Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex

Area-specific information processing in prefrontal cortex during a probabilistic inference task: a multivariate fMRI BOLD time series analysis

INTRODUCTION

Discriminating spatiotemporal stages of information processing involved in complex cognitive processes remains a challenge for neuroscience. This is especially so in prefrontal cortex whose subregions, such as the dorsolateral prefrontal (DLPFC), anterior cingulate (ACC) and orbitofrontal (OFC) cortices are known to have differentiable roles in cognition. Yet it is much less clear how these subregions contribute to different cognitive processes required by a given task. To investigate this, we use functional MRI data recorded from a group of healthy adults during a "Jumping to Conclusions" probabilistic reasoning task.

METHODS

We used a novel approach combining multivariate test statistics with bootstrap-based procedures to discriminate between different task stages reflected in the fMRI blood oxygenation level dependent signal pattern and to unravel differences in task-related information encoded by these regions. Furthermore, we implemented a new feature extraction algorithm that selects voxels from any set of brain regions that are jointly maximally predictive about specific task stages.

RESULTS

Using both the multivariate statistics approach and the algorithm that searches for maximally informative voxels we show that during the Jumping to Conclusions task, the DLPFC and ACC contribute more to the decision making phase comprising the accumulation of evidence and probabilistic reasoning, while the OFC is more involved in choice evaluation and uncertainty feedback. Moreover, we show that in presumably non-task-related regions (temporal cortices) all information there was about task processing could be extracted from just one voxel (indicating the unspecific nature of that information), while for prefrontal areas a wider multivariate pattern of activity was maximally informative.

CONCLUSIONS/SIGNIFICANCE

We present a new approach to reveal the different roles of brain regions during the processing of one task from multivariate activity patterns measured by fMRI. This method can be a valuable tool to assess how area-specific processing is altered in psychiatric disorders such as schizophrenia, and in healthy subjects carrying different genetic polymorphisms.

Neural Firing in the Prefrontal Cortex During Alcohol Intake in Alcohol-Preferring "P" Versus Wistar Rats

BACKGROUND

Neural activity within the prefrontal cortex (PFC) is altered by alcohol and alcohol-associated stimuli and is mediated by genetic susceptibility to alcoholism. However, very little is known about how genetic risk of excessive drinking might mediate neural firing in the PFC during alcohol consumption.

METHODS

To determine how genetic risk influences alcohol seeking, intake, and neural activity, a Pavlovian alcohol consumption task was used-the 2-Way Cued Access Protocol (2CAP). Alcohol-preferring "P" rats and relatives of their (heterogeneous) founding Wistar population were used for these studies. After acquisition of 2CAP, extinction of responding for alcohol was evaluated by substituting water for alcohol. Following these experiments, in vivo electrophysiological recordings were obtained during 2CAP from the PFC in a separate cohort of Wistar and P rats implanted with moveable tetrode microdrives.

RESULTS

P and Wistar rats increased daily alcohol seeking and intake with P rats consuming roughly twice as much alcohol as Wistar. Both rat populations decreased seeking behavior during extinction. However, P rats displayed persistent increases in seeking after controlling for intake versus Wistar. Higher firing rates (FRs) were observed in P rats prior to 2CAP and throughout alcohol and water consumption compared with Wistars that were matched for alcohol-drinking history. Differences in FR were driven, in part, by a larger percentage of neurons in P rats versus Wistars that increased FR compared with those that decreased, or did not change.

CONCLUSIONS

These data provide additional evidence of increased alcohol consumption and persistent alcohol seeking in P versus Wistar rats. Differences in PFC neural firing observed in P rats prior to drinking could be heritable and/or related to an enhanced response to alcohol-associated contextual cues. FR differences observed during alcohol drinking might be related to an augmented sensitivity of PFC neurons to orally consumed alcohol.

Computational psychiatry

Psychiatric disorders such as autism and schizophrenia, arise from abnormalities in brain systems that underlie cognitive, emotional, and social functions. The brain is enormously complex and its abundant feedback loops on multiple scales preclude intuitive explication of circuit functions. In close interplay with experiments, theory and computational modeling are essential for understanding how, precisely, neural circuits generate flexible behaviors and their impairments give rise to psychiatric symptoms. This Perspective highlights recent progress in applying computational neuroscience to the study of mental disorders. We outline basic approaches, including identification of core deficits that cut across disease categories, biologically realistic modeling bridging cellular and synaptic mechanisms with behavior, and model-aided diagnosis. The need for new research strategies in psychiatry is urgent. Computational psychiatry potentially provides powerful tools for elucidating pathophysiology that may inform both diagnosis and treatment. To achieve this promise will require investment in cross-disciplinary training and research in this nascent field.

Prospective memory: a comparative perspective

Prospective memory consists of forming a representation of a future action, temporarily storing that representation in memory, and retrieving it at a future time point. Here, we review the recent development of animal models of prospective memory. We review experiments using rats that focus on the development of time-based and event-based prospective memory. Next, we review a number of prospective-memory approaches that have been used with a variety of non-human primates. Finally, we review selected approaches from the human literature on prospective memory to identify targets for development of animal models of prospective memory. This article is part of a Special Issue entitled: "Tribute to Tom Zentall".

