Neural Basis Research Articles

Cognitive reserve against Alzheimer’s pathology is linked to brain activity during memory formation

The cognitive reserve (CR) hypothesis posits that individuals can differ in how their brain function is disrupted by pathology associated with aging and neurodegeneration. Here, we test this hypothesis in the continuum from cognitively normal to at-risk stages for Alzheimer’s Disease (AD) to AD dementia using longitudinal data from 490 participants of the DELCODE multicentric observational study. Brain function is measured using task fMRI of visual memory encoding. Using a multivariate moderation analysis, we identify a CR-related activity pattern underlying successful memory encoding that moderates the detrimental effect of AD pathological load on cognitive performance. CR is mainly represented by a more pronounced expression of the task-active network encompassing deactivation of the default mode network (DMN) and activation of inferior temporal regions including the fusiform gyrus. We devise personalized fMRI-based CR scores that moderate the impact of AD pathology on cognitive performance and are positively associated with years of education. Furthermore, higher CR scores attenuate the effect of AD pathology on cognitive decline over time. Our findings primarily provide evidence for the maintenance of core cognitive circuits including the DMN as the neural basis of CR. Individual brain activity levels of these areas during memory encoding have prognostic value for future cognitive decline.

Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA.

Advanced computational models and simulations to unravel the complexities of brain function have known a growing interest in recent years in the field of neurosciences, driven by significant technological progress in computing platforms. Multicompartment models, which capture the detailed morphological and functional properties of neural circuits, represent a significant advancement in this area providing more biological coherence than single compartment modeling. These models serve as a cornerstone for exploring the neural basis of sensory processing, learning paradigms, adaptive behaviors, and neurological disorders. Yet, the high complexity of these models presents a challenge for their real-time implementation, which is essential for exploring alternative therapies for neurological disorders such as electroceutics that rely on biohybrid interaction. Here, we present an accessible, user-friendly, and real-time emulator for multicompartment Hodgkin-Huxley neurons on SoC FPGA. Our system enables real-time emulation of multicompartment neurons while emphasizing cost-efficiency, flexibility, and ease of use. We showcase an implementation utilizing a technology that remains underrepresented in the current literature for this specific application. We anticipate that our system will contribute to the enhancement of computation platforms by presenting an alternative architecture for multicompartment computation. Additionally, it constitutes a step toward developing neuromorphic-based neuroprostheses for bioelectrical therapeutics through an embedded real-time platform running at a similar timescale to biological networks.

Diverse frontoparietal connectivity supports semantic prediction and integration in sentence comprehension.

Predictive processing in parietal, temporal, frontal, and sensory cortex allows us to anticipate future meanings to maximize the efficiency of language comprehension, with the temporoparietal junction (TPJ) and inferior frontal gyrus (IFG) thought to be situated towards the top of a predictive hierarchy. Although the regions underpinning this fundamental brain function are well-documented, it remains unclear how they interact to achieve efficient comprehension. To this end we recorded functional magnetic resonance imaging (fMRI) in 22 participants (11 males) while they comprehended sentences presented part-by-part, in which we manipulated the constraint provided by sentential contexts on upcoming semantic information. Using this paradigm, we examined the connectivity patterns of bilateral TPJ and IFG during anticipatory phases (i.e., before the onset of targets) and integration phases (i.e., after the onset of targets). When upcoming semantic content was highly predictable in strong-constraint contexts, both left TPJ and bilateral IFG showed stronger visual coupling, while right TPJ showed stronger connectivity with regions within control, default mode, and visual networks, including IFG, parahippocampal gyrus, posterior cingulate, and fusiform gyrus. These connectivity patterns were weaker when predicted semantic content appeared, in line with predictive coding theory. Conversely, for less predictable content, these connectivity patterns were stronger during the integration phase. Overall, these results suggest that both top-down semantic prediction and bottom-up integration during predictive processing are supported by flexible coupling of frontoparietal regions with control, memory, and sensory systems.Significance Statement Recent work has revealed the neural basis of predictive language comprehension. However, it remains unclear how brain regions change their connectivity in a dynamic fashion to support comprehension in highly predictive and less predictive contexts. Here, we show that stronger frontoparietal connectivity with cognitive control, memory, and sensory areas supports top-down prediction generation in strong-constraint contexts; these connectivity patterns are reduced when the anticipated information appears. This pattern is reversed when upcoming sensory input is unpredictable; connectivity is stronger after word inputs have been presented, allowing semantic integration with preceding low-constraint context. Our findings suggest that both top-down semantic prediction and bottom-up semantic integration in language comprehension rely upon diverse functional coupling of higher-order frontoparietal regions with other brain systems.

