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bioRxiv2024

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.

neuroscience↗

bioRxiv2024

Personality traits vary in their association with brain activity across situations.

Human cognition supports complex behaviour across a range of situations, and traits (such as personality) influence how we react in these different contexts. Although viewing traits as situationally grounded is common in social sciences it is often overlooked in neuroscience. Often studies focus on linking brain activity to trait descriptions of humans examine brain-trait associations in a single task, or, under passive conditions like wakeful rest. These studies, often referred to as brain wide association studies (BWAS) have recently become the subject of controversy because results are often unreliable even with large sample sizes. Although there are important statistical reasons why BWAS yield inconsistent results, we hypothesised that results are inconsistent because the situation in which brain activity is measured will impact the power in detecting a reliable link to a specific trait. To examine this possibility, we performed a state-space analysis in which tasks from the Human Connectome Project (HCP) were organized into a low-dimensional space based on how they activated different large-scale neural systems. We examined how individuals observed brain activity across these different contexts related to their personality. Our analysis found that for multiple personality traits (including Agreeableness, Openness to Experience and Conscientiousness) stronger associations with brain activity emerge in some tasks than others. These data establish that for specific personality traits there are situations in which reliable associations with brain activity can be identified with greater accuracy, highlighting the importance of context-bound views of understanding how brain activity links to trait variation in human behaviour. Significance statementAs a species humans act efficiently in many contexts, however, as individuals our personality makes us more specialised in some situations than others. This "if-then" view of personality is widely accepted in the social sciences but is often overlooked in neuroscience. Here we show adopting a situationally bound view of human traits provides more meaningful descriptions of a brain-trait associations than are possible in traditional brain wide association studies (BWAS) that measure brain activity in a single situation. Our results demonstrate multiple personality traits (including Agreeableness, Openness to Experience and Conscientiousness) show stronger associations with brain activity in some tasks than others, explaining why studies focusing on changes in brain activity at rest can lead to weak or contradictory results.

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bioRxiv2024

Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

Extracting the relationship between high-dimensional recordings of neural activity and complex behav- ior is a ubiquitous problem in systems neuroscience. Toward this goal, encoding and decoding models attempt to infer the conditional distribution of neural activity given behavior and vice versa, while dimensionality reduc- tion techniques aim to extract interpretable low-dimensional representations. Variational autoencoders (VAEs) are flexible deep-learning models commonly used to infer low-dimensional embeddings of neural or behavioral data. However, it is challenging for VAEs to accurately model arbitrary conditional distributions, such as those encountered in neural encoding and decoding, and even more so simultaneously. Here, we present a VAE-based approach for accurately calculating such conditional distributions. We validate our approach on a task with known ground truth and demonstrate the applicability to high-dimensional behavioral time series by retrieving the condi- tional distributions over masked body parts of walking flies. Finally, we probabilistically decode motor trajectories from neural population activity in a monkey reach task and query the same VAE for the encoding distribution of neural activity given behavior. Our approach provides a unifying perspective on joint dimensionality reduction and learning conditional distributions of neural and behavioral data, which will allow for scaling common analyses in neuroscience to todays high-dimensional multi-modal datasets.

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bioRxiv2024

Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control

Understanding individual differences in cognitive control is a central goal in psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures in cognitive neuroscience such as response times or task-based fMRI activity. While prior work has pinpointed the source of the issue -- the vast amount of cross-trial variability within these measures -- solutions remain elusive. Here, we propose one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding of trial-level fMRI data. Using this framework and longitudinal data from the Dual Mechanisms of Cognitive Control project, we estimated individuals neural responses associated with cognitive control within a color-word Stroop task, then assessed the reliability of these individuals responses across a time interval of several months. We show that in many prefrontal and parietal brain regions, test-retest reliability was near maximal, and that only hierarchical models were able to reveal this state of affairs. Further, when compared to traditional univariate contrasts, multivariate decoding enabled individual-level correlations to be estimated with significantly greater precision. We specifically link these improvements in precision to the optimized suppression of cross-trial variability in decoding. Together, these findings not only indicate that cognitive control-related neural responses individuate people in a highly stable manner across time, but also suggest that integrating hierarchical and multivariate models provides a powerful approach for investigating individual differences in cognitive control, one that can effectively address the issue of high-variability measures.