Cognitive control and the anterior cingulate cortex: how conflicting stimuli affect attentional control in the rat

Converging evidence supports the hypothesis that the prefrontal cortex is critical for cognitive control. One prefrontal subregion, the anterior cingulate cortex, is hypothesized to be necessary to resolve response conflicts, disregard salient distractors and alter behavior in response to the generation of an error. These situations all involve goal-oriented monitoring of performance in order to effectively adjust cognitive processes. Several neuropsychological disorders, e.g., schizophrenia, attention deficit hyperactivity and obsessive compulsive disorder, are accompanied by morphological changes in the anterior cingulate cortex. These changes are hypothesized to underlie the impairments on tasks that require cognitive control found in these subjects. A novel conflict monitoring task was used to assess the effects on cognitive control of excitotoxic lesions to anterior cingulate cortex in rats. Prior to surgery all subjects showed improved accuracy on the second of two consecutive, incongruent trials. Lesions to the anterior cingulate cortex abolished this. Lesioned animals had difficulty in adjusting cognitive control on a trial-by-trial basis regardless of whether cognitive changes were increased or decreased. These results support a role for the anterior cingulate cortex in adjustments in cognitive control.

Can we identify non-stationary dynamics of trial-to-trial variability?

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings.

Differential Activation of Fast-Spiking and Regular-Firing Neuron Populations During Movement and Reward in the Dorsal Medial Frontal Cortex

The medial prefrontal cortex is thought to be important for guiding behavior according to an animal's expectations. Efforts to decode the region have focused not only on the question of what information it computes, but also how distinct circuit components become engaged during behavior. We find that the activity of regular-firing, putative projection neurons contains rich information about behavioral context and firing fields cluster around reward sites, while activity among putative inhibitory and fast-spiking neurons is most associated with movement and accompanying sensory stimulation. These dissociations were observed even between adjacent neurons with apparently reciprocal, inhibitory-excitatory connections. A smaller population of projection neurons with burst-firing patterns did not show clustered firing fields around rewards; these neurons, although heterogeneous, were generally less selective for behavioral context than regular-firing cells. The data suggest a network that tracks an animal's behavioral situation while, at the same time, regulating excitation levels to emphasize high valued positions. In this scenario, the function of fast-spiking inhibitory neurons is to constrain network output relative to incoming sensory flow. This scheme could serve as a bridge between abstract sensorimotor information and single-dimensional codes for value, providing a neural framework to generate expectations from behavioral state.

Tracking progress toward a goal in corticostriatal ensembles

When performing sequences of actions, we constantly keep track of our current position in the sequence relative to the overall goal. The present study searched for neural representations of sequence progression in corticostriatal circuits. Neurons within the anterior cingulate cortex (ACC) and its target region in the dorsal striatum (DS) were recorded from simultaneously as rats performed different sequences of lever presses. We analyzed the responses of the neurons to presses occurring in the "first," "second," or "third" serial position regardless of the particular sequence or physical levers. Principal component analysis revealed that the main source of firing rate variance in the ACC was a smooth ramp-like change as the animal progressed through the sequence toward the reward. No such smooth-ramping activity was observed in DS ensembles as firing tended to be tightly linked to each action. In the ACC, the progression in firing was observed only for correct choices and not errors, whereas in the DS, firing associated with each action in a sequence was similar regardless of whether the action was correct or not. Therefore, different forms of a signal exist within corticostriatal circuits that evolve across a sequence of actions, with DS ensembles tracking every action and ACC ensembles tracking actual progress toward the goal.

Inactivation of medial prefrontal cortex or acute stress impairs odor span in rats

The capacity of working memory is limited and is altered in brain disorders including schizophrenia. In rodent working memory tasks, capacity is typically not measured (at least not explicitly). One task that does measure working memory capacity is the odor span task (OST) developed by Dudchenko and colleagues. In separate experiments, the effects of medial prefrontal cortex (mPFC) inactivation or acute stress on the OST were assessed in rats. Inactivation of the mPFC profoundly impaired odor span without affecting olfactory sensitivity. Acute stress also significantly reduced odor span. These findings support a potential role of the OST in developing novel therapeutics for disorders characterized by impaired working memory capacity.

The structure of cognition: attentional episodes in mind and brain

Cognition is organized in a structured series of attentional episodes, allowing complex problems to be addressed through solution of simpler subproblems. A "multiple-demand" (MD) system of frontal and parietal cortex is active in many different kinds of tasks, and using data from neuroimaging, electrophysiology, neuropsychology, and cognitive studies of intelligence, I propose a core role for MD regions in assembly of the attentional episode. Monkey and human data show dynamic neural coding of attended information across multiple MD regions, with rapid communication within and between regions. Neuropsychological and imaging data link MD function to fluid intelligence, explaining some but not all "executive" deficits after frontal lobe lesions. Cognitive studies link fluid intelligence to goal neglect, and the problem of dividing complex task requirements into focused parts. Like the innate releasing mechanism of ethology, I suggest that construction of the attentional episode provides a core organizational principle for complex, adaptive cognition.

Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation

Correlations among neurons are supposed to play an important role in computation and information coding in the nervous system. Empirically, functional interactions between neurons are most commonly assessed by cross-correlation functions. Recent studies have suggested that pairwise correlations may indeed be sufficient to capture most of the information present in neural interactions. Many applications of correlation functions, however, implicitly tend to assume that the underlying processes are stationary. This assumption will usually fail for real neurons recorded in vivo since their activity during behavioral tasks is heavily influenced by stimulus-, movement-, or cognition-related processes as well as by more general processes like slow oscillations or changes in state of alertness. To address the problem of nonstationarity, we introduce a method for assessing stationarity empirically and then “slicing” spike trains into stationary segments according to the statistical definition of weak-sense stationarity. We examine pairwise Pearson cross-correlations (PCCs) under both stationary and nonstationary conditions and identify another source of covariance that can be differentiated from the covariance of the spike times and emerges as a consequence of residual nonstationarities after the slicing process: the covariance of the firing rates defined on each segment. Based on this, a correction of the PCC is introduced that accounts for the effect of segmentation. We probe these methods both on simulated data sets and on in vivo recordings from the prefrontal cortex of behaving rats. Rather than for removing nonstationarities, the present method may also be used for detecting significant events in spike trains.

A review of cell assemblies

Since the cell assembly (CA) was hypothesised, it has gained substantial support and is believed to be the neural basis of psychological concepts. A CA is a relatively small set of connected neurons, that through neural firing can sustain activation without stimulus from outside the CA, and is formed by learning. Extensive evidence from multiple single unit recording and other techniques provides support for the existence of CAs that have these properties, and that their neurons also spike with some degree of synchrony. Since the evidence is so broad and deep, the review concludes that CAs are all but certain. A model of CAs is introduced that is informal, but is broad enough to include, e.g. synfire chains, without including, e.g. holographic reduced representation. CAs are found in most cortical areas and in some sub-cortical areas, they are involved in psychological tasks including categorisation, short-term memory and long-term memory, and are central to other tasks including working memory. There is currently insufficient evidence to conclude that CAs are the neural basis of all concepts. A range of models have been used to simulate CA behaviour including associative memory and more process- oriented tasks such as natural language parsing. Questions involving CAs, e.g. memory persistence, CAs' complex interactions with brain waves and learning, remain unanswered. CA research involves a wide range of disciplines including biology and psychology, and this paper reviews literature directly related to the CA, providing a basis of discussion for this interdisciplinary community on this important topic. Hopefully, this discussion will lead to more formal and accurate models of CAs that are better linked to neuropsychological data.

Abrupt changes in the patterns and complexity of anterior cingulate cortex activity when food is introduced into an environment

Foraging typically involves two distinct phases, an exploration phase where an organism explores its local environment in search of needed resources and an exploitation phase where a discovered resource is consumed. The behavior and cognitive requirements of exploration and exploitation are quite different and yet organisms can quickly and efficiently switch between them many times during a foraging bout. The present study investigated neural activity state dynamics in the anterior cingulate sub-region of the rat medial prefrontal cortex (mPFC) when a reliable food source was introduced into an environment. Distinct and largely independent states were detected using a Hidden Markov Model (HMM) when food was present or absent in the environment. Measures of neural entropy or complexity decreased when rats went from exploring the environment to exploiting a reliable food source. Exploration in the absence of food was associated with many weak activity states, while bouts of food consumption were characterized by fewer stronger states. Widespread activity state changes in the mPFC may help to inform foraging decisions and focus behavior on what is currently most prominent or valuable in the environment.

Where have I been? Where should I go? Spatial working memory on a radial arm maze in a rat model of depression

Disturbances in cognitive functioning are among the most debilitating problems experienced by patients with major depression. Investigations of these deficits in animals help to extend and refine our understanding of human emotional disorder, while at the same time providing valid tools to study higher executive functions in animals. We employ the "learned helplessness" genetic rat model of depression in studying working memory using an eight arm radial maze procedure with temporal delay. This so-called delayed spatial win-shift task consists of three phases, training, delay and test, requiring rats to hold information on-line across a retention interval and making choices based on this information in the test phase. According to a 2×2 factorial design, working memory performance of thirty-one congenitally helpless (cLH) and non-helpless (cNLH) rats was tested on eighteen trials, additionally imposing two different delay durations, 30 s and 15 min, respectively. While not observing a general cognitive deficit in cLH rats, the delay length greatly influenced maze performance. Notably, performance was most impaired in cLH rats tested with the shorter 30 s delay, suggesting a stress-related disruption of attentional processes in rats that are more sensitive to stress. Our study provides direct animal homologues of clinically important measures in human research, and contributes to the non-invasive assessment of cognitive deficits associated with depression.

