Reward value comparison via mutual inhibition in ventromedial prefrontal cortex

Dissociable Representations of Decision Variables within Subdivisions of the Macaque Orbital and Ventrolateral Frontal Cortex

The ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subdivisions of VFC in male macaque monkeys performing a two-choice probabilistic task for different fruit juice outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral Area 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within Area 12o, in contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal Area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subdivisions, while the properties of the reward, such as its flavor, were more strongly represented in Areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates.

Neural mechanisms underpinning metacognitive shifts driven by non-informative predictions

Humans constantly make predictions and such predictions allow us to prepare for future events. Yet, such benefits may come with drawbacks as premature predictions may potentially bias subsequent judgments. Here we examined how prediction influences our perceptual decisions and subsequent confidence judgments, on scenarios where the predictions were arbitrary and independent of the identity of the upcoming stimuli. We defined them as invalid and non-informative predictions. Behavioral results showed that, such non-informative predictions biased perceptual decisions in favor of the predicted choice, and such prediction-induced perceptual bias further increased the metacognitive efficiency. The functional MRI results showed that activities in the medial prefrontal cortex (mPFC) and subgenual anterior cingulate cortex (sgACC) encoded the response consistency between predictions and perceptual decisions. Activity in mPFC predicted the strength of this congruency bias across individuals. Moreover, the parametric encoding of confidence in putamen was modulated by prediction-choice consistency, such that activity in putamen was negatively correlated with confidence rating after inconsistent responses. These findings suggest that predictions, while made arbitrarily, orchestrate the neural representations of choice and confidence judgment.

Irrational choices via a curvilinear representational geometry for value

We make decisions by comparing values, but it is not yet clear how value is represented in the brain. Many models assume, if only implicitly, that the representational geometry of value is linear. However, in part due to a historical focus on noisy single neurons, rather than neuronal populations, this hypothesis has not been rigorously tested. Here, we examine the representational geometry of value in the ventromedial prefrontal cortex (vmPFC), a part of the brain linked to economic decision-making, in two male rhesus macaques. We find that values are encoded along a curved manifold in vmPFC. This curvilinear geometry predicts a specific pattern of irrational decision-making: that decision-makers will make worse choices when an irrelevant, decoy option is worse in value, compared to when it is better. We observe this type of irrational choices in behavior. Together, these results not only suggest that the representational geometry of value is nonlinear, but that this nonlinearity could impose bounds on rational decision-making.

Gaze-centered gating, reactivation, and reevaluation of economic value in orbitofrontal cortex

During economic choice, options are often considered in alternation, until commitment. Nonetheless, neuroeconomics typically ignores the dynamic aspects of deliberation. We trained two male macaques to perform a value-based decision-making task in which two risky offers were presented in sequence at the opposite sides of the visual field, each followed by a delay epoch where offers were invisible. Surprisingly, during the two delays, subjects tend to look at empty locations where the offers had previously appeared, with longer fixations increasing the probability of choosing the associated offer. Spiking activity in orbitofrontal cortex reflects the value of the gazed offer, or of the offer associated with the gazed empty spatial location, even if it is not the most recent. This reactivation reflects a reevaluation process, as fluctuations in neural spiking correlate with upcoming choice. Our results suggest that look-at-nothing gazing triggers the reactivation of a previously seen offer for further evaluation.

Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex in mice during decision making

Attention supports decision making by selecting the features that are relevant for decisions. Selective enhancement of the relevant features and inhibition of distractors has been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when relevance cannot be directly determined, and the attention signal needs to be internally constructed is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC) of mice in an attention-shifting task where relevance of stimulus modalities changed across blocks of trials. In contrast with V1 recordings, decoding of the irrelevant modality gradually declined in ACC after an initial transient. Our analytical proof and a recurrent neural network model of the task revealed mutually inhibiting connections that produced context-gated suppression as observed in mice. Using this RNN model we predicted a correlation between contextual modulation of individual neurons and their stimulus drive, which we confirmed in ACC but not in V1.

Neural interactions in the human frontal cortex dissociate reward and punishment learning

How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.

