Risky Business: The Circuits that Impact Stress-Induced Decision-Making.

How does stress promote risky decision-making? Friedman et al. find that stress disrupts inhibition of striatal circuits by prefrontal cortex, rendering animals insensitive to potential losses. This may help explain how stress contributes to substance abuse and how it can disinhibit automatic behaviors, such as tics in Tourette syndrome.

Efficient production of (S)-limonene and geraniol in Saccharomyces cerevisiae through the utilization of an Erg20 mutant with enhanced GPP accumulation capability.

Monoterpenes and monoterpenoids such as (S)-limonene and geraniol are valuable chemicals with a wide range of applications, including cosmetics, pharmaceuticals, and biofuels. Saccharomyces cerevisiae has proven to be an effective host to produce various terpenes and terpenoids. (S)-limonene and geraniol are produced from geranyl pyrophosphate (GPP) through the enzymatic actions of limonene synthase (LS) and geraniol synthase (GES), respectively. However, a major hurdle in their production arises from the dual functionality of the Erg20, a farnesyl pyrophosphate (FPP) synthase, responsible for generating GPP. Erg20 not only synthesizes GPP by condensing isopentenyl pyrophosphate (IPP) with dimethylallyl pyrophosphate but also catalyzes further condensation of IPP with GPP to produce FPP. In this study, we have tackled this issue by harnessing previously developed Erg20 mutants, Erg20K197G (Erg20G) and Erg20F96W, N127W (Erg20WW), which enhance GPP accumulation. Through a combination of these mutants, we generated a novel Erg20WWG mutant with over four times higher GPP accumulating capability than Erg20WW, as observed through geraniol production levels. The Erg20WWG mutant was fused to the LS from Mentha spicata or the GES from Catharanthus roseus for efficient conversion of GPP to (S)-limonene and geraniol, respectively. Further improvements were achieved by localizing the entire mevalonate pathway and the Erg20WWG-fused enzymes in peroxisomes, while simultaneously downregulating the essential ERG20 gene using the glucose-sensing HXT1 promoter. In the case of (S)-limonene production, additional Erg20WWG-LS was expressed in the cytosol. As a result, the final strains produced 1063 mg/L of (S)-limonene and 1234 mg/L of geraniol by fed-batch biphasic fermentations with ethanol feeding. The newly identified Erg20WWG mutant opens doors for the efficient production of various other GPP-derived chemicals including monoterpene derivatives and cannabinoids.

Coordinated anatomical and functional variability in the human brain during adolescence.

Adolescence represents a time of unparalleled brain development. In particular, developmental changes in morphometric and cytoarchitectural features are accompanied by maturation in the functional connectivity (FC). Here, we examined how three facets of the brain, including myelination, cortical thickness (CT), and resting-state FC, interact in children between the ages of 10 and 15. We investigated the pattern of coordination in these measures by computing correlation matrices for each measure as well as meta-correlations among them both at the regional and network levels. The results revealed consistently higher meta-correlations among myelin, CT, and FC in the sensory-motor cortical areas than in the association cortical areas. We also found that these meta-correlations were stable and little affected by age-related changes in each measure. In addition, regional variations in the meta-correlations were consistent with the previously identified gradient in the FC and therefore reflected the hierarchy of cortical information processing, and this relationship persists in the adult brain. These results demonstrate that heterogeneity in FC among multiple cortical areas are closely coordinated with the development of cortical myelination and thickness during adolescence.

Efficient production of natural sunscreens shinorine, porphyra-334, and mycosporine-2-glycine in Saccharomyces cerevisiae.

