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Westbrook, A.

Publications and source records attributed to Westbrook, A..

15 recordsLinked to original sources

Task-evoked flexibility of intrinsic neural timescales is hierarchically organized, behaviorally relevant, and refined across two developmental stages

Cortical regions integrate temporal information over different timescales at rest, ranging from fast in sensorimotor regions to slow in the association cortex. Here, we study how far regions adapt these timescales to meet task demands, as well as how this \emph{timescale flexibility} is organized across the cortical hierarchy, and how it develops in humans. We quantified rest-to-task changes in intrinsic neural timescales, referred to as \emph{timescale deflections}, using fMRI data from four independent datasets totaling 1,712 participants aged 8--64 years. We found that timescales reduced under cognitive load in all four datasets, most strongly in association cortex, which partially flattened the resting-state timescale hierarchy. Individual differences in timescale deflections were organized along the sensorimotor-association (S-A) axis and tracked cognitive performance, both in adults and in youth. Next, in youth, deflections followed a two-stage developmental course: a generalized reduction throughout childhood, followed by a selective expansion in association cortex from mid-adolescence. Furthermore, the timing of these stages aligned with cortical gradients of inhibitory gene expression and intracortical myelin content, both features implicated in the opening and closing of critical periods of neural plasticity. Overall, our results demonstrate that task-evoked timescale flexibility is hierarchically organized, behaviorally and neurobiologically relevant, and refined across adolescence.

neuroscience↗

From criticality to cognitive effort: scale-invariant EEG dynamics supporting cognitive flexibility are suppressed by effort

The critical brain hypothesis contends that brains operate near a phase transition where excitation and inhibition are balanced, enabling neural dynamics to rapidly adapt and reorganize for cognitive demands. Allocating control resources to maintain stable task representations likely shifts brains away from criticality. Here, we test whether proximity to criticality indexes the balance between flexible adaptation and effortful task engagement. To do so, we adapt a time-resolved measure of scale invariance in EEG amplitude fluctuations (d2), capable of quantifying distance-to-criticality under non-stationary conditions - as during cognitive tasks. We benchmark our measure using ground-truth simulations of a neural mass model and show that d2 is lowest when excitation and inhibition are balanced. Next, we apply d2 to data collected during a task-switching paradigm and find that more demanding trials increased deviation from criticality, whereas greater flexibility, faster responses, and higher accuracy occurred closer to criticality. These effects were region-specific: deviation at posterior electrodes predicted worse performance, while deviations at frontal midline electrodes predicted better performance. Together, these results suggest that deviations from criticality reflect both cognitive load and effort exertion, highlighting EEG amplitude scale-invariance as a sensitive marker of adaptive neural dynamics under cognitive demand.

neuroscience↗

Brain criticality emerges with developmental shifts in frequency-specific excitation-inhibition balance

Adolescent brain maturation involves structural changes effecting a shift in excitation/inhibition (E/I) balance, yet the functional implications of these changes remain unclear. One implication is a shift with respect to criticality. Adult brains, at rest, operate near a critical phase transition - at the boundary between an active, excitation-dominant phase, and an absorbing, inhibition-dominant phase. Special properties emerge when neural systems are balanced at criticality, including maximal susceptibility to perturbation, dynamic range, and information transmission. Thus, a clear picture of how adolescent brain maturation affects E/I balance and criticality is needed to understand how maturational processes shape cognition. Here, we leverage the dynamical properties of longitudinally-collected resting-state EEG recordings during N = 310 sessions from 169 healthy human participants ranging in age from 10 to 33 years old to quantify E/I and proximity to criticality. We find that adult brains operate closer to criticality, including spectrally-widespread increases in long-range temporal correlations and amplitude bistability. We also find band-specific changes in excitation versus inhibition whereby the mechanisms driving low-frequency ({theta} to ) oscillations shift towards lower E/I-possibly because of increasing inhibition-while the mechanisms driving high-frequency ({gamma}) oscillations shift towards higher E/I, possibly because of decreasing inhibition. Opening eyes shifts brains towards lower E/I, and these state-dependent shifts are larger in adults. We simulate developmental effects with a neural mass model of coupled excitatory and inhibitory neurons providing a parsimonious account of how changes in brain dynamics could arise as a function of changes in local connectivity of excitatory and inhibitory neurons. Results indicate developmental movement towards criticality, and greater adaptability to state-specific demands, through adolescence to adulthood, reflecting changes in E/I balance with implications for cognitive development.