Orbitofrontal high-gamma reflects spike-dissociable value and decision mechanisms

The orbitofrontal cortex (OFC) plays a crucial role in value-based decision-making. While previous research has focused on spiking activity in OFC neurons, the role of OFC local field potentials (LFPs) in decision-making remains unclear. LFPs are important because they can reflect synaptic and subthreshold activity not directly coupled to spiking, and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded LFPs and spiking activity using multi-channel vertical probes while monkeys performed a two-option value-based decision-making task. We compared the value- and decision-coding properties of high-gamma range LFPs (HG, 50-150 Hz) to the coding properties of spiking multi-unit activity (MUA) recorded concurrently on the same electrodes. Results show that HG and MUA both represent the values of decision targets, and that their representations have similar temporal profiles in a trial. However, we also identified value-coding properties of HG that were dissociable from the concurrently-measured MUA. On average across channels, HG amplitude increased monotonically with value, whereas the average value encoding in MUA was net neutral. HG also encoded a signal consistent with a comparison between the values of the two targets, a signal which was much weaker in MUA. In individual channels, HG was better able to predict choice outcomes than MUA; however, when simultaneously recorded channels were combined in population-based decoder, MUA provided more accurate predictions than HG. Interestingly, HG value representations were accentuated in channels in or near shallow cortical layers, suggesting a dissociation between neuronal sources of HG and MUA. In summary, we find that HG signals are dissociable from MUA with respect to cognitive variables encoded in prefrontal cortex, evident in the monotonic encoding of value, stronger encoding of value comparisons, and more accurate predictions about behavior. High-frequency LFPs may therefore be a viable - or even preferable - target for BMIs to assist cognitive function, opening the possibility for less invasive access to mental contents that would otherwise be observable only with spike-based measures.

A cognitive map for value-guided choice in ventromedial prefrontal cortex

The prefrontal cortex is crucial for economic decision-making and representing the value of options. However, how such representations facilitate flexible decisions remains unknown. We reframe economic decision-making in prefrontal cortex in line with representations of structure within the medial temporal lobe because such cognitive map representations are known to facilitate flexible behaviour. Specifically, we framed choice between different options as a navigation process in value space. Here we show that choices in a 2D value space defined by reward magnitude and probability were represented with a grid-like code, analogous to that found in spatial navigation. The grid-like code was present in ventromedial prefrontal cortex (vmPFC) local field potential theta frequency and the result replicated in an independent dataset. Neurons in vmPFC similarly contained a grid-like code, in addition to encoding the linear value of the chosen option. Importantly, both signals were modulated by theta frequency - occurring at theta troughs but on separate theta cycles. Furthermore, we found sharp-wave ripples - a key neural signature of planning and flexible behaviour - in vmPFC, which were modulated by accuracy and reward. These results demonstrate that multiple cognitive map-like computations are deployed in vmPFC during economic decision-making, suggesting a new framework for the implementation of choice in prefrontal cortex.

Fractionation of neural reward processing into independent components by novel decoding principle

How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso‑limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.

A view-based decision mechanism for rewards in the primate amygdala

Primates make decisions visually by shifting their view from one object to the next, comparing values between objects, and choosing the best reward, even before acting. Here, we show that when monkeys make value-guided choices, amygdala neurons encode their decisions in an abstract, purely internal representation defined by the monkey's current view but not by specific object or reward properties. Across amygdala subdivisions, recorded activity patterns evolved gradually from an object-specific value code to a transient, object-independent code in which currently viewed and last-viewed objects competed to reflect the emerging view-based choice. Using neural-network modeling, we identified a sequence of computations by which amygdala neurons implemented view-based decision making and eventually recovered the chosen object's identity when the monkeys acted on their choice. These findings reveal a neural mechanism in the amygdala that derives object choices from abstract, view-based computations, suggesting an efficient solution for decision problems with many objects.

Behavioral read-out from population value signals in primate orbitofrontal cortex

The primate orbitofrontal cortex (OFC) has long been recognized for its role in value-based decisions; however, the exact mechanism linking value representations in the OFC to decision outcomes has remained elusive. Here, to address this question, we show, in non-human primates, that trial-wise variability in choices can be explained by variability in value signals decoded from many simultaneously recorded OFC neurons. Mechanistically, this relationship is consistent with the projection of activity within a low-dimensional value-encoding subspace onto a potentially higher-dimensional, behaviorally potent output subspace. Identifying this neural-behavioral link answers longstanding questions about the role of the OFC in economic decision-making and suggests population-level read-out mechanisms for the OFC similar to those recently identified in sensory and motor cortex.

Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex during decision making

Attention is a cognitive faculty that selects part of a larger set of percepts, driven by cues such as stimulus saliency, internal goals or priors. The enhancement of the attended representation and inhibition of distractors have been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when the cue has to be internally constructed from conflicting stimuli, decision rules, and reward contingencies, is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC), an area implicated in ongoing error monitoring and correction during decision conflicts, in a challenging attention-shifting task. In this task, mice had to attend to the rewarded modality when presented identical auditory and visual stimuli in two contexts without direct external cues. In the ACC, the irrelevant stimulus continuously became less decodable than the relevant stimulus as the trial progressed to the decision point. This contrasted strongly with our previous findings in V1 where both relevant and irrelevant stimuli were equally decodable throughout the trial. Using analytical tools and a recurrent neural network (RNN) model, we found that the linearly independent representation of stimulus modalities in ACC was well suited to context-gated suppression of a stimulus modality. We demonstrated that the feedback structure of lateral connections in the RNN consisted of excitatory interactions between cell ensembles representing the same modality and mutual inhibition between cell ensembles representing distinct stimulus modalities. Using this RNN model showing signatures of context-gated suppression, we predicted that the level of contextual modulation of individual neurons should be correlated with their relative responsiveness to the two stimulus modalities used in the task. We verified this prediction in recordings from ACC neurons but not from recordings from V1 neurons. Therefore, ACC effectively operates on low-dimensional neuronal subspaces to combine stimulus related information with internal cues to drive actions under conflict.

Semi-orthogonal subspaces for value mediate a tradeoff between binding and generalization

When choosing between options, we must associate their values with the action needed to select them. We hypothesize that the brain solves this binding problem through neural population subspaces. To test this hypothesis, we examined neuronal responses in five reward-sensitive regions in macaques performing a risky choice task with sequential offers. Surprisingly, in all areas, the neural population encoded the values of offers presented on the left and right in distinct subspaces. We show that the encoding we observe is sufficient to bind the values of the offers to their respective positions in space while preserving abstract value information, which may be important for rapid learning and generalization to novel contexts. Moreover, after both offers have been presented, all areas encode the value of the first and second offers in orthogonal subspaces. In this case as well, the orthogonalization provides binding. Our binding-by-subspace hypothesis makes two novel predictions borne out by the data. First, behavioral errors should correlate with putative spatial (but not temporal) misbinding in the neural representation. Second, the specific representational geometry that we observe across animals also indicates that behavioral errors should increase when offers have low or high values, compared to when they have medium values, even when controlling for value difference. Together, these results support the idea that the brain makes use of semi-orthogonal subspaces to bind features together.

Value dynamics affect choice preparation during decision-making

During decision-making, neurons in the orbitofrontal cortex (OFC) sequentially represent the value of each option in turn, but it is unclear how these dynamics are translated into a choice response. One brain region that may be implicated in this process is the anterior cingulate cortex (ACC), which strongly connects with OFC and contains many neurons that encode the choice response. We investigated how OFC value signals interacted with ACC neurons encoding the choice response by performing simultaneous high-channel count recordings from the two areas in nonhuman primates. ACC neurons encoding the choice response steadily increased their firing rate throughout the decision-making process, peaking shortly before the time of the choice response. Furthermore, the value dynamics in OFC affected ACC ramping-when OFC represented the more valuable option, ACC ramping accelerated. Because OFC tended to represent the more valuable option more frequently and for a longer duration, this interaction could explain how ACC selects the more valuable response.

