Abstract Context Representations in Primate Amygdala and Prefrontal Cortex

Over-representation of fundamental decision variables in the prefrontal cortex underlies decision bias

The brain is organized into anatomically distinct structures consisting of a variety of projection neurons. While such evolutionarily conserved neural circuit organization underlies the innate ability of animals to swiftly adapt to environments, they can cause biased cognition and behavior. Although recent studies have begun to address the causal importance of projection-neuron types as distinct computational units, it remains unclear how projection types are functionally organized in encoding variables during cognitive tasks. This review focuses on the neural computation of decision making in the prefrontal cortex and discusses what decision variables are encoded by single neurons, neuronal populations, and projection type, alongside how specific projection types constrain decision making. We focus particularly on "over-representations" of distinct decision variables in the prefrontal cortex that reflect the biological and subjective significance of the variables for the decision makers. We suggest that task-specific over-representation in the prefrontal cortex involves the refinement of the given decision making, while generalized over-representation of fundamental decision variables is associated with suboptimal decision biases, including pathological ones such as those in patients with psychiatric disorders. Such over-representation of the fundamental decision variables in the prefrontal cortex appear to be tightly constrained by afferent and efferent connections that can be optogenetically intervened on. These ideas may provide critical insights into potential therapeutic targets for psychiatric disorders, including addiction and depression.

Functions of primate amygdala neurons in economic decisions and social decision simulation

Long implicated in aversive processing, the amygdala is now recognized as a key component of the brain systems that process rewards. Beyond reward valuation, recent findings from single-neuron recordings in monkeys indicate that primate amygdala neurons also play an important role in decision-making. The reward value signals encoded by amygdala neurons constitute suitable inputs to economic decision processes by being sensitive to reward contingency, relative reward quantity and temporal reward structure. During reward-based decisions, individual amygdala neurons encode both the value inputs and corresponding choice outputs of economic decision processes. The presence of such value-to-choice transitions in single amygdala neurons, together with other well-defined signatures of decision computation, indicate that a decision mechanism may be implemented locally within the primate amygdala. During social observation, specific amygdala neurons spontaneously encode these decision signatures to predict the choices of social partners, suggesting neural simulation of the partner's decision-making. The activity of these 'simulation neurons' could arise naturally from convergence between value neurons and social, self-other discriminating neurons. These findings identify single-neuron building blocks and computational architectures for decision-making and social behavior in the primate amygdala. An emerging understanding of the decision function of primate amygdala neurons can help identify potential vulnerabilities for amygdala dysfunction in human conditions afflicting social cognition and mental health.

Dynamical prefrontal population coding during defensive behaviours

Coping with threatening situations requires both identifying stimuli that predict danger and selecting adaptive behavioural responses to survive¹. The dorsomedial prefrontal cortex (dmPFC) is a critical structure that is involved in the regulation of threat-related behaviour^2-4. However, it is unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks to successfully drive adaptive responses. Here we used a combination of extracellular recordings, neuronal decoding approaches, pharmacological and optogenetic manipulations to show that, in mice, threat representations and the initiation of avoidance behaviour are dynamically encoded in the overall population activity of dmPFC neurons. Our data indicate that although dmPFC population activity at stimulus onset encodes sustained threat representations driven by the amygdala, it does not predict action outcome. By contrast, transient dmPFC population activity before the initiation of action reliably predicts avoided from non-avoided trials. Accordingly, optogenetic inhibition of prefrontal activity constrained the selection of adaptive defensive responses in a time-dependent manner. These results reveal that the adaptive selection of defensive responses relies on a dynamic process of information linking threats with defensive actions, unfolding within prefrontal networks.

Neural representation of abstract task structure during generalization

Cognitive models in psychology and neuroscience widely assume that the human brain maintains an abstract representation of tasks. This assumption is fundamental to theories explaining how we learn quickly, think creatively, and act flexibly. However, neural evidence for a verifiably generative abstract task representation has been lacking. Here, we report an experimental paradigm that requires forming such a representation to act adaptively in novel conditions without feedback. Using functional magnetic resonance imaging, we observed that abstract task structure was represented within left mid-lateral prefrontal cortex, bilateral precuneus, and inferior parietal cortex. These results provide support for the neural instantiation of the long-supposed abstract task representation in a setting where we can verify its influence. Such a representation can afford massive expansions of behavioral flexibility without additional experience, a vital characteristic of human cognition.

Orbitofrontal State Representations Are Related to Choice Adaptations and Reward Predictions

Animals can categorize the environment into "states," defined by unique sets of available action-outcome contingencies in different contexts. Doing so helps them choose appropriate actions and make accurate outcome predictions when in each given state. State maps have been hypothesized to be held in the orbitofrontal cortex (OFC), an area implicated in decision-making and encoding information about outcome predictions. Here we recorded neural activity in OFC in 6 male rats to test state representations. Rats were trained on an odor-guided choice task consisting of five trial blocks containing distinct sets of action-outcome contingencies, constituting states, with unsignaled transitions between them. OFC neural ensembles were analyzed using decoding algorithms. Results indicate that the vast majority of OFC neurons contributed to representations of the current state at any point in time, independent of odor cues and reward delivery, even at the level of individual neurons. Across state transitions, these representations gradually integrated evidence for the new state; the rate at which this integration happened in the prechoice part of the trial was related to how quickly the rats' choices adapted to the new state. Finally, OFC representations of outcome predictions, often thought to be the primary function of OFC, were dependent on the accuracy of OFC state representations.SIGNIFICANCE STATEMENT A prominent hypothesis proposes that orbitofrontal cortex (OFC) tracks current location in a "cognitive map" of state space. Here we tested this idea in detail by analyzing neural activity recorded in OFC of rats performing a task consisting of a series of states, each defined by a set of available action-outcome contingencies. Results show that most OFC neurons contribute to state representations and that these representations are related to the rats' decision-making and OFC reward predictions. These findings suggest new interpretations of emotional dysregulation in pathologies, such as addiction, which have long been known to be related to OFC dysfunction.

