Behavior needs neural variability

From pre-stimulus activity to the contents of consciousness - A spatiotemporal view: Reply to comments on "Beyond task response-Pre-stimulus activity modulates contents of consciousness"

What are the exact neuronal mechanisms of pre-post-stimulus interaction and how can that account for the intrinsically subjective nature of the contents of consciousness? This is the key question lurking behind the various excellent and very thoughtful commentaries to our target article which we group along four main topics and questions. (i) What is the role of neural features like alpha power, phase dynamics, trial-to-trial variability and fractal scale-free dynamics in yielding pre-post-stimulus interaction and its conscious contents. (ii) What do we mean by 'content' of consciousness? This concerns its meaning, its characterization as internal or external, and its relation to the basic subjectivity of consciousness. (iii) How does our approach stand to other theories of consciousness like the Dendritic Integration Theory (DIT), GNWT and IIT? This concerns the convergence among the different theories that highlight distinct aspects. (iv) How can we detail the spatiotemporal shaping of the contents of consciousness including their intrinsically subjective nature through pre-post-stimulus interaction? This concerns the details of how the non-additive pre-post-stimulus interaction shapes the subjective nature of our experience of conscious contents, that is, how the neuronal activity connects to the phenomenal features of consciousness. Together, we conclude that the contents of consciousness are shaped primarily in a temporal-dynamic and spatial-topographic way through the non-additive pre-post-stimulus interaction. Such spatiotemporal shaping of the contents in our consciousness constitutes their intrinsically subjective nature which must be distinguished from their (more objective) modulation by cognitive, sensory, affective, and motor functions.

Bridging the gap of brain and experience - Converging Neurophenomenology with Spatiotemporal Neuroscience

Neuroscience faces the challenge of connecting brain and mind, with the mind manifesting in first-person experience while the brain's neural activity can only be investigated in third-person perspective. To connect neural and mental states, Neurophenomenology provides a methodological toolkit for systematically linking first-person subjective experience with third-person objective observations of the brain's neural activity. However, beyond providing a systematic methodological strategy ('disciplined circularity'), it leaves open how neural activity and subjective experience are related among themselves, independent of our methodological strategy. The recently introduced Spatiotemporal Neuroscience suggests that neural activity and subjective experience share a commonly underlying feature as their "common currency", notably analogous spatiotemporal dynamics. Can Spatiotemporal Neuroscience inform Neurophenomenology to allow for a deeper and more substantiative connection of first-person experience and third-person neural activity? The goal of our paper is to show how Spatiotemporal Neuroscience and Neurophenomenology can be converged and integrated with each other to gain better understanding of the brain-mind connection. We describe their convergence on theoretical grounds which, subsequently, is illustrated by empirical examples like self, meditation, and depression. In conclusion, we propose that the integration of Neurophenomenology and Spatiotemporal Neuroscience can provide complementary insights, enrich both fields, allows for deeper understanding of brain-mind connection, and opens the door for developing novel methodological approaches in their empirical investigation.

Metacontrol-related aperiodic neural activity decreases but strategic adjustment thereof increases from childhood to adulthood

The concept of "metacontrol" pertains to the ability to effectively balance cognitive-control styles between extreme persistence and extreme flexibility. Metacontrol relies on the frontal lobe's integrity and is reflected in the degree of aperiodic neural activity in the EEG power spectrum. Given that the frontal lobe undergoes major changes through childhood and adolescence, we predicted that aperiodic neural activity would be more pronounced in underage participants (N = 76, 8 ~ 17 years old) than in young adults (N = 90, 18 ~ 30 years old) performing a Go/Nogo task. As expected, younger participants showed lower aperiodic exponents, indicating a higher level of aperiodic neural activity. We also predicted that adults would be more effective in tailoring their aperiodic brain activity to task-specific requirements. In line with our expectations, adults significantly reduced aperiodic activity in the more control-demanding Nogo condition, whereas underage participants showed no difference between conditions. Age predicted both the reduction of aperiodic activity from the pre-stimulus to the post-stimulus period and the greater reduction of this activity in the more control-demanding conditions. Our findings suggest that aperiodic exponents reflect ontogenetic changes in metacontrol, which in turn can be characterized by a systematic reduction of aperiodic neural activity, and a more strategic adjustment of this activity to task demands with increasing age.

Unraveling the roles of spatial working memory sustained and selective neurons in prefrontal cortex

The heart of goal-directed behavior organization is working memory. Recent studies have emphasized the critical role of the prefrontal cortex (PFC) in working memory, highlighting elevated spiking levels in PFC neurons during working-memory delays. As a higher-order cortex, PFC contains various types of neurons with complex receptive fields, making it challenging to identify task-engaged neurons, particularly during the working memory periods when firing rates are lower compared to stimulus periods. While previous studies have primarily focused on neurons selective for sensory stimuli, there are also task-sustained neurons that are not selective for specific stimulus characteristics. In this study, we differentiate between working memory (WM)-sustained neurons, which show task-related activity without stimulus spatial selectivity, and working memory (WM)-selective neurons, which are selective for the location of the stimulus. To investigate their roles, we investigated the neural activities of the lateral PFC neurons in two macaque monkeys during a spatial working memory task. Fano factor analysis revealed that the neuronal variability of both WM-selective and WM-sustained neurons was similar and significantly higher than that of non-active neurons (neurons not modulated by the task). Moreover, the Fano factor of active neurons diminished during error trials compared to correct trials. The spike phase locking (SPL) value was measured to evaluate the coupling of local field potentials (LFPs) phases to spike times, considering neural network characteristics. SPL results indicated that both WM-selective neurons and WM-sustained neurons exhibited higher SPL in the alpha/beta-band compared to non-active neurons. Additionally, the alpha/beta-band SPL of working memory-active neurons decreased during error trials. In summary, despite the non-stimulus-specific activation of WM-sustained neurons, they may contribute to task performance alongside WM-selective neurons.

From neural activity to behavioral engagement: temporal dynamics as their "common currency" during music

The human cortex is highly dynamic as manifest in its vast ongoing temporal repertoire. Similarly, human behavior is also variable over time with, for instance, fluctuating response times. How the brain's ongoing dynamics relates to the fluctuating dynamics of behavior such as emotions remains yet unclear, though. We measure median frequency (MF) in a dynamic way (D-MF) to investigate the dynamics in both electroencephalography (EEG) neural activity and the subjects' continuous behavioral assessment of their perceived emotional engagement changes during five different music pieces. Our main findings are: (i) significant differences in the frequency dynamics, e.g., D-MF, of the subjects' behavioral engagement ratings between the five music pieces, (ii) significant differences in the, e.g., D-MF, of the music pieces' EEG-based neural activity, and (iii) there is a unidirectional relationship from neural to behavioral during the five music pieces as measured through correlation and Granger causality between their respective D-MF's. Together, we demonstrate that neural dynamics relates to behavioral dynamics through the shared fluctuations in their dynamics. This highlights the key role of dynamics in connecting neural and behavioral activity as their "common currency."

