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Kunimatsu, J.

Publications and source records attributed to Kunimatsu, J..

11 recordsLinked to original sources

Respiratory suppression enhances voluntary control of human action

Athletes often regulate their breathing to optimize motor performance, suggesting a close link between respiration and movement control. Previous studies have shown that respiration modulates neuronal oscillations and influences behavior. However, how respiratory control directly affects motor behavior remains unclear. Here, we demonstrate that breath holding during the preparatory period facilitates voluntary movement. We examined respiratory patterns while participants performed two types of eye movement tasks: pro-saccade and anti-saccade tasks. In the anti-saccade task, participants were required to suppress a reflexive saccade toward a visual target and instead generate a voluntary saccade in the opposite direction. To assess whether motivation modulates respiratory control, the tasks were conducted under two motivational conditions. Most participants suppressed their breath during the anticipatory period preceding saccade initiation, regardless of task type, and this tendency was more pronounced under high motivation. In the anti-saccade task, stronger respiratory adjustments were associated with shorter reaction times, whereas no such relationship was observed in the pro-saccade task. Moreover, intentional breath holding during the preparatory period selectively reduced reaction times in the anti-saccade task. These findings indicate that individuals spontaneously hold their breath to facilitate voluntary actions in highly motivated situations, and that this effect can be reproduced through intentional breath holding. We propose that anticipatory breath holding may play a functional role in proactively modulating neural processes underlying voluntary movement control.

neuroscience↗

Sustained visual signals in the primate cerebellar dentate nucleus drive associative learning

A number of studies have suggested that the cerebellum has cognitive functions; however, the underlying neuronal mechanisms remain unclear. In this study, we demonstrated that sustained visual signals in the cerebellar dentate nucleus represent the visuomotor associative information. We recorded neural activity from the dentate nucleus when monkeys performed a learning task involving the association between visual objects and saccade directions. We found that sustained visual activity was greater in the region during learning than during memory retrieval. This enhancement disappeared under the uncertain reward condition, wherein the monkey did not perform the heuristic learning strategy. Furthermore, sustained visual signals changed the response to visual objects depending on the associated saccade direction. This direction selectivity was positively correlated with modulation during learning. These results suggest that sustained visual signals in the dentate nucleus reflect learning motivation and drive learning by increasing the strength of discrimination among visual objects.

physiology↗

Formation of brain-wide neural geometry during visual item recognition in monkeys

Neural dynamics assumes to reflect computations that relay and transform information in the brain. Previous studies have identified the neural population dynamics in many individual brain regions as a trajectory geometry, preserving a manifest of computational motifs. However, whether these populations share particular geometric patterns across brain-wide neural populations remains unclear. Here, by mapping neural dynamics widely across temporal/frontal/limbic regions in the cortical and subcortical structures of monkeys, we show that 10 neural populations, including 2,500 neurons, propagate visual item information in a stochastic manner. We found that visual inputs predominantly evoked rotational dynamics in the higher-order visual area, TE and its downstream striatum tail, while curvy/straight dynamics appeared frequently downstream in the orbitofrontal/hippocampal network. These geometric changes were not deterministic but rather stochastic according to their respective emergence rates. Our meta-analysis results indicated that visual information propagates as a heterogeneous mixture of stochastic neural population signals in the brain.

neuroscience↗

A method for evaluating hunger and thirst in monkeys by measuring blood ghrelin and osmolality levels

Hunger and thirst drive animals consumption behavior and regulate their decision-making regarding rewards. We previously assessed the thirst states of monkeys by measuring blood osmolality under controlled water access and related the thirst states to risky decisions for fluid rewards. However, hunger assessments in monkeys have been poorly performed. Moreover, the lack of precise measures for hunger states leads to another issue regarding how hunger and thirst states interact with each other in each individual. Thus, when controlling food access to motivate subject performances, how these two physiological needs are satisfied in captive monkeys remains unclear. Here, we measured blood ghrelin levels and osmolality for hunger and thirst, respectively, in four captive macaques. Using enzyme-linked immunosorbent assay, we found that the levels of blood ghrelin, a widely measured hunger-related peptide hormone in humans, were high after 20 h of no food access (with ad libitum water), which is a typical controlled food access condition. One hour after consuming the regular dry meal, the value decreased in three out of four monkeys to within the range of individual blood ghrelin levels. Additionally, blood osmolality measured from the same blood sample, the standard hematological index of hydration status, increased after consuming regular dry meals with no water access. Thus, ghrelin and osmolality may provide a precise reflection of the physiological states of individual monkeys for hunger and thirst, suggesting that these indices can be used as a tool for monitoring hunger and thirst levels that mediate the animals decision-making for consuming rewards.