Prediction errors and valence: From single units to multidimensional encoding in the amygdala

The amygdala-one of the primary structures of the limbic system-is comprised of interconnected nuclei situated within the temporal lobe. It has a well-established role in the modulation of negative affective states, as well as in fear processing. However, its vast projections with diverse brain regions-ranging from the cortex to the brainstem-are suggestive of its more complex involvement in affective or motivational aspects of cognitive processing. The amygdala can play an invaluable role in context-dependent associative learning, unsigned prediction error learning, influencing outcome selection, and multidimensional encoding. In this review, we delve into the amygdala's role in associative learning and outcome selection, emphasizing its intrinsic involvement in the appropriate context-dependent modulation of motivated behavior.

Learning to Synchronize: Midfrontal Theta Dynamics during Rule Switching

In recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model uses bursts at theta frequency to flexibly bind appropriate task modules by synchrony. The 64-channel EEG signal was recorded while 27 human subjects (female: 21, male: 6) performed a probabilistic reversal learning task. In line with the Sync model, postfeedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via Akaike information criterion) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporoparietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful, for example, when multiple stimulus-action mappings must be retained and used.SIGNIFICANCE STATEMENT Everyday life requires flexibility in switching between several rules. A key question in understanding this ability is how the brain mechanistically coordinates such switches. The current study tests a recent computational framework (Sync model) that proposed how midfrontal theta oscillations coordinate activity in hierarchically lower task-related areas. In line with predictions of this Sync model, midfrontal theta power was stronger when rule switches were most likely (strong negative prediction error), especially in subjects who obtained a better model fit. Additionally, also theta phase connectivity between midfrontal and task-related areas was increased after negative feedback. Thus, the data provided support for the hypothesis that the brain uses theta power and synchronization for flexibly switching between rules.

Anterior Cingulate Cortex and the Control of Dynamic Behavior in Primates

The brain mechanism for controlling continuous behavior in dynamic contexts must mediate action selection and learning across many timescales, responding differentially to the level of environmental uncertainty and volatility. In this review, we argue that a part of the frontal cortex known as the anterior cingulate cortex (ACC) is particularly well suited for this function. First, the ACC is interconnected with prefrontal, parietal, and subcortical regions involved in valuation and action selection. Second, the ACC integrates diverse, behaviorally relevant information across multiple timescales, producing output signals that temporally encapsulate decision and learning processes and encode high-dimensional information about the value and uncertainty of future outcomes and subsequent behaviors. Third, the ACC signals behaviorally relevant information flexibly, displaying the capacity to represent information about current and future states in a valence-, context-, task- and action-specific manner. Fourth, the ACC dynamically controls instrumental- and non-instrumental information seeking behaviors to resolve uncertainty about future outcomes. We review electrophysiological and circuit disruption studies in primates to develop this point, discuss its relationship to novel therapeutics for neuropsychiatric disorders in humans, and conclude by relating ongoing research in primates to studies of medial frontal cortical regions in rodents.

The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex

The curse of dimensionality plagues models of reinforcement learning and decision making. The process of abstraction solves this by constructing variables describing features shared by different instances, reducing dimensionality and enabling generalization in novel situations. Here, we characterized neural representations in monkeys performing a task described by different hidden and explicit variables. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training, which requires a particular geometry of neural representations. Neural ensembles in prefrontal cortex, hippocampus, and simulated neural networks simultaneously represented multiple variables in a geometry reflecting abstraction but that still allowed a linear classifier to decode a large number of other variables (high shattering dimensionality). Furthermore, this geometry changed in relation to task events and performance. These findings elucidate how the brain and artificial systems represent variables in an abstract format while preserving the advantages conferred by high shattering dimensionality.

Low-dimensional dynamics for working memory and time encoding

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear "ramping" component of each neuron's firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

Mesoscopic-scale functional networks in the primate amygdala

The primate amygdala performs multiple functions that may be related to the anatomical heterogeneity of its nuclei. Individual neurons with stimulus- and task-specific responses are not clustered in any of the nuclei, suggesting that single-units may be too-fine grained to shed light on the mesoscale organization of the amygdala. We have extracted from local field potentials recorded simultaneously from multiple locations within the primate (Macaca mulatta) amygdala spatially defined and statistically separable responses to visual, tactile, and auditory stimuli. A generalized eigendecomposition-based method of source separation isolated coactivity patterns, or components, that in neurophysiological terms correspond to putative subnetworks. Some component spatial patterns mapped onto the anatomical organization of the amygdala, while other components reflected integration across nuclei. These components differentiated between visual, tactile, and auditory stimuli suggesting the presence of functionally distinct parallel subnetworks.