Prefrontal dopamine and behavioral flexibility: shifting from an "inverted-U" toward a family of functions

Studies on prefrontal cortex (PFC) dopamine (DA) function have revealed its essential role in mediating a variety of cognitive and executive functions. A general principle that has emerged (primarily from studies on working memory) is that PFC DA, acting on D₁ receptors, regulates cognition in accordance to an “inverted-U” shaped function, so that too little or too much activity has detrimental effects on performance. However, contemporary studies have indicated that the receptor mechanisms through which mesocortical DA regulates different aspects of behavioral flexibility can vary considerably across different DA receptors and cognitive operations. This article will review psychopharmacological and neurochemical data comparing and contrasting the cognitive effects of antagonism and stimulation of different DA receptors in the medial PFC. Thus, set-shifting is dependent on a co-operative interaction between PFC D₁ and D₂ receptors, yet, supranormal stimulation of these receptors does not appear to have detrimental effects on this function. On the other hand, modification of cost/benefit decision biases in situations involving reward uncertainty is regulated in complex and sometimes opposing ways by PFC D₁ vs. D₂ receptors. When viewed collectively, these findings suggest that the “inverted-U” shaped dose-response curve underlying D₁ receptor modulation of working memory is not a one-size-fits-all function. Rather, it appears that mesocortical DA exerts its effects via a family of functions, wherein reduced or excessive DA activity can have a variety of effects across different cognitive domains.

Reward-related activity in the medial prefrontal cortex is driven by consumption

An emerging literature suggests that the medial prefrontal cortex (mPFC) is crucial for the ability to track behavioral outcomes over time and has a critical role in successful foraging. Here, we examine this issue by analyzing changes in neuronal spike activity and local field potentials in the rat mPFC in relation to the consumption of rewarding stimuli. Using multi-electrode recording methods, we simultaneously recorded from ensembles of neurons and field potentials in the mPFC during the performance of an operant-delayed alternation task and a variable-interval licking procedure. In both tasks, we found that consummatory behavior (licking) activates many mPFC neurons and is associated with theta-band phase locking by mPFC field potentials. Many neurons that were modulated by the delivery of reward were also modulated when rats emitted bouts of licks during the period of consumption. The majority of these licking-modulated neurons were found in the rostral part of the prelimbic cortex, a region that is heavily interconnected with the gustatory insular cortex and projects to subcortical feeding-related centers. Based on the tight coupling between spike activity, theta-band phase locking, and licking behavior, we suggest that reward-related activity in the mPFC is driven by consummatory behavior.

The role of medial prefrontal cortex in memory and decision making

Some have claimed that the medial prefrontal cortex (mPFC) mediates decision making. Others suggest mPFC is selectively involved in the retrieval of remote long-term memory. Yet others suggests mPFC supports memory and consolidation on time scales ranging from seconds to days. How can all these roles be reconciled? We propose that the function of the mPFC is to learn associations between context, locations, events, and corresponding adaptive responses, particularly emotional responses. Thus, the ubiquitous involvement of mPFC in both memory and decision making may be due to the fact that almost all such tasks entail the ability to recall the best action or emotional response to specific events in a particular place and time. An interaction between multiple memory systems may explain the changing importance of mPFC to different types of memories over time. In particular, mPFC likely relies on the hippocampus to support rapid learning and memory consolidation.

The role of medial prefrontal cortex in memory and decision making

Some have claimed that the medial prefrontal cortex (mPFC) mediates decision making. Others suggest mPFC is selectively involved in the retrieval of remote long-term memory. Yet others suggests mPFC supports memory and consolidation on time scales ranging from seconds to days. How can all these roles be reconciled? We propose that the function of the mPFC is to learn associations between context, locations, events, and corresponding adaptive responses, particularly emotional responses. Thus, the ubiquitous involvement of mPFC in both memory and decision making may be due to the fact that almost all such tasks entail the ability to recall the best action or emotional response to specific events in a particular place and time. An interaction between multiple memory systems may explain the changing importance of mPFC to different types of memories over time. In particular, mPFC likely relies on the hippocampus to support rapid learning and memory consolidation.

A preclinical assessment of d.l-govadine as a potential antipsychotic and cognitive enhancer

Tetrahydroprotoberberines (THPBs) are compounds derived from traditional Chinese medicine and increasing preclinical evidence suggests efficacy in treatment of a wide range of symptoms observed in schizophrenia. A receptor-binding profile of the THPB, d.l-govadine (d.l-Gov), reveals high affinity for dopamine and noradrenaline receptors, efficacy as a D2 receptor antagonist, brain penetrance in the 10-300 ng/g range, and thus motivated an assessment of the antipsychotic and pro-cognitive properties of this compound in the rat. Increased dopamine efflux in the prefrontal cortex and nucleus accumbens, measured by microdialysis, is observed following subcutaneous injection of the drug. d.l-Gov inhibits both conditioned avoidance responding (CAR) and amphetamine-induced locomotion (AIL) at lower doses than clozapine (CAR ED50: d.l-Gov 0.72 vs. clozapine 7.70 mg/kg; AIL ED50: d.l-Gov 1.70 vs. clozapine 4.27 mg/kg). Catalepsy is not detectable at low biologically relevant doses, but is observed at higher doses. Consistent with previous reports, acute d-amphetamine disrupts latent inhibition (LI) while a novel finding of enhanced LI is observed in sensitized animals. Treatment with d.l-Gov prior to conditioned stimulus (CS) pre-exposure restores LI to levels observed in controls in both sensitized animals and those treated acutely with d-amphetamine. Finally, possible pro-cognitive properties of d.l-Gov are assessed with the spatial delayed win-shift task. Subcutaneous injection of 1.0 mg/kg d.l-Gov failed to affect errors at a 30-min delay, but decreased errors observed at a 12-h delay. Collectively, these data provide the first evidence that d.l-Gov may have antipsychotic properties in conjunction with pro-cognitive effects, lending further support to the hypothesis that THPBs are a class of compounds which merit serious consideration as novel treatments for schizophrenia.