The Unity of Consciousness and the Practical Ethics of Neural Organoid Research

This article investigates a critical yet underexplored structural aspect of consciousness in the context of the practical ethics of neural organoid research: the unity of consciousness. We advocate for the necessity of the unified field, which has garnered substantial support from both philosophical and empirical standpoints, although the debate remains unresolved. We highlight the brainstem as a potential source of the unified conscious field, a structure already under scrutiny in neural organoid research in relation to conditions such as Parkinson’s disease and post-COVID-19 syndromes. We argue that if unity is a necessary feature of consciousness, consciousness is contingent upon a specific biological system without which consciousness cannot arise, thereby narrowing the range of neural organoids of ethical concern. Furthermore, the transplantation of neural organoids into animals has emerged as a practical concern, with ethical implications varying based on the necessity of the unity of consciousness. We argue that transplantation evades a significant ethical dilemma if unity is necessary and if the organoids to be transplanted lack the neural basis for the unified field of consciousness.

Primate thalamic nuclei select abstract rules and shape prefrontal dynamics.

Flexible behavior depends on abstract rules to generalize beyond specific instances, and outcome monitoring to adjust actions. Cortical circuits are posited to read out rules from high-dimensional representations of task-relevant variables in prefrontal cortex (PFC). We instead hypothesized that converging inputs from PFC, directly or via basal ganglia (BG), enable thalamus to select rules. We measured activity across PFC and connected thalamic nuclei of monkeys applying rules. Abstract rule information first appeared in ventroanterior thalamus (VA) - the main thalamic hub between BG and PFC. Mediodorsal thalamus (MD) also represented rule information before PFC, persisting to help maintain activation of relevant PFC cell ensembles. MD, a major recipient of midbrain dopamine input, was first to represent information about behavioral outcomes. A PFC-BG-thalamus model reproduced key findings, and thalamic-lesion modeling disrupted PFC rule representations. This suggests that thalamus selects high-level cognitive information from PFC and monitors behavioral outcomes of these selections.

Neural bases of social facilitation and inhibition: how peer presence affects elementary eye movements.

Social Facilitation/Inhibition (SFI) refers to how others' presence influences task performance positively or negatively. Our previous study revealed that peer presence modulated saccadic eye movements, a fundamental sensorimotor activity. Pro- and anti-saccades were either facilitated or inhibited depending on trial block complexity (Tricoche et al., 2020). In the present fMRI study, we adapted our paradigm to investigate the neural basis of SFI on saccades. Considering inter- and intra-individual variabilities, we evaluated the shared and distinct neural patterns between social facilitation and inhibition. We predicted an involvement of the saccade-related and attention networks, alongside the Theory-of-Mind (ToM) network, with opposite activity changes between facilitation and inhibition. Results confirmed peer presence modulation in fronto-parietal areas related to saccades and attention, in opposite directions for facilitation and inhibition. Additionally, the ventral attention network was modulated during inhibition. Default mode regions, including ToM areas, were also modulated. Finally, pupil size, often linked to arousal, increased with peers and correlated with dorsal attention regions and anterior insula activities. These results suggest that SFI engages task-specific and domain-general networks, modulated differently based on observed social effect. Attention network seemed to play a central role at both basic (linked to arousal or vigilance) and cognitive control levels.

Functional connectivity in procrastination and emotion regulation

Procrastination, an irrational delay of intended action, leads to numerous adverse effects in many life domains, such as low academic performance, poor mental health, and financial distress. Previous studies have revealed a substantial negative correlation between emotional regulation and procrastination. However, the neural basis for the association between emotion regulation and procrastination remains unclear. Therefore, we employed the voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to explore the neural substrates underlying how emotion regulation is responsible for procrastination (N = 243). In line with our hypothesis, the results showed a significant negative correlation between emotion regulation ability and procrastination. Additionally, the VBM analysis showed that emotion regulation ability was positively correlated with gray matter (GM) volumes in the right dorsal-lateral prefrontal cortex (dlPFC). The mediation analysis revealed that emotion regulation ability mediated the relationship between the GM volumes of the right dlPFC and procrastination. Furthermore, the RSFC results indicated that right dlPFC-left insula functional connectivity was positively associated with emotion regulation ability. Emotion regulation ability further mediated the relationship between the right dlPFC-left insula functional connectivity and procrastination. The current findings suggest that the neural pathway related to cognitive control over aversive emotion may be responsible for the close relationship between emotion regulation and procrastination, which provides a novel perspective for explaining the tight association between emotion regulation and procrastination.

