Distributed and Mixed Information in Monosynaptic Inputs to Dopamine Neurons

Distinct dynamics and intrinsic properties in ventral tegmental area populations mediate reward association and motivation

Ventral tegmental area (VTA) dopamine neurons regulate reward-related associative learning and reward-driven motivated behaviors, but how these processes are coordinated by distinct VTA neuronal subpopulations remains unresolved. Here, we compare the contribution of two primarily dopaminergic and largely non-overlapping VTA subpopulations, all VTA dopamine neurons and VTA GABAergic neurons of the mouse midbrain, to these processes. We find that the dopamine subpopulation that projects to the nucleus accumbens (NAc) core preferentially encodes reward-predictive cues and prediction errors. In contrast, the subpopulation that projects to the NAc shell preferentially encodes goal-directed actions and relative reward anticipation. VTA GABA neuron activity strongly contrasts VTA dopamine population activity and preferentially encodes reward outcome and retrieval. Electrophysiology, targeted optogenetics, and whole-brain input mapping reveal multiple convergent sources that contribute to the heterogeneity among VTA dopamine subpopulations that likely underlies their distinct encoding of reward-related associations and motivation that defines their functions in these contexts.

Mixed Representations of Sound and Action in the Auditory Midbrain

Linking sensory input and its consequences is a fundamental brain operation. During behavior, the neural activity of neocortical and limbic systems often reflects dynamic combinations of sensory and task-dependent variables, and these "mixed representations" are suggested to be important for perception, learning, and plasticity. However, the extent to which such integrative computations might occur outside of the forebrain is less clear. Here, we conduct cellular-resolution two-photon Ca2+ imaging in the superficial "shell" layers of the inferior colliculus (IC), as head-fixed mice of either sex perform a reward-based psychometric auditory task. We find that the activity of individual shell IC neurons jointly reflects auditory cues, mice's actions, and behavioral trial outcomes, such that trajectories of neural population activity diverge depending on mice's behavioral choice. Consequently, simple classifier models trained on shell IC neuron activity can predict trial-by-trial outcomes, even when training data are restricted to neural activity occurring prior to mice's instrumental actions. Thus, in behaving mice, auditory midbrain neurons transmit a population code that reflects a joint representation of sound, actions, and task-dependent variables.

Impact of Unitary Synaptic Inhibition on Spike Timing in Ventral Tegmental Area Dopamine Neurons

Midbrain dopamine neurons receive convergent synaptic input from multiple brain areas, which perturbs rhythmic pacemaking to produce the complex firing patterns observed in vivo. This study investigated the impact of single and multiple inhibitory inputs on ventral tegmental area (VTA) dopamine neuron firing in mice of both sexes using novel experimental measurements and modeling. We first measured unitary inhibitory postsynaptic currents produced by single axons using both minimal electrical stimulation and minimal optical stimulation of rostromedial tegmental nucleus and ventral pallidum afferents. We next determined the phase resetting curve, the reversal potential for GABAA receptor-mediated inhibitory postsynaptic currents (IPSCs), and the average interspike membrane potential trajectory during pacemaking. We combined these data in a phase oscillator model of a VTA dopamine neuron, simulating the effects of unitary inhibitory postsynaptic conductances (uIPSGs) on spike timing and rate. The effect of a uIPSG on spike timing was predicted to vary according to its timing within the interspike interval or phase. Simulations were performed to predict the pause duration resulting from the synchronous arrival of multiple uIPSGs and the changes in firing rate and regularity produced by asynchronous uIPSGs. The model data suggest that asynchronous inhibition is more effective than synchronous inhibition, because it tends to hold the neuron at membrane potentials well positive to the IPSC reversal potential. Our results indicate that small fluctuations in the inhibitory synaptic input arriving from the many afferents to each dopamine neuron are sufficient to produce highly variable firing patterns, including pauses that have been implicated in reinforcement.

Rethinking dopamine-guided action sequence learning

As opposed to those requiring a single action for reward acquisition, tasks necessitating action sequences demand that animals learn action elements and their sequential order and sustain the behaviour until the sequence is completed. With repeated learning, animals not only exhibit precise execution of these sequences but also demonstrate enhanced smoothness and efficiency. Previous research has demonstrated that midbrain dopamine and its major projection target, the striatum, play crucial roles in these processes. Recent studies have shown that dopamine from the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) serve distinct functions in action sequence learning. The distinct contributions of dopamine also depend on the striatal subregions, namely the ventral, dorsomedial and dorsolateral striatum. Here, we have reviewed recent findings on the role of striatal dopamine in action sequence learning, with a focus on recent rodent studies.

Ventral pallidum GABA and glutamate neurons drive approach and avoidance through distinct modulation of VTA cell types

The ventral pallidum (VP) contains GABA and glutamate neurons projecting to ventral tegmental area (VTA) whose stimulation drives approach and avoidance, respectively. Yet little is known about the mechanisms by which VP cell types shape VTA activity and drive behavior. Here, we found that both VP GABA and glutamate neurons were activated during approach to reward or by delivery of an aversive stimulus. Stimulation of VP GABA neurons inhibited VTA GABA, but activated dopamine and glutamate neurons. Remarkably, stimulation-evoked activation was behavior-contingent such that VTA recruitment was inhibited when evoked by the subject's own action. Conversely, VP glutamate neurons activated VTA GABA, as well as dopamine and glutamate neurons, despite driving aversion. However, VP glutamate neurons evoked dopamine in aversion-associated ventromedial nucleus accumbens (NAc), but reduced dopamine release in reward-associated dorsomedial NAc. These findings show how heterogeneous VP projections to VTA can be engaged to shape approach and avoidance behaviors.

Human Substantia Nigra Neurons Encode Reward Expectations

Dopamine (DA) signals originating from substantia nigra (SN) neurons are centrally involved in the regulation of motor and reward processing. DA signals behaviorally relevant events where reward outcomes differ from expectations (reward prediction errors, RPEs). RPEs play a crucial role in learning optimal courses of action and in determining response vigor when an agent expects rewards. Nevertheless, how reward expectations, crucial for RPE calculations, are conveyed to and represented in the dopaminergic system is not fully understood, especially in the human brain where the activity of DA neurons is difficult to study. One possibility, suggested by evidence from animal models, is that DA neurons explicitly encode reward expectations. Alternatively, they may receive RPE information directly from upstream brain regions. To address whether SN neuron activity directly reflects reward expectation information, we directly examined the encoding of reward expectation signals in human putative DA neurons by performing single-unit recordings from the SN of patients undergoing neurosurgery. Patients played a two-armed bandit decision-making task in which they attempted to maximize reward. We show that neuronal firing rates (FR) of putative DA neurons during the reward expectation period explicitly encode reward expectations. First, activity in these neurons was modulated by previous trial outcomes, such that FR were greater after positive outcomes than after neutral or negative outcome trials. Second, this increase in FR was associated with shorter reaction times, consistent with an invigorating effect of DA neuron activity during expectation. These results suggest that human DA neurons explicitly encode reward expectations, providing a neurophysiological substrate for a signal critical for reward learning.