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bioRxiv2024

Accurate localization of cortical and subcortical sources of M/EEG signals by a convolutional neural network with a realistic head conductivity model: Validation with M/EEG simulation, evoked potentials, and invasive recordings

While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established non-invasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Particularly challenging is the accurate localization of subcortical sources of M/EEG, which remains a subject of debate. To address this issue, we propose a four-layered convolutional neural network (4LCNN) designed to precisely locate both cortical and subcortical source activity underlying M/EEG signals. The 4LCNN was trained using a vast dataset generated by forward M/EEG simulations based on a realistic head volume conductor model. The 4LCNN implicitly learns the characteristics of M/EEG and their sources from the training data without need for explicitly formulating and fine-tuning optimal priors, a common challenge in conventional M/EEG source imaging techniques. We evaluated the efficacy of the 4LCNN model on a validation dataset comprising forward M/EEG simulations and two types of real experimental data from humans: 1) somatosensory evoked potentials recorded by EEG, and 2) simultaneous recordings from invasive electrodes implanted in the brain and MEG signals. Our results demonstrate that the 4LCNN provides robust and superior estimation accuracy compared to conventional M/EEG source imaging methods, aligning well with established neuroscience knowledge. Notably, the accuracy of the subcortical regions was as accurate as that of the cortical regions. The 4LCNN method, as a data-driven approach, enables accurate source localization of M/EEG signals, including in subcortical regions, suggesting future contributions to various research endeavors such as contributions to the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases and basic brain functions.

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bioRxiv2024

Quick, Don't Move!: Wh-Movement and Wh-In-Situ Structures in Rapid Parallel Reading - EEG studies in English, Urdu, and Mandarin Chinese

A fundamental question in the cognitive neuroscience of language is how grammatical representations are reflected in the organization and activity of the brain. This is challenging in part because superficial differences between languages, e.g., word order, exert different demands on the memory systems that process these structures. Here, we present an electroencephalography (EEG) study to investigate the brains responses to wh-constructions in English, Urdu, and Mandarin Chinese. In addition to the different word orders, writing systems, and morphological typologies of the 3 languages, these languages diverge with regard to how wh-constructions are formed: English requires filler-gap dependencies for wh-objects, whereas Urdu and Mandarin Chinese do not. We use a rapid parallel reading task, in which short sentences are displayed in parallel for 200ms to mitigate the different demands placed on memory systems. We show that neural responses distinguish wh-object constructions from their controls in midline anterior (Urdu, Mandarin Chinese) and right posterior sensors (English, Mandarin Chinese), from 200-400ms (English, Mandarin Chinese) and 500-800ms (English, Urdu). Although there is no detectable uniform, language-invariant response to wh-constructions across languages, there are a number of shared features in the evoked response between any pair of languages, i.e., wh-in-situ constructions generate an evoked response in midline anterior sensors. Moreover, behavioral evidence shows a robust cross-language cost of processing wh-object constructions, regardless of their surface form. This demonstrates that readers of diverse languages can process some grammatical information in a short 200ms fixation, and that the RPVP methodology may enable new ways of linking cognitive neuroscience of language to comparative syntax, i.e., the systematic description of similarities and differences between grammatical structures.

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bioRxiv2024

Meta-Reinforcement Learning reconciles surprise, value and control in the anterior cingulate cortex.

The role of the dorsal anterior cingulate cortex (dACC) in cognition is a frequently studied yet highly debated topic in neuroscience. Most authors agree that the dACC is involved in either cognitive control (e.g. voluntary inhibition of automatic responses) or monitoring (e.g. comparing expectations with outcomes, detecting errors, tracking surprise). A consensus on which theoretical perspective best explains dACC contribution to behaviour is still lacking, as two distinct sets of studies report dACC activation in tasks requiring surprise tracking for performance monitoring and cognitive control without involving surprise monitoring, respectively. This creates a theoretical impasse, as no single current account can reconcile these findings. Here we propose a novel hypothesis on dACC function that integrates both the monitoring and the cognitive control perspectives in a unifying, meta-Reinforcement Learning framework, in which cognitive control is optimized by meta-learning based on tracking Bayesian surprise. We tested the quantitative predictions from our theory in two different functional neuroimaging studies at the basis of the current theory crisis. We show that the meta-Reinforcement Learning perspective successfully captures all the neuroimaging results by predicting both cognitive control and monitoring functions, resolving the theoretical impasse about dACC function within an integrative framework. In sum, our results suggest that dACC function can be framed as a meta-learning optimisation of cognitive control, providing an integrative perspective on its roles in cognitive control, surprise tracking, and performance monitoring. Significance statementAn important debate in cognitive neuroscience concerns the role of the anterior cingulate cortex (ACC) in cognition. Two effective and competing frameworks suggest a role for the ACC in surprise monitoring or optimizing cognitive control, respectively. So far, none of these frameworks has succeeded as a unified theory of ACC function. In this study, we reanalyzed previous neuroimaging data on ACC activity during cognitive tasks using a novel computational perspective: meta-Reinforcement Learning. We show that this computational framework can explain a variety of data on ACC function that, globally, could not be captured by any of the previous models. We propose that meta-Reinforcement Learning offers a unified theory of ACC cognitive and computational function.