An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data

For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean-input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx) model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ("in vivo-like") input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a "high-throughput" model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available.

Topography of descending projections from anterior insular and medial prefrontal regions to the lateral habenula of the epithalamus in the rat

The epithalamic lateral nucleus of the habenula (LHb) plays a key role in regulating firing of dopamine and serotonin neurons in the midbrain and is thereby involved in various cognitive and affective behaviors. It is not yet clear, however, from where the LHb receives cognitive and affective information relevant to its regulation of the midbrain monoaminergic systems. The prefrontal cortex would be among the ideal sources. Here, using anterograde and retrograde tracer injections in the rat brain, we characterized the topography of the corticohabenular projections. Following injections of cholera toxin subunit B into the LHb, retrogradely labeled neurons were produced in the anterior insular, cingulate, prelimbic and infralimbic cortices. Consistent with this retrograde tracing, injections of biotinylated dextran amine (BDA) into these cortical regions labeled robust terminals in the LHb. Our quantification of the BDA-impregnated varicosities revealed that projections from the anterior insula terminated mainly in the intersection regions of the lateral and ventral two-thirds of the LHb, while projections from the cingulate cortex terminated mainly in the lateral two-thirds of the LHb. By comparison, BDA-labeled terminals originating from the medial prefrontal regions were contained mainly in the medial plus ventral one-third of LHb. Based on these data, we hypothesize that LHb provides a link for conveying cognitive and affective information from prefrontal and insular regions to the midbrain monoaminergic centers.

Action and outcome activity state patterns in the anterior cingulate cortex

Although there are numerous theories regarding anterior cingulate cortex (ACC) function, most suggest that it is involved in some form of action or outcome processing. The present study characterized the dominant patterns of ACC activity on a task in which actions and outcomes could vary independently. Patterns of activity were detected using a modified form of principal component analysis (PCA), termed constrained PCA in which a regression procedure was applied prior to PCA to eliminate the contribution of nontask-related activity. When trials were grouped according to outcome, a PC was found in all subjects and sessions that had large fluctuations during actions but only differentiated correct versus error trials prior to the end of the delay and again at time of the outcome. Another PC was always present that separated right from left lever presses, but only around the time of the actual lever press. Individual neurons exhibited significant selectivities for trials involving different actions and/or outcomes. Of the ACC neurons that exhibited significant outcome selectivity, the majority fired more on error trials. The present study revealed separate as well as integrated action and outcome monitoring in the ACC, especially, although not exclusively, under conditions when an error is likely.

Contextual encoding by ensembles of medial prefrontal cortex neurons

Contextual representations serve to guide many aspects of behavior and influence the way stimuli or actions are encoded and interpreted. The medial prefrontal cortex (mPFC), including the anterior cingulate subregion, has been implicated in contextual encoding, yet the nature of contextual representations formed by the mPFC is unclear. Using multiple single-unit tetrode recordings in rats, we found that different activity patterns emerged in mPFC ensembles when animals moved between different environmental contexts. These differences in activity patterns were significantly larger than those observed for hippocampal ensembles. Whereas ≈11% of mPFC cells consistently preferred one environment over the other across multiple exposures to the same environments, optimal decoding (prediction) of the environmental setting occurred when the activity of up to ≈50% of all mPFC neurons was taken into account. On the other hand, population activity patterns were not identical upon repeated exposures to the very same environment. This was partly because the state of mPFC ensembles seemed to systematically shift with time, such that we could sometimes predict the change in ensemble state upon later reentry into one environment according to linear extrapolation from the time-dependent shifts observed during the first exposure. We also observed that many strongly action-selective mPFC neurons exhibited a significant degree of context-dependent modulation. These results highlight potential differences in contextual encoding schemes by the mPFC and hippocampus and suggest that the mPFC forms rich contextual representations that take into account not only sensory cues but also actions and time.

Stimulation with a low-amplitude, digitized synaptic signal to invoke robust activity within neuronal networks on multielectrode arrays

Multielectrode arrays (MEAs) are used for analysis of neuronal activity. Here we report two variations on commonly accepted techniques that increase the precision of extracellular electrical stimulation: (i) the use of a low-amplitude recorded spontaneous synaptic signal as a stimulus waveform and (ii) the use of a specific electrode within the array adjacent to the stimulus electrode as a hard-grounded stimulus signal return path. Both modifications remained compatible with manipulation of neuronal networks. In addition, localized stimulation with the low-amplitude synaptic signal allowed selective stimulation or inhibition of otherwise spontaneous signals. These findings indicate that minimizing the area of the culture impacted by external stimulation allows modulation of signaling patterns within subpopulations of neurons in culture. The simple modifications described herein may be useful for precise monitoring and manipulation of neuronal networks.