Phasic dopamine signals are reduced in the spontaneously hypertensive rat and increased by methylphenidate

The spontaneously hypertensive rat (SHR) is a selectively bred animal strain that is frequently used to model attention-deficit hyperactivity disorder (ADHD) because of certain genetically determined behavioural characteristics. To test the hypothesis that the characteristically altered response to positive reinforcement in SHRs may be due to altered phasic dopamine response to reward, we measured phasic dopamine signals in the SHRs and Sprague Dawley (SD) rats using in vivo fast-scan cyclic voltammetry. The effects of the dopamine reuptake inhibitor, methylphenidate, on these signals were also studied. Phasic dopamine signals during the pairing of a sensory cue with electrical stimulation of midbrain dopamine neurons were significantly smaller in the SHRs than in the SD rats. Over repeated pairings, the dopamine response to the sensory cue increased, whereas the response to the electrical stimulation of dopamine neurons decreased, similarly in both strains. However, the final amplitude of the response to the sensory cue after pairing was significantly smaller in SHRs than in the SD rats. Methylphenidate increased responses to sensory cues to a significantly greater extent in the SHRs than in the SD rats, due largely to differences in the low dose effect. At a higher dose, methylphenidate increased responses to sensory cues and electrical stimulation similarly in SHRs and SD rats. The smaller dopamine responses may explain the reduced salience of reward-predicting cues previously reported in the SHR, whereas the action of methylphenidate on the cue response suggests a potential mechanism for the therapeutic effects of low-dose methylphenidate in ADHD.

Dopamine across timescales and cell types: Relevance for phenotypes in Parkinson's disease progression

Dopamine neurons in the substantia nigra pars compacta (SNc) synthesize and release dopamine, a critical neurotransmitter for movement and learning. SNc dopamine neurons degenerate in Parkinson's Disease (PD), causing a host of motor and non-motor symptoms. Here, we review recent conceptual advances in our basic understanding of the dopamine system - including our rapidly advancing knowledge of dopamine neuron heterogeneity - with special attention to their importance for understanding PD. In PD patients, dopamine neuron degeneration progresses from lateral SNc to medial SNc, suggesting clinically relevant heterogeneity in dopamine neurons. With technical advances in dopamine system interrogation, we can understand the relevance of this heterogeneity for PD progression and harness it to develop new treatments.

Glutamate inputs send prediction error of reward, but not negative value of aversive stimuli, to dopamine neurons

Midbrain dopamine neurons are thought to signal reward prediction errors (RPEs), but the mechanisms underlying RPE computation, particularly the contributions of different neurotransmitters, remain poorly understood. Here, we used a genetically encoded glutamate sensor to examine the pattern of glutamate inputs to dopamine neurons in mice. We found that glutamate inputs exhibit virtually all of the characteristics of RPE rather than conveying a specific component of RPE computation, such as reward or expectation. Notably, whereas glutamate inputs were transiently inhibited by reward omission, they were excited by aversive stimuli. Opioid analgesics altered dopamine negative responses to aversive stimuli into more positive responses, whereas excitatory responses of glutamate inputs remained unchanged. Our findings uncover previously unknown synaptic mechanisms underlying RPE computations; dopamine responses are shaped by both synergistic and competitive interactions between glutamatergic and GABAergic inputs to dopamine neurons depending on valences, with competitive interactions playing a role in responses to aversive stimuli.

Prediction error in dopamine neurons during associative learning

Dopamine neurons have long been thought to facilitate learning by broadcasting reward prediction error (RPE), a teaching signal used in machine learning, but more recent work has advanced alternative models of dopamine's computational role. Here, I revisit this critical issue and review new experimental evidences that tighten the link between dopamine activity and RPE. First, I introduce the recent observation of a gradual backward shift of dopamine activity that had eluded researchers for over a decade. I also discuss several other findings, such as dopamine ramping, that were initially interpreted to conflict but later found to be consistent with RPE. These findings improve our understanding of neural computation in dopamine neurons.

Sensory Cues Potentiate VTA Dopamine Mediated Reinforcement

Sensory cues are critical for shaping decisions and invigorating actions during reward seeking. Dopamine neurons in the ventral tegmental area (VTA) are central in this process, supporting associative learning in Pavlovian and instrumental settings. Studies of intracranial self-stimulation (ICSS) behavior, which show that animals will work hard to receive stimulation of dopamine neurons, support the notion that dopamine transmits a reward or value signal to support learning. Recent studies have begun to question this, however, emphasizing dopamine's value-free functions, leaving its contribution to behavioral reinforcement somewhat muddled. Here, we investigated the role of sensory stimuli in dopamine-mediated reinforcement, using an optogenetic ICSS paradigm in tyrosine hydroxylase (TH)-Cre rats. We find that while VTA dopamine neuron activation in the absence of explicit external cues is sufficient to maintain robust self-stimulation, the presence of cues dramatically potentiates ICSS behavior. Our results support a framework where dopamine can have some base value as a reinforcer, but the impact of this signal is modulated heavily by the sensory learning context.

Ventral pallidum neurons projecting to the ventral tegmental area reinforce but do not invigorate reward-seeking behavior

Reward-predictive cues acquire motivating and reinforcing properties that contribute to the escalation and relapse of drug use in addiction. The ventral pallidum (VP) and ventral tegmental area (VTA) are two key nodes in brain reward circuitry implicated in addiction and cue-driven behavior. In the current study, we use in vivo fiber photometry and optogenetics to record from and manipulate VP→VTA in rats performing a discriminative stimulus task to determine the role these neurons play in invigoration and reinforcement by reward cues. We find that VP→VTA neurons are active during reward consumption and that optogenetic stimulation of these neurons biases choice behavior and is reinforcing. Critically, we find no encoding of reward-seeking vigor, and optogenetic stimulation does not enhance the probability or vigor of reward seeking in response to cues. Our results suggest that VP→VTA activity is more important for reinforcement than for invigoration of reward seeking by cues.