Multidimensional processing in the amygdala

Brain-wide circuits that coordinate affective and social behaviours intersect in the amygdala. Consequently, amygdala lesions cause a heterogeneous array of social and non-social deficits. Social behaviours are not localized to subdivisions of the amygdala even though the inputs and outputs that carry social signals are anatomically restricted to distinct subnuclear regions. This observation may be explained by the multidimensional response properties of the component neurons. Indeed, the multitudes of circuits that converge in the amygdala enlist the same subset of neurons into different ensembles that combine social and non-social elements into high-dimensional representations. These representations may enable flexible, context-dependent social decisions. As such, multidimensional processing may operate in parallel with subcircuits of genetically identical neurons that serve specialized and functionally dissociable functions. When combined, the activity of specialized circuits may grant specificity to social behaviours, whereas multidimensional processing facilitates the flexibility and nuance needed for complex social behaviour.

Different Multidimensional Representations across the Amygdalo-Prefrontal Network during an Approach-Avoidance Task

The prelimbic (PL) area and basolateral amygdala (lateral [LA] and basolateral [BL] nuclei) have closely related functions and similar extrinsic connectivity. Reasoning that the computational advantage of such redundancy should be reflected in differences in how these structures represent information, we compared the coding properties of PL and amygdala neurons during a task that requires rats to produce different conditioned defensive or appetitive behaviors. Rather than unambiguous regional differences in the identities of the variables encoded, we found gradients in how the same variables are represented. Whereas PL and BL neurons represented many different parameters through minor variations in firing rates, LA cells coded fewer task features with stronger changes in activity. At the population level, whereas valence could be easily distinguished from amygdala activity, PL neurons could distinguish both valence and trial identity as well as or better than amygdala neurons. Thus, PL has greater representational capacity.

Hippocampal Network Reorganization Underlies the Formation of a Temporal Association Memory

Episodic memory requires linking events in time, a function dependent on the hippocampus. In "trace" fear conditioning, animals learn to associate a neutral cue with an aversive stimulus despite their separation in time by a delay period on the order of tens of seconds. But how this temporal association forms remains unclear. Here we use two-photon calcium imaging of neural population dynamics throughout the course of learning and show that, in contrast to previous theories, hippocampal CA1 does not generate persistent activity to bridge the delay. Instead, learning is concomitant with broad changes in the active neural population. Although neural responses were stochastic in time, cue identity could be read out from population activity over longer timescales after learning. These results question the ubiquity of seconds-long neural sequences during temporal association learning and suggest that trace fear conditioning relies on mechanisms that differ from persistent activity accounts of working memory.

The Role of Amygdala in Self-Conscious Emotions in a Patient With Acquired Bilateral Damage

Shame plays a fundamental role in the regulation of our social behavior. One intriguing question is whether amygdala might play a role in processing this emotion. In the present single-case study, we tested a patient with acquired damage of bilateral amygdalae and surrounding areas as well as healthy controls on shame processing and other social cognitive tasks. Results revealed that the patient's subjective experience of shame, but not of guilt, was more reduced than in controls, only when social standards were violated, while it was not different than controls in case of moral violations. The impairment in discriminating between normal social situations and violations also emerged. Taken together, these findings suggest that the role of the amygdala in processing shame might reflect its relevance in resolving ambiguity and uncertainty, in order to correctly detect social violations and to generate shame feelings.

Neural Trajectories in the Supplementary Motor Area and Motor Cortex Exhibit Distinct Geometries, Compatible with Different Classes of Computation

The supplementary motor area (SMA) is believed to contribute to higher order aspects of motor control. We considered a key higher order role: tracking progress throughout an action. We propose that doing so requires population activity to display low "trajectory divergence": situations with different future motor outputs should be distinct, even when present motor output is identical. We examined neural activity in SMA and primary motor cortex (M1) as monkeys cycled various distances through a virtual environment. SMA exhibited multiple response features that were absent in M1. At the single-neuron level, these included ramping firing rates and cycle-specific responses. At the population level, they included a helical population-trajectory geometry with shifts in the occupied subspace as movement unfolded. These diverse features all served to reduce trajectory divergence, which was much lower in SMA versus M1. Analogous population-trajectory geometry, also with low divergence, naturally arose in networks trained to internally guide multi-cycle movement.

Incidental binding between predictive relations

Knowledge of predictive relations is a core aspect of learning. Beyond individual relations, we also represent intuitive theories of the world, which include interrelated sets of relations. We asked whether individual predictive relations learned incidentally in the same context become associatively bound and whether they spontaneously influence later learning. Participants performed a cover task while watching three sequences of events. Each sequence contained the same set of events, but differed in how the events related to each other. The first two sequences each had two strong predictive relations (R1 & R2, and R3 & R4). The third contained either a consistent pairing of relations (R1 & R2) or an inconsistent pairing (R1 & R3). We found that participants' learning of the individual relations in the third sequence was affected by pairing consistency, suggesting the mind associates relations to each other as part of the intrinsic way it learns about the world. This was despite participants' minimal ability to verbally describe most of the relations they had learned. Thus, participants spontaneously developed the expectation that pairs of relations should cohere, and this affected their ability to learn new evidence. Such associative binding of relational information may help us build intuitive theories.

Adaptive learning under expected and unexpected uncertainty

The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning depends on whether they are representative of a typically experienced range of outcomes or signal a change in the reward environment. Successful learning and decision-making therefore require the ability to estimate expected uncertainty (related to the variability of outcomes) and unexpected uncertainty (related to the variability of the environment). Understanding the bases and effects of these two types of uncertainty and the interactions between them - at the computational and the neural level - is crucial for understanding adaptive learning. Here, we examine computational models and experimental findings to distil computational principles and neural mechanisms for adaptive learning under uncertainty.

