patterns in large-scale brain networks associated with negative emotions and overeating. We used a large sample of young women aged 17-22 years (N = 360) to examine how social support influences the synchrony of five intrinsic networks (executive control network [ECN], default mode network, salience network [SN], basal ganglia network, and precuneus network [PN]) and how these networks influence negative affect and overeating. Additionally, we explored these analyses in another sample of males (N = 136). After statistically controlling for differences in age and head movement, we observed significant associations of higher levels of social support with increased intra- and inter-network functional synchrony, particularly for ECN-centered network connectivity. Subsequent chain-mediated analyses showed that social support predicted overeating through the ECN-SN and ECN-PN network connectivity and negative emotions. However, these results were not found in men. These findings suggest that social support influences the synergistic patterns within and between intrinsic networks related to inhibitory control, emotion salience, self-referential thinking, and reward sensitivity. Furthermore, they reveal that social support and its neural markers may play a key role in young women's emotional health and eating behavior.

A meta-analytical review of the impact of mindfulness on creativity: Framing current lines of research and defining moderator variables

Findings relating to the impact of mindfulness interventions on creative performance remain inconsistent, perhaps because of discrepancies between study designs, including variability in the length of mindfulness interventions, the absence of control groups or the tendencies to explore creativity as one unitary construct. To derive a clearer understanding of the impact that mindfulness interventions may exert on creative performance, two meta-analytical reviews were conducted, drawing respectively on studies using a control group design (n = 20) and studies using a pretest-posttest design (n = 17). A positive effect was identified between mindfulness and creativity, both for control group designs (d = 0.42, 95% CIs [0.29, 0.54]) and pretest-posttest designs (d = 0.59, 95% CIs [0.38, 0.81]). Subgroup analysis revealed that intervention length, creativity task (i.e., divergent vs. convergent thinking tasks) and control group type, were significant moderators for control group studies, whereas only intervention length was a significant moderator for pretest-posttest studies. Overall, the findings support the use of mindfulness as a tool to enhance creative performance, with more advantageous outcomes for convergent as opposed to divergent thinking tasks. We discuss the implications of study design and intervention length as key factors of relevance to future research aimed at advancing theoretical accounts of the relationship between mindfulness and creativity.

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.

Creative thinking and brain network development in schoolchildren

Fostering creative minds has always been a premise to ensure adaptation to new challenges of human civilization. While some alternative educational settings (i.e., Montessori) were shown to nurture creative skills, it is unknown how they impact underlying brain mechanisms across the school years. This study assessed creative thinking and resting-state functional connectivity via fMRI in 75 children (4-18 y.o.) enrolled either in Montessori or traditional schools. We found that pedagogy significantly influenced creative performance and underlying brain networks. Replicating past work, Montessori-schooled children showed higher scores on creative thinking tests. Using static functional connectivity analysis, we found that Montessori-schooled children showed decreased within-network functional connectivity of the salience network. Moreover, using dynamic functional connectivity, we found that traditionally-schooled children spent more time in a brain state characterized by high intra-default mode network connectivity. These findings suggest that pedagogy may influence brain networks relevant to creative thinking-particularly the default and salience networks. Further research is needed, like a longitudinal study, to verify these results given the implications for educational practitioners. A video abstract of this article can be viewed at https://www.youtube.com/watch?v=xWV_5o8wB5g . RESEARCH HIGHLIGHTS: Most executive jobs are prospected to be obsolete within several decades, so creative skills are seen as essential for the near future. School experience has been shown to play a role in creativity development, however, the underlying brain mechanisms remained under-investigated yet. Seventy-five 4-18 years-old children, from Montessori or traditional schools, performed a creativity task at the behavioral level, and a 6-min resting-state MR scan. We uniquely report preliminary evidence for the impact of pedagogy on functional brain networks.