An opponent striatal circuit for distributional reinforcement learning

Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards - an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2,3, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions4. To fill this gap, we used high-density probes (Neuropixels) to acutely record striatal activity from well-trained, water-restricted mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons - D1 and D2 MSNs - contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs5-15 to reap the computational benefits of distributional RL.

Inhibitory Pedunculopontine Neurons Gate Dopamine-Mediated Motor Actions of Unsigned Valence

BACKGROUND

The pedunculopontine nucleus (PPN) maintains a bidirectional connectivity with the basal ganglia that supports their shared roles in the selection and execution of motor actions. Previous studies identified a role for PPN neurons in goal-directed behavior, but the cellular substrates underlying this function have not been elucidated. We recently revealed the existence of a monosynaptic GABAergic input from the PPN that inhibits dopamine neurons of the substantia nigra. Activation of this pathway interferes with the execution of learned motor sequences when the actions are rewarded, even though the inhibition of dopamine neurons did not shift the value of the action, hence suggesting executive control over the gating of behavior.

OBJECTIVE

To test the attributes of the inhibition of dopamine neurons by the PPN in the context of goal-directed behavior regardless of whether the outcome is positively or negatively reinforced.

METHODS

We delivered optogenetic stimulation to PPN GABAergic axon terminals in the substantia nigra during a battery of behavioral tasks with positive and negative valence.

RESULTS

Inhibition of dopamine neurons by PPN optogenetic activation during an appetitive task impaired the initiation and overall execution of the behavioral sequence without affecting the consumption of reward. During an active avoidance task, the same activation impaired the ability of mice to avoid a foot shock, but their escape response was unaffected. In addition, responses to potential threats were significantly attenuated.

CONCLUSION

Our results show that PPN GABAergic neurons modulate learned, goal-directed behavior of unsigned valence without affecting overall motor behavior.

Reduced neural encoding of utility prediction errors in cocaine addiction

Influential accounts of addiction posit alterations in adaptive behavior driven by deficient dopaminergic prediction errors (PEs), signaling the discrepancy between actual and expected reward. Dopamine neurons encode these error signals in subjective terms, calibrated by individual risk preferences, as "utility" PEs. It remains unclear, however, whether people with drug addiction have PE deficits or their computational source. Here, using an analogous task to prior single-unit studies with known expectancies, we show that fMRI-measured PEs similarly reflect utility PEs. Relative to control participants, people with chronic cocaine addiction demonstrate reduced utility PEs in the dopaminoceptive ventral striatum, with similar trends in orbitofrontal cortex. Dissecting this PE signal into its subcomponent terms attributed these reductions to weaker striatal responses to received reward/utility, whereas suppression of activity with reward expectation was unchanged. These findings support that addiction may fundamentally disrupt PE signaling and reveal an underappreciated role for perceived reward value in this mechanism.

Postsynaptic cell type and synaptic distance do not determine efficiency of monosynaptic rabies virus spread measured at synaptic resolution

Retrograde monosynaptic tracing using glycoprotein-deleted rabies virus is an important component of the toolkit for investigation of neural circuit structure and connectivity. It allows for the identification of first-order presynaptic connections to cell populations of interest across both the central and peripheral nervous system, helping to decipher the complex connectivity patterns of neural networks that give rise to brain function. Despite its utility, the factors that influence the probability of transsynaptic rabies spread are not well understood. While it is well established that expression levels of rabies glycoprotein used to trans-complement G-deleted rabies can result in large changes in numbers of inputs labeled per starter cell (convergence index [CI]), it is not known how typical values of CI relate to the proportions of synaptic contacts or input neurons labeled. And it is not known whether inputs to different cell types, or synaptic contacts that are more proximal or distal to the cell body, are labeled with different probabilities. Here, we use a new rabies virus construct that allows for the simultaneous labeling of pre- and postsynaptic specializations to quantify the proportion of synaptic contacts labeled in mouse primary visual cortex. We demonstrate that with typical conditions about 40% of first-order presynaptic excitatory synapses to cortical excitatory and inhibitory neurons are labeled. We show that using matched tracing conditions there are similar proportions of labeled contacts onto L4 excitatory pyramidal, somatostatin (Sst) inhibitory, and vasoactive intestinal peptide (Vip) starter cell types. Furthermore, we find no difference in the proportions of labeled excitatory contacts onto postsynaptic sites at different subcellular locations.

Cell type specificity for circuit output in the midbrain dopaminergic system

Midbrain dopaminergic neurons are a relatively small group of neurons in the mammalian brain controlling a wide range of behaviors. In recent years, increasingly sophisticated tracing, imaging, transcriptomic, and machine learning approaches have provided substantial insights into the anatomical, molecular, and functional heterogeneity of dopaminergic neurons. Despite this wealth of new knowledge, it remains unclear whether and how the diverse features defining dopaminergic subclasses converge to delineate functional ensembles within the dopaminergic system. Here, we review recent studies investigating various aspects of dopaminergic heterogeneity and discuss how development, behavior, and disease influence subtype characteristics. We then outline what further approaches could be pursued to gain a more inclusive picture of dopaminergic diversity, which could be crucial to understanding the functional architecture of this system.

Aberrations in temporal dynamics of cognitive processing induced by Parkinson's disease and Levodopa

The motor symptoms of Parkinson's disease (PD) have been shown to significantly improve by Levodopa. However, despite the widespread adoption of Levodopa as a standard pharmaceutical drug for the treatment of PD, cognitive impairments linked to PD do not show visible improvement with Levodopa treatment. Furthermore, the neuronal and network mechanisms behind the PD-induced cognitive impairments are not clearly understood. In this work, we aim to explain these cognitive impairments, as well as the ones exacerbated by Levodopa, through examining the differential dynamic patterns of the phase-amplitude coupling (PAC) during cognitive functions. EEG data recorded in an auditory oddball task performed by a cohort consisting of controls and a group of PD patients during both on and off periods of Levodopa treatment were analyzed to derive the temporal dynamics of the PAC across the brain. We observed distinguishing patterns in the PAC dynamics, as an indicator of information binding, which can explain the slower cognitive processing associated with PD in the form of a latency in the PAC peak time. Thus, considering the high-level connections between the hippocampus, the posterior and prefrontal cortices established through the dorsal and ventral striatum acting as a modulatory system, we posit that the primary issue with cognitive impairments of PD, as well as Levodopa's cognitive deficit side effects, can be attributed to the changes in temporal dynamics of dopamine release influencing the modulatory function of the striatum.