Specialized medial prefrontal-amygdala coordination in other-regarding decision preference

Social behaviors recruit multiple cognitive operations that require interactions between cortical and subcortical brain regions. Interareal synchrony may facilitate such interactions between cortical and subcortical neural populations. However, it remains unknown how neurons from different nodes in the social brain network interact during social decision-making. Here, we investigated oscillatory neuronal interactions between the basolateral amygdala and the rostral anterior cingulate gyrus of the medial prefrontal cortex while monkeys expressed context-dependent positive or negative other-regarding preference (ORP), where decisions impacted the reward received by another monkey. Synchronization between the two nodes was enhanced for positive ORP, but suppressed for negative ORP. These interactions occurred in beta and gamma frequency bands depending on the area contributing spikes, exhibited a specific directionality of information flow associated with positive ORP, and could be used to decode social decisions. These findings suggest that specialized coordination in the medial prefrontal-amygdala network underlies social-decision preference.

The contribution of nonhuman primate research to the understanding of emotion and cognition and its clinical relevance

Psychiatric disorders are often conceptualized as arising from dysfunctional interactions between neural systems mediating cognitive and emotional processes. Mechanistic insights into these interactions have been lacking in part because most work in emotions has occurred in rodents, often without concurrent manipulations of cognitive variables. Nonhuman primate (NHP) model systems provide a powerful platform for investigating interactions between cognitive operations and emotions due to NHPs' strong homology with humans in behavioral repertoire and brain anatomy. Recent electrophysiological studies in NHPs have delineated how neural signals in the amygdala, a brain structure linked to emotion, predict impending appetitive and aversive stimuli. In addition, abstract conceptual information has also been shown to be represented in the amygdala and in interconnected brain structures such as the hippocampus and prefrontal cortex. Flexible adjustments of emotional behavior require the ability to apply conceptual knowledge and generalize to different, often novel, situations, a hallmark example of interactions between cognitive and emotional processes. Elucidating the neural mechanisms that explain how the brain processes conceptual information in relation to emotional variables promises to provide important insights into the pathophysiology accounting for symptoms in neuropsychiatric disorders.

Dichotomous organization of amygdala/temporal-prefrontal bundles in both humans and monkeys

The interactions of anterior temporal structures, and especially the amygdala, with the prefrontal cortex are pivotal to learning, decision-making, and socio-emotional regulation. A clear anatomical description of the organization and dissociation of fiber bundles linking anterior temporal cortex/amygdala and prefrontal cortex in humans is still lacking. Using diffusion imaging techniques, we reconstructed fiber bundles between these anatomical regions in human and macaque brains. First, by studying macaques, we assessed which aspects of connectivity known from tracer studies could be identified with diffusion imaging. Second, by comparing diffusion imaging results in humans and macaques, we estimated the patterns of fibers coursing between human amygdala and prefrontal cortex and compared them with those in the monkey. In posterior prefrontal cortex, we observed a prominent and well-preserved bifurcation of bundles into primarily two fiber systems-an amygdalofugal path and an uncinate path-in both species. This dissociation fades away in more rostral prefrontal regions.

Fear-Related Signals in the Primary Visual Cortex

Neuronal responses in the primary visual cortex (V1) are driven by simple stimuli, but these stimulus-evoked responses can be markedly modulated by non-sensory factors, such as attention and reward [1], and shaped by perceptual training [2]. In real-life situations, neutral visual stimuli can become emotionally tagged by experience, resulting in altered perceptual abilities to detect and discriminate these stimuli [3-5]. Human imaging [4] and electroencephalography (EEG) studies [6-9] have shown that visual fear learning (the acquisition of aversive emotion associated with a visual stimulus) affects the activities in visual cortical areas as early as in V1. However, it remains elusive as to whether the fear-related activities seen in the early visual cortex have to do with feedback influences from other cortical areas; it is also unclear whether and how the response properties of V1 cells are modified during the fear learning. In the current study, we addressed these issues by recording from V1 of awake monkeys implanted with an array of microelectrodes. We found that responses of V1 neurons were rapidly modified when a given orientation of grating stimulus was repeatedly associated with an aversive stimulus. The output visual signals from V1 cells conveyed, from their response outset, fear-related signals that were specific to the fear-associated grating orientation and visual-field location. The specific fear signals were independent of neurons' orientation preferences and were present even though the fear-associated stimuli were rendered invisible. Our findings suggest a bottom-up mechanism that allows for proactive labeling of visual inputs that are predictive of imminent danger.

Categorical encoding of decision variables in orbitofrontal cortex

A fundamental and recurrent question in systems neuroscience is that of assessing what variables are encoded by a given population of neurons. Such assessments are often challenging because neurons in one brain area may encode multiple variables, and because neuronal representations might be categorical or non-categorical. These issues are particularly pertinent to the representation of decision variables in the orbitofrontal cortex (OFC)-an area implicated in economic choices. Here we present a new algorithm to assess whether a neuronal representation is categorical or non-categorical, and to identify the encoded variables if the representation is indeed categorical. The algorithm is based on two clustering procedures, one variable-independent and the other variable-based. The two partitions are then compared through adjusted mutual information. The present algorithm overcomes limitations of previous approaches and is widely applicable. We tested the algorithm on synthetic data and then used it to examine neuronal data recorded in the primate OFC during economic decisions. Confirming previous assessments, we found the neuronal representation in OFC to be categorical in nature. We also found that neurons in this area encode the value of individual offers, the binary choice outcome and the chosen value. In other words, during economic choice, neurons in the primate OFC encode decision variables in a categorical way.