Code conflict in an event file task is reflected by aperiodic neural activity

We investigated the relationship between aperiodic electroencephalography (EEG) activity and code conflict, hypothesizing that the former might serve as an indicator of the latter. We analyzed EEG and behavioral outcomes of a sample performing the event file task, which assesses code conflict in co-occurring or temporally overlapping stimulus and response features. To quantify aperiodic activity, we employed the fitting oscillations & one-over-f algorithm. The behavioral results revealed a typical partial-repetition cost effect, indicating that performance is impaired if the stimulus repeats while the response alternates, or vice versa. This suggests that the previously combined shape and response were stored in an event file and retrieved when any one of these components was repeated. Notably, this effect was also evident in the aperiodic exponent, which was lower for partial repetitions than for full repetitions or alternations, implying increased cortical noise, a higher excitatory E/I ratio, and noisier decision-making processes. The scalp distribution of this effect aligns with its sensorimotor characteristics. Thus, we interpret these findings as promising preliminary evidence that the aperiodic exponent may serve as a valuable neural marker of code conflict.

Disentangling sources of variability in decision-making

Even the most highly-trained observers presented with identical choice-relevant stimuli will reliably exhibit substantial trial-to-trial variability in the timing and accuracy of their choices. Despite being a pervasive feature of choice behaviour and a prominent phenotype for numerous clinical disorders, the capability to disentangle the sources of such intra-individual variability (IIV) remains limited. In principle, computational models of decision-making offer a means of parsing and estimating these sources, but methodological limitations have prevented this potential from being fully realized in practice. In this Review, we first discuss current limitations of algorithmic models for understanding variability in decision-making behaviour. We then highlight recent advances in behavioural paradigm design, novel analyses of cross-trial behavioural and neural dynamics, and the development of neurally grounded computational models that are now making it possible to link distinct components of IIV to well-defined neural processes. Taken together, we demonstrate how these methods are opening up new avenues for systematically analysing the neural origins of IIV, paving the way for a more refined, holistic understanding of decision-making in health and disease.

Modulation of brain signal variability in visual cortex reflects aging, GABA, and behavior

Moment-to-moment neural variability has been shown to scale positively with the complexity of stimulus input. However, the mechanisms underlying the ability to align variability to input complexity are unknown. Using a combination of behavioral methods, computational modeling, fMRI, MR spectroscopy, and pharmacological intervention, we investigated the role of aging and GABA in neural variability during visual processing. We replicated previous findings that participants expressed higher variability when viewing more complex visual stimuli. Additionally, we found that such variability modulation was associated with higher baseline visual GABA levels and was reduced in older adults. When pharmacologically increasing GABA activity, we found that participants with lower baseline GABA levels showed a drug-related increase in variability modulation while participants with higher baseline GABA showed no change or even a reduction, consistent with an inverted-U account. Finally, higher baseline GABA and variability modulation were jointly associated with better visual-discrimination performance. These results suggest that GABA plays an important role in how humans utilize neural variability to adapt to the complexity of the visual world.

Foraging animals use dynamic Bayesian updating to model meta-uncertainty in environment representations

Foraging theory predicts animal behavior in many contexts. In patch-based foraging behaviors, the marginal value theorem (MVT) gives the optimal strategy for deterministic environments whose parameters are fully known to the forager. In natural settings, environmental parameters exhibit variability and are only partially known to the animal based on its experience, creating uncertainty. Models of uncertainty in foraging are well established. However, natural environments also exhibit unpredicted changes in their statistics. As a result, animals must ascertain whether the currently observed quality of the environment is consistent with their internal models, or whether something has changed, creating meta-uncertainty. Behavioral strategies for optimizing foraging behavior under meta-uncertainty, and their neural underpinnings, are largely unknown. Here, we developed a novel behavioral task and computational framework for studying patch-leaving decisions in head-fixed and freely moving mice in conditions of meta-uncertainty. We stochastically varied between-patch travel time, as well as within-patch reward depletion rate. We find that, when uncertainty is minimal, mice adopt patch residence times in a manner consistent with the MVT and not explainable by simple ethologically motivated heuristic strategies. However, behavior in highly variable environments was best explained by modeling both first- and second-order uncertainty in environmental parameters, wherein local variability and global statistics are captured by a Bayesian estimator and dynamic prior, respectively. Thus, mice forage under meta-uncertainty by employing a hierarchical Bayesian strategy, which is essential for efficiently foraging in volatile environments. The results provide a foundation for understanding the neural basis of decision-making that exhibits naturalistic meta-uncertainty.

Executive control fluctuations underlie behavioral variability in anthropoids

In complex tasks requiring cognitive control, humans show trial-by-trial alterations in response time (RT), which are evident even when sensory-motor or other contextual aspects of the task remain stable. Exaggerated intra-individual RT variability is associated with brain injuries and frequently seen in aging and neuropsychological disorders. In this opinion, we discuss recent electrophysiology and imaging studies in humans and neurobiological studies in monkeys that indicate RT variability is linked with executive control fluctuation and that prefrontal cortical regions play essential, but dissociable, roles in such fluctuation of control and the resulting behavioral variability. We conclude by discussing emerging models proposing that both extremes of behavioral variability (significantly higher or lower) might reflect aberrant alterations in various aspects of decision-making processes.

Beyond the uniform creative brain: Inter-individual variability in functional connectivity correlates with creativity

Creativity, characterized by the pursuit of uniqueness and novelty, highlights the importance of individual variability, which have been a key focus in cognitive and behavioral research on creativity. However, most studies on the neural basis of creativity have primarily focused on consistent patterns of brain activity across individuals, with little attention to the variability in brain function. In this study, inter-subject representational similarity analysis was employed to investigate the relationship between inter-individual variability in resting-state functional connectivity and creative ability. The results revealed significant positive correlations between individual variability in functional connectivity maps of multiple brain regions, including the superior frontal gyrus, orbital gyrus, precuneus, cingulate gyrus, and lateral occipital cortex, and variability in creative ability. Notably, both intra-network variability within the default mode network (DMN) and visual network, as well as inter-network variability among the DMN, visual, sensorimotor, dorsal attention, and fronto-parietal networks, were linked to the variability in creative ability. The variations in functional connectivity patterns effectively distinguished individuals with high creative ability from those with lower ability. By examining creativity from the perspective of individual variability, this study provides new insights into the neural mechanisms underlying creativity.