Mycosporine-like amino acids (MAAs) are promising natural sunscreens mainly produced in marine organisms. Until now, metabolic engineering efforts to produce MAAs in heterologous hosts have mainly focused on shinorine production, and the low production levels are still not suitable for industrial applications. In this study, we successfully developed Saccharomyces cerevisiae strains that can efficiently produce various disubstituted MAAs, including shinorine, porphyra-334, and mycosporine-2-glycine (M2G), which are formed by conjugating serine, threonine, and glycine to mycosporine-glycine (MG), respectively. We first generated an MG-producing strain by multiple integration of the biosynthetic genes from cyanobacteria and applying metabolic engineering strategies to increase sedoheptulose-7-phosphate pool, a substrate for MG production. Next, five mysD genes from cyanobacteria, which encode D-Ala-D-Ala ligase homologues that conjugate an amino acid to MG, were introduced into the MG-producing strain to determine the substrate preference of each MysD enzyme. MysDs from Lyngbya sp., Nostoclinckia, and Euhalothece sp. showed high specificity toward serine, threonine, and glycine, resulting in efficient production of shinorine, porphyra-334, and M2G, respectively. This is the first report on the production of porphyra-334 and M2G in S. cerevisiae. Furthermore, we identified that the substrate specificity of MysD was determined by the omega loop region of 43-45 amino acids predicted based on its structural homology to a D-Ala-D-Ala ligase from Thermus thermophilus involved in peptidoglycan biosynthesis. The substrate specificities of two MysD enzymes were interchangeable by swapping the omega loop region. Using the engineered strain expressing mysD from Lyngbya sp. or N. linckia, up to 1.53 g/L shinorine or 1.21 g/L porphyra-334 was produced by fed-batch fermentation in a 5-L bioreactor, the highest titer reported so far. These results suggest that S. cerevisiae is a promising host for industrial production of different types of MAAs, providing a sustainable and eco-friendly alternative for the development of natural sunscreens.

Multiple timescales of neural dynamics and integration of task-relevant signals across cortex.

A long-lasting challenge in neuroscience has been to find a set of principles that could be used to organize the brain into distinct areas with specific functions. Recent studies have proposed the orderly progression in the time constants of neural dynamics as an organizational principle of cortical computations. However, relationships between these timescales and their dependence on response properties of individual neurons are unknown, making it impossible to determine how mechanisms underlying such a computational principle are related to other aspects of neural processing. Here, we developed a comprehensive method to simultaneously estimate multiple timescales in neuronal dynamics and integration of task-relevant signals along with selectivity to those signals. By applying our method to neural and behavioral data during a dynamic decision-making task, we found that most neurons exhibited multiple timescales in their response, which consistently increased from parietal to prefrontal and cingulate cortex. While predicting rates of behavioral adjustments, these timescales were not correlated across individual neurons in any cortical area, resulting in independent parallel hierarchies of timescales. Additionally, none of these timescales depended on selectivity to task-relevant signals. Our results not only suggest the existence of multiple canonical mechanisms for increasing timescales of neural dynamics across cortex but also point to additional mechanisms that allow decorrelation of these timescales to enable more flexibility.

Confluence of Timing and Reward Biases in Perceptual Decision-Making Dynamics.

Although the decisions of our daily lives often occur in the context of temporal and reward structures, the impact of such regularities on decision-making strategy is poorly understood. Here, to explore how temporal and reward context modulate strategy, we trained 2 male rhesus monkeys to perform a novel perceptual decision-making task with asymmetric rewards and time-varying evidence reliability. To model the choice and response time patterns, we developed a computational framework for fitting generalized drift-diffusion models, which flexibly accommodate diverse evidence accumulation strategies. We found that a dynamic urgency signal and leaky integration, in combination with two independent forms of reward biases, best capture behavior. We also tested how temporal structure influences urgency by systematically manipulating the temporal structure of sensory evidence, and found that the time course of urgency was affected by temporal context. Overall, our approach identified key components of cognitive mechanisms for incorporating temporal and reward structure into decisions.SIGNIFICANCE STATEMENT In everyday life, decisions are influenced by many factors, including reward structures and stimulus timing. While reward and timing have been characterized in isolation, ecologically valid decision-making involves a multiplicity of factors acting simultaneously. This raises questions about whether the same decision-making strategy is used when these two factors are concurrently manipulated. To address these questions, we trained rhesus monkeys to perform a novel decision-making task with both reward asymmetry and temporal uncertainty. In order to understand their strategy and hint at its neural mechanisms, we used the new generalized drift diffusion modeling framework to model both reward and timing mechanisms. We found two of each reward and timing mechanisms are necessary to explain our data.