Of Pathways, Processes, and Orbitofrontal Cortex

Orbitofrontal cortex (OFC) predicts the consequences of our actions and updates our expectations based on experienced outcomes. In this issue of Neuron, Groman et al. (2019) precisely ablate pathways between the OFC, amygdala, and nucleus accumbens to reveal their separable contributions to reinforcement learning.

Subcortical Substrates of Explore-Exploit Decisions in Primates

The explore-exploit dilemma refers to the challenge of deciding when to forego immediate rewards and explore new opportunities that could lead to greater rewards in the future. While motivational neural circuits facilitate learning based on past choices and outcomes, it is unclear whether they also support computations relevant for deciding when to explore. We recorded neural activity in the amygdala and ventral striatum of rhesus macaques as they solved a task that required them to balance novelty-driven exploration with exploitation of what they had already learned. Using a partially observable Markov decision process (POMDP) model to quantify explore-exploit trade-offs, we identified that the ventral striatum and amygdala differ in how they represent the immediate value of exploitative choices and the future value of exploratory choices. These findings show that subcortical motivational circuits are important in guiding explore-exploit decisions.

Correlated Neural Activity across the Brains of Socially Interacting Bats

Social interactions occur between multiple individuals, but what is the detailed relationship between the neural dynamics across their brains? To address this question across timescales and levels of neural activity, we used wireless electrophysiology to simultaneously record from pairs of bats engaged in a wide range of natural social interactions. We found that neural activity was remarkably correlated between their brains over timescales from seconds to hours. The correlation depended on a shared social environment and was most prominent in high frequency local field potentials (>30 Hz), followed by local spiking activity. Furthermore, the degree of neural correlation covaried with the extent of social interactions, and an increase in correlation preceded their initiation. These results show that inter-brain correlation is an inherent feature of natural social interactions, reveal the domain of neural activity where it is most prominent, and provide a foundation for studying its functional role in social behaviors.

Functional brain connectivity in a rodent seizure model of autistic-like behavior

OBJECTIVE

There is increasing evidence that Autism Spectrum Disorder (ASD) is a disorder of functional connectivity with both human and rodent studies demonstrating alterations in connectivity. Here, we hypothesized that early-life seizures (ELS) in rats would interrupt normal brain connectivity and result in autistic-like behavior (ALB).

METHODS

Following 50 seizures, adult rats were tested in the social interaction and social novelty tests and then underwent qualitative and quantitative intracranial electroencephalography (EEG) monitoring in the medial prefrontal cortex (PFC) and the hippocampal subfields, CA3 and CA1.

RESULTS

Rats with ELS showed deficits in social interaction and novelty, and compared with control, rats had marked increases in coherence within the hippocampus (CA3-CA1) and between the hippocampus and PFC during the awake and sleep states indicating hyperconnectivity. In addition, sleep spindle density was significantly reduced in rats with ELS. There were no differences in voltage correlations and power spectral densities between the ELS and control rats in any bandwidths.

CONCLUSION

Taken together, these findings indicate that ELS can result in ALB and alter functional connectivity as measured by coherence and sleep spindle density. These findings implicate altered connectivity as a robust neural signature for ALB following ELS.

National Institutes of Health initiatives for advancing scientific developments in clinical neuropsychology

OBJECTIVE

The current review briefly addresses the history of neuropsychology as a context for discussion of developmental milestones that have advanced the profession, as well as areas where the progression has lagged. More recently in the digital/information age, utilization and incorporation of emerging technologies has been minimal, which has stagnated ongoing evolution of the practice of neuropsychology despite technology changing many aspects of daily living. These authors advocate for embracing National Institutes of Health (NIH) initiatives, or interchangeably referred to as transformative opportunities, for the behavioral and social sciences. These initiatives address the need for neuropsychologists to transition from fragmented and data-poor approaches to integrated and data-rich scientific approaches that ultimately improve translational applications. Specific to neuropsychology is the need for the adoption of novel means of brain-behavior characterizations.

METHOD

Narrative review Conclusions: Clinical neuropsychology has reached a developmental plateau where it is ready to embrace the measurement science and technological advances which have been readily adopted by the human neurosciences. While there are ways in which neuropsychology is making inroads into these areas, a great deal of growth is needed to maintain relevance as a scientific discipline (see Figures 1, 2, and 3) consistent with NIH initiatives to advance scientific developments. Moreover, implications of such progress require discussion and modification of training, ethical, and legal mandates of the practice of neuropsychology.

More rapid reversal learning following overtraining in the rat is evidence that behavioural and cognitive flexibility are dissociable

Cognitive flexibility is a term used to describe the brain processes underlying the phenomenon of adaptive change in behaviour in response to changed contingencies in the internal or external environment. Cognitive flexibility is often assessed in complex tasks measuring perceptual attentional shifting or response or task switching, but, arguably, reversal learning is a simple assay of cognitive flexibility. Reversal learning requires the detection of a changed outcome, the cessation of a previously-rewarded response and the selection of an alternative, previously-unrewarded, response. This study addressed the issue of the relationship between reversal learning and cognitive flexibility. In a single testing session, rats completed a series of 2-alternative forced-choice discriminations between digging bowls. The bowls differed according to both the medium within the bowl and the odor of the bowl. Having learned which cue (one of the odors or one of the digging media) indicated the food-baited bowl, half the rats were given additional trials of "over-training". To test reversal learning, the meaning of the cues predictive of reward/non-reward was then switched. There was a robust effect of over-training, with over-trained rats performing reversal learning in fewer trials than rats trained to criterion only. The pattern of errors supported the hypothesis that more rapid reversing results from the formation of an attentional set. This is the same attentional mechanism that results in less rapid shifting or switching. We conclude that the behavioural flexibility demonstrated in reversal learning does not provide a scale on which cognitive flexibility can be measured.