The Role of Cognition in Divergent Thinking: Implications for Successful Aging

Promoting active and successful aging has become crucial to improve quality of life in later adulthood and reduce the impact of cognitive decline. Increasing evidence suggested that the ability to think creatively (e.g., via divergent thinking), similar to cognitive reserve, could represent a beneficial factor against the negative effects of aging. However, there is still little evidence investigating the relationships between divergent thinking, cognitive functions, and cognitive reserve in late adulthood. The present study explored these relationships in a sample of 98 individuals ranging from 61 to 88 years old (mean age: 72.44 ± 6.35). Results showed that visual, but not verbal, divergent thinking was affected by aging. Interestingly, visual divergent thinking performance was predicted by both the cognitive component of crystallized intelligence and cognitive reserve. Only the crystallized component of intelligence was found to mediate the aging effect on visual divergent thinking performance. These results suggest that in later adulthood a potential shift strategy to prior knowledge and semantic components over executive and control components of cognition could underlie a preserved ability to think divergently and, plausibly, creatively. Limitations of the study and implications for successful aging are discussed.

The reciprocal relationship between openness and creativity: from neurobiology to multicultural environments

The desire for novelty and variety in experiences, which may manifest in an inclination to engage with individuals from a diverse range of cultural backgrounds, collectively constitutes the personality dimension known as "Openness to Experience." Empirical research has identified a positive correlation between trait openness and various expressions of creativity, such as divergent ideation, innovative problem-solving strategies, and cumulative creative accomplishments. This nexus between openness to interpersonal diversity, as an aspect of the larger personality trait of openness, and creativity has precipitated considerable scholarly interest across the disciplines of personality, social and organizational psychology, and neuroscientific investigation. In this paper, we review the neurobehavioral properties, including the cognitive processes and neural mechanisms, that connect these two constructs. Further, we explore how culture influences levels of openness and creativity in individuals and consider how creativity predisposes individuals toward openness to a plethora of experiences, including those occurring in culturally diverse contexts. This reciprocal entanglement of creativity and openness has been shown to foster a reduction in biases, augment conflict resolution capabilities, and generally yield superior outcomes in multicultural environments.

Preventing prefrontal dysfunction by tDCS modulates stress-induced creativity impairment in women: an fNIRS study

Stress is a major external factor threatening creative activity. The study explored whether left-lateralized activation in the dorsolateral prefrontal cortex manipulated through transcranial direct current stimulation could alleviate stress-induced impairment in creativity. Functional near-infrared spectroscopy was used to explore the underlying neural mechanisms. Ninety female participants were randomly assigned to three groups that received stress induction with sham stimulation, stress induction with true stimulation (anode over the left and cathode over the right dorsolateral prefrontal cortex), and control manipulation with sham stimulation, respectively. Participants underwent the stress or control task after the transcranial direct current stimulation manipulation, and then completed the Alternative Uses Task to measure creativity. Behavioral results showed that transcranial direct current stimulation reduced stress responses in heart rate and anxiety. The functional near-infrared spectroscopy results revealed that transcranial direct current stimulation alleviated dysfunction of the prefrontal cortex under stress, as evidenced by higher activation of the dorsolateral prefrontal cortex and frontopolar cortex, as well as stronger inter-hemispheric and intra-hemispheric functional connectivity within the prefrontal cortex. Further analysis demonstrated that the cortical regulatory effect prevented creativity impairment induced by stress. The findings validated the hemispheric asymmetry hypothesis regarding stress and highlighted the potential for brain stimulation to alleviate stress-related mental disorders and enhance creativity.

Creative mindset reduces racial ingroup bias in empathic neural responses

Spontaneous racial categorization of other-race individuals provides a cognitive basis of racial ingroup biases in empathy and prosocial behavior. In two experiments, we investigated whether fostering a creativity mindset reduces racial ingroup biases in empathy and undermines spontaneous racial categorization of other-race faces. Before and after a creative mindset priming procedure that required the construction of novel objects using discreteness, we recorded electroencephalography signals to Asian and White faces with painful or neutral expressions from Chinese adults to assess neural activities underlying racial ingroup biases in empathy and spontaneous racial categorization of faces. We found that a frontal-central positive activity within 200 ms after face onset (P2) showed greater amplitudes to painful (vs. neutral) expressions of Asian compared with White faces and exhibited repetition suppression in response to White faces. These effects, however, were significantly reduced by creative mindset priming. Moreover, the creative mindset priming enhanced the P2 amplitudes to others' pain to a larger degree in participants who created more novel objects. The priming effects were not observed in control participants who copied objects constructed by others. Our findings suggest that creative mindsets may reduce racial ingroup biases in empathic neural responses by undermining spontaneous racial categorization of faces.

Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks

Cognitive neuroscience assumes that different mental abilities correspond to at least partly separable brain subnetworks and strives to understand their relationships. However, single-task approaches typically revealed multiple brain subnetworks to be involved in performance. Here, we chose a bottom-up approach of investigating the association between structural and functional brain subnetworks, on the one hand, and domain-specific cognitive abilities, on the other. Structural network was identified using machine-learning graph neural network by clustering anatomical brain properties measured in 838 individuals enroled in the WU-Minn Young Adult Human Connectome Project. Functional network was adapted from seven Resting State Networks (7-RSN). We then analyzed the results of 15 cognitive tasks and estimated five latent abilities: fluid reasoning (Gf), crystallized intelligence (Gc), memory (Mem), executive functions (EF), and processing speed (Gs). In a final step we determined linear associations between these independently identified ability and brain entities. We found no one-to-one mapping between latent abilities and brain subnetworks. Analyses revealed that abilities are associated with properties of particular combinations of brain subnetworks. While some abilities are more strongly associated to within-subnetwork connections, others are related with connections between multiple subnetworks. Importantly, domain-specific abilities commonly rely on node(s) as hub(s) to connect with other subnetworks. To test the robustness of our findings, we ran the analyses through several defensible analytical decisions. Together, the present findings allow a novel perspective on the distinct nature of domain-specific cognitive abilities building upon unique combinations of associated brain subnetworks.

What's behind deliberation? The effect of task-related mind-wandering on post-incubation creativity

Previous studies have already suggested that the deliberate nature of Mind-Wandering (MW) is critical for promoting creative performance. However, the deliberate nature of MW may be mixed up with task-relatedness. Whether the deliberate nature or task-relatedness of MW is responsible for such positive influence remains unclear. The present study tried to address this issue by investigating the influence of deliberate MW (MW-d) and task-related MW (MW-r) on post-incubation creative performance. Our result showed that MW-d is positively correlated with MW-r and spontaneous MW (MW-s) is highly positively correlated with task-unrelated MW (MW-u). Meanwhile, after controlling the possible confounding variables (i.e., the pre-incubation creative performance, the performance during distraction task, and motivation on creative ideation), both MW-d and MW-r predicted participants' AUT performance after incubation. However, the prediction model based on MW-r was stable while the MW-d-based prediction model was not. These findings indicate that the task-relatedness of MW, instead of its deliberate nature, might have a positive influence on subsequent creative performance.

Creative writing as a means to recover from early psychosis- Experiences from a group intervention

BACKGROUND

In this paper, we explore the subjective experiences of a group intervention in creative writing (CW) for young adults being treated for psychosis.

METHOD

A qualitative and exploratory design was applied. Five out of eight patients who were offered a course in CW with two-hour weekly sessions for 12 weeks took part in this study. The five participants who followed through were interviewed after project termination. Systematic text condensation was applied to the transcribed interviews.

RESULTS

The analysis revealed three overarching themes: a) the group was valued as a creative community, b) there was safety in the structured yet flexible framing of the course, c) the participants experienced creative freedom that enabled a feeling of mastery.

CONCLUSION

CW was well conceived. The feelings of connectedness and mastery were prominent. The participants experienced growth on several levels. Our findings support previous work on arts therapy as a means to recovery.

Brain-based graph-theoretical predictive modeling to map the trajectory of transdiagnostic symptoms of anhedonia, impulsivity, and hypomania from the human functional connectome

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM's mean square error (MSE) to that of a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region for information spread) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders, highlighting transdiagnostic generalization. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level. ClinicalTrials.gov identifier: NCT01976975.

Default mode network spatio-temporal electrophysiological signature and causal role in creativity