Multitasking practice eliminates modality-based interference by separating task representations in sensory brain regions.

The debate on the neural basis of multitasking costs evolves around neural overlap between concurrently performed tasks. Recent evidence suggests that training-related reductions in representational overlap in fronto-parietal brain regions predict multitasking improvements. Cognitive theories assume that overlap of task representations may lead to unintended information exchange between tasks (i.e., crosstalk). Modality-based crosstalk was suggested as a source for multitasking costs in multisensory settings. Robust findings of increased costs for certain modality mappings may be explained by crosstalk between the stimulus modality in one task and sensory action consequences in the concurrently performed task. Whether modality-based crosstalk emerges from representational overlap in general fronto-parietal multitasking regions or modality-specific regions is not known yet. In this functional neuroimaging study, we investigate neural overlap during multitasking performance in humans, focusing on modality compatibility by employing multivariate pattern analysis and modality-specific practice interventions in three groups (total N = 54, 24 females). We observed significant differences between modality compatible and modality incompatible single-task representations, specifically in the auditory cortex but not in fronto-parietal regions. Notably, improved auditory decoding accuracy related to modality incompatible tasks was predictive of performance gains in the corresponding dual task along with complete elimination of modality-specific dual-task costs. This predictive relationship was evident only in the group practicing modality incompatible mappings, suggesting that specific practice on task sets with modality overlap influenced both neural representations and subsequent multitasking performance. This study contributes to the integration of cognitive theory and neuroscience and the role of task representations in dual-task interference.Significance Statement In a society dominated by multitasking, understanding its neurocognitive basis and plasticity is crucial for key aspects of everyday tasks. We investigate the neural mechanisms behind multitasking limitations, offering insights for targeted cognitive interventions. The study builds upon established theories of cognitive multitasking and imaging research, addressing the concept of modality-based crosstalk - the unintended exchange of modality-based information between tasks. Through functional brain imaging and pattern analysis, we examined how neural task representations contribute to performance costs in dual tasks with varying degrees of modality overlap. Notably, our findings demonstrate a practice-related decrease in neural overlap which is associated with substantial multitasking improvements, specifically in the auditory cortex, emphasizing the contribution of sensory regions to flexible multidimensional task representations.

Nature and nurture in fruit fly hearing.

As for human language learning and birdsong acquisition, fruit flies adjust their auditory perception based on past sound experiences. This phenomenon is known as song preference learning in flies. Recent advancements in omics databases, such as the single-cell transcriptome and brain connectomes, have been integrated into traditional molecular genetics, making the fruit fly an outstanding model for studying the neural basis of "Nature and Nurture" in auditory perception and behaviors. This minireview aims to provide an overview of song preference in flies, including the nature of the phenomenon and its underlying neural mechanisms. Specifically, we focus on the neural circuitry involved in song preference learning, with which auditory experiences shape the song preference of flies. This shaping process depends on an integration hub that processes external sensory stimuli and internal states to enable flexible control of behavior. We also briefly review recent findings on the signals that feed into this integration hub, modulating song preference of flies in an experience-dependent manner.

A fronto-insular network underlies individual variations in anger expression and control

Abstract Anger can be deconstructed into distinct components: a tendency to outwardly express it (anger-out) and the capability to manage it (anger control). These aspects exhibit individual differences that vary across a continuum. Notably, the capacity to express and control anger is of great importance to modulate our reactions in interpersonal situations. The aim of this study was to test the hypothesis that anger expression and control are negatively correlated and that both can be decoded by the same patterns of grey and white matter features of a fronto-temporal brain network. To this aim, a data fusion unsupervised machine learning technique, known as transposed Independent Vector Analysis (tIVA), was used to decompose the brain into covarying GM–WM networks and then backward regression was used to predict both anger expression and control from a sample of 212 healthy subjects. Confirming our hypothesis, results showed that anger control and anger expression are negatively correlated, the more individuals control anger, the less they externalize it. At the neural level, individual differences in anger expression and control can be predicted by the same GM–WM network. As expected, this network included lateral and medial frontal regions, the insula, temporal regions, and the precuneus. The higher the concentration of GM–WM in this brain network, the higher the level of externalization of anger, and the lower the anger control. These results expand previous findings regarding the neural bases of anger by showing that individual differences in anger control and expression can be predicted by morphometric features.