Reinforcement Learning during Adolescence in Rats.

The most dynamic period of postnatal brain development occurs during adolescence, the period between childhood and adulthood. Neuroimaging studies have observed morphologic and functional changes during adolescence, and it is believed that these changes serve to improve the functions of circuits that underlie decision-making. Direct evidence in support of this hypothesis, however, has been limited because most preclinical decision-making paradigms are not readily translated to humans. Here, we developed a reversal-learning protocol for the rapid assessment of adaptive choice behavior in dynamic environments in rats as young as postnatal day 30. A computational framework was used to elucidate the reinforcement-learning mechanisms that change in adolescence and into adulthood. Using a cross-sectional and longitudinal design, we provide the first evidence that value-based choice behavior in a reversal-learning task improves during adolescence in male and female Long-Evans rats and demonstrate that the increase in reversal performance is due to alterations in value updating for positive outcomes. Furthermore, we report that reversal-learning trajectories in adolescence reliably predicted reversal performance in adulthood. This novel behavioral protocol provides a unique platform for conducting biological and systems-level analyses of the neurodevelopmental mechanisms of decision-making.SIGNIFICANCE STATEMENT The neurodevelopmental adaptations that occur during adolescence are hypothesized to underlie age-related improvements in decision-making, but evidence to support this hypothesis has been limited. Here, we describe a novel behavioral protocol for rapidly assessing adaptive choice behavior in adolescent rats with a reversal-learning paradigm. Using a computational approach, we demonstrate that age-related changes in reversal-learning performance in male and female Long-Evans rats are linked to specific reinforcement-learning mechanisms and are predictive of reversal-learning performance in adulthood. Our behavioral protocol provides a unique platform for elucidating key components of adolescent brain function.

Neurochemical and Behavioral Dissections of Decision-Making in a Rodent Multistage Task.

Flexible decision-making in dynamic environments requires both retrospective appraisal of reinforced actions and prospective reasoning about the consequences of actions. These complementary reinforcement-learning systems can be characterized computationally with model-free and model-based algorithms, but how these processes interact at a neurobehavioral level in normal and pathological states is unknown. Here, we developed a translationally analogous multistage decision-making (MSDM) task to independently quantify model-free and model-based behavioral mechanisms in rats. We provide the first direct evidence that male rats, similar to humans, use both model-free and model-based learning when making value-based choices in the MSDM task and provide novel analytic approaches for independently quantifying these reinforcement-learning strategies. Furthermore, we report that ex vivo dopamine tone in the ventral striatum and orbitofrontal cortex correlate with model-based, but not model-free, strategies, indicating that the biological mechanisms mediating decision-making in the multistage task are conserved in rats and humans. This new multistage task provides a unique behavioral platform for conducting systems-level analyses of decision-making in normal and pathological states.SIGNIFICANCE STATEMENT Decision-making is influenced by both a retrospective "model-free" system and a prospective "model-based" system in humans, but the biobehavioral mechanisms mediating these learning systems in normal and disease states are unknown. Here, we describe a translationally analogous multistage decision-making task to provide a behavioral platform for conducting neuroscience studies of decision-making in rats. We provide the first evidence that choice behavior in rats is influenced by model-free and model-based systems and demonstrate that model-based, but not model-free, learning is associated with corticostriatal dopamine tone. This novel behavioral paradigm has the potential to yield critical insights into the mechanisms mediating decision-making alterations in mental disorders.

Neural basis of reinforcement learning and decision making.

Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal's knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain.

Persistent Spiking Activity Underlies Working Memory.

