Therapeutic doses of ketamine acutely attenuate the aversive effect of losses during decision-making.

The discovery of rapid-acting antidepressant, ketamine has opened a pathway to a new generation of treatments for depression, and inspired neuroscientific investigation based on a new perspective that non-adaptive changes in the intrinsic excitatory and inhibitory circuitry might underlie the pathophysiology of depression. Nevertheless, it still remains largely unknown how the hypothesized molecular and synaptic levels of changes in the circuitry might mediate behavioral and neuropsychological changes underlying depression, and how ketamine might restore adaptive behavior. Here, we used computational models to analyze behavioral changes induced by therapeutic doses of ketamine, while rhesus macaques were iteratively making decisions based on gains and losses of tokens. When administered intramuscularly or intranasally, ketamine reduced the aversiveness of undesirable outcomes such as losses of tokens without significantly affecting the evaluation of gains, behavioral perseveration, motivation, and other cognitive aspects of learning such as temporal credit assignment and time scales of choice and outcome memory. Ketamine's potentially antidepressant effect was separable from other side effects such as fixation errors, which unlike outcome evaluation, was readily countered with strong motivation to avoid errors. We discuss how the acute effect of ketamine to reduce the initial impact of negative events could potentially mediate longer-term antidepressant effects through mitigating the cumulative effect of those events produced by slowly decaying memory, and how the disruption-resistant affective memory might pose challenges in treating depression. Our study also invites future investigations on ketamine's antidepressant action over diverse mood states and with affective events exerting their impacts at diverse time scales.

The first high-quality genome assembly and annotation of Patiria pectinifera.

The blue bat star, a highly adaptive species in the East Sea of Korea, has displayed remarkable success in adapting to recent climate change. The genetic mechanisms behind this success were not well-understood, prompting our report on the first chromosome-level assembly of the Patiria genus. We assembled the genome using Nanopore and Illumina sequences, yielding a total length of 615 Mb and a scaffold N50 of 24,204,423 bp. Hi-C analysis allowed us to anchor the scaffold sequences onto 22 pseudochromosomes. K-mer based analysis revealed 5.16% heterozygosity rate of the genome, higher than any previously reported echinoderm species. Our transposable element analysis exposed a substantial number of genome-wide retrotransposons and DNA transposons. These results offer valuable resources for understanding the evolutionary mechanisms behind P. pectinifera's successful adaptation in fluctuating environments.

Transient neuronal suppression for exploitation of new sensory evidence.

In noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify and respond via saccade to the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals' behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset but sensitive to stimulus strength. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is important for dynamic perceptual decision making.

Timescales of Cognition in the Brain.

We live in a world that changes on many timescales. To learn and make decisions appropriately, the human brain has evolved to integrate various types of information, such as sensory evidence and reward feedback, on multiple timescales. This is reflected in cortical hierarchies of timescales consisting of heterogeneous neuronal activities and expression of genes related to neurotransmitters critical for learning. We review the recent findings on how timescales of sensory and reward integration are affected by the temporal properties of sensory and reward signals in the environment. Despite existing evidence linking behavioral and neuronal timescales, future studies must examine how neural computations at multiple timescales are adjusted and combined to influence behavior flexibly.

NMDAR Neurotransmission Needed for Persistent Neuronal Firing: Potential Roles in Mental Disorders.

The dorsolateral prefrontal cortex (dlPFC) generates the mental representations that are the foundation of abstract thought, and provides top-down regulation of emotion through projections to the medial PFC and cingulate cortices. Physiological recordings from dlPFC Delay cells have shown that the generation of mental representations during working memory relies on NMDAR neurotransmission, with surprisingly little contribution from AMPAR. Systemic administration of low "antidepressant" doses of the NMDAR antagonist, ketamine, erodes these representations and reduces dlPFC Delay cell firing. In contrast to the dlPFC, V1 neuronal firing to visual stimuli depends on AMPAR, with much less contribution from NMDAR. Similarly, neurons in the dlPFC that respond to sensory events (cue cells, response feedback cells) rely on AMPAR, and systemic ketamine increases their firing. Insults to NMDAR transmission, and the impaired ability for dlPFC to generate mental representations, may contribute to cognitive deficits in schizophrenia, e.g., from genetic insults that weaken NMDAR transmission, or from blockade of NMDAR by kynurenic acid. Elevated levels of kynurenic acid in dlPFC may also contribute to cognitive deficits in other disorders with pronounced neuroinflammation (e.g., Alzheimer's disease), or peripheral infections where kynurenine can enter brain (e.g., delirium from sepsis, "brain fog" in COVID19). Much less is known about NMDAR actions in the primate cingulate cortices. However, NMDAR neurotransmission appears to process the affective and visceral responses to pain and other aversive experiences mediated by the cingulate cortices, which may contribute to sustained alterations in mood state. We hypothesize that the very rapid, antidepressant effects of intranasal ketamine may involve the disruption of NMDAR-generated aversive mood states by the anterior and subgenual cingulate cortices, providing a "foot in the door" to allow the subsequent return of top-down regulation by higher PFC areas. Thus, the detrimental vs. therapeutic effects of NMDAR blockade may be circuit dependent.