Anterior cingulate neurons in the rat map anticipated effort and reward to their associated action sequences

Goal-directed behaviors require the consideration and expenditure of physical effort. The anterior cingulate cortex (ACC) appears to play an important role in evaluating effort and reward and in organizing goal-directed actions. Despite agreement regarding the involvement of the ACC in these processes, the way in which effort-, reward-, and motor-related information is registered by networks of ACC neurons is poorly understood. To contrast ACC responses to effort, reward, and motor behaviors, we trained rats on a reversal task in which the selected paths on a track determined the level of effort or reward. Effort was presented in the form of an obstacle that was climbed to obtain reward. We used single-unit recordings to identify neural correlates of effort- and reward-guided behaviors. During periods of outcome anticipation, 52% of recorded ACC neurons responded to the specific route taken to the reward while 21% responded prospectively to effort and 12% responded prospectively to reward. In addition, effort- and reward-selective neurons typically responded to the route, suggesting that these cells integrated motor-related activity with expectations of future outcomes. Furthermore, the activity of ACC neurons did not discriminate between choice and forced trials or respond to a more generalized measure of outcome value. Nearly all neural responses to effort and reward occurred after path selection and were restricted to discrete temporal/spatial stages of the task. Together, these findings support a role for the ACC in integrating route-specific actions, effort, and reward in the service of sustaining discrete movements through an effortful series of goal-directed actions.

Postsynaptic diacylglycerol lipase mediates retrograde endocannabinoid suppression of inhibition in mouse prefrontal cortex

Depolarization-induced suppression of inhibition (DSI) is a prevailing form of endocannabinoid signalling. However, several discrepancies have arisen regarding the roles played by the two major brain endocannabinoids, 2-arachidonoylglycerol (2-AG) and anandamide, in mediating DSI. Here we studied endocannabinoid signalling in the prefrontal cortex (PFC), where several components of the endocannabinoid system have been identified, but endocannabinoid signalling remains largely unexplored. In voltage clamp recordings from mouse PFC pyramidal neurons, depolarizing steps significantly suppressed IPSCs induced by application of the cholinergic agonist carbachol. DSI in PFC neurons was abolished by extra- or intracellular application of tetrahydrolipstatin (THL), an inhibitor of the 2-AG synthesis enzyme diacylglycerol lipase (DAGL). Moreover, DSI was enhanced by inhibiting 2-AG degradation, but was unaffected by inhibiting anandamide degradation. THL, however, may affect other enzymes of lipid metabolism and does not selectively target the α (DAGLα) or β (DAGLβ) isoforms of DAGL. Therefore, we studied DSI in the PFC of DAGLα(-/-) and DAGLβ(-/-) mice generated via insertional mutagenesis by gene-trapping with retroviral vectors. Gene trapping strongly reduced DAGLα or DAGLβ mRNA levels in a locus-specific manner. In DAGLα(-/-) mice cortical levels of 2-AG were significantly decreased and DSI was completely abolished, whereas DAGLβ deficiency did not alter cortical 2-AG levels or DSI. Importantly, cortical levels of anandamide were not significantly affected in DAGLα(-/-) or DAGLβ(-/-) mice. The chronic decrease of 2-AG levels in DAGLα(-/-) mice did not globally alter inhibitory transmission or the response of cannabinoid-sensitive synapses to cannabinoid receptor stimulation, although it altered some intrinsic membrane properties. Finally, we found that repetitive action potential firing of PFC pyramidal neurons suppressed synaptic inhibition in a DAGLα-dependent manner. These results show that DSI is a prominent form of endocannabinoid signalling in PFC circuits. Moreover, the close agreement between our pharmacological and genetic studies indicates that 2-AG synthesized by postsynaptic DAGLα mediates DSI in PFC neurons.

Dopamine and serotonin interactively modulate prefrontal cortex neurons in vitro

BACKGROUND

Dopamine (DA) and serotonin (5-HT) are released in cortex under similar circumstances, and many psychiatric drugs bind to both types of receptors, yet little is known about how they interact.

METHODS

To characterize the nature of these interactions, the current study used in vitro patch-clamp recordings to measure the effects of DA and/or 5-HT on pyramidal cells in layer V of the medial prefrontal cortex.

RESULTS

Either DA or 5-HT applied in isolation increased the evoked excitability of prefrontal cortex neurons, as shown previously. Coapplication of DA and 5-HT produced either a larger increase in excitability than when either was given alone or a significant decrease that was never observed when either was given alone. Dopamine or 5-HT also "primed" neurons to respond in an exaggerated manner to the subsequent application of the other monoamine.

CONCLUSIONS

These data reveal the unappreciated interactive nature of neuromodulation in cortex by showing that the combined effects of DA and 5-HT can be different from their effects recorded in isolation. On the basis of these findings, we present a theory of how DA and 5-HT might synergistically modulate cortical circuits during various tasks.

Attracting dynamics of frontal cortex ensembles during memory-guided decision-making

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states.

What is the Functional Relevance of Prefrontal Cortex Entrainment to Hippocampal Theta Rhythms?