Deep Brain Stimulation Response Circuits in Obsessive-Compulsive Disorder

In the field of deep brain stimulation (DBS), 2 major themes are currently making significant progress. The first of these is the framework of connectomic DBS, in which circuits that are associated with improvements of specific symptoms are described and targeted to improve and potentially personalize treatment. The second theme is related to the concept of brain sensing and adaptive DBS, which are aimed at identifying neural biomarkers that may guide stimulation in a closed-loop fashion. In DBS for obsessive-compulsive disorder (OCD), substantial progress has been made on both ends over the last 5 years. Together, the results have begun to draw a picture of exactly which circuit is associated with treatment response and how it may be affected by dysfunctional brain activity that may be attenuated using DBS. This knowledge, if further refined and validated, will define where, when, and how to stimulate which patients with OCD. We review the key studies from recent years with the aim of aggregating and condensing findings along both spatial and temporal domains. The result is a concept that anatomically defines a circuit that is likely dysfunctional in patients with typical OCD phenotypes and that may be adaptively targeted using DBS to maximally improve symptoms.

Connecting brain and mind through temporo-spatial dynamics: Towards a theory of common currency

Despite major progress in our understanding of the brain, the connection of neural and mental features, that is, brain and mind, remains yet elusive. In our 2020 target paper ("Is temporospatial dynamics the 'common currency' of brain and mind? Spatiotemporal Neuroscience") we proposed the "Common currency hypothesis": temporo-spatial dynamics are shared by neural and mental features, providing their connection. The current paper aims to further support and extend the original description of such common currency into a first outline of a "Common currency theory" (CCT) of neuro-mental relationship. First, we extend the range of examples to thoughts, meditation, depression and attention all lending support that temporal characteristics, (i.e. dynamics) are shared by both neural and mental features. Second, we now also show empirical examples of how spatial characteristics, i.e., topography, are shared by neural and mental features; this is illustrated by topographic reorganization of both neural and mental states in depression and meditation. Third, considering the neuro-mental connection in theoretical terms, we specify their relationship by distinct forms of temporospatial correspondences, ranging on a continuum from simple to complex. In conclusion, we extend our initial hypothesis about the key role of temporo-spatial dynamics in neuro-mental relationship into a first outline of an integrated mind-brain theory, the "Common currency theory" (CCT).

Catecholaminergic Modulation of Metacontrol Is Reflected in Aperiodic EEG Activity and Predicted by Baseline GABA+ and Glx Concentrations

The ability to balance between being persistent versus flexible during cognitive control is referred to as "metacontrol" and reflected in the exponent of aperiodic neural activity. Theoretical considerations suggest that metacontrol is affected by the interplay of the GABAergic, glutamatergic, and catecholaminergic systems. Moreover, evidence suggests that fronto-striatal structures play an important role. Yet, the nexus between neurobiochemistry and structural neuroanatomy when it comes to the foundations of metacontrol is not understood. To examine this, we investigated how an experimental manipulation of catecholaminergic signaling via methylphenidate (MHP) and baseline levels of GABA and glutamate in the anterior cingulate cortex (ACC), supplementary motor area (SMA), and striatum as assessed via MR spectroscopy altered task performance and associated aperiodic activity (assessed via EEG) during a conflict monitoring task. We investigated N = 101 healthy young adults. We show that the EEG-aperiodic exponent was elevated during task performance, as well as during cognitively challenging task conditions requiring more persistent processing and was further enhanced by MPH administration. Correlation analyses also provided evidence for an important role of individual characteristics and dispositions as reflected by the observed role of GABA+ and Glx baseline levels in the ACC, the SMA, and the striatum. Our observations point to an important role of catecholamines in the amino acid neurotransmitter-driven regulation of metacontrol and task-specific (changes in) metacontrol biases. The results suggest an interplay of the GABA/Glx and the catecholaminergic system in prefrontal-basal ganglia structures crucial for metacontrol.

Impact of hearing loss on brain signal variability in older adults under different auditory load conditions

Introduction

The moment-by-moment variability in brain signals, a newly recognized indicator, demonstrates both the adaptability of an individual's brain as a unique trait and the distribution of neural resources within that individual in response to constantly shifting task requirements. This study aimed to explore brain signal variability in older adults using oxyhemoglobin (HbO) variability derived from fNIRS during tasks with increasing signal-to-noise ratio (SNR) loads and to assess the effects of varying degrees of hearing loss on speech recognition performance and related brain signal variability patterns.

Methods

Eighty-one participants were categorized into three groups: healthy controls (n = 30, aged 65.5 ± 3.4), mild hearing loss (n = 25, aged 66.0 ± 3.7), and moderate to severe hearing loss (n = 26, aged 67.5 ± 3.7). Speech perception was tested under quiet, 5 dB SNR, and 0 dB SNR conditions.

Results

Results revealed that the brain signal variability increased with higher SNR loads in healthy older adults, indicating enhanced neural resource allocation with the SNR load. In contrast, we found that hearing loss reduced brain signal variability during speech recognition tasks, especially in noisy conditions, in the mild hearing loss and moderate to severe hearing loss groups, possibly indicating decreased neural processing efficiency. Additionally, a positive correlation between brain signal variability and speech recognition performance was observed in healthy control participants across all SNR conditions, suggesting that brain signal variability could dynamically respond to the precise level of auditory environment demands. However, this relationship was only significant at the 5 dB SNR condition in hearing loss groups.

Discussion

Taken together, this study underscores the significant impact of hearing loss on brain signal variability modulation in auditory cognitive tasks and highlights the need for further research to understand the underlying neural mechanisms.

Multiple brain activation patterns for the same perceptual decision-making task

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leverage trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimate the brain activations for each trial. We then cluster the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we find multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibits strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes are characterized by activations in different task-positive areas. The default mode network subtype is characterized by behavioral signatures that are similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.

The Promise of Investigating Neural Variability in Psychiatric Disorders

Researchers have begun to use the synergy of psychiatry and neuroscience to identify biomarkers that can be used to diagnose mental health disorders, predict their progression, and forecast treatment efficacy. However, biomarkers have achieved limited success to date, potentially due to a narrow focus on specific aspects of brain signals. This highlights a critical need for methodologies that can fully exploit the potential of neuroscience to transform psychiatric practice. In recent years, there has been emerging evidence of the ubiquity and importance of moment-to-moment neural variability for brain function. Single-neuron recordings and computational models have demonstrated the significance of variability even at the microscopic level. Concurrently, studies involving healthy humans using neuroimaging recording techniques have strongly indicated that neural variability, which in the past was dismissed as undesirable noise, is an important substrate for cognition. Given the cognitive disruption seen in several psychiatric disorders, neural variability is a promising biomarker in this context, and careful consideration of design choices is necessary to advance the field. In this review, we provide an overview of the significance and substrates of neural variability across different recording modalities and spatial scales. We also review the existing evidence that supports its relevance in the study of psychiatric disorders. Finally, we advocate for future research to investigate neural variability within disorder-relevant, task-based paradigms and longitudinal designs. Supported by computational models of brain activity, this framework holds the potential for advancing precision psychiatry in a powerful and experimentally feasible manner.

