Biology subjects

Chao, Z. C.

Publications and source records attributed to Chao, Z. C..

10 recordsLinked to original sources

bioRxiv2025

Short Oxygen Pulses Enhance Creative Problem-Solving

Creativity is central to human innovation, yet it often fluctuates from moment to moment. Identifying simple interventions to reliably boost creativity has broad scientific and societal value. Here, we tested whether short-pulse oxygen inhalation enhances creative problem-solving. Sixty participants performed two established tasks: the Alternative Uses Test (AUT), capturing divergent idea generation, and the Fusion Innovation Test (FIT), assessing both divergent and convergent thinking. Oxygen ([~]40% FiO2) was delivered in 1-minute pulses at 3-4-minute intervals, designed to align with intrinsic brain flexibility rhythms. Responses were scored for novelty, feasibility, and goal attainment using a validated GPT-based method. Linear mixed-effects regression revealed that oxygen significantly enhances both the quality and quantity of creative ideas across tasks. These findings demonstrate that a safe, low-cost physiological intervention can augment creative performance, providing a new link between oxygen metabolism, neural flexibility, and problem-solving. Significance statementHuman progress depends on creativity, yet individuals often struggle to access their full creative potential. We demonstrate that brief cycles of enriched oxygen inhalation enhance creativity across distinct problem-solving tasks. This safe and low-cost protocol increased both originality and productivity, pointing to oxygen metabolism as a previously underappreciated driver of flexible thinking. The work introduces a novel, scalable approach to support creativity at a time when human innovation is essential alongside advances in artificial intelligence.

neuroscience↗

bioRxiv2025

A Shared Neural Marker Predicts Creative Performance Across Distinct Problem-Solving Tasks

Creativity is essential for innovation, yet the brain mechanisms supporting its moment-to-moment variability remain unclear. We hypothesize that creativity depends on dynamic fluctuations in neural flexibility, which determine the potential to generate creative solutions. Here, we identify a shared neural marker of "creativity potential" that predicts upcoming performance across distinct problem-solving tasks. Twenty-eight participants completed the Alternative Uses Test (AUT), a measure of divergent thinking, and the Fusion Innovation Test (FIT), which integrates divergent and convergent thinking. Responses were scored for novelty, feasibility, and goal attainment using validated GPT-based automated evaluation. EEG signals recorded prior to problem onset were used to decode single-trial creativity scores. A decoding model based on coherence features achieved robust performance (r = 0.45, leave-one-trial-out) and generalized across individuals (r = 0.34, leave-one-subject-out). Feature weights revealed a creativity potential network (CPN), characterized by frontal-temporal interactions in beta frequency band. Applying the model to resting-state recordings revealed [~]3-minute cycles of creativity potential, suggesting intrinsic brain dynamics shape readiness for creative problem-solving. These findings establish a shared neural marker of creativity that transcends task boundaries and individuals. Beyond advancing our understanding of creative cognition, this work opens the possibility of monitoring creativity potential in real time, with implications for neurofeedback and creativity enhancement in daily life. Significance statementCreativity allows us to generate novel and useful ideas, yet our ability to be creative fluctuates from moment to moment. Here we identify a neural marker of "creativity potential" that predicts upcoming creative performance across two distinct problem-solving tasks. Using EEG and GPT-based automated evaluation, we show that preparatory brain activity encodes creativity potential, generalizing across tasks and individuals. Furthermore, creativity potential fluctuates in intrinsic [~]3-minute cycles during rest. These results advance our understanding of the neural basis of creativity and provide a foundation for real-time monitoring and neurofeedback applications that may help individuals enhance their creative capacity.

neuroscience↗

bioRxiv2025

Acetylcholine Enhances Deviance Detection in Hodgkin-Huxley Neuronal Networks

The brains ability to detect unexpected events, deviance detection (DD), is critical for survival. While DD has been computationally explained by synaptic plasticity, the role of neuromodulators like acetylcholine (ACh) remains less understood. Here, we examine how ACh modulates DD without invoking additional plasticity mechanisms. Using a cholinergic-sensitive Hodgkin-Huxley network of 200 neurons arranged in 2D space and stimulated via five spatially distinct inputs (A-E), we implemented an oddball paradigm with three conditions: standard (80% A, 20% B), deviant (20% A, 80% B), and a multi-standard control (20% each of A-E). ACh levels were controlled by the conductance of the slow K+ current. In the absence of ACh, the network already exhibited DD, responding more strongly to deviant A compared to control A. Notably, introducing a small amount of ACh amplified DD, while further increases suppressed it. Maximal DD occurred when strong spike frequency adaptation to standard B reduced competition, and enhanced phase-locking synchronized the networks response to deviant A. These findings reveal how neuromodulation can shape context-sensitive neural computation, optimizing detection of salient events through a dynamic balance of suppression and synchronization.