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bioRxiv2024

Impact of microchannel width on axons for brain-on-chip applications

Technologies for axon guidance for in vitro disease models and bottom up investigations are increasingly being used in neuroscience research. One of the most prevalent patterning methods is using polydimethylsiloxane (PDMS) microstructures due to compatibility with microscopy and electrophysiology which enables systematic tracking of axon development with precision and efficiency. Previous investigations of these guidance platforms have noted axons tend to follow edges and avoid sharp turns; however, the specific impact of spatial constraints remains only partially explored. We investigated the influence of microchannel width beyond a constriction point, as well as the number of available microchannels, on axon growth dynamics. Further, by manipulating the size of micron/submicron-sized PDMS tunnels we investigated the space restriction that prevents growth cone penetration showing that restrictions smaller than 350nm were sufficient to exclude axons. This research offers insights into the interplay of spatial constraints, axon development, and neural behavior. The findings are important for designing in vitro platforms and in vivo neural interfaces for both fundamental neuroscience and translational applications in rapidly evolving neural implant technologies.

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bioRxiv2024

Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer

Accurate prediction of the brains task reactivity from resting-state functional magnetic resonance imaging (fMRI) data remains a significant challenge in neuroscience. Traditional statistical approaches often fail to capture the complex, nonlinear spatiotemporal patterns of brain function. This study introduces SwiFUN (Swin fMRI UNet Transformer), a novel deep-learning framework designed to predict 3D task activation maps directly from resting-state fMRI scans. SwiFUN leverages advanced techniques such as shifted window-based self-attention, which helps to understand complex patterns by focusing on varying parts of the data sequentially, and a contrastive learning strategy to better capture individual differences among subjects. When applied to predicting emotion-related task activation in adults (UK Biobank, n=7,038) and children (ABCD, n=4,944), SwiFUN consistently achieved higher overall prediction accuracy than existing methods across all contrasts; it demonstrated an improvement of up to 27% for the FACES-PLACES contrast in ABCD data. The resulting task activation maps revealed individual differences across cortical and subcortical regions associated with sex, age, and depressive symptoms. This scalable, transformer-based approach potentially reduces the need for task-based fMRI in clinical settings, marking a promising direction for future neuroscience and clinical research that enhances our ability to understand and predict brain function.

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bioRxiv2024

AxoDen: An Algorithm for the Automated Quantification of Axonal Density in defined Brain Regions

The rodent brain contains 70,000,000+ neurons interconnected via complex axonal circuits with varying architectures. Neural pathologies are often associated with anatomical changes in these axonal projections and synaptic connections. Notably, axonal density variations of local and long-range projections increase or decrease as a function of the strengthening or weakening, respectively, of the information flow between brain regions. Traditionally, histological quantification of axonal inputs relied on assessing the mean fluorescence intensity within a rectangle placed in the brain region-of-inter-est. Despite yielding valuable insights, this conventional method is notably susceptible to background fluorescence, post-acquisition adjustments, and inter-researcher variability. Additionally, it fails to account for the non-uniform innervation across brain regions, thus overlooking critical data such as innervation percentages and axonal distribution patterns. In response to these challenges, we introduce AxoDen, an open-source semi-automated platform designed to increase the speed and rigor of axon quantifications for basic neuroscience discovery. AxoDen processes user-defined brain regions-of-interests incorporating dynamic thresholding of grayscales-transformed images to facilitate binarized pixel measure-ments. Thereby AxoDen segregates the image content into signal and non-signal categories, effectively eliminating background interference and enabling the exclusive measurement of fluorescence from axonal projections. AxoDen provides detailed and accurate representations of axonal density and spatial distribution. AxoDens advanced yet user-friendly platform enhances the reliability and efficiency of axonal density analysis and facilitates access to unbiased high-quality data analysis with no technical background or coding experience required. AxoDen is freely available to everyone as a valuable neuroscience tool for dissecting axonal innervation patterns in precisely defined brain regions.