There has been considerable interest in the importance of oscillations in the brain and in how these oscillations relate to the firing of single neurons. Recently a number of studies have shown that the spiking of individual neurons in the medial prefrontal cortex (mPFC) become entrained to the hippocampal (HPC) theta rhythm. We recently showed that theta-entrained mPFC cells lost theta-entrainment specifically on error trials even though the firing rates of these cells did not change (Hyman et al., 2010). This implied that the level of HPC theta-entrainment of mPFC units was more predictive of trial outcome than differences in firing rates and that there is more information encoded by the mPFC on working memory tasks than can be accounted for by a simple rate code. Nevertheless, the functional meaning of mPFC entrainment to HPC theta remains a mystery. It is also unclear as to whether there are any differences in the nature of the information encoded by theta-entrained and non-entrained mPFC cells. In this review we discuss mPFC entrainment to HPC theta within the context of previous results as well as provide a more detailed analysis of the Hyman et al. (2010) data set. This re-analysis revealed that theta-entrained mPFC cells selectively encoded a variety of task-relevant behaviors and stimuli while never theta-entrained mPFC cells were most strongly attuned to errors or the lack of expected rewards. In fact, these error responsive neurons were responsible for the error representations exhibited by the entire ensemble of mPFC neurons. A theta reset was also detected in the post-error period. While it is becoming increasingly evident that mPFC neurons exhibit correlates to virtually all cues and behaviors, perhaps phase-locking directs attention to the task-relevant representations required to solve a spatially based working memory task while the loss of theta-entrainment at the start of error trials may represent a shift of attention away from these representations. The subsequent theta reset following error commission, when coupled with the robust responses of never theta-entrained cells, could produce a potent error-evoked signal used to alert the rat to changes in the relationship between task-relevant cues and reward expectations.

A reservoir of time constants for memory traces in cortical neurons

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

Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses

Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.

Neurons in the rat anterior cingulate cortex dynamically encode cost-benefit in a spatial decision-making task

Optimal decision-making often requires an assessment of the costs and benefits associated with each available course of action. Previous studies have shown that lesions to the anterior cingulate cortex (ACC) impair cost-benefit decision-making in laboratory animals, but the neural mechanisms underlying the deficit are not well understood. We recorded from ACC neurons in freely moving rats as they performed a spatial decision-making task whereby, in the baseline configuration "2:6B," rats could pursue two or six food pellets, the latter obtained by climbing a barrier [high cost, high reward (HCHR)]. In this configuration, the mean percentage of HCHR choices was 69 +/- 4%, and a substantial portion of ACC neurons (63%) exhibited significantly higher firing for one goal trajectory versus the other; for 94% of these cells, higher firing was associated with the HCHR option. This HCHR bias was not simply attributable to the larger reward, the barrier, or behavioral preference. In intersession and intrasession manipulations involving at least one barrier (2:6B, 2B:6B, and 2:2B), ACC activity rapidly adapted and was consistently biased toward the economically advantageous option relative to the configuration. Interestingly, when only a difference in reward magnitude was presented (2:6, no barrier, HCHR choices of 84 +/- 4%), ACC activity was minimal and nonbiased. One interpretation of our data is that the ACC encodes a relative, integrated cost-benefit representation of available choice options that is biased toward the "better" option in terms of effort/outcome ratio. This representation may be specifically recruited when an assessment of reward and effort is required to optimally perform a task.

Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning

One of the most intriguing aspects of adaptive behavior involves the inference of regularities and rules in ever-changing environments. Rules are often deduced through evidence-based learning which relies on the prefrontal cortex (PFC). This is a highly dynamic process, evolving trial by trial and therefore may not be adequately captured by averaging single-unit responses over numerous repetitions. Here, we employed advanced statistical techniques to visualize the trajectories of ensembles of simultaneously recorded medial PFC neurons on a trial-by-trial basis as rats deduced a novel rule in a set-shifting task. Neural populations formed clearly distinct and lasting representations of familiar and novel rules by entering unique network states. During rule acquisition, the recorded ensembles often exhibited abrupt transitions, rather than evolving continuously, in tight temporal relation to behavioral performance shifts. These results support the idea that rule learning is an evidence-based decision process, perhaps accompanied by moments of sudden insight.

Working Memory Performance Correlates with Prefrontal-Hippocampal Theta Interactions but not with Prefrontal Neuron Firing Rates

Performance of memory tasks is impaired by lesions to either the medial prefrontal cortex (mPFC) or the hippocampus (HPC); although how these two areas contribute to successful performance is not well understood. mPFC unit activity is temporally affected by hippocampal-theta oscillations, with almost half the mPFC population entrained to theta in behaving animals, pointing to theta interactions as the mechanism enabling collaborations between these two areas. mPFC neurons respond to sensory stimuli and responses in working memory tasks, though the function of these correlated firing rate changes remains unclear because similar responses are reported during mPFC dependent and independent tasks. Using a DNMS task we compared error trials vs. correct trials and found almost all mPFC cells fired at similar rates during both error and correct trials (92%), however theta-entrainment of mPFC neurons declined during error performance as only 17% of cells were theta-entrained (during correct trials 46% of the population was theta-entrained). Across the population, error and correct trials did not differ in firing rate, but theta-entrainment was impaired. Periods of theta-entrainment and firing rate changes appeared to be independent variables, and only theta-entrainment was correlated with successful performance, indicating mPFC-HPC theta-range interactions are the key to successful DNMS performance.