neuroscience↗

Aberrant E-I Balance and Brain Criticality in Major Depressive Disorder

Brain criticality and complexity are increasingly recognized as promising biomarkers for psychiatric disorders. In Major Depressive Disorder (MDD), disordered neural dynamics have been reported, but their nature and consistency remain incompletely understood. Here, we study brain criticality, excitation-inhibition (E/I) balance, combined excitation- inhibition strength (E+I), and complexity of brain dynamics associated with MDD. Using resting-state EEG from 183 patients with MDD and 133 healthy controls (HC), we identified disruptions of critical dynamics and excitation-inhibition balance which discriminate groups. We found that amplitude bistability is lower, and long-range temporal correlations are weaker in MDD, implying deviation from criticality. Excitation-inhibition metrics show frequency-specific alterations in MDD. Estimates of excitation-inhibition ratios (E/I) derived from the statistical properties of amplitude fluctuations show higher values in HC than MDD in the {theta} band, indicating relative over-excitation, and lower values in the {gamma} band, indicating relative over-inhibition. An excitation- inhibition strength index reflecting combined excitatory and inhibitory drive (E+I), was decreased in {theta} through {beta} bands and increased in {gamma} in MDD. Collectively, excitation-inhibition measures suggest decreased inhibitory drive in the mechanisms underlying {theta} oscillations in MDD and increased inhibitory drive in the mechanisms underlying {gamma} oscillations. Classification using least absolute shrinkage and selection operator (LASSO) regression achieved high accuracy and the predictive feature set includes measures of criticality, E/I ratios, and combined E+I strength. These findings elucidate pathological alterations of brain dynamics in MDD and define a complex system fingerprint, supporting the development of biomarkers for diagnosis and treatment.

neuroscience↗

E/I ratio and net E+I strength are differentially affected across brain disorders

Neuronal oscillations wax and wane near criticality, switching between high- and low-power states. However, the link between this bistability and excitation-inhibition balance is not well understood. Using a computational model, we demonstrate that bistability is influenced by two key factors: the excitatory-inhibitory (E/I) ratio and the combined strength of excitatory and inhibitory activity (E+I). We developed algorithms to infer both metrics from electrophysiological data and validated them in vivo. In intracranial recordings from epilepsy patients, both E/I and E+I increased during seizures, while interictally, E+I remained elevated in seizure zones as E/I decreased--consistent with compensatory inhibition. Extending the framework to scalp EEG from eight disorders, we found that Alzheimers disease is better classified by E/I changes, whereas autism spectrum disorder and ADHD are distinguished by E+I alterations. Our findings highlight the importance of bistability in understanding EI regulation and offer handles for diagnostic and treatment decision support.

neuroscience↗

Striatal Dopamine Can Enhance Learning, Both Fast and Slow, and Also Make it Cheaper

Associations can be learned incrementally, via reinforcement learning (RL), or stored instantly in working memory (WM). While WM is fast, it is also capacity-limited and effortful. Striatal dopamine may promote RL plasticity, and WM, by facilitating updating and effort exertion. Yet, prior studies have failed to distinguish between dopamines effects on RL versus WM. N = 100 participants completed a paradigm isolating these systems in a double-blind study measuring dopamine synthesis with [18F]-FDOPA imaging and manipulating dopamine with methylphenidate and sulpiride. Learning is enhanced among high synthesis capacity individuals and by methylphenidate, but impaired by sulpiride. Methylphenidate also blunts effort cost learning. Computational modeling reveals that individuals with high dopamine synthesis rely more on WM, while methylphenidate boosts their RL rates. The D2 antagonist sulpiride reduces accuracy due to diminished WM involvement and faster WM decay. We conclude that dopamine enhances both slow RL, and fast WM, by promoting plasticity and reducing effort sensitivity. These results also highlight the need to control for dopamines effects on WM when testing its effects on RL.

animal behavior and cognition↗

Expected Costs of Mental Efforts are Updated When People Exert Effort, not by Prospective Information