Glutamate inputs send prediction error of reward but not negative value of aversive stimuli to dopamine neurons

Midbrain dopamine neurons are thought to signal reward prediction errors (RPEs) but the mechanisms underlying RPE computation, particularly contributions of different neurotransmitters, remain poorly understood. Here we used a genetically-encoded glutamate sensor to examine the pattern of glutamate inputs to dopamine neurons. We found that glutamate inputs exhibit virtually all of the characteristics of RPE, rather than conveying a specific component of RPE computation such as reward or expectation. Notably, while glutamate inputs were transiently inhibited by reward omission, they were excited by aversive stimuli. Opioid analgesics altered dopamine negative responses to aversive stimuli toward more positive responses, while excitatory responses of glutamate inputs remained unchanged. Our findings uncover previously unknown synaptic mechanisms underlying RPE computations; dopamine responses are shaped by both synergistic and competitive interactions between glutamatergic and GABAergic inputs to dopamine neurons depending on valences, with competitive interactions playing a role in responses to aversive stimuli.

Dopaminergic neurons dynamically update sensory values during olfactory maneuver

Dopaminergic neurons (DANs) drive associative learning to update the value of sensory cues, but their contribution to the assessment of sensory values outside the context of association remains largely unexplored. Here, we show in Drosophila that DANs in the mushroom body encode the innate value of odors and constantly update the current value by inducing plasticity during olfactory maneuver. Our connectome-based network model linking all the way from the olfactory neurons to DANs reproduces the characteristics of DAN responses, proposing a concrete circuit mechanism for computation. Downstream of DANs, odors alone induce value- and dopamine-dependent changes in the activity of mushroom body output neurons, which store the current value of odors. Consistent with this neural plasticity, specific sets of DANs bidirectionally modulate flies' steering in a virtual olfactory environment. Thus, the DAN circuit known for discrete, associative learning also continuously updates odor values in a nonassociative manner.

Neurocircuits for motivation

The nervous system coordinates various motivated behaviors such as feeding, drinking, and escape to promote survival and evolutionary fitness. Although the precise behavioral repertoires required for distinct motivated behaviors are diverse, common features such as approach or avoidance suggest that common brain substrates are required for a wide range of motivated behaviors. In this Review, I describe a framework by which neural circuits specified for some innate drives regulate the activity of ventral tegmental area (VTA) dopamine neurons to reinforce ongoing or planned actions to fulfill motivational demands. This framework may explain why signaling from VTA dopamine neurons is ubiquitously involved in many types of diverse volitional motivated actions, as well as how sensory and interoceptive cues can initiate specific goal-directed actions.

Sensory cues potentiate VTA dopamine mediated reinforcement

Sensory cues are critical for shaping decisions and invigorating actions during reward seeking. Dopamine neurons in the ventral tegmental area (VTA) are critical in this process, supporting associative learning in Pavlovian and instrumental settings. Studies of intracranial self stimulation (ICSS) behavior, which show that animals will work hard to receive stimulation of dopamine neurons, support the notion that dopamine transmits a reward or value signal to support learning. Recent studies have begun to question this, however, emphasizing dopamine's value-free functions, leaving its contribution to behavioral reinforcement somewhat muddled. Here, we investigated the role of sensory stimuli in dopamine-mediated reinforcement, using an optogenetic ICSS paradigm in tyrosine hydroxylase (TH)-cre rats. We find that while VTA dopamine neuron activation in the absence of any external cueing stimulus is sufficient to maintain robust self stimulation, the presence of cues dramatically potentiates ICSS behavior. Our results support a framework where dopamine can have some base value as a reinforcer, but the impact of this signal is modulated heavily by the sensory learning context.

Biologically plausible local synaptic learning rules robustly implement deep supervised learning

In deep neural networks, representational learning in the middle layer is essential for achieving efficient learning. However, the currently prevailing backpropagation learning rules (BP) are not necessarily biologically plausible and cannot be implemented in the brain in their current form. Therefore, to elucidate the learning rules used by the brain, it is critical to establish biologically plausible learning rules for practical memory tasks. For example, learning rules that result in a learning performance worse than that of animals observed in experimental studies may not be computations used in real brains and should be ruled out. Using numerical simulations, we developed biologically plausible learning rules to solve a task that replicates a laboratory experiment where mice learned to predict the correct reward amount. Although the extreme learning machine (ELM) and weight perturbation (WP) learning rules performed worse than the mice, the feedback alignment (FA) rule achieved a performance equal to that of BP. To obtain a more biologically plausible model, we developed a variant of FA, FA_Ex-100%, which implements direct dopamine inputs that provide error signals locally in the layer of focus, as found in the mouse entorhinal cortex. The performance of FA_Ex-100% was comparable to that of conventional BP. Finally, we tested whether FA_Ex-100% was robust against rule perturbations and biologically inevitable noise. FA_Ex-100% worked even when subjected to perturbations, presumably because it could calibrate the correct prediction error (e.g., dopaminergic signals) in the next step as a teaching signal if the perturbation created a deviation. These results suggest that simplified and biologically plausible learning rules, such as FA_Ex-100%, can robustly facilitate deep supervised learning when the error signal, possibly conveyed by dopaminergic neurons, is accurate.

Selective encoding of reward predictions and prediction errors by globus pallidus subpopulations

Basal ganglia (BG) circuits help guide and invigorate actions using predictions of future rewards (values). Within the BG, the globus pallidus pars externa (GPe) may play an essential role in aggregating and distributing value information. We recorded from the GPe in unrestrained rats performing both Pavlovian and instrumental tasks to obtain rewards and distinguished neuronal subtypes by their firing properties across the wake/sleep cycle and optogenetic tagging. In both tasks, the parvalbumin-positive (PV+), faster-firing "prototypical" neurons showed strong, sustained modulation by value, unlike other subtypes, including the "arkypallidal" cells that project back to striatum. Furthermore, we discovered that a distinct minority (7%) of GP cells display slower, pacemaker-like firing and encode reward prediction errors (RPEs) almost identically to midbrain dopamine neurons. These cell-specific forms of GPe value representation help define the circuit mechanisms by which the BG contribute to motivation and reinforcement learning.