Transcranial Direct Current Stimulation over the Posterior Parietal Cortex Increases Nontarget Retrieval during Visual Working Memory.

Visual working memory (VWM) requires precise feature binding. Previous studies have revealed a close relationship between the posterior parietal cortex (PPC) and feature binding during VWM; this study further examined their causal relationship through three transcranial direct current stimulation (tDCS) experiments. In Experiment 1 (N = 57), participants underwent three sessions of tDCS separately, including PPC stimulation, occipital cortex stimulation, and sham stimulation, and completed delayed estimation tasks for orientations before and after stimulation. Results showed that tDCS over PPC selectively prolonged recall response time (RT) and increased the probability of nontarget responses (a.k.a. failure of feature binding, pNT). In Experiment 2 (N = 29), combining metacognition estimation, we further investigated whether the effects of PPC stimulation were attributed to misbinding (i.e., participants self-reported "remembered" in nontarget responses) or informed guessing trials (participants self-reported "forgotten" in nontarget responses). We replicated the main findings in Experiment 1 and observed greater tDCS effects of PPC on RT in informed guessing trials while there are comparable effects on pNT in these two types of trials. In Experiment 3 (N = 28), we then examined whether the tDCS effects over PPC specifically influenced the memory retrieval process by using a change detection task. We found that PPC stimulation did not influence the recognition RT or accuracy. Together, this study provided direct causal evidence supporting the specific involvement of PPC in feature binding during VWM retrieval, from both aspects of speed and response preference, expanding our understanding of the neural basis of feature binding in VWM.

Role of the medial posterior parietal cortex in orchestrating attention and reaching.

The interplay between attention, alertness and motor planning is crucial for our manual interactions. To investigate the neural bases of this interaction, and challenging the views that attention cannot be disentangled from motor planning, we instructed human volunteers of both sexes to plan and execute reaching movements while attending to the target, while attending elsewhere, or without constraining attention. We recorded reaction times to reach initiation and pupil diameter and interfered with the functions of the medial posterior parietal cortex (mPPC) with online repetitive transcranial magnetic stimulation to test the causal role of this cortical region in the interplay between spatial attention and reaching. We found that mPPC plays a key role in the spatial association of reach planning and covert attention. Moreover, we have found that alertness, measured by pupil size, is a good predictor of the promptness of reach initiation only if we plan a reach to attended targets, and mPPC is causally involved in this coupling. Different from previous understanding, we suggest that mPPC is neither involved in reach planning per se, nor in sustained covert attention in absence of a reach plan, but it is specifically involved in attention functional to reaching.Significance Statement Attention is required to perform dexterous arm movements. In this work we show the neural bases of the interplay between attention and reaching preparation, with the aim to provide information useful to address effective rehabilitation strategies to treat functional deficits observed in attention-related diseases. We discuss how brain areas are involved in orchestrating attention and reaching by signaling the alignment of their spatial coordinates. Moreover, we found that pupil size changes during reach preparation are related to reach initiation, suggesting a coordination between vigilance and reach promptness when preparing a reach to attended targets.

Madhyarama Neuron: Unveiling the Neural Basis of Energy Balance and its Therapeutic Potential in Mental Health

Madhyarama Neuron: Unveiling the Neural Basis of Energy Balance and its Therapeutic Potential in Mental Health

Global changes in the pattern of connectivity in developmental prosopagnosia.