To understand why people avoid mental effort, it is crucial to reveal the mechanisms by which we learn and decide about mental effort costs. This study investigated whether mental effort cost learning aligns with temporal-difference (TD) learning or alternative mechanisms. Model-based fMRI analyses showed no correlation between cost prediction errors (CPEs) and activity in the dorsomedial frontal cortex/dorsal anterior cingulate cortex (dmFC/dACC) or striatum at the time of a fully informative effort cue about upcoming effort demands, contradicting the TD hypothesis. Instead, CPEs correlate with dmFC/dACC (positively) and caudate (negatively) activity at effort completion. Furthermore, only activity patterns at effort completion predict subsequent choices. These results show that decision policies are updated retrospectively at effort completion, updating expected costs with prediction error between experienced effort and prior expectations, demonstrating mental effort cost learning is retrospective, and imply that adaptive learning of mental effort cost does not follow canonical TD learning. Significance StatementUnderstanding how people learn about mental effort costs is essential for advancing theories of motivation and cognitive control. However, the algorithms supporting such learning remain unclear. This study addressed this gap and found that temporal-difference learning, commonly used to explain reward learning, could not account for how people learn about effort. Instead, decision policies were updated retrospectively at effort completion, based on a prediction error between experienced effort and prior expectations. These findings reveal that mental effort cost learning is fundamentally retrospective and imply that it relies on mechanisms distinct from canonical temporal-difference learning.

neuroscience↗

Striatal dopamine synthesis capacity reflects smartphone social activity

Striatal dopamine has been implicated in social behavior across humans, rodents, and non-human primates in artificial laboratory settings with highly-practiced tasks and fixed reward contingencies. Whether striatal dopamine drives naturalistic, spontaneous social behavior remains unclear. Here, we leverage day-to-day logs of unconstrained smartphone behavior and establish a novel link between smartphone social activity and individual differences in striatal dopamine synthesis capacity using [18F]-DOPA PET in (N=22) healthy adult humans. We find a strong relationship such that a higher proportion of social app interactions correlates with lower dopamine synthesis capacity in the bi-lateral putamen. Permutation tests and penalized regressions provide evidence that this link between dopamine synthesis capacity and social versus non-social smartphone taps is specific. These observations provide a key empirical grounding for current speculations about dopamines role in digital social behavior.

neuroscience↗

Baseline dopamine predicts individual variation in methylphenidate's effects on cognitive motivation

The cognitive enhancing effects of methylphenidate are well established, but the mechanisms remain unclear. We recently demonstrated that methylphenidate boosts cognitive motivation by enhancing the weight on the benefits of a cognitive task in a manner that depended on striatal dopamine. Here we considered the complementary hypothesis that methylphenidate might also act by changing the weight on the opportunity cost of a cognitive task. To this end, fifty healthy participants (25 women) completed a novel cognitive effort discounting task that was sensitive to opportunity cost, and required choices between task and leisure. They were tested on methylphenidate, sulpiride or placebo and also underwent an [18F]DOPA PET scan to quantify baseline dopamine synthesis capacity. Methylphenidate boosted choices of cognitive effort over leisure across the group, and this effect was greatest in participants with more striatal dopamine at baseline. The effects of sulpiride did not reach significance. This study strengthens the motivational account of methylphenidates effects on cognition and suggests that methylphenidate reduces the cost of mental labor by increasing striatal dopamine.

neuroscience↗

Dopamine Promotes Cognitive Effort by Biasing the Benefits Versus Costs of Cognitive Work

Stimulants like methylphenidate are increasingly used for cognitive enhancement, but precise mechanisms are unknown. We found that methylphenidate boosts willingness to expend cognitive effort by altering the benefit-to-cost ratio of cognitive work. Willingness to expend effort was greater for participants with higher striatal dopamine synthesis capacity, while methylphenidate and sulpiride - a selective D2 receptor antagonist - increased cognitive motivation more for participants with lower synthesis capacity. A sequential sampling model informed by momentary gaze revealed that decisions to expend effort are related to amplification of benefit-versus-cost information attended early in the decision process, while the effect of benefits is strengthened with higher synthesis capacity and by methylphenidate. These findings demonstrate that methylphenidate boosts the perceived benefits-versus-costs of cognitive effort by modulating striatal dopamine signaling. One Sentence SummaryStriatal dopamine increases cognitive effort by respectively amplifying and attenuating the subjective benefits and costs of cognitive control.

neuroscience↗

Quantifying the cost of cognitive stability and flexibility

Exerting cognitive control is known to carry a subjective effort cost and people are generally biased to avoid it. Recent theorizing suggests that the cost of cognitive effort serves as a motivational signal to bias people away from excessive focusing and towards more cognitive flexibility. We asked whether the effort cost of stable distractor resistance is higher than that of flexible updating of working memory representations. We tested this prediction by using (i) a delayed response paradigm in which we manipulate demands for distractor resistance and flexible updating, as well as (ii) a subsequent cognitive effort discounting paradigm that allows us to quantify subjective effort costs. We demonstrate, in two different samples (28 and 62 participants) that participants discount tasks both high in distractor resistance and flexible updating when comparing with taking a break. As predicted, when directly contrasting distractor resistance and flexible updating the subjective cost of performing a task requiring distractor resistance is higher than that requiring flexible updating.