Single-neuron spiking variability in hippocampus dynamically tracks sensory content during memory formation in humans

During memory formation, the hippocampus is presumed to represent the content of stimuli, but how it does so is unknown. Using computational modelling and human single-neuron recordings, we show that the more precisely hippocampal spiking variability tracks the composite features of each individual stimulus, the better those stimuli are later remembered. We propose that moment-to-moment spiking variability may provide a new window into how the hippocampus constructs memories from the building blocks of our sensory world.

Spike reliability is cell type specific and shapes excitation and inhibition in the cortex

Neurons encode information in the highly variable spiking activity of neuronal populations, so that different repetitions of the same stimulus can generate action potentials that vary significantly in terms of the count and timing. How does spiking variability originate, and does it have a functional purpose? Leveraging large-scale intracellular electrophysiological data, we relate the spiking reliability of cortical neurons in-vitro during the intracellular injection of current resembling synaptic inputs to their morphologic, electrophysiologic, and transcriptomic classes. Our findings demonstrate that parvalbumin+ (PV) interneurons, a subclass of inhibitory neurons, show high reliability compared to other neuronal subclasses, particularly excitatory neurons. Through computational modeling, we predict that the high reliability of PV interneurons allows for strong and precise inhibition in downstream neurons, while the lower reliability of excitatory neurons allows for integrating multiple synaptic inputs leading to a spiking rate code. These findings illuminate how spiking variability in different neuronal classes affect information propagation in the brain, leading to precise inhibition and spiking rate codes.

Broadscale dampening of uncertainty adjustment in the aging brain

The ability to prioritize among input features according to relevance enables adaptive behaviors across the human lifespan. However, relevance often remains ambiguous, and such uncertainty increases demands for dynamic control. While both cognitive stability and flexibility decline during healthy ageing, it is unknown whether aging alters how uncertainty impacts perception and decision-making, and if so, via which neural mechanisms. Here, we assess uncertainty adjustment across the adult lifespan (N = 100; cross-sectional) via behavioral modeling and a theoretically informed set of EEG-, fMRI-, and pupil-based signatures. On the group level, older adults show a broad dampening of uncertainty adjustment relative to younger adults. At the individual level, older individuals whose modulation more closely resembled that of younger adults also exhibit better maintenance of cognitive control. Our results highlight neural mechanisms whose maintenance plausibly enables flexible task-set, perception, and decision computations across the adult lifespan.

Flexibility of intrinsic neural timescales during distinct behavioral states

Recent neuroimaging studies demonstrate a heterogeneity of timescales prevalent in the brain's ongoing spontaneous activity, labeled intrinsic neural timescales (INT). At the same time, neural timescales also reflect stimulus- or task-related activity. The relationship of the INT during the brain's spontaneous activity with their involvement in task states including behavior remains unclear. To address this question, we combined calcium imaging data of spontaneously behaving mice and human electroencephalography (EEG) during rest and task states with computational modeling. We obtained four primary findings: (i) the distinct behavioral states can be accurately predicted from INT, (ii) INT become longer during behavioral states compared to rest, (iii) INT change from rest to task is correlated negatively with the variability of INT during rest, (iv) neural mass modeling shows a key role of recurrent connections in mediating the rest-task change of INT. Extending current findings, our results show the dynamic nature of the brain's INT in reflecting continuous behavior through their flexible rest-task modulation possibly mediated by recurrent connections.

Individual differences in the consistency of neural and behavioural responses to speech sounds

There are documented individual differences among adults in the consistency of speech sound processing, both at neural and behavioural levels. Some adults show more consistent neural responses to speech sounds than others, as measured by an event-related potential called the frequency-following response (FFR); similarly, some adults show more consistent behavioural responses to native speech sounds than others, as measured by two-alternative forced choice (2AFC) and visual analog scaling (VAS) tasks. Adults also differ in how successfully they can perceive non-native speech sounds. Interestingly, it remains unclear whether these differences are related within individuals. In the current study, native English-speaking adults completed native phonetic perception tasks (2AFC and VAS), a non-native (German) phonetic perception task, and an FFR recording session. From these tasks, we derived measures of the consistency of participants' neural and behavioural responses to native speech as well as their non-native perception ability. We then examined the relationships among individual differences in these measures. Analysis of the behavioural measures revealed that more consistent responses to native sounds predicted more successful perception of unfamiliar German sounds. Analysis of neural and behavioural data did not reveal clear relationships between FFR consistency and our phonetic perception measures. This multimodal work furthers our understanding of individual differences in speech processing among adults, and may eventually lead to individualized approaches for enhancing non-native language acquisition in adulthood.

How measurement noise limits the accuracy of brain-behaviour predictions

Major efforts in human neuroimaging strive to understand individual differences and find biomarkers for clinical applications by predicting behavioural phenotypes from brain imaging data. To identify generalisable and replicable brain-behaviour prediction models, sufficient measurement reliability is essential. However, the selection of prediction targets is predominantly guided by scientific interest or data availability rather than psychometric considerations. Here, we demonstrate the impact of low reliability in behavioural phenotypes on out-of-sample prediction performance. Using simulated and empirical data from four large-scale datasets, we find that reliability levels common across many phenotypes can markedly limit the ability to link brain and behaviour. Next, using 5000 participants from the UK Biobank, we show that only highly reliable data can fully benefit from increasing sample sizes from hundreds to thousands of participants. Our findings highlight the importance of measurement reliability for identifying meaningful brain-behaviour associations from individual differences and underscore the need for greater emphasis on psychometrics in future research.

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes.

Beyond the serotonin deficit hypothesis: communicating a neuroplasticity framework of major depressive disorder

The serotonin deficit hypothesis explanation for major depressive disorder (MDD) has persisted among clinicians and the general public alike despite insufficient supporting evidence. To combat rising mental health crises and eroding public trust in science and medicine, researchers and clinicians must be able to communicate to patients and the public an updated framework of MDD: one that is (1) accessible to a general audience, (2) accurately integrates current evidence about the efficacy of conventional serotonergic antidepressants with broader and deeper understandings of pathophysiology and treatment, and (3) capable of accommodating new evidence. In this article, we summarize a framework for the pathophysiology and treatment of MDD that is informed by clinical and preclinical research in psychiatry and neuroscience. First, we discuss how MDD can be understood as inflexibility in cognitive and emotional brain circuits that involves a persistent negativity bias. Second, we discuss how effective treatments for MDD enhance mechanisms of neuroplasticity-including via serotonergic interventions-to restore synaptic, network, and behavioral function in ways that facilitate adaptive cognitive and emotional processing. These treatments include typical monoaminergic antidepressants, novel antidepressants like ketamine and psychedelics, and psychotherapy and neuromodulation techniques. At the end of the article, we discuss this framework from the perspective of effective science communication and provide useful language and metaphors for researchers, clinicians, and other professionals discussing MDD with a general or patient audience.