Mixed representations of sound and action in the auditory midbrain

Linking sensory input and its consequences is a fundamental brain operation. Accordingly, neural activity of neo-cortical and limbic systems often reflects dynamic combinations of sensory and behaviorally relevant variables, and these "mixed representations" are suggested to be important for perception, learning, and plasticity. However, the extent to which such integrative computations might occur in brain regions upstream of the forebrain is less clear. Here, we conduct cellular-resolution 2-photon Ca2+ imaging in the superficial "shell" layers of the inferior colliculus (IC), as head-fixed mice of either sex perform a reward-based psychometric auditory task. We find that the activity of individual shell IC neurons jointly reflects auditory cues and mice's actions, such that trajectories of neural population activity diverge depending on mice's behavioral choice. Consequently, simple classifier models trained on shell IC neuron activity can predict trial-by-trial outcomes, even when training data are restricted to neural activity occurring prior to mice's instrumental actions. Thus in behaving animals, auditory midbrain neurons transmit a population code that reflects a joint representation of sound and action.

Dopamine release in the nucleus accumbens promotes REM sleep and cataplexy

Patients with the sleep disorder narcolepsy suffer from excessive daytime sleepiness, disrupted nighttime sleep, and cataplexy-the abrupt loss of postural muscle tone during wakefulness, often triggered by strong emotion. The dopamine (DA) system is implicated in both sleep-wake states and cataplexy, but little is known about the function of DA release in the striatum and sleep disorders. Recording DA release in the ventral striatum revealed orexin-independent changes across sleep-wake states as well as striking increases in DA release in the ventral, but not dorsal, striatum prior to cataplexy onset. Tonic low-frequency stimulation of ventral tegmental efferents in the ventral striatum suppressed both cataplexy and rapid eye movement (REM) sleep, while phasic high-frequency stimulation increased cataplexy propensity and decreased the latency to REM sleep. Together, our findings demonstrate a functional role of DA release in the striatum in regulating cataplexy and REM sleep.

A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost

Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.

Ventral pallidum GABA and glutamate neurons drive approach and avoidance through distinct modulation of VTA cell types

The ventral pallidum (VP) contains GABA and glutamate (Glut) neurons projecting to ventral tegmental area (VTA) whose stimulation drives approach and avoidance, respectively. Yet little is known about the cell-type-specific mechanisms by which VP projections to VTA drive behavior. Here, we found that both VP GABA and Glut neurons were activated during approach to reward or delivery of an aversive stimulus. Stimulation of VP GABA neurons inhibited VTA GABA, but activated dopamine (DA) and glutamate neurons. Remarkably, this cell-type-specific recruitment was behavior-contingent such that VTA recruitment was inhibited when evoked by the subject's own action. Conversely, VP Glut neurons activated VTA GABA, as well as DA and Glut neurons, despite driving aversion. However, VP Glut neurons evoked DA in reward-associated ventromedial nucleus accumbens (NAc), but reduced DA in aversion-associated dorsomedial NAc. These findings show how heterogeneous VP cell types can engage VTA cell types to shape approach and avoidance behaviors.

Striatal dopamine regulates sleep states and narcolepsy-cataplexy

Disruptions to sleep can be debilitating and have a severe effect on daily life. Patients with the sleep disorder narcolepsy suffer from excessive daytime sleepiness, disrupted nighttime sleep, and cataplexy - the abrupt loss of postural muscle tone (atonia) during wakefulness, often triggered by strong emotion. The dopamine (DA) system is implicated in both sleep-wake states and cataplexy, but little is known about the function of DA release in the striatum - a major output region of midbrain DA neurons - and sleep disorders. To better characterize the function and pattern of DA release in sleepiness and cataplexy, we combined optogenetics, fiber photometry, and sleep recordings in a murine model of narcolepsy (orexin-/-; OX KO) and in wildtype mice. Recording DA release in the ventral striatum revealed OX-independent changes across sleep-wake states as well as striking increases in DA release in the ventral, but not dorsal, striatum prior to cataplexy onset. Tonic low frequency stimulation of ventral tegmental efferents in the ventral striatum suppressed both cataplexy and REM sleep, while phasic high frequency stimulation increased cataplexy propensity and decreased the latency to rapid eye movement (REM) sleep. Together, our findings demonstrate a functional role of DA release in the striatum in regulating cataplexy and REM sleep.

Rewarding-unrewarding prediction signals under a bivalent context in the primate lateral hypothalamus

Animals can expect rewards under equivocal situations. The lateral hypothalamus (LH) is thought to process motivational information by producing valence signals of reward and punishment. Despite rich studies using rodents and non-human primates, these signals have been assessed separately in appetitive and aversive contexts; therefore, it remains unclear what information the LH encodes in equivocal situations. To address this issue, macaque monkeys were conditioned under a bivalent context in which reward and punishment were probabilistically delivered, in addition to appetitive and aversive contexts. The monkeys increased approaching behavior similarly in the bivalent and appetitive contexts as the reward probability increased. They increased avoiding behavior under the bivalent and aversive contexts as the punishment probability increased, but the mean frequency was lower under the bivalent context than under the aversive context. The population activity correlated with these mean behaviors. Moreover, the LH produced fine prediction signals of reward expectation, uncertainty, and predictability consistently in the bivalent and appetitive contexts by recruiting context-independent and context-dependent subpopulations of neurons, while it less produced punishment signals in the aversive and bivalent contexts. Further, neural ensembles encoded context information and "rewarding-unrewarding" and "reward-punishment" valence. These signals may motivate individuals robustly in equivocal environments.

Modulation of aversive value coding in the vertebrate and invertebrate brain

Avoiding potentially dangerous situations is key for the survival of any organism. Throughout life, animals learn to avoid environments, stimuli or actions that can lead to bodily harm. While the neural bases for appetitive learning, evaluation and value-based decision-making have received much attention, recent studies have revealed more complex computations for aversive signals during learning and decision-making than previously thought. Furthermore, previous experience, internal state and systems level appetitive-aversive interactions seem crucial for learning specific aversive value signals and making appropriate choices. The emergence of novel methodologies (computation analysis coupled with large-scale neuronal recordings, neuronal manipulations at unprecedented resolution offered by genetics, viral strategies and connectomics) has helped to provide novel circuit-based models for aversive (and appetitive) valuation. In this review, we focus on recent vertebrate and invertebrate studies yielding strong evidence that aversive value information can be computed by a multitude of interacting brain regions, and that past experience can modulate future aversive learning and therefore influence value-based decisions.