neuroscience↗

bioRxiv2025

Spatiotemporal Dynamics of fMRI Signal Changes Induced by High Concentration Normobaric Oxygen Inhalation

While it is well known that oxygen supports the brains metabolic demands, it remains unclear how increased oxygen concentration influences intrinsic neural activity over time and across brain regions. Using resting-state functional magnetic resonance imaging (fMRI), we examined the dynamic responses to high-concentration normobaric oxygen across distinct phases of exposure and withdrawal. We revealed three patterns in the BOLD signals: increased activation during inhalation, an undershoot following immediate oxygen withdrawal, and reactivation even without continued oxygen. These responses were most pronounced in the default mode network (DMN), but also exhibited spatiotemporally heterogeneous patterns across the brain, a map we term Brain Oxygen Sensitivity Topography (BOST). Functional connectivity analyses further revealed increased between-network connectivity during inhalation and enhanced within-network connectivity in the DMN during the aftereffect. This spatiotemporal heterogeneity and transient network reorganization suggests that distinct physiological processes are engaged at each phase, enabling us to predict how different oxygen protocols will enhance specific cognitive functions.

neuroscience↗

bioRxiv2025

Creativity Potential Networks: Brain Markers for Novelty and Feasibility of Upcoming Divergent Thinking Solutions

The brains resting-state activity can serve as an indicator of cognitive flexibility and predict the likelihood of an upcoming Aha experience. This suggests that spontaneous neural dynamics reflect a persons readiness for creative insight and underscore the potential of resting-state measures as biomarkers for anticipating creative breakthroughs. However, solutions accompanied by an Aha experience are not always truly creative, so it may be more valuable to identify biomarkers specifically linked to novelty and usefulness--two key dimensions of creative performance. To achieve this, we recruit 49 participants to complete the Alternative Uses Test, in which unconventional uses for everyday items are generated. We evaluate the responses for both novelty and feasibility using automated GPT-based methods and analyze resting-state EEG prior to the test. We find that creative performance is better predicted by interactions between different brain areas than by the activation of individual regions. Specifically, the degree centrality of theta-band functional connectivity in the right parietal and occipital areas correlates with novelty, while connectivity in the right middle and inferior frontal areas is associated with more feasible answers. These findings highlight distinct resting-state brain networks underlying the "creative potential" for novelty and feasibility, which could be leveraged to monitor and enhance brain flexibility. Significance statementOur study introduces the Creativity Potential Network (CPN), a resting-state brain network that can predict the novelty and feasibility of the upcoming solution in creative problem-solving. We show that the CPN is represented by communication between brain areas, and that the networks for novelty and feasibility are spatially distinct. This work provides a potential method to assess the potential to be creative without relying on behavioral measures and could be combined with neurofeedback to monitor and enhance brain flexibility.

neuroscience↗

bioRxiv2024

Effects of Spatial Constraints of Inhibitory Connectivity on the Dynamical Development of Criticality in Spiking Networks

Neural systems are hypothesized to operate near criticality, enhancing their capacity for optimal information processing, transmission and storage capabilities. Criticality has typically been studied in spiking neural networks and related systems organized in random or full connectivity, with the balance of excitation and inhibition being a key determinant of the critical point of the system. However, given that neurons in the brain are spatially distributed, with their distances significantly influencing connectivity and signal timing, it is unclear how the spatial organization of excitatory and inhibitory connectivity influences the networks self-organization towards criticality. Here, we systematically constrain the distance and density of inhibitory connectivity in two-dimensional spiking networks and allow synaptic weights to self-organize with activity-dependent excitatory and inhibitory plasticity in the presence of a low level of stochastic intrinsic activity. We then investigate the relationship between inhibitory connectivity, synaptic weights, and the resulting network activity during and after development. We find that networks with longer-range inhibitory synapses tend towards more supercritical behavior compared to networks with a similar number of shorter-range inhibitory synapses. We show that this distance dependence is a consequence of weaker long-range synapses after development due to the presence of synaptic delays, which shift most spike pairs outside of the potentiation window of the inhibitory learning rule.