The default mode network (DMN) is a widely distributed, intrinsic brain network thought to play a crucial role in internally-directed cognition. It subserves self-referential thinking, recollection of the past, mind wandering, and creativity. Knowledge about the electrophysiology underlying DMN activity is scarce, due to the difficulty to simultaneously record from multiple distant cortical areas with commonly-used techniques. The present study employs stereo-electroencephalography depth electrodes in 13 human patients undergoing monitoring for epilepsy, obtaining high spatiotemporal resolution neural recordings across multiple canonical DMN regions. Our results offer a rare insight into the temporal evolution and spatial origin of theta (4-8Hz) and gamma signals (30-70Hz) during two DMN-associated higher cognitive functions: mind-wandering and alternate uses. During the performance of these tasks, DMN activity is defined by a specific pattern of decreased theta coupled with increased gamma power. Critically, creativity and mind wandering engage the DMN with different dynamics: creativity recruits the DMN strongly during the covert search of ideas, while mind wandering displays the strongest modulation of DMN during the later recall of the train of thoughts. Theta band power modulations, predominantly occurring during mind wandering, do not show a predominant spatial origin within the DMN. In contrast, gamma power effects were similar for mind wandering and creativity and more strongly associated to lateral temporal nodes. Interfering with DMN activity through direct cortical stimulation within several DMN nodes caused a decrease in creativity, specifically reducing the originality of the alternate uses, without affecting creative fluency or mind wandering. These results suggest that DMN activity is flexibly modulated as a function of specific cognitive processes and supports its causal role in creative thinking. Our findings shed light on the neural constructs supporting creative cognition and provide causal evidence for the role of DMN in the generation of original connections among concepts.

Similarities and Distinctions between Cortical Neural Substrates That Underlie Generation of Malevolent Creative Ideas

Creativity can be driven by negative intentions, and this is called malevolent creativity (MC). It is a type of creativity that serves antisocial purposes and deliberately leads to harmful or immoral results. A possible classification indicates that there are three kinds of MC in daily life: hurting people, lying, and playing tricks. This study aimed to explore similar and distinct neural substrates underlying these different kinds of MC idea generation. The participants were asked to perform different MC tasks, and their neural responses were recorded using a functional near-infrared spectroscopy device. The findings revealed that most regions within the prefrontal and temporal lobes [e.g., the right dorsolateral prefrontal cortex (rDLPFC), and right angular gyrus] were involved in the three MC tasks. However, the right frontopolar cortex (rFPC) was more activated and less coupled with the rDLPFC and right precuneus during the lying task than during the other tasks. Thus, rFPC may play an important role in constructing novel lies. In the lying task, individuals were more selfish and less compassionate. In the playing tricks and hurting people tasks, there was less neural coupling between the rDLPFC and the left inferior frontal gyrus/right inferior parietal lobule than that in the lying task. This may imply that selfish motivation is released when individuals try to ignore victims' distress or generate aggressive tricks in hurting people or playing tricks tasks. These findings indicate that the three kinds of MC idea generation involve common cortical regions related to creative idea generation and moral judgment, whereas differences in cortical responses exist because of their unique features.

Connectome-based prediction of eating disorder-associated symptomatology

BACKGROUND

Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).

METHODS

CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.

RESULTS

The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.

CONCLUSIONS

These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

Task and Resting-State Functional Connectivity Predict Driving Violations

Aberrant driving behaviors cause accidents; however, there is a lack of understanding of the neural mechanisms underlying these behaviors. To address this issue, a task and resting-state functional connectivity was used to predict aberrant driving behavior and associated personality traits. The study included 29 right-handed participants with driving licenses issued for more than 1 year. During the functional magnetic resonance imaging experiment, participants first recorded their resting state and then watched a driving video while continuously rating the risk and speed on each block. Functional connectome-based predictive modeling was employed for whole brain tasks and resting-state functional connectivity to predict driving behavior (violation, error, and lapses), sensation-seeking, and impulsivity. Resting state and task-based functional connectivity were found to significantly predict driving violations, with resting state significantly predicting lapses and task-based functional connectivity showing a tendency to predict errors. Conversely, neither impulsivity nor sensation-seeking was associated with functional connectivity. The results suggest a significant association between aberrant driving behavior, but a nonsignificant association between impulsivity and sensation-seeking, and task-based or resting state functional connectivity. This could provide a deeper understanding of the neural processing underlying reckless driving that may ultimately be used to prevent accidents.

Corrigendum: Enhanced intrinsic functional connectivity in the visual system of visual artist: implications for creativity

[This corrects the article DOI: 10.3389/fnins.2023.1114771.].