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bioRxiv2024

Transcranial Brain Atlas Based on Photon Measurement Density Function in a Triple-Parameter Standard Channel Space

Functional near-infrared spectroscopy (fNIRS) is a widely used transcranial brain imaging technique in neuroscience research. Nevertheless, its lack of anatomical information poses challenges for designing appropriate optode montage and localizing fNIRS signals to underlying anatomical regions. The photon measurement density function (PMDF) is often employed to address these issues, as it accurately measures the sensitivity of a fNIRS signal channel to perturbations of absorption coefficients at any brain locations. However, existing PMDF-based localization methods have two limitations: a limited channel space, and PMDF estimation based on single standard head models, which differ anatomically from individual subjects. To overcome these limitations, this study proposes a continuous fNIRS standard channel space and constructs a PMDF-based transcranial brain atlas (PMDF-TBA) by calculating PMDFs based on MRI images of 48 adults. PMDF-TBA contains group-averaged sensitivities of channels to gray matter and brain regions of atlases, such as Brodmann, AAL2, and LPBA40. Through leave-one-out cross-validation, we evaluated the prediction ability of PMDF-TBA for sensitivity of unseen individuals and found that it outperformed PMDFs based on single standard head models. Thus, PMDF-TBA serves as a more generalizable fNIRS spatial localization tool. Therefore, PMDF-TBA can be utilized to optimize optode montage design, improve channel sensitivity to target brain regions, and assist in the source localization of fNIRS data, thereby promoting the application of fNIRS in neuroscience research.

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bioRxiv2024

RESPAN: an accurate, unbiased and automated pipeline for analysis of dendritic morphology and dendritic spine mapping

Quantification of dendritic spines is essential for studying synaptic connectivity, yet most current approaches require manual adjustments or the combination of multiple software tools for optimal results. Here, we present Restoration Enhanced SPine And Neuron Analysis (RESPAN), an open-source pipeline integrating state-of-the-art deep learning for image restoration, segmentation, and analysis in an easily deployable, user-friendly interface. Leveraging content-aware restoration to enhance signal, contrast, and isotropic resolution further enhances RESPANs robust detection of spines, dendritic branches, and soma across a wide variety of samples, including challenging datasets such as those from live imaging and in vivo 2-photon microscopy with limited signal. Extensive validation against expert annotations and comparison with other software demonstrates RESPANs superior accuracy and reproducibility across multiple imaging modalities. RESPAN offers significant improvements in usability over currently available approaches, streamlining and democratizing access to a combination of advanced capabilities through an accessible resource for the neuroscience community. MOTIVATIONAccurate and unbiased reconstructions of neuronal morphology and quantification of dendritic spines are widely used in neuroscience but remain a significant challenge for efficient large-scale analysis. Current methods rely heavily on parameter optimization between images and manual annotation, introducing bias and creating bottlenecks that limit large-scale studies. Additionally, existing automated tools often require complex workflows across multiple software platforms and lack integrated validation capabilities. We developed RESPAN to address these limitations by providing a comprehensive, automated pipeline that combines state-of-the-art deep learning approaches for image restoration, model training, image segmentation and analysis within one user-friendly graphic interface. This enables rapid, unbiased analysis of dendritic spine morphology across diverse imaging modalities while maintaining high accuracy and reproducibility.

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bioRxiv2024

Interactive data exploration websites for large-scale electrophysiology

Methodological advances in neuroscience have enabled the collection of massive datasets which demand innovative approaches for scientific communication. Existing platforms for data storage lack intuitive tools for data exploration, limiting our ability to interact effectively with these brain-wide datasets. We introduce two public websites: (Data and Atlas) developed for the International Brain Laboratory which provide access to millions of behavioral trials and hundreds of thousands of individual neurons. These interfaces allow users to discover both the raw and processed brain-wide data released by the IBL at the scale of the whole brain, individual sessions, trials, and neurons. By hosting these data interfaces as websites they are available cross-platform with no installation. By releasing each sites code as a modular open-source framework, other researchers can easily develop their own web interfaces and explore their own data. As neuroscience datasets continue to expand, customizable web interfaces offer a glimpse into a future of streamlined data exploration and act as blueprints for future tools.