The role of the orbitofrontal cortex in alcohol use, abuse, and dependence

One of the major functions of the orbitofrontal cortex (OFC) is to promote flexible motivated behavior. It is no surprise, therefore, that recent work has demonstrated a prominent impact of chronic drug use on the OFC and a potential role for OFC disruption in drug abuse and addiction. Among drugs of abuse, the use of alcohol is particularly salient with respect to OFC function. Although a number of studies in humans have implicated OFC dysregulation in alcohol use disorders, animal models investigating the association between OFC and alcohol use are only beginning to be developed, and there is still a great deal to be revealed. The goal of this review is to consider what is currently known regarding the role of the OFC in alcohol use and dependence. I will first provide a brief, general overview of current views of OFC function and its contributions to drug seeking and addiction. I will then discuss research to date related to the OFC and alcohol use, both in human clinical populations and in non-human models. Finally I will consider issues and strategies to guide future study that may identify this brain region as a key player in the transition from moderated to problematic alcohol use and dependence.

Midline thalamic inputs to the amygdala: Ultrastructure and synaptic targets

One of the main subcortical inputs to the basolateral nucleus of the amygdala (BL) originates from a group of dorsal thalamic nuclei located at or near the midline, mainly from the central medial (CMT), and paraventricular (PVT) nuclei. Although similarities among the responsiveness of BL, CMT, and PVT neurons to emotionally arousing stimuli suggest that these thalamic inputs exert a significant influence over BL activity, little is known about the synaptic relationships that mediate these effects. Thus, the present study used Phaseolus vulgaris-leucoagglutinin (PHAL) anterograde tracing and electron microscopy to shed light on the ultrastructural properties and synaptic targets of CMT and PVT axon terminals in the rat BL. Virtually all PHAL-positive CMT and PVT axon terminals formed asymmetric synapses. Although CMT and PVT axon terminals generally contacted dendritic spines, a substantial number ended on dendritic shafts. To determine whether these dendritic shafts belonged to principal or local-circuit cells, calcium/calmodulin-dependent protein kinase II (CAMKIIα) immunoreactivity was used as a selective marker of principal BL neurons. In most cases, dendritic shafts postsynaptic to PHAL-labeled CMT and PVT terminals were immunopositive for CaMKIIα. Overall, these results suggest that CMT and PVT inputs mostly target principal BL neurons such that when CMT or PVT neurons fire, little feed-forward inhibition counters their excitatory influence over principal cells. These results are consistent with the possibility that CMT and PVT inputs constitute major determinants of BL activity.

Long time-scales in primate amygdala neurons support aversive learning

Associative learning forms when there is temporal relationship between a stimulus and a reinforcer, yet the inter-trial-interval (ITI), which is usually much longer than the stimulus-reinforcer-interval, contributes to learning-rate and memory strength. The neural mechanisms that enable maintenance of time between trials remain unknown, and it is unclear if the amygdala can support time scales at the order of dozens of seconds. We show that the ITI indeed modulates rate and strength of aversive-learning, and that single-units in the primate amygdala and dorsal-anterior-cingulate-cortex signal confined periods within the ITI, strengthen this coding during acquisition of aversive-associations, and diminish during extinction. Additionally, pairs of amygdala-cingulate neurons synchronize during specific periods suggesting a shared circuit that maintains the long temporal gap. The results extend the known roles of this circuit and suggest a mechanism that maintains trial-structure and temporal-contingencies for learning.

Neural Circuit Motifs in Valence Processing

How do our brains determine whether something is good or bad? How is this computational goal implemented in biological systems? Given the critical importance of valence processing for survival, the brain has evolved multiple strategies to solve this problem at different levels. The psychological concept of "emotional valence" is now beginning to find grounding in neuroscience. This review aims to bridge the gap between psychology and neuroscience on the topic of emotional valence processing. Here, I highlight a subset of studies that exemplify circuit motifs that repeatedly appear as implementational systems in valence processing. The motifs I identify as being important in valence processing include (1) Labeled Lines, (2) Divergent Paths, (3) Opposing Components, and (4) Neuromodulatory Gain. Importantly, the functionality of neural substrates in valence processing is dynamic, context-dependent, and changing across short and long timescales due to synaptic plasticity, competing mechanisms, and homeostatic need.

Multi-dimensional Coding by Basolateral Amygdala Neurons

Conditioned appetitive and aversive responses (CRs) are thought to result from the activation of specific subsets of valence-coding basolateral amygdala (BLA) neurons. Under this model, the responses of BLA cells to conditioned stimuli (CSs) and the activity that drives CRs are closely related. We tested the strength of this correlation using a task where rats could emit different CRs in response to the same CSs. At odds with this model, the CS responses and CR-related activity of individual BLA cells were separable. Moreover, while the incidence of valence-coding cells did not exceed chance, at the population level there was similarity between valence coding for CSs and CRs. In fact, both lateral and basolateral neurons concurrently encoded multiple task features and behaviors. Thus, conditioned emotional behaviors may not depend on the recruitment of single cells that explicitly encode individual task variables but from multiplexed representations distributed across the BLA.

Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks

Large-scale neural recordings have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while individual neurons exhibit complex selectivity. Understanding how low-dimensional computations on mixed, distributed representations emerge from the structure of the recurrent connectivity and inputs to cortical networks is a major challenge. Here, we study a class of recurrent network models in which the connectivity is a sum of a random part and a minimal, low-dimensional structure. We show that, in such networks, the dynamics are low dimensional and can be directly inferred from connectivity using a geometrical approach. We exploit this understanding to determine minimal connectivity required to implement specific computations and find that the dynamical range and computational capacity quickly increase with the dimensionality of the connectivity structure. This framework produces testable experimental predictions for the relationship between connectivity, low-dimensional dynamics, and computational features of recorded neurons.

Social Hierarchy Representation in the Primate Amygdala Reflects the Emotional Ambiguity of Our Social Interactions

Group living can help individuals defend against predators and acquire nutrition. However, conflicts between group members can arise (food sharing, mating, etc), requiring individuals to know the social status of each member to promote survival. In our recent paper, we sought to understand how the brain represents the social status of monkeys living in the same colony. Primates learn the social status of their peers through experience, including observation and direct interactions, just like they learn the rewarding or aversive nature of stimuli that predict different types of reinforcement. Group members may thereby be viewed as differing in value. We found in the amygdala, a brain area specialized for emotion, a neural representation of social hierarchy embedded in the same neuronal ensemble engaged in the assignment of motivational significance to previously neutral stimuli. Interestingly, we found 2 subpopulations of amygdala neurons encoding the social status of individuals in an opposite manner. In response to a stimulus, one population encodes similarly appetitive nonsocial images and dominant monkeys as well as aversive nonsocial stimuli and submissive monkeys. The other population encodes the opposite pattern later in time. This mechanism could reflect the emotional ambiguity we face in social situations as each interaction is potentially positive (eg, food access, protection, promotion) or negative (eg, aggression, bullying).

Just above Chance: Is It Harder to Decode Information from Prefrontal Cortex Hemodynamic Activity Patterns?

The prefrontal cortex (PFC) is central to flexible, goal-directed cognition, and understanding its representational code is an important problem in cognitive neuroscience. In humans, multivariate pattern analysis (MVPA) of fMRI blood oxygenation level-dependent (BOLD) measurements has emerged as an important approach for studying neural representations. Many previous studies have implicitly assumed that MVPA of fMRI BOLD is just as effective in decoding information encoded in PFC neural activity as it is in visual cortex. However, MVPA studies of PFC have had mixed success. Here we estimate the base rate of decoding information from PFC BOLD activity patterns from a meta-analysis of published MVPA studies. We show that PFC has a significantly lower base rate (55.4%) than visual areas in occipital (66.6%) and temporal (71.0%) cortices and one that is close to chance levels. Our results have implications for the design and interpretation of MVPA studies of PFC and raise important questions about its functional organization.

Amygdala activity for the modulation of goal-directed behavior in emotional contexts

Choosing valuable objects and rewarding actions is critical for survival. While such choices must be made in a way that suits the animal’s circumstances, the neural mechanisms underlying such context-appropriate behavior are unclear. To address this question, we devised a context-dependent reward-seeking task for macaque monkeys. Each trial started with the appearance of one of many visual scenes containing two or more objects, and the monkey had to choose the good object by saccade to get a reward. These scenes were categorized into two dimensions of emotional context: dangerous versus safe and rich versus poor. We found that many amygdala neurons were more strongly activated by dangerous scenes, by rich scenes, or by both. Furthermore, saccades to target objects occurred more quickly in dangerous than in safe scenes and were also quicker in rich than in poor scenes. Thus, amygdala neuronal activity and saccadic reaction times were negatively correlated in each monkey. These results suggest that amygdala neurons facilitate targeting saccades predictably based on aspects of emotional context, as is necessary for goal-directed and social behavior.

The amygdala is known to control passive fear responses (e.g., freezing), but it is unclear if it also contributes to active behaviors. To reach certain goals, we (humans and animals) often need to go through fearful environments. We hypothesized that the amygdala contributes to such an active behavior and devised a new foraging task for macaque monkeys in which various emotional contexts changed across many environments. This “exciting” task provoked extremely fast learning and high-capacity memory of objects and environments, and thereby caused extremely fast goal-directed behaviors. We found that the goal-directed behavior was affected by the emotional context in two dimensions (dangerous–safe and rich–poor) separately from the object values. Then, many neurons in the amygdala responded to the environments before any object appeared and did so selectively, depending on the emotional context of the environment. The neuronal activity was tightly correlated with the reaction time of goal-directed behavior across the contexts: faster behavior in dangerous or rich context. These results suggest that the amygdala facilitates goal-directed behavior by focusing on emotional contexts. Such a function is also important for emotional–social behavior and its disorder, including averted eye gaze in autism.

Lisdexamfetamine Targets Amygdala Mechanisms That Bias Cognitive Control in Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Prefrontal-limbic circuits that form the neural architecture for emotion to influence behavior have been implicated in the pathophysiology of attention-deficit/hyperactivity disorder (ADHD) and represent a potentially important target of medication treatment that has not been substantively evaluated. This study tested the effect of the psychostimulant prodrug lisdexamfetamine dimesylate on amygdala activation and connectivity during the emotional bias of response execution and inhibition.

METHODS

Twenty-five adults with ADHD were scanned twice with event-related functional magnetic resonance imaging while performing an emotional go/no-go task after 3 to 4 weeks of lisdexamfetamine treatment and 3 weeks off medication in a randomized, counterbalanced, hybrid crossover design. Drug, trial type, and face emotion (happy, sad, or neutral) were included as within-subjects factors in repeated measures analyses of activation and connectivity.