How can ethology inform the neuroscience of fear, aggression and dominance?

The study of behaviour is dominated by two approaches. On the one hand, ethologists aim to understand how behaviour promotes adaptation to natural contexts. On the other, neuroscientists aim to understand the molecular, cellular, circuit and psychological origins of behaviour. These two complementary approaches must be combined to arrive at a full understanding of behaviour in its natural setting. However, methodological limitations have restricted most neuroscientific research to the study of how discrete sensory stimuli elicit simple behavioural responses under controlled laboratory conditions that are only distantly related to those encountered in real life. Fortunately, the recent advent of neural monitoring and manipulation tools adapted for use in freely behaving animals has enabled neuroscientists to incorporate naturalistic behaviours into their studies and to begin to consider ethological questions. Here, we examine the promises and pitfalls of this trend by describing how investigations of rodent fear, aggression and dominance behaviours are changing to take advantage of an ethological appreciation of behaviour. We lay out current impediments to this approach and propose a framework for the evolution of the field that will allow us to take maximal advantage of an ethological approach to neuroscience and to increase its relevance for understanding human behaviour.

Within-Individual BOLD Signal Variability and its Implications for Task-Based Cognition: A Systematic Review

Within-individual blood oxygen level-dependent (BOLD) signal variability, intrinsic moment-to-moment signal fluctuations within a single individual in specific voxels across a given time course, is a relatively new metric recognized in the neuroimaging literature. Within-individual BOLD signal variability has been postulated to provide information beyond that provided by mean-based analysis. Synthesis of the literature using within-individual BOLD signal variability methodology to examine various cognitive domains is needed to understand how intrinsic signal fluctuations contribute to optimal performance. This systematic review summarizes and integrates this literature to assess task-based cognitive performance in healthy groups and few clinical groups. Included papers were published through October 17, 2022. Searches were conducted on PubMed and APA PsycInfo. Studies eligible for inclusion used within-individual BOLD signal variability methodology to examine BOLD signal fluctuations during task-based functional magnetic resonance imaging (fMRI) and/or examined relationships between task-based BOLD signal variability and out-of-scanner behavioral measure performance, were in English, and were empirical research studies. Data from each of the included 19 studies were extracted and study quality was systematically assessed. Results suggest that variability patterns for different cognitive domains across the lifespan (ages 7-85) may depend on task demands, measures, variability quantification method used, and age. As neuroimaging methods explore individual-level contributions to cognition, within-individual BOLD signal variability may be a meaningful metric that can inform understanding of neurocognitive performance. Further research in understudied domains/populations, and with consistent quantification methods/cognitive measures, will help conceptualize how intrinsic BOLD variability impacts cognitive abilities in healthy and clinical groups.

Behavioural stochastic resonance across the lifespan

Stochastic resonance (SR) is the phenomenon wherein the introduction of a suitable level of noise enhances the detection of subthreshold signals in non linear systems. It manifests across various physical and biological systems, including the human brain. Psychophysical experiments have confirmed the behavioural impact of stochastic resonance on auditory, somatic, and visual perception. Aging renders the brain more susceptible to noise, possibly causing differences in the  SR phenomenon between young and elderly individuals. This study investigates the impact of noise on motion detection accuracy throughout the lifespan, with 214 participants ranging in age from 18 to 82. Our objective was to determine the optimal noise level to induce an SR-like response in both young and old populations. Consistent with existing literature, our findings reveal a diminishing advantage with age, indicating that the efficacy of noise addition progressively diminishes. Additionally, as individuals age, peak performance is achieved with lower levels of noise. This study provides the first insight into how SR changes across the lifespan of healthy adults and establishes a foundation for understanding the pathological alterations in perceptual processes associated with aging.

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer's disease: a magnetoencephalography study

Understanding the nature and onset of neurophysiological changes, and the selective vulnerability of central hub regions in the functional network, may aid in managing the growing impact of Alzheimer's disease on society. However, the precise neurophysiological alterations occurring in the pre-clinical stage of human Alzheimer's disease remain controversial. This study aims to provide increased insights on quantitative neurophysiological alterations during a true early stage of Alzheimer's disease. Using high spatial resolution source-reconstructed magnetoencephalography, we investigated regional and whole-brain neurophysiological changes in a unique cohort of 11 cognitively unimpaired individuals with pathogenic mutations in the presenilin-1 or amyloid precursor protein gene and a 1:3 matched control group (n = 33) with a median age of 49 years. We examined several quantitative magnetoencephalography measures that have been shown robust in detecting differences in sporadic Alzheimer's disease patients and are sensitive to excitation-inhibition imbalance. This includes spectral power and functional connectivity in different frequency bands. We also investigated hub vulnerability using the hub disruption index. To understand how magnetoencephalography measures change as the disease progresses through its pre-clinical stage, correlations between magnetoencephalography outcomes and various clinical variables like age were analysed. A comparison of spectral power between mutation carriers and controls revealed oscillatory slowing, characterized by widespread higher theta (4-8 Hz) power, a lower posterior peak frequency and lower occipital alpha 2 (10-13 Hz) power. Functional connectivity analyses presented a lower whole-brain (amplitude-based) functional connectivity in the alpha (8-13 Hz) and beta (13-30 Hz) bands, predominantly located in parieto-temporal hub regions. Furthermore, we found a significant hub disruption index for (phase-based) functional connectivity in the theta band, attributed to both higher functional connectivity in 'non-hub' regions alongside a hub disruption. Neurophysiological changes did not correlate with indicators of pre-clinical disease progression in mutation carriers after multiple comparisons correction. Our findings provide evidence that oscillatory slowing and functional connectivity differences occur before cognitive impairment in individuals with autosomal dominant mutations leading to early onset Alzheimer's disease. The nature and direction of these alterations are comparable to those observed in the clinical stages of Alzheimer's disease, suggest an early excitation-inhibition imbalance, and fit with the activity-dependent functional degeneration hypothesis. These insights may prove useful for early diagnosis and intervention in the future.