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.

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.

All about pain pharmacology: what pain physicians should know.

From the perspective of the definition of pain, pain can be divided into emotional and sensory components, which originate from potential and actual tissue damage, respectively. The pharmacologic treatment of the emotional pain component includes antianxiety drugs, antidepressants, and antipsychotics. The anti-anxiety drugs have anti-anxious, sedative, and somnolent effects. The antipsychotics are effective in patients with positive symptoms of psychosis. On the other hand, the sensory pain component can be divided into nociceptive and neuropathic pain. Non-steroidal anti-inflammatory drugs (NSAIDs) and opioids are usually applied for somatic and visceral nociceptive pain, respectively; anticonvulsants and antidepressants are administered for the treatment of neuropathic pain with positive and negative symptoms, respectively. The NSAIDs, which inhibit the cyclo-oxygenase pathway, exhibit anti-inflammatory, antipyretic, and analgesic effects; however, they have a therapeutic ceiling. The adverse reactions (ADRs) of the NSAIDs include gastrointestinal problems, generalized edema, and increased bleeding tendency. The opioids, which bind to the opioid receptors, present an analgesic effect only, without anti-inflammatory, antipyretic, or ceiling effects. The ADRs of the opioids start from itching and nausea/vomiting to cardiovascular and respiratory depression, as well as constipation. The anticonvulsants include carbamazepine, related to sodium channel blockade, and gabapentin and pregabalin, related to calcium blockade. The antidepressants show their analgesic actions mainly through inhibiting the reuptake of serotonin or norepinephrine. Most drugs, except NSAIDs, need an updose titration period. The principle of polypharmacy for analgesia in case of mixed components of pain is increasing therapeutic effects while reducing ADRs, based on the origin of the pain.

Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty.

Value-based decision making often involves integration of reward outcomes over time, but this becomes considerably more challenging if reward assignments on alternative options are probabilistic and non-stationary. Despite the existence of various models for optimally integrating reward under uncertainty, the underlying neural mechanisms are still unknown. Here we propose that reward-dependent metaplasticity (RDMP) can provide a plausible mechanism for both integration of reward under uncertainty and estimation of uncertainty itself. We show that a model based on RDMP can robustly perform the probabilistic reversal learning task via dynamic adjustment of learning based on reward feedback, while changes in its activity signal unexpected uncertainty. The model predicts time-dependent and choice-specific learning rates that strongly depend on reward history. Key predictions from this model were confirmed with behavioral data from non-human primates. Overall, our results suggest that metaplasticity can provide a neural substrate for adaptive learning and choice under uncertainty.

Neural Basis of Strategic Decision Making.

Human choice behaviors during social interactions often deviate from the predictions of game theory. This might arise partly from the limitations in the cognitive abilities necessary for recursive reasoning about the behaviors of others. In addition, during iterative social interactions, choices might change dynamically as knowledge about the intentions of others and estimates for choice outcomes are incrementally updated via reinforcement learning. Some of the brain circuits utilized during social decision making might be general-purpose and contribute to isomorphic individual and social decision making. By contrast, regions in the medial prefrontal cortex (mPFC) and temporal parietal junction (TPJ) might be recruited for cognitive processes unique to social decision making.

A hierarchy of intrinsic timescales across primate cortex.

Specialization and hierarchy are organizing principles for primate cortex, yet there is little direct evidence for how cortical areas are specialized in the temporal domain. We measured timescales of intrinsic fluctuations in spiking activity across areas and found a hierarchical ordering, with sensory and prefrontal areas exhibiting shorter and longer timescales, respectively. On the basis of our findings, we suggest that intrinsic timescales reflect areal specialization for task-relevant computations over multiple temporal ranges.

Neural correlates of strategic reasoning during competitive games.