Homeostatic Reinforcement Theory Accounts for Sodium Appetitive State- and Taste-Dependent Dopamine Responding

Seeking and consuming nutrients is essential to survival and the maintenance of life. Dynamic and volatile environments require that animals learn complex behavioral strategies to obtain the necessary nutritive substances. While this has been classically viewed in terms of homeostatic regulation, recent theoretical work proposed that such strategies result from reinforcement learning processes. This theory proposed that phasic dopamine (DA) signals play a key role in signaling potentially need-fulfilling outcomes. To examine links between homeostatic and reinforcement learning processes, we focus on sodium appetite as sodium depletion triggers state- and taste-dependent changes in behavior and DA signaling evoked by sodium-related stimuli. We find that both the behavior and the dynamics of DA signaling underlying sodium appetite can be accounted for by a homeostatically regulated reinforcement learning framework (HRRL). We first optimized HRRL-based agents to sodium-seeking behavior measured in rodents. Agents successfully reproduced the state and the taste dependence of behavioral responding for sodium as well as for lithium and potassium salts. We then showed that these same agents account for the regulation of DA signals evoked by sodium tastants in a taste- and state-dependent manner. Our models quantitatively describe how DA signals evoked by sodium decrease with satiety and increase with deprivation. Lastly, our HRRL agents assigned equal preference for sodium versus the lithium containing salts, accounting for similar behavioral and neurophysiological observations in rodents. We propose that animals use orosensory signals as predictors of the internal impact of the consumed good and our results pose clear targets for future experiments. In sum, this work suggests that appetite-driven behavior may be driven by reinforcement learning mechanisms that are dynamically tuned by homeostatic need.

Ventral pallidal regulation of motivated behaviors and reinforcement

The interconnected nuclei of the ventral basal ganglia have long been identified as key regulators of motivated behavior, and dysfunction of this circuit is strongly implicated in mood and substance use disorders. The ventral pallidum (VP) is a central node of the ventral basal ganglia, and recent studies have revealed complex VP cellular heterogeneity and cell- and circuit-specific regulation of reward, aversion, motivation, and drug-seeking behaviors. Although the VP is canonically considered a relay and output structure for this circuit, emerging data indicate that the VP is a central hub in an extensive network for reward processing and the regulation of motivation that extends beyond classically defined basal ganglia borders. VP neurons respond temporally faster and show more advanced reward coding and prediction error processing than neurons in the upstream nucleus accumbens, and regulate the activity of the ventral mesencephalon dopamine system. This review will summarize recent findings in the literature and provide an update on the complex cellular heterogeneity and cell- and circuit-specific regulation of motivated behaviors and reinforcement by the VP with a specific focus on mood and substance use disorders. In addition, we will discuss mechanisms by which stress and drug exposure alter the functioning of the VP and produce susceptibility to neuropsychiatric disorders. Lastly, we will outline unanswered questions and identify future directions for studies necessary to further clarify the central role of VP neurons in the regulation of motivated behaviors. Significance: Research in the last decade has revealed a complex cell- and circuit-specific role for the VP in reward processing and the regulation of motivated behaviors. Novel insights obtained using cell- and circuit-specific interrogation strategies have led to a major shift in our understanding of this region. Here, we provide a comprehensive review of the VP in which we integrate novel findings with the existing literature and highlight the emerging role of the VP as a linchpin of the neural systems that regulate motivation, reward, and aversion. In addition, we discuss the dysfunction of the VP in animal models of neuropsychiatric disorders.

Degeneracy and stability in neural circuits of dopamine and serotonin neuromodulators: A theoretical consideration

Degenerate neural circuits perform the same function despite being structurally different. However, it is unclear whether neural circuits with interacting neuromodulator sources can themselves degenerate while maintaining the same neuromodulatory function. Here, we address this by computationally modeling the neural circuits of neuromodulators serotonin and dopamine, local glutamatergic and GABAergic interneurons, and their possible interactions, under reward/punishment-based conditioning tasks. The neural modeling is constrained by relevant experimental studies of the VTA or DRN system using, e.g., electrophysiology, optogenetics, and voltammetry. We first show that a single parsimonious, sparsely connected neural circuit model can recapitulate several separate experimental findings that indicated diverse, heterogeneous, distributed, and mixed DRNVTA neuronal signaling in reward and punishment tasks. The inability of this model to recapitulate all observed neuronal signaling suggests potentially multiple circuits acting in parallel. Then using computational simulations and dynamical systems analysis, we demonstrate that several different stable circuit architectures can produce the same observed network activity profile, hence demonstrating degeneracy. Due to the extensive D2-mediated connections in the investigated circuits, we simulate the D2 receptor agonist by increasing the connection strengths emanating from the VTA DA neurons. We found that the simulated D2 agonist can distinguish among sub-groups of the degenerate neural circuits based on substantial deviations in specific neural populations' activities in reward and punishment conditions. This forms a testable model prediction using pharmacological means. Overall, this theoretical work suggests the plausibility of degeneracy within neuromodulator circuitry and has important implications for the stable and robust maintenance of neuromodulatory functions.

Pupillary dynamics of mice performing a Pavlovian delay conditioning task reflect reward-predictive signals

Pupils can signify various internal processes and states, such as attention, arousal, and working memory. Changes in pupil size have been associated with learning speed, prediction of future events, and deviations from the prediction in human studies. However, the detailed relationships between pupil size changes and prediction are unclear. We explored pupil size dynamics in mice performing a Pavlovian delay conditioning task. A head-fixed experimental setup combined with deep-learning-based image analysis enabled us to reduce spontaneous locomotor activity and to track the precise dynamics of pupil size of behaving mice. By setting up two experimental groups, one for which mice were able to predict reward in the Pavlovian delay conditioning task and the other for which mice were not, we demonstrated that the pupil size of mice is modulated by reward prediction and consumption, as well as body movements, but not by unpredicted reward delivery. Furthermore, we clarified that pupil size is still modulated by reward prediction even after the disruption of body movements by intraperitoneal injection of haloperidol, a dopamine D2 receptor antagonist. These results suggest that changes in pupil size reflect reward prediction signals. Thus, we provide important evidence to reconsider the neuronal circuit involved in computing reward prediction error. This integrative approach of behavioral analysis, image analysis, pupillometry, and pharmacological manipulation will pave the way for understanding the psychological and neurobiological mechanisms of reward prediction and the prediction errors essential to learning and behavior.