The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour

A common or multiple-demand (MD) pattern of frontal and parietal activity is associated with diverse cognitive demands, and with standard tests of fluid intelligence. In intelligent behaviour, goals are achieved by assembling a series of sub-tasks, creating structured mental programs. Single cell and functional magnetic resonance imaging (fMRI) data indicate a key role for MD cortex in defining and controlling the parts of such programs, with focus on the specific content of a current cognitive operation, rapid reorganization as mental focus is changed, and robust separation of successive task steps. Resembling the structured problem-solving of symbolic artificial intelligence, the mental programs of MD cortex appear central to intelligent thought and action.

Post-learning infusion of anisomycin into the anterior cingulate cortex impairs instrumental acquisition through an effect on reinforcer valuation

The integrity of the rodent anterior cingulate cortex (ACC) is essential for various aspects of instrumental behavior, but it is not clear if the ACC is important for the acquisition of a simple instrumental response. Here, it was demonstrated that post-session infusions of anisomycin into the rat ACC completely prevented the acquisition of instrumental responding. The experimental use of post-session intracranial infusions of plasticity inhibitors is assumed to affect local consolidation of plasticity, but not behavioral task performance. However, in associative appetitive conditioning, post-session intracranial infusion of pharmaco-active compounds could actually interfere with subsequent task performance indirectly through retrospective effects on the valuation of ingested rewards. Thus, it was subsequently demonstrated that the intracranial infusion of anisomycin into the ACC after sucrose pellet consumption significantly reduced subsequent pellet consumption, suggesting that the infusion of anisomycin into the ACC produced conditioned taste avoidance. In the third experiment, an innovative procedure was introduced that dissociated the effects of intracranial infusions after conditioning sessions on task-learning and unconditioned stimulus valuation. With this procedure, the infusion of anisomycin into the ACC after instrumental sessions did not affect instrumental reinforcer valuation or the acquisition of instrumental responding, suggesting that plasticity in the ACC is not necessary for the acquisition of instrumental behavior.

The self-organization of explicit attitudes

How do minds produce explicit attitudes over several hundred milliseconds? Speeded evaluative measures have revealed implicit biases beyond cognitive control and subjective awareness, yet mental processing may culminate in an explicit attitude that feels personally endorsed and corroborates voluntary intentions. We argue that self-reported explicit attitudes derive from a continuous, temporally dynamic process, whereby multiple simultaneously conflicting sources of information self-organize into a meaningful mental representation. While our participants reported their explicit (like vs. dislike) attitudes toward White versus Black people by moving a cursor to a "like" or "dislike" response box, we recorded streaming x- and y-coordinates from their hand-movement trajectories. We found that participants' hand-movement paths exhibited greater curvature toward the "dislike" response when they reported positive explicit attitudes toward Black people than when they reported positive explicit attitudes toward White people. Moreover, these trajectories were characterized by movement disorder and competitive velocity profiles that were predicted under the assumption that the deliberate attitudes emerged from continuous interactions between multiple simultaneously conflicting constraints.

Neural Correlates of Novel Object and Novel Location Recognition Behavior in the Mouse Anterior Cingulate Cortex

The anterior cingulate cortex (ACC) is a component of the limbic system implicated in a wide variety of functions spanning motor and sensory information processing, memory, attention, novelty detection, and comparisons of expectation versus outcome. It remains unclear how much of this functional diversity stems from differences in methodology or interpretation versus truly reflecting the range of processes in which the ACC is involved. In the present study, ACC neuronal activity was examined in freely behaving mice (C57BL6/J) under conditions allowing investigation of many of the cited functions in conditions free from externally applied rules: tests of novel object and novel location recognition memory. Behavioral activity and neuronal activity were recorded first in the open field, during the initial exposure and subsequent familiarization to two identical objects, and finally during the recognition memory tests. No discernible stable firing correlates of ACC neurons were found in the open field, but the addition of objects led to lasting changes in the firing patterns of many ACC neurons around one or both of the object locations. During the novel location test, some neurons followed the familiar object to its new location, others fired exclusively where the object had been, and yet others fired to both current and former object locations. Many of these same features were observed during tests of object recognition memory. However, the magnitude of the neuronal preference for the novel or the familiar object was markedly greater than that observed during either the tests of location recognition or novel object preferences in animals that did not exhibit the expected behavior. The present study reveals, for the first time, single-neuron correlates of object and location recognition behaviors in the rodent ACC and suggests that neurons of the ACC provide a distributed representation of all of the salient features of a task.

Hierarchical coding for sequential task events in the monkey prefrontal cortex

The frontal lobes play a key role in sequential organization of behavior. Little is known, however, of the way frontal neurons code successive phases of a structured task plan. Using correlational analysis, we asked how a population of frontal cells represents the multiple events of a complex sequential task. Monkeys performed a conventional cue-target association task, with distinct cue, delay, and target phases. Across the population of recorded cells, we examined patterns of activity for different task phases, and in the same phase, for different stimulus objects. The results show hierarchical representation of task events. For different task phases, there were different, approximately orthogonal patterns of activity across the population of neurons. Modulations of each basic pattern encoded stimulus information within each phase. By orthogonal coding, the frontal lobe may control transitions between the discrete steps of a mental program; by correlated coding within each step, similar operations may be applied to different stimulus content.