Although human and animal behaviors are largely shaped by reinforcement and punishment, choices in social settings are also influenced by information about the knowledge and experience of other decision-makers. During competitive games, monkeys increased their payoffs by systematically deviating from a simple heuristic learning algorithm and thereby countering the predictable exploitation by their computer opponent. Neurons in the dorsomedial prefrontal cortex (dmPFC) signaled the animal's recent choice and reward history that reflected the computer's exploitative strategy. The strength of switching signals in the dmPFC also correlated with the animal's tendency to deviate from the heuristic learning algorithm. Therefore, the dmPFC might provide control signals for overriding simple heuristic learning algorithms based on the inferred strategies of the opponent.

Cortical signals for rewarded actions and strategic exploration.

In stable environments, decision makers can exploit their previously learned strategies for optimal outcomes, while exploration might lead to better options in unstable environments. Here, to investigate the cortical contributions to exploratory behavior, we analyzed single-neuron activity recorded from four different cortical areas of monkeys performing a matching-pennies task and a visual search task, which encouraged and discouraged exploration, respectively. We found that neurons in multiple regions in the frontal and parietal cortex tended to encode signals related to previously rewarded actions more reliably than unrewarded actions. In addition, signals for rewarded choices in the supplementary eye field were attenuated during the visual search task and were correlated with the tendency to switch choices during the matching-pennies task. These results suggest that the supplementary eye field might play a unique role in encouraging animals to explore alternative decision-making strategies.

Single-nucleotide resolution of RNAs up to 59 nucleotides by high-performance liquid chromatography.

Ion-pair, reverse-phase high-performance liquid chromatography (HPLC) is a standard analytical platform for separating, purifying, and analyzing RNAs. However, a single-nucleotide resolution by using HPLC is currently limited to RNAs shorter than 25 nucleotides (nt). Here we describe a method of separating three RNA aptamers with 57, 58, and 59nt on an XBridge ion-pair, reverse-phase HPLC column by a single-nucleotide resolution. Under a similar condition, we also show the capability of our method to resolve two structurally different, yet sequence or mass identical, 59-nt aptamers. We establish that the optimal condition to achieve a single-nucleotide resolution correlates to 50°C and zero magnesium concentration in mobile phases. The ion-pairing agent, the buffer, and the solvent we use are also compatible for post-HPLC analysis such as mass spectrometry. Therefore, our method provides a new way of detecting, analyzing, and separating RNAs by conformation or structure and extends the ability to separate RNAs that are longer than 25nt by single-nucleotide resolution.

Neural basis of learning and preference during social decision-making.

Social decision-making is arguably the most complex cognitive function performed by the human brain. This is due to two unique features of social decision-making. First, predicting the behaviors of others is extremely difficult. Second, humans often take into consideration the well-beings of others during decision-making, but this is influenced by many contextual factors. Despite such complexity, studies on the neural basis of social decision-making have made substantial progress in the last several years. They demonstrated that the core brain areas involved in reinforcement learning and valuation, such as the ventral striatum and orbitofrontal cortex, make important contribution to social decision-making. Furthermore, the contribution of brain systems implicated for theory of mind during decision-making is being elucidated. Future studies are expected to provide additional details about the nature of information channeled through these brain areas.

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.

The prefrontal cortex and hybrid learning during iterative competitive games.

Behavioral changes driven by reinforcement and punishment are referred to as simple or model-free reinforcement learning. Animals can also change their behaviors by observing events that are neither appetitive nor aversive when these events provide new information about payoffs available from alternative actions. This is an example of model-based reinforcement learning and can be accomplished by incorporating hypothetical reward signals into the value functions for specific actions. Recent neuroimaging and single-neuron recording studies showed that the prefrontal cortex and the striatum are involved not only in reinforcement and punishment, but also in model-based reinforcement learning. We found evidence for both types of learning, and hence hybrid learning, in monkeys during simulated competitive games. In addition, in both the dorsolateral prefrontal cortex and orbitofrontal cortex, individual neurons heterogeneously encoded signals related to actual and hypothetical outcomes from specific actions, suggesting that both areas might contribute to hybrid learning.

A reservoir of time constants for memory traces in cortical neurons.

According to reinforcement learning theory of decision making, reward expectation is computed by integrating past rewards with a fixed timescale. In contrast, we found that a wide range of time constants is available across cortical neurons recorded from monkeys performing a competitive game task. By recognizing that reward modulates neural activity multiplicatively, we found that one or two time constants of reward memory can be extracted for each neuron in prefrontal, cingulate and parietal cortex. These timescales ranged from hundreds of milliseconds to tens of seconds, according to a power law distribution, which is consistent across areas and reproduced by a 'reservoir' neural network model. These neuronal memory timescales were weakly, but significantly, correlated with those of monkey's decisions. Our findings suggest a flexible memory system in which neural subpopulations with distinct sets of long or short memory timescales may be selectively deployed according to the task demands.