neuroscience↗

Neuronal mechanism of the encoding of socially familiar faces in the striatum tail

Although we can quickly locate a familiar person even in a crowd, the underlying neuronal mechanism remains unclear. Recently, we found that the striatum tail (STRt), which is part of the basal ganglia, is sensitive to long-term reward history. Here, we show that long-term value-coding neurons are involved in the detection of socially familiar faces. Many STRt neurons respond to facial images, especially to those of socially familiar persons. Additionally, we found that these face-responsive neurons also encode the stable values of many objects based on long-term reward experiences. Interestingly, the strength of neuronal modulation of social familiarity bias (familiar or unfamiliar) and object value bias (high-valued or low-valued) were positively correlated. These results suggest that both social familiarity and stable object-value information are mediated by a common neuronal mechanism. This mechanism may contribute to the rapid detection of familiar faces in real-world contexts. TeaserThe common mechanism mediating social familiarity and stable object-value information may contribute to rapid detection of familiar faces.

neuroscience↗

Distinct roles of the orbitofrontal cortex, ventral striatum, and dopamine neurons in counterfactual thinking of decision outcomes

Individuals often assess past decisions by comparing what was gained with what would have been gained had they acted differently. Thoughts of past alternatives that counter what actually happened are called "counterfactuals". Recent theories emphasize the role of the prefrontal cortex in processing counterfactual outcomes in decision-making, although how subcortical regions contribute to this process remains to be elucidated. Here we report a clear distinction among the roles of the orbitofrontal cortex, ventral striatum and midbrain dopamine neurons in processing counterfactual outcomes in monkeys. Our findings suggest that actually-gained and counterfactual outcome signals are both processed in the cortico-subcortical network constituted by these regions but in distinct manners, and integrated only in the orbitofrontal cortex in a way to compare these outcomes. This study extends the prefrontal theory of counterfactual thinking and provides key insights regarding how the prefrontal cortex cooperates with subcortical regions to make decisions using counterfactual information. TeaserCortical and subcortical systems both contribute to counterfactual thinking of decision outcomes but in distinct manners.

neuroscience↗

Noninvasive method for monitoring breathing patterns in monkeys

Respiration is strongly linked to internal states such as arousal, emotion, and even cognitive processes and provides objective biological information to estimate these states in humans and animals. However, the measurement of respiration has not been established in macaque monkeys that have been widely used as model animals for understanding various higher brain functions. In the present study, we developed a method to monitor the respiration of behaving monkeys. We first measured the temperature of their nasal breathing, which changes between inspiration and expiration phases, in an anesthetized condition and estimated the respiration pattern. We compared the estimated pattern with that obtained by a conventional chest band method that has been used in humans and applies to anesthetized, but not behaving, monkeys. These respiration patterns matched well, suggesting that the measurement of nasal air temperature can be used to monitor the respiration of monkeys. Furthermore, we confirmed that the respiration frequency in behaving monkeys monitored by the measurement of nasal air temperature was not affected by the orofacial movement of licking to obtain the liquid reward. We next examined the frequency of respiration when they listened to music or white noise. The respiratory frequency was higher when the monkeys listened to music than the noise. This result is consistent with a phenomenon in humans and indicates the accuracy of our monitoring method. These data suggest that the measurement of nasal air temperature enables us to monitor the respiration of behaving monkeys and thereby estimate their internal states. Significance StatementWhile respiration is linked with internal processing, such as emotional and cognitive states, methods have not been established for physiological research on monkeys. We developed a novel method that obtained respiration signals by measuring the nasal air temperatures of behaving monkeys. Our method was able to continuously track the respiration pattern without distortions evoked by orofacial movements to lick the liquid reward. The respiratory frequency increased while listening to music than when listening to white noise for all monkeys. These results demonstrate that nasal air temperature measurements can be used to monitor the respiration patterns of aroused monkeys, allowing us to understand their internal state. This will be useful for investigating the underlying neuronal mechanism of neuropsychiatric disorders using monkeys.

neuroscience↗

Comparison of neural population dynamics in the regression subspace between continuous and categorical task parameters

Neural population dynamics, presumably fundamental computational units in the brain, provide a key framework for understanding information processing in the sensory, cognitive, and motor functions. However, neural population dynamics is not explicitly related to the conventional analytic framework for single-neuron activity, i.e., representational models that analyze neuronal modulations associated with cognitive and motor parameters. In this study, we applied a recently developed state-space analysis to incorporate the representational models into the dynamic model in combination with these parameters. We compared neural population dynamics between continuous and categorical task parameters during two visual recognition tasks, using the datasets originally designed for a single-neuron approach. We successfully extracted neural population dynamics in the regression subspace, which represent modulation dynamics for both continuous and categorical task parameters with reasonable temporal characteristics. Furthermore, we combined the classical optimal-stimulus analysis paradigm for the single-neuron approach (i.e., stimulus identified as maximum neural responses) into the dynamic model, and found that the most prominent modulation dynamics at the lower dimension were derived from these optimal responses. Thus, our approach provides a unified framework for incorporating knowledge acquired with the single-neuron approach into the dynamic model as a standard procedure for describing neural modulation dynamics in the brain.