Persistent activity generated in the PFC during the delay period of working memory tasks represents information about stimuli held in memory and determines working memory performance. Alternative models of working memory, depending on the rhythmicity of discharges or exclusively on short-term synaptic plasticity, are inconsistent with the neurophysiological data.Dual Perspectives Companion Paper:Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not, by Mikael Lundqvist, Pawel Herman, and Earl K. Miller.

Dopamine D3 Receptor Availability Is Associated with Inflexible Decision Making.

UNLABELLED: Dopamine D2/3 receptor signaling is critical for flexible adaptive behavior; however, it is unclear whether D2, D3, or both receptor subtypes modulate precise signals of feedback and reward history that underlie optimal decision making. Here, PET with the radioligand [(11)C]-(+)-PHNO was used to quantify individual differences in putative D3 receptor availability in rodents trained on a novel three-choice spatial acquisition and reversal-learning task with probabilistic reinforcement. Binding of [(11)C]-(+)-PHNO in the midbrain was negatively related to the ability of rats to adapt to changes in rewarded locations, but not to the initial learning. Computational modeling of choice behavior in the reversal phase indicated that [(11)C]-(+)-PHNO binding in the midbrain was related to the learning rate and sensitivity to positive, but not negative, feedback. Administration of a D3-preferring agonist likewise impaired reversal performance by reducing the learning rate and sensitivity to positive feedback. These results demonstrate a previously unrecognized role for D3 receptors in select aspects of reinforcement learning and suggest that individual variation in midbrain D3 receptors influences flexible behavior. Our combined neuroimaging, behavioral, pharmacological, and computational approach implicates the dopamine D3 receptor in decision-making processes that are altered in psychiatric disorders. SIGNIFICANCE STATEMENT: Flexible decision-making behavior is dependent upon dopamine D2/3 signaling in corticostriatal brain regions. However, the role of D3 receptors in adaptive, goal-directed behavior has not been thoroughly investigated. By combining PET imaging with the D3-preferring radioligand [(11)C]-(+)-PHNO, pharmacology, a novel three-choice probabilistic discrimination and reversal task and computational modeling of behavior in rats, we report that naturally occurring variation in [(11)C]-(+)-PHNO receptor availability relates to specific aspects of flexible decision making. We confirm these relationships using a D3-preferring agonist, thus identifying a unique role of midbrain D3 receptors in decision-making processes.

Neuronal basis of age-related working memory decline.

Many of the cognitive deficits of normal ageing (forgetfulness, distractibility, inflexibility and impaired executive functions) involve prefrontal cortex (PFC) dysfunction. The PFC guides behaviour and thought using working memory, which are essential functions in the information age. Many PFC neurons hold information in working memory through excitatory networks that can maintain persistent neuronal firing in the absence of external stimulation. This fragile process is highly dependent on the neurochemical environment. For example, elevated cyclic-AMP signalling reduces persistent firing by opening HCN and KCNQ potassium channels. It is not known if molecular changes associated with normal ageing alter the physiological properties of PFC neurons during working memory, as there have been no in vivo recordings, to our knowledge, from PFC neurons of aged monkeys. Here we characterize the first recordings of this kind, revealing a marked loss of PFC persistent firing with advancing age that can be rescued by restoring an optimal neurochemical environment. Recordings showed an age-related decline in the firing rate of DELAY neurons, whereas the firing of CUE neurons remained unchanged with age. The memory-related firing of aged DELAY neurons was partially restored to more youthful levels by inhibiting cAMP signalling, or by blocking HCN or KCNQ channels. These findings reveal the cellular basis of age-related cognitive decline in dorsolateral PFC, and demonstrate that physiological integrity can be rescued by addressing the molecular needs of PFC circuits.

Symbol addition by monkeys provides evidence for normalized quantity coding.

Weber's law can be explained either by a compressive scaling of sensory response with stimulus magnitude or by a proportional scaling of response variability. These two mechanisms can be distinguished by asking how quantities are added or subtracted. We trained Rhesus monkeys to associate 26 distinct symbols with 0-25 drops of reward, and then tested how they combine, or add, symbolically represented reward magnitude. We found that they could combine symbolically represented magnitudes, and they transferred this ability to a novel symbol set, indicating that they were performing a calculation, not just memorizing the value of each combination. The way they combined pairs of symbols indicated neither a linear nor a compressed scale, but rather a dynamically shifting, relative scaling.