Daily Dynamics of Resting-State Electroencephalographic Theta and Gamma Fluctuations Are Associated With Cognitive Performance in Healthy Aging

OBJECTIVES

Healthy age-related cognitive changes are highly heterogeneous across individuals. This variability is increasingly explained through the lens of spontaneous fluctuations of brain activity, now considered a powerful index of age-related changes. However, brain activity is a biological process modulated by circadian rhythms, and how these fluctuations evolve throughout the day is under investigation.

METHODS

We analyzed data from 101 healthy late middle-aged participants from the Cognitive Fitness in Aging study (68 women and 33 men; aged 50-69 years). Participants completed 5 electroencephalographic (EEG) recordings of spontaneous resting-state activity on the same day. We used weighted phase-lag index (wPLI) analyses as an index of the functional synchrony between brain regions couplings, and we computed daily global PLI fluctuation rates of the 5 recordings to assess the association with cognitive performance and β-amyloid and tau/neuroinflammation pathological markers.

RESULTS

We found that theta and gamma daily fluctuations in the salience-control executive internetwork (SN-CEN) are associated with distinct mechanisms underlying cognitive heterogeneity in aging. Higher levels of SN-CEN theta daily fluctuations appear to be deleterious for memory performance and were associated with higher tau/neuroinflammation rates. In contrast, higher levels of gamma daily fluctuations are positively associated with executive performance and were associated with lower rate of β-amyloid deposition.

DISCUSSION

Thus, accounting for daily EEG fluctuations of brain activity contributes to a better understanding of subtle brain changes underlying individuals' cognitive performance in healthy aging. Results also provide arguments for considering the time of day when assessing cognition for old adults in a clinical context.

Aperiodic neural activity reflects metacontrol in task-switching

"Metacontrol" refers to the ability to find the right balance between more persistent and more flexible cognitive control styles, depending on task demands. Recent research on tasks involving response conflict regulation indicates a consistent link between aperiodic EEG activity and task conditions that demand a more or less persistent control style. In this study, we explored whether this connection between metacontrol and aperiodic activity also applies to cognitive flexibility. We examined EEG and behavioral data from two separate samples engaged in a task-switching paradigm, allowing for an internal replication of our findings. Both studies revealed that aperiodic activity significantly decreased during task switching compared to task repetition. Our results support the predictions of metacontrol theory but contradict those of traditional control theories which would have predicted the opposite pattern of results. We propose that aperiodic activity observed in EEG signals serves as a valid indicator of dynamic neuroplasticity in metacontrol, suggesting that truly adaptive metacontrol does not necessarily bias processing towards persistence in response to every control challenge, but chooses between persistence and flexibility biases depending on the nature of the challenge.

Multiple Brain Activation Patterns for the Same Perceptual Decision-Making Task

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.

Suppressing the Maintenance of Information in Working Memory Alters Long-term Memory Traces

The sensory recruitment hypothesis conceptualizes information in working memory as being activated representations of information in long-term memory. Accordingly, changes made to an item in working memory would be expected to influence its subsequent retention. Here, we tested the hypothesis that suppressing information from working memory, which can reduce short-term access to that information, may also alter its long-term neural representation. We obtained fMRI data (n = 25; 13 female / 12 male participants) while participants completed a working memory removal task with scene images as stimuli, followed by a final surprise recognition test of the examined items. We applied a multivariate pattern analysis to the data to quantify the engagement of suppression on each trial, to track the contents of working memory during suppression, and to assess representational changes afterward. Our analysis confirms previous reports that suppression of information in working memory involves focused attention to target and remove unwanted information. Furthermore, our findings provide new evidence that even a single dose of suppression of an item in working memory can (if engaged with sufficient strength) produce lasting changes in its neural representation, particularly weakening the unique, item-specific features, which leads to forgetting. Our study sheds light on the underlying mechanisms that contribute to the suppression of unwanted thoughts and highlights the dynamic interplay between working memory and long-term memory.

Reassessing the Functional Significance of Blood Oxygen Level Dependent Signal Variability

BOLD signal variability (SDBOLD) has emerged as a unique measure of the adaptive properties of neural systems that facilitate fast, stable responding, based on claims that SDBOLD is independent of mean BOLD signal (meanBOLD) and is a powerful predictor of behavioral performance. We challenge these two claims. First, the apparent independence of SDBOLD and meanBOLD may reflect the presence of deactivations; we hypothesize that although SDBOLD may not be related to raw meanBOLD, it will be linearly related to "absolute" meanBOLD. Second, the observed relationship between SDBOLD and performance may be an artifact of using fixed-length trials longer than RTs. Such designs provide opportunities to toggle between on- and off-task states, and fast responders likely engage in more frequent state-switching, thereby artificially elevating SDBOLD. We hypothesize that SDBOLD will be higher and more strongly related to performance when using such fixed-length trials relative to self-paced trials that terminate upon a response. We test these two hypotheses in an fMRI study using blocks of fixed-length or self-paced trials. Results confirmed both hypotheses: (1) SDBOLD was robustly related with absolute meanBOLD, and (2) toggling between on- and off-task states during fixed-length trials reliably contributed to SDBOLD. Together, these findings suggest that a reappraisal of the functional significance of SDBOLD as a unique marker of cognitive performance is warranted.

Abrupt and spontaneous strategy switches emerge in simple regularised neural networks

Humans sometimes have an insight that leads to a sudden and drastic performance improvement on the task they are working on. Sudden strategy adaptations are often linked to insights, considered to be a unique aspect of human cognition tied to complex processes such as creativity or meta-cognitive reasoning. Here, we take a learning perspective and ask whether insight-like behaviour can occur in simple artificial neural networks, even when the models only learn to form input-output associations through gradual gradient descent. We compared learning dynamics in humans and regularised neural networks in a perceptual decision task that included a hidden regularity to solve the task more efficiently. Our results show that only some humans discover this regularity, and that behaviour is marked by a sudden and abrupt strategy switch that reflects an aha-moment. Notably, we find that simple neural networks with a gradual learning rule and a constant learning rate closely mimicked behavioural characteristics of human insight-like switches, exhibiting delay of insight, suddenness and selective occurrence in only some networks. Analyses of network architectures and learning dynamics revealed that insight-like behaviour crucially depended on a regularised gating mechanism and noise added to gradient updates, which allowed the networks to accumulate "silent knowledge" that is initially suppressed by regularised gating. This suggests that insight-like behaviour can arise from gradual learning in simple neural networks, where it reflects the combined influences of noise, gating and regularisation. These results have potential implications for more complex systems, such as the brain, and guide the way for future insight research.