neuroscience↗

Dynamic prospect theory - two core economic decision theories coexist in the gambling behavior of monkeys

Research in behavioral economics and reinforcement learning has given rise to two influential theories describing human economic choice under uncertainty. The first, prospect theory, assumes that decision-makers use static mathematical functions, utility and probability weighting, to calculate the values of alternatives. The second, reinforcement learning theory, posits that dynamic mathematical functions update the values of alternatives based on experience through reward prediction error (RPE). To date, these theories have been examined in isolation without reference to one another. Therefore, it remains unclear whether RPE affects a decision-makers utility and/or probability weighting functions, or whether these functions are indeed static as in prospect theory. Here, we propose a dynamic prospect theory model that combines prospect theory and RPE, and test this combined model using choice data on gambling behavior of captive macaques. We found that under standard prospect theory, monkeys, like humans, had a concave utility function. Unlike humans, monkeys exhibited a concave, rather than inverse-S shaped, probability weighting function. Our dynamic prospect theory model revealed that probability distortions, not the utility of rewards, solely and systematically varied with RPE: after a positive RPE, the estimated probability weighting functions became more concave, suggesting more optimistic belief about receiving rewards and over-weighted subjective probabilities at all probability levels. Thus, the probability perceptions in laboratory monkeys are not static even after extensive training, and are governed by a dynamic function well captured by the algorithmic feature of reinforcement learning. This novel evidence supports combining these two major theories to capture choice behavior under uncertainty. Significance statementWe propose and test a new decision theory under uncertainty by combining pre-existing two influential theories in the neuroeconomics: prospect theory from economics and prediction error theory from reinforcement learning. Collecting a large dataset (over 60,000 gambling decisions) from laboratory monkeys enables us to test the hybrid model of these two core decision theories reliably. Our results showed over-weighted subjective probabilities at all probability levels after lucky win, indicating that positive prediction error systematically bias decision-makers more optimistically about receiving rewards. This trial-by-trial prediction-error dynamics in probability perception provides outperformed performance of the model compared to the standard static prospect theory. Thus, both static and dynamic elements coexist in monkeys risky decision-making, an evidence contradicting the assumption of prospect theory.

animal behavior and cognition↗

Tonic firing mode of midbrain dopamine neurons continuously tracks reward values changing moment-by-moment

Appropriate actions are taken based on the values of future rewards. The phasic activity of midbrain dopamine neurons signals these values. Because reward values often change over time, even on a subsecond-by-subsecond basis, appropriate action selection requires continuous value monitoring. However, the phasic dopamine activity, which is sporadic and has a short duration, likely fails continuous monitoring. Here, we demonstrate a tonic firing mode of dopamine neurons that effectively tracks changing reward values. We recorded dopamine neuron activity in monkeys during a Pavlovian procedure in which the value of a cued reward gradually increased or decreased. Dopamine neurons tonically increased and decreased their activity as the reward value changed. This tonic activity was evoked more strongly by non-burst spikes than burst spikes producing a conventional phasic activity. Our findings suggest that dopamine neurons change their firing mode to effectively signal reward values, which could underlie action selection in changing environments.

neuroscience↗

Amygdala controls saccade and gaze physically, motivationally, and socially

The amygdala is uniquely sensitive to emotional events. However, it is not understood whether and how the amygdala uses such emotional signals to control behavior, especially eye movements. We therefore injected muscimol (GABAA agonist) into the central nucleus of amygdala (CeA) in monkeys. This unilateral temporary inactivation suppressed saccades to contralateral but not ipsilateral targets, resulting in longer latencies, hypometric amplitudes, and slower velocity. During free viewing of movies, gaze was distributed mostly in the ipsilateral hemifield. Moreover, CeA inactivation disrupted the tendency of gaze toward social interaction images, which were normally focused on continuously. Conversely, optogenetic stimulation of CeA facilitated saccades to the contralateral side. These findings suggest that CeA controls spatially selective gaze and attention in emotional contexts, and provide a new framework for understanding psychiatric disorders related to amygdala dysfunction.\n\nHighlightsO_LICentral amygdala facilitates contralateral saccades selectively.\nC_LIO_LISaccade facilitation is related to motivational goals and social interaction.\nC_LIO_LIThe amygdala thus controls goal-directed behavior based on emotional contexts.\nC_LI

neuroscience↗