Nigrostriatal dopamine pathway regulates auditory discrimination behavior

The auditory striatum, the tail portion of dorsal striatum in basal ganglia, is implicated in perceptual decision-making, transforming auditory stimuli to action outcomes. Despite its known connections to diverse neurological conditions, the dopaminergic modulation of sensory striatal neuronal activity and its behavioral influences remain unknown. We demonstrated that the optogenetic inhibition of dopaminergic projections from the substantia nigra pars compacta to the auditory striatum specifically impairs mouse choice performance but not movement in an auditory frequency discrimination task. In vivo dopamine and calcium imaging in freely behaving mice revealed that this dopaminergic projection modulates striatal tone representations, and tone-evoked striatal dopamine release inversely correlated with the evidence strength of tones. Optogenetic inhibition of D1-receptor expressing neurons and pharmacological inhibition of D1 receptors in the auditory striatum dampened choice performance accuracy. Our study uncovers a phasic mechanism within the nigrostriatal system that regulates auditory decisions by modulating ongoing auditory perception.

The auditory striatum, the tail portion of dorsal striatum, is implicated in decision-making. This study uncovers a phasic mechanism within the nigrostriatal system that regulates auditory decisions by modulating ongoing auditory perception.

A gradual temporal shift of dopamine responses mirrors the progression of temporal difference error in machine learning

A large body of evidence has indicated that the phasic responses of midbrain dopamine neurons show a remarkable similarity to a type of teaching signal (temporal difference (TD) error) used in machine learning. However, previous studies failed to observe a key prediction of this algorithm: that when an agent associates a cue and a reward that are separated in time, the timing of dopamine signals should gradually move backward in time from the time of the reward to the time of the cue over multiple trials. Here we demonstrate that such a gradual shift occurs both at the level of dopaminergic cellular activity and dopamine release in the ventral striatum in mice. Our results establish a long-sought link between dopaminergic activity and the TD learning algorithm, providing fundamental insights into how the brain associates cues and rewards that are separated in time.

Mesoaccumbal Dopamine Heterogeneity: What Do Dopamine Firing and Release Have to Do with It?

Ventral tegmental area (VTA) dopamine (DA) neurons are often thought to uniformly encode reward prediction errors. Conversely, DA release in the nucleus accumbens (NAc), the prominent projection target of these neurons, has been implicated in reinforcement learning, motivation, aversion, and incentive salience. This contrast between heterogeneous functions of DA release versus a homogeneous role for DA neuron activity raises numerous questions regarding how VTA DA activity translates into NAc DA release. Further complicating this issue is increasing evidence that distinct VTA DA projections into defined NAc subregions mediate diverse behavioral functions. Here, we evaluate evidence for heterogeneity within the mesoaccumbal DA system and argue that frameworks of DA function must incorporate the precise topographic organization of VTA DA neurons to clarify their contribution to health and disease.

A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning

Classical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.

Racing and Pacing in the Reward System: A Multi-Clock Circadian Control Over Dopaminergic Signalling

Level of motivation, responsiveness to rewards and punishment, invigoration of exploratory behaviours, and motor performance are subject to daily fluctuations that emerge from circadian rhythms in neuronal activity of the midbrain’s dopaminergic system. While endogenous circadian rhythms are weak in the ventral tegmental area and substantia nigra pars compacta, daily changes in expression of core clock genes, ion channels, neurotransmitter receptors, dopamine-synthesising enzymes, and dopamine transporters, accompanied by changes in electrical activity, are readily observed in these nuclei. These processes cause dopamine levels released in structures innervated by midbrain dopaminergic neurons (e.g., the striatum) to oscillate in a circadian fashion. Additionally, growing evidence show that the master circadian clock located in the suprachiasmatic nucleus of the hypothalamus (SCN) rhythmically influences the activity of the dopaminergic system through various intermediate targets. Thus, circadian changes in the activity of the dopaminergic system and concomitant dopamine release observed on a daily scale are likely to be generated both intrinsically and entrained by the master clock. Previous studies have shown that the information about the value and salience of stimuli perceived by the animal is encoded in the neuronal activity of brain structures innervating midbrain dopaminergic centres. Some of these structures themselves are relatively autonomous oscillators, while others exhibit a weak endogenous circadian rhythm synchronised by the SCN. Here, we place the dopaminergic system as a hub in the extensive network of extra-SCN circadian oscillators and discuss the possible consequences of its daily entrainment for animal physiology and behaviour.

Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning

Identifying the circuits responsible for cognition and understanding their embedded computations is a challenge for neuroscience. We establish here a hierarchical cross-scale approach, from behavioral modeling and fMRI in task-performing mice to cellular recordings, in order to disentangle local network contributions to olfactory reinforcement learning. At mesoscale, fMRI identifies a functional olfactory-striatal network interacting dynamically with higher-order cortices. While primary olfactory cortices respectively contribute only some value components, the downstream olfactory tubercle of the ventral striatum expresses comprehensively reward prediction, its dynamic updating, and prediction error components. In the tubercle, recordings reveal two underlying neuronal populations with non-redundant reward prediction coding schemes. One population collectively produces stabilized predictions as distributed activity across neurons; in the other, neurons encode value individually and dynamically integrate the recent history of uncertain outcomes. These findings validate a cross-scale approach to mechanistic investigations of higher cognitive functions in rodents.

Where and how the brain learns from experience is not fully understood. Here the authors use a hierarchical approach from behavioural modelling to systems fMRI to cellular coding reveals brain mechanisms for history informed updating of future predictions.

Choice-selective sequences dominate in cortical relative to thalamic inputs to NAc to support reinforcement learning

How are actions linked with subsequent outcomes to guide choices? The nucleus accumbens, which is implicated in this process, receives glutamatergic inputs from the prelimbic cortex and midline regions of the thalamus. However, little is known about whether and how representations differ across these input pathways. By comparing these inputs during a reinforcement learning task in mice, we discovered that prelimbic cortical inputs preferentially represent actions and choices, whereas midline thalamic inputs preferentially represent cues. Choice-selective activity in the prelimbic cortical inputs is organized in sequences that persist beyond the outcome. Through computational modeling, we demonstrate that these sequences can support the neural implementation of reinforcement-learning algorithms, in both a circuit model based on synaptic plasticity and one based on neural dynamics. Finally, we test and confirm a prediction of our circuit models by direct manipulation of nucleus accumbens input neurons.