Developmental prosopagnosia is a neurodevelopmental condition characterized by difficulties in recognizing the identity of a person from their face. While current theories of the neural basis of developmental prosopagnosia focus on the face processing network, successful recognition of face identities requires broader integration of neural signals across the whole brain. Here, we asked whether disruptions in global functional and structural connectivity contribute to the face recognition difficulties observed in developmental prosopagnosia. We found that the left temporal pole was less functionally connected to the rest of the brain in developmental prosopagnosia. This was driven by weaker contralateral connections to the middle and inferior temporal gyri, as well as to the medial prefrontal cortex. The pattern of global connectivity in the left temporal pole was also disrupted in developmental prosopagnosia. Critically, these changes in global functional connectivity were only evident when participants viewed faces. Structural connectivity analysis revealed localized reductions in connectivity between the left temporal pole and a number of regions, including the fusiform gyrus, inferior temporal gyrus, and orbitofrontal cortex. Our findings underscore the importance of whole-brain integration in supporting typical face recognition and provide evidence that disruptions in connectivity involving the left temporal pole may underlie the characteristic difficulties of developmental prosopagnosia.

Leveraging deep single-soma RNA sequencing to explore the neural basis of human somatosensation.

The versatility of somatosensation arises from heterogeneous dorsal root ganglion (DRG) neurons. However, soma transcriptomes of individual human (h)DRG neurons-critical information to decipher their functions-are lacking due to technical difficulties. In this study, we isolated somata from individual hDRG neurons and conducted deep RNA sequencing (RNA-seq) to detect, on average, over 9,000 unique genes per neuron, and we identified 16 neuronal types. These results were corroborated and validated by spatial transcriptomics and RNAscope in situ hybridization. Cross-species analyses revealed divergence among potential pain-sensing neurons and the likely existence of human-specific neuronal types. Molecular-profile-informed microneurography recordings revealed temperature-sensing properties across human sensory afferent types. In summary, by employing single-soma deep RNA-seq and spatial transcriptomics, we generated an hDRG neuron atlas, which provides insights into human somatosensory physiology and serves as a foundation for translational work.

A New Artificial Intelligence-Based System for Optimal Electricity Arbitrage of a Second-Life Battery Station in Day-Ahead Markets

Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls below 70%, it needs to be replaced, and these discarded batteries are typically sent for recycling. Nevertheless, there is an opportunity to repurpose these worn-out batteries for a second life in electric power systems. This study focuses on the arbitrage situation of a second-life battery (SLB) facility in day-ahead electricity markets. This approach not only contributes to balancing supply and demand in the electric power system but also allows the battery facility to achieve significant gains. We propose an artificial intelligence system that integrates optimized deep learning algorithms for market price predictions with a mixed-integer linear programming (MILP) model for market participation and arbitrage decisions. Our system predicts prices for the next 24 h using Neural Hierarchical Interpolation for Time Series (N-HiTS) and decides when to enter the market using the MILP model and incorporating the predicted data and the statuses of the batteries. We compare the accuracy of our trained deep learning model with other deep learning models such as recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS). We test the efficiency of the proposed system using real-world Turkish day-ahead market data. According to the results obtained, this study concludes that substantial gains can be achieved with the predicted prices and the optimal operating model. A facility with a total battery energy capacity of 5.133 MWh can generate a profit of USD 539 in one day, showcasing the potential of our study. Our new system’s approach provides proof of concept of new research opportunities for the participation of SLB facilities in day-ahead markets.

Positive and negative affect, related mental health traits, and cognitive performance: shared genetic architecture and potential causality.

Altered affect and cognitive dysfunction are transdiagnostic, burdensome, and pervasive features of many psychiatric conditions which remain poorly understood and have few efficacious treatments. Research on the genetic architecture of these phenotypes and causal relationships between them may provide insight into their aetiology and comorbidity. Using data from the Lifelines Cohort Study, we conducted genome-wide association studies (GWAS) on positive and negative affect and four cognitive domains (working memory, reaction time, visual learning and memory, executive function). Using publicly available large GWAS on related - albeit distinct-phenotypes (depression, anxiety, wellbeing, general cognitive ability [GCA]) we conducted genetic correlation and Mendelian randomization (MR) analyses to examine genetic overlap and causal relationships. We identified one genome-wide hit ( p <5×10 -8 ) for reaction time, and many loci with suggestive associations ( p <5×10 -6 ; N range= 11-20 independent hits) for other phenotypes. For most phenotypes, gene mapping and tissue expression analysis of suggestive hits from the GWAS showed increased gene expression in brain tissue compared to other tissues. As predicted, negative affect is genetically correlated with mental health phenotypes (depression r g =0.51; anxiety r g =0.70; wellbeing r g = -0.71) and cognitive domains are genetically correlated with GCA and brain volume ( r g ≤ 0.66). Genetic correlations between negative and positive affect suggest that they are dissociable constructs ( r g = -0.18) with negative affect having higher genetic overlap with GCA than positive affect ( r g =-0.19 vs -0.06). This could indicate that negative affect has a higher shared neural basis with GCA than positive affect and/or GCA and negative affect may exhibit causal relationships. MR analyses suggest potential causal effects of higher GCA on reduced negative affect, reduced risk of depression and anxiety, and higher wellbeing, but little impact on positive affect. We also report evidence for potential causal effects of depression and lower wellbeing on reduced GCA. Taken together, these results suggests that GCA may be a valid target for negative affect (but not positive affect) and depression and wellbeing may be valid targets for GCA.