Force Fluctuations During Role-Differentiated Bimanual Movements Reflect Cognitive Impairments in Older Adults: A Cohort Sequential Study

During role-differentiated bimanual movements (RDBM), an object is typically stabilized with 1 hand and manipulated with the other. RDBM require coupling both hands for coordinated action (achieved through interhemispheric connections), but also inhibition of crosstalk to avoid involuntary movements in the stabilizing hand. We investigated how healthy cognitive aging and mild cognitive impairments (MCI) affect force stabilization during an RDBM in a cohort sequential study design with up to 4 measurement points over 32 months. In total, 132 older adults (>80 years) participated in this study, 77 were cognitively healthy individuals (CHI) and 55 presented with MCI. Participants performed a visuomotor bimanual force-tracking task. They either produced a constant force with both hands (bimanual constant) or a constant force with 1 and an alternating force with the other hand (role-differentiated). We investigated force fluctuations of constant force production using the coefficient of variation (CV), detrended fluctuation analysis (DFA), and sample entropy (SEn). Results showed higher CV and less complex variability structure (higher DFA and lower SEn) during the role-differentiated compared to the bimanual constant task. Furthermore, CHI displayed a more complex variability structure during the bimanual constant, but a less complex structure during the role-differentiated task than MCI. Interestingly, this complexity reduction was more pronounced in CHI than MCI individuals, suggesting different changes in the control mechanisms. Although understanding these changes requires further research, potential causes might be structural deteriorations leading to less efficient (intra- and interhemispheric) networks because of MCI, or an inability to appropriately divert the focus of attention.

Evaluating and Comparing Measures of Aperiodic Neural Activity

Neuro-electrophysiological recordings contain prominent aperiodic activity - meaning irregular activity, with no characteristic frequency - which has variously been referred to as 1/f (or 1/f-like activity), fractal, or 'scale-free' activity. Previous work has established that aperiodic features of neural activity is dynamic and variable, relating (between subjects) to healthy aging and to clinical diagnoses, and also (within subjects) tracking conscious states and behavioral performance. There are, however, a wide variety of conceptual frameworks and associated methods for the analyses and interpretation of aperiodic activity - for example, time domain measures such as the autocorrelation, fractal measures, and/or various complexity and entropy measures, as well as measures of the aperiodic exponent in the frequency domain. There is a lack of clear understanding of how these different measures relate to each other and to what extent they reflect the same or different properties of the data, which makes it difficult to synthesize results across approaches and complicates our overall understanding of the properties, biological significance, and demographic, clinical, and behavioral correlates of aperiodic neural activity. To address this problem, in this project we systematically survey the different approaches for measuring aperiodic neural activity, starting with an automated literature analysis to curate a collection of the most common methods. We then evaluate and compare these methods, using statistically representative time series simulations. In doing so, we establish consistent relationships between the measures, showing that much of what they capture reflects shared variance - though with some notable idiosyncrasies. Broadly, frequency domain methods are more specific to aperiodic features of the data, whereas time domain measures are more impacted by oscillatory activity. We extend this analysis by applying the measures to a series of empirical EEG and iEEG datasets, replicating the simulation results. We conclude by summarizing the relationships between the multiple methods, emphasizing opportunities for reexamining previous findings and for future work.

EEG spectral attractors identify a geometric core of brain dynamics

Multidimensional reconstruction of brain attractors from electroencephalography (EEG) data enables the analysis of geometric complexity and interactions between signals in state space. Utilizing resting-state data from young and older adults, we characterize periodic (traditional frequency bands) and aperiodic (broadband exponent) attractors according to their geometric complexity and shared dynamical signatures, which we refer to as a geometric cross-parameter coupling. Alpha and aperiodic attractors are the least complex, and their global shapes are shared among all other frequency bands, affording alpha and aperiodic greater predictive power. Older adults show lower geometric complexity but greater coupling, resulting from dedifferentiation of gamma activity. The form and content of resting-state thoughts were further associated with the complexity of attractor dynamics. These findings support a process-developmental perspective on the brain's dynamic core, whereby more complex information differentiates out of an integrative and global geometric core.

Variation in moment-to-moment brain state engagement changes across development and contributes to individual differences in executive function

Neural variability, or variation in brain signals, facilitates dynamic brain responses to ongoing demands. This flexibility is important during development from childhood to young adulthood, a period characterized by rapid changes in experience. However, little is known about how variability in the engagement of recurring brain states changes during development. Such investigations would require the continuous assessment of multiple brain states concurrently. Here, we leverage a new computational framework to study state engagement variability (SEV) during development. A consistent pattern of SEV changing with age was identified across cross-sectional and longitudinal datasets (N>3000). SEV developmental trajectories stabilize around mid-adolescence, with timing varying by sex and brain state. SEV successfully predicts executive function (EF) in youths from an independent dataset. Worse EF is further linked to alterations in SEV development. These converging findings suggest SEV changes over development, allowing individuals to flexibly recruit various brain states to meet evolving needs.

Beta-band neural variability reveals age-related dissociations in human working memory maintenance and deletion

Maintaining and removing information in mind are 2 fundamental cognitive processes that decline sharply with age. Using a combination of beta-band neural oscillations, which have been implicated in the regulation of working memory contents, and cross-trial neural variability, an undervalued property of brain dynamics theorized to govern adaptive cognitive processes, we demonstrate an age-related dissociation between distinct working memory functions-information maintenance and post-response deletion. Load-dependent decreases in beta variability during maintenance predicted memory performance of younger, but not older adults. Surprisingly, the post-response phase emerged as the predictive locus of working memory performance for older adults, with post-response beta variability correlated with memory performance of older, but not younger adults. Single-trial analysis identified post-response beta power elevation as a frequency-specific signature indexing memory deletion. Our findings demonstrate the nuanced interplay between age, beta dynamics, and working memory, offering valuable insights into the neural mechanisms of cognitive decline in agreement with the inhibition deficit theory of aging.

Abnormal resting-state EEG phase dynamics distinguishes major depressive disorder and bipolar disorder

Changes in EEG have been reported in both major depressive disorder (MDD) and bipolar disorder (BD). Specifically, power changes in EEG alpha and theta frequency bands during rest and task are known in both disorders. This leaves open whether there are changes in yet another component of the electrophysiological EEG signal, namely phase-related processes that may allow for distinguishing MDD and BD. For that purpose, we investigate EEG-based spontaneous phase in the resting state of MDD, BD and healthy controls. Our main findings show: (i) decreased spontaneous phase variability in frontal theta of both MDD and BD compared to HC; (ii) decreased spontaneous phase variability in central-parietal alpha in MDD compared to both BD and HC; (iii) increased delays or lags of alpha phase cycles in MDD (but not in BD), which (iv) correlate with the decreased phase variability in MDD. Together, we show similar (decreased frontal theta variability) and distinct (decreased central-parietal alpha variability with increased lags or delays) findings in the spontaneous phase dynamics of MDD and BD. This suggests potential relevance of theta and alpha phase dynamics in distinguishing MDD and BD in clinical differential-diagnosis.