neuroscience↗

bioRxiv2024

Temporal Prediction through Integration of Probability Distributions of Event Timings at Multiple Levels

Our brain uses prior experience to anticipate the timing of upcoming events. This dynamical process can be modeled using a hazard function derived from the probability distribution of event timings. However, the contexts of an event can lead to various probability distributions for the same event, and it remains unclear how the brain integrates these distributions into a coherent temporal prediction. In this study, we create a local-global foreperiod paradigm consisting of a sequence of paired trials, where in each trial, participants respond to a target signal after a specified time interval (i.e. foreperiod) following a warning cue. The prediction of the target onset in the second trial can be based on the probability distribution of the second foreperiod (local level) and its conditional probability given the foreperiod in the first trial (global level). These probability distributions are then transformed into hazard functions to represent the local and global temporal predictions. Reaction times to the target signal are best explained by incorporating both local and global predictions, indicating that both levels of temporal information contribute to making predictions. We further show that electroencephalographic source signals are best reconstructed when integrating both predictions. Specifically, the local and global predictions are separately encoded in the posterior and anterior brain regions, and to achieve synergy between both predictions, a third region--particularly the right posterior cingulate area--is needed. Our study reveals brain networks that integrate multilevel temporal information, providing a comprehensive view of hierarchical predictive coding of time.

neuroscience↗

bioRxiv2024

Omission-responsive neurons encode negative prediction error and probability in the auditory cortex

Predictive coding posits the brain predicts incoming sensory information and signals prediction errors when actual input differs from expectations. Positive prediction errors occur when input exceeds predictions, while negative prediction errors arise when input falls short. Specific neurons are theorized to encode negative prediction errors, linked to responses to omitted expected inputs. However, the information encoded in omission responses remains unclear. We recorded single-unit activity in rat auditory cortex during an omission paradigm with varying tone probabilities. We identified neurons selectively responding to omissions, with responses increasing with evidence accumulation and correlating with tone predictability--key characteristics of negative prediction-error neurons. Interestingly, these neurons showed selective omission responses but broad tone responses, revealing an asymmetry in error signaling. We propose a circuit model with laterally interconnected prediction-error neurons reproducing this asymmetry. Our model demonstrates that lateral connections enhance precision and efficiency of prediction encoding, supported by the free energy principle.

neuroscience↗

bioRxiv2023

Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities

Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants in the expected pattern of sensory input. Studies show that MMN diminishes when a sequence pattern becomes more predictable over a longer timescale. This implies that MMN is comprised of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms which peak early in the central-frontal area and late in a more frontal area, respectively. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multi-tiered neural marker, applicable in clinical settings.

neuroscience↗

bioRxiv2023

Crossmodal Hierarchical Predictive Coding for Audiovisual Sequences in Human Brain

Predictive-coding theory proposes that the brain actively predicts sensory inputs based on prior knowledge. While this theory has been extensively researched within individual sensory modalities, there is a crucial need for empirical evidence supporting hierarchical predictive processing across different modalities to further generalize the theory. Here, we examine how crossmodal knowledge is represented and learned in the brain by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) in humans during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at two hierarchical levels: stimulus-to-stimulus transition (local level) and multi-stimulus sequence structure (global level). With a model-fitting approach, we decompose the EEG data using three distinct predictive-coding models: one with no audiovisual integration, one with audiovisual integration at the global level, and one with audiovisual integration at both the local and global levels. The best-fitting model demonstrates that audiovisual integration occurs at both levels. This highlights a convergence of auditory and visual information to construct crossmodal predictions, even in the more basic interactions that occur between individual stimuli. Furthermore, we reveal the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and show that auditory and visual prediction-error signals are progressively redirected to the central-parietal area of the brain as learning progresses. Our findings unveil a crossmodal predictive coding mechanism, where the unimodal framework is implemented through more distributed brain networks to process hierarchical crossmodal knowledge.

neuroscience↗