Opponent Identity Influences Value Learning in Simple Games.

Context plays a pivotal role in many decision-making scenarios, including social interactions wherein the identities and strategies of other decision makers often shape our behaviors. However, the neural mechanisms for tracking such contextual information are poorly understood. Here, we investigated how opponent identity affects human reinforcement learning during a simulated competitive game against two independent computerized opponents. We found that strategies of participants were affected preferentially by the outcomes of the previous interactions with the same opponent. In addition, reinforcement signals from the previous trial were less discriminable throughout the brain after the opponent changed, compared with when the same opponent was repeated. These opponent-selective reinforcement signals were particularly robust in right rostral anterior cingulate and right lingual regions, where opponent-selective reinforcement signals correlated with a behavioral measure of opponent-selective reinforcement learning. Therefore, when choices involve multiple contextual frames, such as different opponents in a game, decision making and its neural correlates are influenced by multithreaded histories of reinforcement. Overall, our findings are consistent with the availability of temporally overlapping, context-specific reinforcement signals. SIGNIFICANCE STATEMENT: In real-world decision making, context plays a strong role in determining the value of an action. Similar choices take on different values depending on setting. We examined the contextual dependence of reward-based learning and reinforcement signals using a simple two-choice matching-pennies game played by humans against two independent computer opponents that were randomly interleaved. We found that human subjects' strategies were highly dependent on opponent context in this game, a fact that was reflected in select brain regions' activity (rostral anterior cingulate and lingual cortex). These results indicate that human reinforcement histories are highly dependent on contextual factors, a fact that is reflected in neural correlates of reinforcement signals.

A two-stage model of concurrent interval timing in monkeys.

Accurate timing is critical for a wide range of cognitive processes and behaviors. In addition, complex environments frequently necessitate the simultaneous timing of multiple intervals, and behavioral performance in concurrent timing can constrain formal models of timing behavior and provide important insights into the corresponding neural mechanisms. However, the accuracy of such concurrent timing has not been rigorously examined. We developed a novel behavioral paradigm in which rhesus monkeys were incentivized to time two independent intervals. The onset asynchrony of two overlapping intervals varied randomly, thereby discouraging the animals from adopting any habitual responses. We found that only the first response for each interval was strongly indicative of the internal timing of that interval, consistent with previous findings and a two-stage model. In addition, the temporal precision of the first response was comparable in the single-interval and concurrent-interval conditions, although the first saccade to the second interval tended to occur sooner than in the single-interval condition. Finally, behavioral responses during concurrent timing could be well accounted for by a race between two independent stochastic processes resembling those in the single-interval condition. The fact that monkeys can simultaneously monitor and respond to multiple temporal intervals indicates that the neural mechanisms for interval timing must be sufficiently flexible for concurrent timing.

Insulin-like growth factor binding protein-3 enhances etoposide-induced cell growth inhibition by suppressing the NF-κB activity in gastric cancer cells.

Nuclear factor-kappaB (NF-κB) is a transcription factor that is activated in various neoplasms, including gastric cancer. Insulin-like growth factor binding protein-3 (IGFBP-3) is a potent tumor suppressor and is significantly suppressed in a variety of cancers. Although IGFBP-3 has been reported to have antiproliferative and proapoptotic effects, the precise mechanisms underlying the action of IGFBP-3 have not been elucidated. In this study, we found an inverse correlation between NF-κB activity and IGFBP-3 expression in patients with gastric cancer. Overexpression of IGFBP-3 resulted in significant inhibition of total and phosphorylated p65 NF-κB and IκB proteins in gastric cancer cells. IGFBP-3 further inhibited the expression of NF-κB-regulated cell adhesion molecules, ICAM-1 and VCAM-1. Finally, the growth inhibition induced by etoposide was significantly enhanced by IGFBP-3 overexpression along with concomitant suppression of NF-κB activity. These findings indicate that IGFBP-3 enhances etoposide-induced cell growth inhibition by blocking the NF-κB signaling pathway in gastric cancer cells. Furthermore, our data suggest that IGFBP-3 could be used as an adjuvant in the treatment of gastric cancer.