An extended amygdala-midbrain circuit controlling cocaine withdrawal-induced anxiety and reinstatement

Although midbrain dopamine (DA) circuits are central to motivated behaviors, our knowledge of how experience modifies these circuits to facilitate subsequent behavioral adaptations is limited. Here we demonstrate the selective role of a ventral tegmental area DA projection to the amygdala (VTADA→amygdala) for cocaine-induced anxiety but not cocaine reward or sensitization. Our rabies virus-mediated circuit mapping approach reveals a persistent elevation in spontaneous and task-related activity of inhibitory GABAergic cells from the bed nucleus of the stria terminalis (BNST) and downstream VTADA→amygdala cells that can be detected even after a single cocaine exposure. Activity in BNSTGABA→midbrain cells is related to cocaine-induced anxiety but not reward or sensitization, and silencing this projection prevents development of anxiety during protracted withdrawal after cocaine administration. Finally, we observe that VTADA→amygdala cells are strongly activated after a challenge exposure to cocaine and that activity in these cells is necessary and sufficient for reinstatement of cocaine place preference.

Dopamine neurons evaluate natural fluctuations in performance quality

Many motor skills are learned by comparing ongoing behavior to internal performance benchmarks. Dopamine neurons encode performance error in behavioral paradigms where error is externally induced, but it remains unknown whether dopamine also signals the quality of natural performance fluctuations. Here, we record dopamine neurons in singing birds and examine how spontaneous dopamine spiking activity correlates with natural fluctuations in ongoing song. Antidromically identified basal ganglia-projecting dopamine neurons correlate with recent, and not future, song variations, consistent with a role in evaluation, not production. Furthermore, maximal dopamine spiking occurs at a single vocal target, consistent with either actively maintaining the existing song or shifting the song to a nearby form. These data show that spontaneous dopamine spiking can evaluate natural behavioral fluctuations unperturbed by experimental events such as cues or rewards.

Learning and producing skilled behavior requires an internal measure of performance. Duffy et al. examine dopamine neurons’ relationship to natural song in singing birds. Spontaneous dopamine activity correlates with song fluctuations in a manner consistent with evaluation of natural behavioral variations, independent of external perturbations, cues, or rewards.

Songbird subthalamic neurons project to dopaminergic midbrain and exhibit singing-related activity

Skill learning requires motor output to be evaluated against internal performance benchmarks. In songbirds, ventral tegmental area (VTA) dopamine neurons (DA) signal performance errors important for learning, but it remains unclear which brain regions project to VTA and how these inputs may contribute to DA error signaling. Here, we find that the songbird subthalamic nucleus (STN) projects to VTA and that STN microstimulation can excite VTA neurons. We also discover that STN receives inputs from motor cortical, auditory cortical, and ventral pallidal brain regions previously implicated in song evaluation. In the first neural recordings from songbird STN, we discover that the activity of most STN neurons is associated with body movements and not singing, but a small fraction of neurons exhibits precise song timing and performance error signals. Our results place the STN in a pathway important for song learning, but not song production, and expand the territories of songbird brain potentially associated with song learning.NEW & NOTEWORTHY Songbird subthalamic (STN) neurons exhibit singing-related signals and are interconnected with the motor cortical nucleus, auditory pallium, ventral pallidum, and ventral tegmental area, areas important for song generation and learning.

The prediction-error hypothesis of schizophrenia: new data point to circuit-specific changes in dopamine activity

Schizophrenia is a severe psychiatric disorder affecting 21 million people worldwide. People with schizophrenia suffer from symptoms including psychosis and delusions, apathy, anhedonia, and cognitive deficits. Strikingly, schizophrenia is characterised by a learning paradox involving difficulties learning from rewarding events, whilst simultaneously 'overlearning' about irrelevant or neutral information. While dysfunction in dopaminergic signalling has long been linked to the pathophysiology of schizophrenia, a cohesive framework that accounts for this learning paradox remains elusive. Recently, there has been an explosion of new research investigating how dopamine contributes to reinforcement learning, which illustrates that midbrain dopamine contributes in complex ways to reinforcement learning, not previously envisioned. This new data brings new possibilities for how dopamine signalling contributes to the symptomatology of schizophrenia. Building on recent work, we present a new neural framework for how we might envision specific dopamine circuits contributing to this learning paradox in schizophrenia in the context of models of reinforcement learning. Further, we discuss avenues of preclinical research with the use of cutting-edge neuroscience techniques where aspects of this model may be tested. Ultimately, it is hoped that this review will spur to action more research utilising specific reinforcement learning paradigms in preclinical models of schizophrenia, to reconcile seemingly disparate symptomatology and develop more efficient therapeutics.

Increased expression of pathological markers in Parkinson's disease dementia post-mortem brains compared to dementia with Lewy bodies

BACKGROUND

Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are common age-related neurodegenerative diseases comprising Lewy body spectrum disorders associated with cortical and subcortical Lewy body pathology. Over 30% of PD patients develop PD dementia (PDD), which describes dementia arising in the context of established idiopathic PD. Furthermore, Lewy bodies frequently accompany the amyloid plaque and neurofibrillary tangle pathology of Alzheimer's disease (AD), where they are observed in the amygdala of approximately 60% of sporadic and familial AD. While PDD and DLB share similar pathological substrates, they differ in the temporal onset of motor and cognitive symptoms; however, protein markers to distinguish them are still lacking.

METHODS

Here, we systematically studied a series of AD and PD pathogenesis markers, as well as mitochondria, mitophagy, and neuroinflammation-related indicators, in the substantia nigra (SN), temporal cortex (TC), and caudate and putamen (CP) regions of human post-mortem brain samples from individuals with PDD and DLB and condition-matched controls.

RESULTS

We found that p-APPT668 (TC), α-synuclein (CP), and LC3II (CP) are all increased while the tyrosine hydroxylase (TH) (CP) is decreased in both PDD and DLB compared to control. Also, the levels of Aβ42 and DD2R, IBA1, and p-LRRK2S935 are all elevated in PDD compared to control. Interestingly, protein levels of p-TauS199/202 in CP and DD2R, DRP1, and VPS35 in TC are all increased in PDD compared to DLB.

CONCLUSIONS

Together, our comprehensive and systematic study identified a set of signature proteins that will help to understand the pathology and etiology of PDD and DLB at the molecular level.