Adolescent-to-adult gains in cognitive flexibility are adaptively supported by reward sensitivity, exploration, and neural variability

Cognitive flexibility exhibits dynamic changes throughout development, with different forms of flexibility showing dissociable developmental trajectories. In this review, we propose that an adolescent-specific mode of flexibility in the face of changing environmental contingencies supports the emergence of adolescent-to-adult gains in cognitive shifting efficiency. We first describe how cognitive shifting abilities monotonically improve from childhood to adulthood, accompanied by increases in brain state flexibility, neural variability, and excitatory/inhibitory balance. We next summarize evidence supporting the existence of a dopamine-driven, adolescent peak in flexible behavior that results in reward seeking, undirected exploration, and environmental sampling. We propose a neurodevelopmental framework that relates these adolescent behaviors to the refinement of neural phenotypes relevant to mature cognitive flexibility, and thus highlight the importance of the adolescent period in fostering healthy neurocognitive trajectories.

The central role of the individual in the history of brains

Every species' brain, body and behavior is shaped by the contingencies of their evolutionary history; these exert pressures that change their developmental trajectories. There is, however, another set of contingencies that shape us and other animals: those that occur during a lifetime. In this perspective piece, we show how these two histories are intertwined by focusing on the individual. We suggest that organisms--their brains and behaviors--are not solely the developmental products of genes and neural circuitry but individual centers of action unfolding in time. To unpack this idea, we first emphasize the importance of variation and the central role of the individual in biology. We then go over "errors in time" that we often make when comparing development across species. Next, we reveal how an individual's development is a process rather than a product by presenting a set of case studies. These show developmental trajectories as emerging in the contexts of the "the actual now" and "the presence of the past". Our consideration reveals that individuals are slippery-they are never static; they are a set of on-going, creative activities. In light of this, it seems that taking individual development seriously is essential if we aspire to make meaningful comparisons of neural circuits and behavior within and across species.

Interindividual aperiodic resting-state EEG activity predicts cognitive-control styles

The ability to find the right balance between more persistent and more flexible cognitive-control styles is known as "metacontrol." Recent findings suggest a relevance of aperiodic EEG activity and task conditions that are likely to elicit a specific metacontrol style. Here we investigated whether individual differences in aperiodic EEG activity obtained off-task (during resting state) predict individual cognitive-control styles under task conditions that pose different demands on metacontrol. We analyzed EEG resting-state data, task-EEG, and behavioral outcomes from a sample of N = 65 healthy participants performing a Go/Nogo task. We examined aperiodic activity as indicator of "neural noise" in the EEG power spectrum, and participants were assigned to a high-noise or low-noise group according to a median split of the exponents obtained for resting state. We found that off-task aperiodic exponents predicted different cognitive-control styles in Go and Nogo conditions: Overall, aperiodic exponents were higher (i.e., noise was lower) in the low-noise group, who however showed no difference between Go and Nogo trials, whereas the high-noise group exhibited significant noise reduction in the more persistence-heavy Nogo condition. This suggests that trait-like biases determine the default cognitive-control style, which however can be overwritten or compensated for under challenging task demands. We suggest that aperiodic activity in EEG signals represents valid indicators of highly dynamic arbitration between metacontrol styles, representing the brain's capability to reorganize itself and adapt its neural activity patterns to changing environmental conditions.

Protocol and preregistration for the CODEC project: measuring, modelling and mechanistically understanding the nature of cognitive variability in early childhood

BACKGROUND

Children's cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning. The relative dearth of empirical work on variability, historically limited due to a lack of suitable data and quantitative methodology, has left crucial questions unanswered, which the CODEC (COgnitive Dynamics in Early Childhood) study aims to address.

METHOD

The CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a 'burst' week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms.

DISCUSSION

CODEC's unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC's longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal 'burst' design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children.

TRIAL REGISTRATION

ClinicalTrials.gov - NCT06330090.

Reliability of variability and complexity measures for task and task-free BOLD fMRI

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

Beyond task response-Pre-stimulus activity modulates contents of consciousness

The current discussion on the neural correlates of the contents of consciousness (NCCc) focuses mainly on the post-stimulus period of task-related activity. This neglects the substantial impact of the spontaneous or ongoing activity of the brain as manifest in pre-stimulus activity. Does the interaction of pre- and post-stimulus activity shape the contents of consciousness? Addressing this gap in our knowledge, we review and converge two recent lines of findings, that is, pre-stimulus alpha power and pre- and post-stimulus alpha trial-to-trial variability (TTV). The data show that pre-stimulus alpha power modulates post-stimulus activity including specifically the subjective features of conscious contents like confidence and vividness. At the same time, alpha pre-stimulus variability shapes post-stimulus TTV reduction including the associated contents of consciousness. We propose that non-additive rather than merely additive interaction of the internal pre-stimulus activity with the external stimulus in the alpha band is key for contents to become conscious. This is mediated by mechanisms on different levels including neurophysiological, neurocomputational, neurodynamic, neuropsychological and neurophenomenal levels. Overall, considering the interplay of pre-stimulus intrinsic and post-stimulus extrinsic activity across wider timescales, not just evoked responses in the post-stimulus period, is critical for identifying neural correlates of consciousness. This is well in line with both processing and especially the Temporo-spatial theory of consciousness (TTC).

Linking Cognitive Integrity to Working Memory Dynamics in the Aging Human Brain

Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and MEG recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuospatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/nonmatch decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared with younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Spike Reliability is Cell-Type Specific and Shapes Excitation and Inhibition in the Cortex

Neurons encode information in the highly variable spiking activity of neuronal populations, so that different repetitions of the same stimulus can generate action potentials that vary significantly in terms of the count and timing. How does spiking variability originate, and does it have a functional purpose? Leveraging the Allen Institute cell types dataset, we relate the spiking reliability of cortical neurons in-vitro during the intracellular injection of current resembling synaptic inputs to their morphologic, electrophysiologic, and transcriptomic classes. Our findings demonstrate that parvalbumin+ (PV) interneurons, a subclass of inhibitory neurons, show high reliability compared to other neuronal subclasses, particularly excitatory neurons. Through computational modeling, we predict that the high reliability of PV interneurons allows for strong and precise inhibition in downstream neurons, while the lower reliability of excitatory neurons allows for integrating multiple synaptic inputs leading to a spiking rate code. These findings illuminate how spiking variability in different neuronal classes affect information propagation in the brain, leading to precise inhibition and spiking rate codes.