Signals for previous goal choice persist in the dorsomedial, but not dorsolateral striatum of rats.

The cortico-basal ganglia network has been proposed to consist of parallel loops serving distinct functions. However, it is still uncertain how the content of processed information varies across different loops and how it is related to the functions of each loop. We investigated this issue by comparing neuronal activity in the dorsolateral (sensorimotor) and dorsomedial (associative) striatum, which have been linked to habitual and goal-directed action selection, respectively, in rats performing a dynamic foraging task. Both regions conveyed significant neural signals for the animal's goal choice and its outcome. Moreover, both regions conveyed similar levels of neural signals for action value before the animal's goal choice and chosen value after the outcome of the animal's choice was revealed. However, a striking difference was found in the persistence of neural signals for the animal's chosen action. Signals for the animal's goal choice persisted in the dorsomedial striatum until the outcome of the animal's next goal choice was revealed, whereas they dissipated rapidly in the dorsolateral striatum. These persistent choice signals might be used for causally linking temporally discontiguous responses and their outcomes in the dorsomedial striatum, thereby contributing to its role in goal-directed action selection.

Chronic Ethanol Exposure Produces Persistent Impairment in Cognitive Flexibility and Decision Signals in the Striatum.

Lack of cognitive flexibility is a hallmark of substance use disorders and has been associated with drug-induced synaptic plasticity in the dorsomedial striatum (DMS). Yet the possible impact of altered plasticity on real-time striatal neural dynamics during decision-making is unclear. Here, we identified persistent impairments induced by chronic ethanol (EtOH) exposure on cognitive flexibility and striatal decision signals. After a substantial withdrawal period from prior EtOH vapor exposure, male, but not female, rats exhibited reduced adaptability and exploratory behavior during a dynamic decision-making task. Reinforcement learning models showed that prior EtOH exposure enhanced learning from rewards over omissions. Notably, neural signals in the DMS related to the decision outcome were enhanced, while those related to choice and choice-outcome conjunction were reduced, in EtOH-treated rats compared to the controls. These findings highlight the profound impact of chronic EtOH exposure on adaptive decision-making, pinpointing specific changes in striatal representations of actions and outcomes as underlying mechanisms for cognitive deficits.

Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations.

Foraging theory has been a remarkably successful approach to understanding the behavior of animals in many contexts. In patch-based foraging contexts, the marginal value theorem (MVT) shows that the optimal strategy is to leave a patch when the marginal rate of return declines to the average for the environment. However, the MVT is only valid in deterministic environments whose statistics are known to the forager; naturalistic environments seldom meet these strict requirements. As a result, the strategies used by foragers in naturalistic environments must be empirically investigated. We developed a novel behavioral task and a corresponding computational framework for studying patch-leaving decisions in head-fixed and freely moving mice. We varied between-patch travel time, as well as within-patch reward depletion rate, both deterministically and stochastically. We found that mice adopt patch residence times in a manner consistent with the MVT and not explainable by simple ethologically motivated heuristic strategies. Critically, behavior was best accounted for by a modified form of the MVT wherein environment representations were updated based on local variations in reward timing, captured by a Bayesian estimator and dynamic prior. Thus, we show that mice can strategically attend to, learn from, and exploit task structure on multiple timescales simultaneously, thereby efficiently foraging in volatile environments. The results provide a foundation for applying the systems neuroscience toolkit in freely moving and head-fixed mice to understand the neural basis of foraging under uncertainty.

Neurocomputational mechanisms involved in adaptation to fluctuating intentions of others.

Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.