Connectome-based fingerprint of motor impairment is stable along the course of Parkinson's disease

Functional alterations in brain connectivity have previously been described in Parkinson's disease, but it is not clear whether individual differences in connectivity profiles might be also linked to severity of motor-symptom manifestation. Here we investigated the relevance of individual functional connectivity patterns measured with resting-state fMRI with respect to motor-symptom severity in Parkinson's disease, through a whole-brain, data-driven approach (connectome-based predictive modeling). Neuroimaging and clinical data of Parkinson's disease patients from the Parkinson's Progression Markers Initiative were derived at baseline (session 1, n = 81) and at follow-up (session 2, n = 53). Connectome-based predictive modeling protocol was implemented to predict levels of motor impairment from individual connectivity profiles. The resulting predictive model comprised a network mainly involving functional connections between regions located in the cerebellum, and in the motor and frontoparietal networks. The predictive power of the model was stable along disease progression, as the connectivity within the same network could predict levels of motor impairment, even at a later stage of the disease. Finally, connectivity profiles within this network could be identified at the individual level, suggesting the presence of individual fingerprints within resting-state fMRI connectivity associated with motor manifestations in Parkinson's disease.

The neurobiology of openness as a personality trait

Openness is a multifaceted behavioral disposition that encompasses personal, interpersonal, and cultural dimensions. It has been suggested that the interindividual variability in openness as a personality trait is influenced by various environmental and genetic factors, as well as differences in brain functional and structural connectivity patterns along with their various associated cognitive processes. Alterations in degree of openness have been linked to several aspects of health and disease, being impacted by both physical and mental health, substance use, and neurologic conditions. This review aims to explore the current state of knowledge describing the neurobiological basis of openness and how individual differences in openness can manifest in brain health and disease.

Representing creative thought: A representational similarity analysis of creative idea generation and evaluation

Dual process theories of creativity suggest that creative thought is supported by both a generation phase, where unconstrained ideas are generated and combined in novel ways, and an evaluation phase, where those ideas are filtered for usefulness in context. Neurocognitively, both the default mode network (DMN) and the executive control network (ECN) have been implicated in generation and evaluation, respectively. Importantly, generating and evaluating ideas implies that the same information, reflected in patterns of neural activity, must be present in both phases, suggesting that information should be 'reinstated' (i.e. multidimensional patterns must reappear) within and/or between network nodes. In the present study, we used representational similarity analysis (RSA) to investigate the extent to which nodes of the DMN and ECN reinstate information between a generation phase, in which participants generated novel or appropriate word associations to single nouns, and an evaluation phase, where we presented the associations back to participants to evaluate them. We showed strong evidence for reinstatement within the ECN dorsal lateral prefrontal cortex during the novel association task, and within the DMN medial prefrontal cortex during the appropriate association task. We additionally showed between network reinstatement between the ECN dorsal lateral prefrontal cortex and the DMN posterior parietal cortex during the novelty task. These results demonstrate the importance of both within- and between-informational reinstatement for generating and evaluating ideas, and implicate both the DMN and ECN in dual process models of creativity.

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available

Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Partly due to patient privacy, computational, and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases - without needing the raw data - allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).

The challenges and prospects of brain-based prediction of behaviour

Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.

A predictive coding approach to psychedelic virtual-induced hallucinations and creative cognition in aging

Recent research has investigated the potential of psychedelic substances in treating various neurological and psychiatric disorders. In particular, there has been a growth in studies concerning the intersection of psychedelics, Virtual Reality (VR), and Cognitive Flexibility (CF). Indeed, the use of immersive technology allows the simulation of the perceptual and cognitive effects of psychedelic substances without the potential risks associated with them. CF is strongly associated with creative cognition, a complex cognitive mechanism involved in creative thinking and associated with the prefrontal cortex and the neural networks supporting executive functions, memory, attention, and spontaneous modes of thought. The Bayesian brain approach, which is rooted in predictive coding, has emerged as a promising framework for understanding the effects of psychedelic hallucinations on cognitive functioning. Psychedelic substances may enhance creativity by inducing a state of CF, allowing for a wider range of associations and possibilities to be explored and increasing openness to experience. A decline in cognitive abilities, including creative processing and divergent thinking, is observed during the aging process. In particular, studies on Mild Cognitive Impairment (MCI) show poorer performance in executive functions, including CF. The present paper suggests that psychedelic hallucinations induced by VR may help optimize the balance between top-down expectations and bottom-up sensory information. Therefore, enhanced CF and creativity may be crucial during the aging process for maintaining cognitive functions and preventing pathological conditions.