RESULTS

Lisdexamfetamine was associated with increased right amygdala activation and reduced psychophysiological interactions with the orbital aspect of the left inferior frontal gyrus specifically for responses to sad faces compared with placebo, but there was no effect on the accuracy of response execution or inhibition. The relative gain in right amygdala activation in response to sad faces for lisdexamfetamine was correlated with a reduction in symptoms of ADHD.

CONCLUSIONS

Treatment with lisdexamfetamine potentiates affective encoding in amygdala, purportedly via catecholaminergic mechanisms, but functionally disconnects the amygdala from inferior frontal regions that encode behavioral significance-resulting in reduced emotional bias of cognitive control. Pinpointing the neurophysiologic underpinnings of therapeutic improvement with lisdexamfetamine represents a first step in developing targeted approaches to treatment of ADHD.

Basolateral amygdala circuitry in positive and negative valence

All organisms must solve the same fundamental problem: they must acquire rewards and avoid danger in order to survive. A key challenge for the nervous system is therefore to connect motivationally salient sensory stimuli to neural circuits that engage appropriate valence-specific behavioral responses. Anatomical, behavioral, and electrophysiological data have long suggested that the amygdala plays a central role in this process. Here we review experimental efforts leveraging recent technological advances to provide previously unattainable insights into the functional, anatomical, and genetic identity of neural populations within the amygdala that connect sensory stimuli to valence-specific behavioral responses.

Mixed selectivity encoding and action selection in the prefrontal cortex during threat assessment

The medial prefrontal cortex (mPFC) regulates expression of emotional behavior. The mPFC combines multivariate information from its inputs, and depending on the imminence of threat, activates downstream networks that either increase or decrease the expression of anxiety-related motor behavior and autonomic activation. Here, we selectively highlight how subcortical input to the mPFC from two example structures, the amygdala and ventral hippocampus, help shape mixed selectivity encoding and action selection during emotional processing. We outline a model where prefrontal subregions modulate behavior along orthogonal motor dimensions, and exhibit connectivity that selects for expression of one behavioral strategy while inhibiting the other.

Shared neural coding for social hierarchy and reward value in primate amygdala

The social brain hypothesis posits that dedicated neural systems process social information. In support of this, neurophysiological data have shown that some brain regions are specialized for representing faces. It remains unknown, however, whether distinct anatomical substrates also represent more complex social variables, such as the hierarchical rank of individuals within a social group. Here we show that the primate amygdala encodes the hierarchical rank of individuals in the same neuronal ensembles that encode the rewards associated with nonsocial stimuli. By contrast, orbitofrontal and anterior cingulate cortices lack strong representations of hierarchical rank while still representing reward values. These results challenge the conventional view that dedicated neural systems process social information. Instead, information about hierarchical rank-which contributes to the assessment of the social value of individuals within a group-is linked in the amygdala to representations of rewards associated with nonsocial stimuli.

Effects of inference on dopaminergic prediction errors depend on orbitofrontal processing

Dopaminergic reward prediction errors in monkeys reflect inferential reward predictions that well-trained animals can make when associative rules change. Here, in a new analysis of previously described data, we test whether dopaminergic error signals in rats are influenced by inferential predictions and whether such effects depend on the orbitofrontal cortex (OFC). Dopamine neurons were recorded from controls or rats with ipsilateral OFC lesions during performance of a choice task in which odor cues signaled the availability of sucrose reward in 2 wells. To induce prediction errors, we manipulated either the timing or number of rewards delivered in each well across blocks of trials. Of importance, a change in reward at 1 well predicted a change in reward at the other on later trials. We compared behavior and neural activity on trials when such inference was possible versus trials involving the same reward change when inference was not possible. Rats responded faster when they could infer an increase in reward compared to when the same reward was coming but they could not infer a change. This inferential prediction was reflected in the firing of dopamine neurons in controls, which changed less to unexpected delivery (or omission) of reward and more to the new high-value cue on inference versus noninference trials. These effects were absent in dopamine neurons recorded in rats with ipsilateral OFC lesions. Thus, dopaminergic error signals recorded in rats are influenced by both experiential and inferential reward predictions, and the effects of inferential predictions depend on OFC. (PsycINFO Database Record

Decoupled choice-driven and stimulus-related activity in parietal neurons may be misrepresented by choice probabilities

Trial-by-trial correlations between neural responses and choices (choice probabilities) are often interpreted to reflect a causal contribution of neurons to task performance. However, choice probabilities may arise from top-down, rather than bottom-up, signals. We isolated distinct sensory and decision contributions to single-unit activity recorded from the dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas of monkeys during perception of self-motion. Superficially, neurons in both areas show similar tuning curves during task performance. However, tuning in MSTd neurons primarily reflects sensory inputs, whereas choice-related signals dominate tuning in VIP neurons. Importantly, the choice-related activity of VIP neurons is not predictable from their stimulus tuning, and these factors are often confounded in choice probability measurements. This finding was confirmed in a subset of neurons for which stimulus tuning was measured during passive fixation. Our findings reveal decoupled stimulus and choice signals in the VIP area, and challenge our understanding of choice signals in the brain.Choice-related signals in neuronal activity may reflect bottom-up sensory processes, top-down decision-related influences, or a combination of the two. Here the authors report that choice-related activity in VIP neurons is not predictable from their stimulus tuning, and that dominant choice signals can bias the standard metric of choice preference (choice probability).