neuroscience↗

The Subjective Value of Cognitive Effort is Encoded by a Domain-General Valuation Network

Cognitive control is necessary for goal-directed behavior, yet people treat control as costly, discounting goal value by cognitive demands in a similar manner as they would for delayed or risky outcomes. It is unclear, however, whether a putatively domain-general valuation network implicated in other cost domains also encodes the subjective value (SV) of cognitive effort. Here, we demonstrate that a valuation network, centered on the ventromedial prefrontal cortex and ventral striatum, also encodes SV during cognitive effort-based decision-making. We doubly dissociate this network from a primarily frontoparietal network recruited as a function of decision difficulty. We also find evidence that SV signals predict choice and are influenced by state and trait motivation, including sensitivity to reward and anticipated task performance. These findings unify cognitive effort with other cost domains, and inform physiological mechanisms of SV representations underlying the willingness to expend cognitive effort.

neuroscience↗

Distinct timescales of RNA regulators enable the construction of a genetic pulse generator

To build complex genetic networks with predictable behaviours, synthetic biologists use libraries of modular parts that can be characterized in isolation and assembled together to create programmable higher-order functions. Characterization experiments and computational models for gene regulatory parts operating in isolation are routinely employed to predict the dynamics of interconnected parts and guide the construction of new synthetic devices. Here, we individually characterize two modes of RNA-based transcriptional regulation, using small transcription activating RNAs (STARs) and CRISPR interference (CRISPRi), and show how their distinct regulatory timescales can be used to engineer a composed feedforward loop that creates a pulse of gene expression. We use a cell-free transcription-translation system (TXTL) to rapidly characterize the system, and we apply Bayesian inference to extract kinetic parameters for an ODE-based mechanistic model. We then demonstrate in simulation and verify with TXTL experiments that the simultaneous regulation of a single gene target with STARs and CRISPRi leads to a pulse of gene expression. Our results suggest the modularity of the two regulators in an integrated genetic circuit, and we anticipate that construction and modeling frameworks that can leverage this modularity will become increasingly important as synthetic circuits increase in complexity.

synthetic biology↗

Computational design of Small Transcription Activating RNAs (STARs) for versatile and dynamic gene regulation

A longstanding goal of synthetic biology has been the programmable control of cellular functions. Central to this goal is the creation of versatile regulatory toolsets that allow for programmable control of gene expression. Of the many regulatory molecules available, RNA regulators offer the intriguing possibility of de novo design - allowing for the bottom-up molecular-level design of genetic control systems. Here we present a computational design approach for the creation of a bacterial regulator called Small Transcription Activating RNAs (STARs) and create a library of high-performing and orthogonal STARs that achieve up to [~]9000-fold gene activation. We then demonstrate the versatility of RNA-based transcription control by showing the broad utility of STARs - from acting synergistically with existing constitutive and inducible regulators, to reprogramming cellular phenotypes and controlling multigene metabolic pathway expression. Finally, we combine these new STARs with themselves and CRISPRi transcriptional repressors to deliver new types of RNA-based genetic circuitry that allow for sophisticated and temporal control of gene expression.

synthetic biology↗

Comparative genomics of beetle-vectored fungal pathogens reveals a reduction in genome size and independent evolution of pathogenicity of two tree pathogens

O_LIGeosmithia morbida is an emerging fungal pathogen which serves as a paradigm for examining the evolutionary processes behind pathogenicity because it is one of two known pathogens within a genus of mostly saprophytic, beetle-associated, fungi. This pathogen causes thousand cankers disease in black walnut trees and is vectored into the host via the walnut twig beetle. G. morbida was first detected in western US and currently threatens the timber industry concentrated in eastern US.\nC_LIO_LIWe sequenced the genomes of G. morbida and two non-pathogenic Geosmithia species and compared these species to other fungal pathogens and nonpathogens to identify genes under positive selection in G. morbida that may be associated with pathogenicity.\nC_LIO_LIG. morbida possesses one of the smallest genomes among the fungal species observed in this study, and one of the smallest fungal pathogen genomes to date. The enzymatic profile is this pathogen is very similar to its relatives.\nC_LIO_LIOur findings indicate that genome reduction is an important adaptation during the evolution of a specialized lifestyle in fungal species that occupy a specific niche, such as beetle vectored tree pathogens. We also present potential genes under selection in G. morbida that could be important for adaptation to a pathogenic lifestyle.\nC_LI

evolutionary biology↗