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bioRxiv2024

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as "default" or "visual". Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

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bioRxiv2024

Decoding in the fourth dimension: Classification of temporal patterns and their generalization across locations

Neuroscience research has increasingly used decoding techniques, in which multivariate statistical methods identify patterns in neural data that allow the classification of experimental conditions or participant groups. Typically, the features used for decoding are spatial in nature, including voxel patterns and electrode locations. However, the strength of many neurophysiological recording techniques such as electroencephalography or magnetoencephalography is in their rich temporal, rather than spatial, content. The present report proposes a new decoding method that relies on the time information contained in neural time series. This information is then used in a subsequent step, generalization across location (GAL), which characterizes the relationship between sensor locations based on their ability to cross-decode. Two datasets are used to demonstrate usage of this method, referred to as time-GAL, involving (1) event-related potentials in response to affective pictures and (2) steady-state visual evoked potentials in response to aversively conditioned grating stimuli. In both cases, experimental conditions were successfully decoded based on the temporal features contained in the neural time series. Cross-decoding occurred in regions known to be involved in visual and affective processing. We conclude that the time-GAL approach holds promise for analyzing neural time series from a wide range of paradigms and measurement domains providing an assumption-free method to quantifying differences in temporal patterns of neural information processing and whether these patterns are shared across sensor locations. Author summaryDecoding and classification approaches are widely used in computational biology. In the field of neuroscience, pattern classification approaches typically use spatial information. In many instances, neural time series however are best defined by the their temporal, rather than spatial, features. Here, we propose a novel decoding approach taking advantage of the temporal information. Specifically, we utilize the waveform of neural time series as features for decoding experimental conditions and quantify each decoders generalization across locations (GAL) in multi-channel recordings. We illustrate the usage of our open source toolbox using two datasets, showing the sensitivity of the method to systematic condition differences in temporal dynamics along with its ability to capture and quantify spatial dependencies between recording locations.

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bioRxiv2024

An Open-Source Deep Learning-Based GUI Toolbox For Automated Auditory Brainstem Response Analyses (ABRA)

Hearing loss is a pervasive global health challenge with profound impacts on communication, cognitive function, and quality of life. Recent studies have established age-related hearing loss as a significant risk factor for dementia, highlighting the importance of hearing loss research. Auditory brainstem responses (ABRs), which are electrophysiological recordings of acoustically evoked synchronized neural activity from the auditory nerve and brainstem, serve as in vivo correlates for sensory hair cell and synaptic function, hearing sensitivity, and other critical readouts of auditory pathway physiology, making them highly valuable for both basic neuroscience and clinical research. Despite their utility, traditional ABR analyses rely heavily on subjective manual interpretation, which may introduce variability and pose challenges for reproducibility across studies. Here, we introduce Auditory Brainstem Response Analyzer (ABRA), a novel suite of open-source ABR analysis tools powered by deep learning, which automates and standardizes ABR waveform analysis. ABRA employs convolutional neural networks trained on diverse datasets collected from multiple experimental settings, achieving rapid and unbiased extraction of key ABR metrics, including peak amplitude, latency, and auditory threshold estimates. We demonstrate that ABRAs deep learning models provide performance comparable to expert human annotators while dramatically reducing analysis time and enhancing reproducibility across datasets from different laboratories. By bridging hearing research, sensory neuroscience, and advanced computational techniques, ABRA facilitates broader interdisciplinary insights into auditory function. An online version of the tool is available for use at no cost at https://abra.ucsd.edu.

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bioRxiv2024

A multimodal fMRI dataset unifying naturalistic processes with a rich array of experimental tasks

Cognitive neuroscience has advanced significantly due to the availability of openly shared datasets. Large sample sizes, large amounts of data per person, and diversity in tasks and data types are all desirable, but are difficult to achieve in a single dataset. Here, we present an open dataset with N = 101 participants and 6 hours of scanning per participant, with 6 multifaceted cognitive tasks including 2 hours of naturalistic movie viewing. This datasets combination of ample sample size, extensive data per participant, more than 600 iso hours worth of data, and a wide range of experimental conditions -- including cognitive, affective, social, and somatic/interoceptive tasks -- positions it uniquely for probing important questions in cognitive neuroscience.

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bioRxiv2024

Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control

Nonergodicity and Simpsons paradox present significant, yet underappreciated challenges in neuroscience. Leveraging brain imaging and behavioral data from over 4,000 children and a Bayesian computational model of cognitive dynamics, we investigated brain-behavior relationships underlying cognitive control at both between-subjects and within-subjects levels. Strikingly, we observed a reversal of associations of inhibitory control brain activations with dynamic behavioral measures when comparing between-subjects and within-subjects analyses, revealing the nonergodic nature of these processes. This nonergodicity was pervasive throughout the brain but most pronounced in the salience network. Additionally, within-subjects analysis uncovered dissociated brain representations of reactive and proactive control processes, as well as distinct brain-behavior associations for individuals who adaptively versus maladaptively regulated cognitive control. Our findings offer insights into dynamic neural mechanisms of cognitive control during a critical developmental period. This work highlights the importance of embracing nonergodicity in human neuroscience, with implications for both theoretical understanding and applications to AI and psychopathology.

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