A neural implementation of cognitive reserve: Insights from a longitudinal fMRI study of set-switching in aging

Aging is often accompanied by changes in brain structure and executive functions, particularly in tasks involving cognitive flexibility, such as task-switching. However, substantial individual differences in the degree of cognitive impairment indicate that some individuals can cope with brain changes more effectively than others, suggesting higher cognitive reserve (CR). This study identified a neural basis for CR by examining the longitudinal relationship between task-related brain activation, structural brain changes, and changes in cognitive performance during an executive task-switching paradigm including single and dual conditions. Fifty-two older individuals were assessed at baseline and followed up after five years. Structural brain changes related to task-switching performance were analyzed using elastic net regression. Task-related functional brain activation was measured using ordinal trends canonical variate analysis (OrT CVA), capturing patterns of activation increasing from single to dual conditions. A differential task-related expression score (dOrT) was calculated as the difference in pattern expression scores between single and dual conditions at baseline. A linear regression model tested whether dOrT moderated the impact of brain changes on changes in switch cost over five years. Results showed a significant interaction between changes in brain structure and dOrT activation on switch cost change, indicating a moderation effect of task-related activation. Higher dOrT buffered the impact of brain structural decline on switch costs, enabling older adults to better cope with age-related brain structural changes and preserve cognitive flexibility. These findings suggest that these task-related activation patterns represent a neural basis for CR.

Enhancing prediction of human traits and behaviors through ensemble learning of traditional and novel resting-state fMRI connectivity analyses

Recent advances in cognitive neuroscience have focused on using resting-state functional connectivity (RSFC) data from fMRI scans to more accurately predict human traits and behaviors. Traditional approaches generally analyze RSFC by correlating averaged time-series data across regions of interest (ROIs) or networks, which may overlook important spatial signal patterns. To address this limitation, we introduced a novel linear regression technique that estimates RSFC by predicting spatial brain activity patterns in a target ROI from those in a seed ROI. We applied both traditional and our novel RSFC estimation methods to a large-scale dataset from the Human Connectome Project and the Brain Genomics Superstruct Project, analyzing resting-state fMRI data to predict sex, age, personality traits, and psychological task performance. To enhance prediction accuracy, we developed an ensemble learner that combines these qualitatively different methods using a weighted average approach. Our findings revealed that hierarchical clustering of RSFC patterns using our novel method displays distinct whole-brain grouping patterns compared to the traditional approach. Importantly, the ensemble model, integrating these diverse weak learners, outperformed the traditional RSFC method in predicting human traits and behaviors. Notably, the predictions from the traditional and novel methods showed relatively low similarity, indicating that our novel approach captures unique and previously undetected information about human traits and behaviors through fine-grained local spatial patterns of neural activation. These results highlight the potential of combining traditional and innovative RSFC analysis techniques to enrich our understanding of the neural basis of human traits and behaviors.

Metacontrol Regulates Creative Thinking: An EEG Complexity Analysis Based on Multiscale Entropy.

Previous studies have shown that creative thinking is associated with metacontrol, but its neural basis is unknown. The present study explored the neural basis of both by assessing EEG complexity through multiscale entropy. Subjects were engaged in a metacontrol task and an Alternative Uses Task, grouped according to task performance, and the EEG was analysed by multiscale entropy. The results showed that EEG complexity was significantly higher in the high-metacontrol and high-creativity groups than in the low-metacontrol and low-creativity groups, respectively, at high time scales. The metacontrol adaptability score and multipurpose task score were significantly and positively correlated with the EEG complexity at multiple electrode sites. It suggests that metacontrol and creativity are dependent on the activation of long-duration neural networks.