Electrophysiological signatures of inequity-dependent reward encoding in the human OFC

Social decision making requires the integration of reward valuation and social cognition systems, both dependent on the orbitofrontal cortex (OFC). How these two OFC functions interact is largely unknown. We recorded intracranial activity from the OFC of ten patients making choices in a social context where reward inequity with a social counterpart varied and could be either advantageous or disadvantageous. We find that OFC high-frequency activity (HFA; 70-150 Hz) encodes self-reward, consistent with previous reports. We also observe encoding of the social counterpart's reward, as well as the type of inequity being experienced. Additionally, we find evidence of inequity-dependent reward encoding: depending on the type of inequity, electrodes rapidly and reversibly switch between different reward-encoding profiles. These results provide direct evidence for encoding of self- and other rewards in the human OFC and highlight the dynamic nature of encoding in the OFC as a function of social context.

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

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

Economic value in the Brain: A meta-analysis of willingness-to-pay using the Becker-DeGroot-Marschak auction

Forming and comparing subjective values (SVs) of choice options is a critical stage of decision-making. Previous studies have highlighted a complex network of brain regions involved in this process by utilising a diverse range of tasks and stimuli, varying in economic, hedonic and sensory qualities. However, the heterogeneity of tasks and sensory modalities may systematically confound the set of regions mediating the SVs of goods. To identify and delineate the core brain valuation system involved in processing SV, we utilised the Becker-DeGroot-Marschak (BDM) auction, an incentivised demand-revealing mechanism which quantifies SV through the economic metric of willingness-to-pay (WTP). A coordinate-based activation likelihood estimation meta-analysis analysed twenty-four fMRI studies employing a BDM task (731 participants; 190 foci). Using an additional contrast analysis, we also investigated whether this encoding of SV would be invariant to the concurrency of auction task and fMRI recordings. A fail-safe number analysis was conducted to explore potential publication bias. WTP positively correlated with fMRI-BOLD activations in the left ventromedial prefrontal cortex with a sub-cluster extending into anterior cingulate cortex, bilateral ventral striatum, right dorsolateral prefrontal cortex, right inferior frontal gyrus, and right anterior insula. Contrast analysis identified preferential engagement of the mentalizing-related structures in response to concurrent scanning. Together, our findings offer succinct empirical support for the core structures participating in the formation of SV, separate from the hedonic aspects of reward and evaluated in terms of WTP using BDM, and show the selective involvement of inhibition-related brain structures during active valuation.

Abstract Value Encoding in Neural Populations But Not Single Neurons

An important open question in neuroeconomics is how the brain represents the value of offers in a way that is both abstract (allowing for comparison) and concrete (preserving the details of the factors that influence value). Here, we examine neuronal responses to risky and safe options in five brain regions that putatively encode value in male macaques. Surprisingly, we find no detectable overlap in the neural codes used for risky and safe options, even when the options have identical subjective values (as revealed by preference) in any of the regions. Indeed, responses are weakly correlated and occupy distinct (semi-orthogonal) encoding subspaces. Notably, however, these subspaces are linked through a linear transform of their constituent encodings, a property that allows for comparison of dissimilar option types. This encoding scheme allows these regions to multiplex decision related processes: they can encode the detailed factors that influence offer value (here, risky and safety) but also directly compare dissimilar offer types. Together these results suggest a neuronal basis for the qualitatively different psychological properties of risky and safe options and highlight the power of population geometry to resolve outstanding problems in neural coding.SIGNIFICANCE STATEMENT To make economic choices, we must have some mechanism for comparing dissimilar offers. We propose that the brain uses distinct neural codes for risky and safe offers, but that these codes are linearly transformable. This encoding scheme has the dual advantage of allowing for comparison across offer types while preserving information about offer type, which in turn allows for flexibility in changing circumstances. We show that responses to risky and safe offers exhibit these predicted properties in five different reward-sensitive regions. Together, these results highlight the power of population coding principles for solving representation problems in economic choice.

Neurons in human pre-supplementary motor area encode key computations for value-based choice

Adaptive behaviour in real-world environments requires that choices integrate several variables, including the novelty of the options under consideration, their expected value and uncertainty in value estimation. Here, to probe how integration over decision variables occurs during decision-making, we recorded neurons from the human pre-supplementary motor area (preSMA), ventromedial prefrontal cortex and dorsal anterior cingulate. Unlike the other areas, preSMA neurons not only represented separate pre-decision variables for each choice option but also encoded an integrated utility signal for each choice option and, subsequently, the decision itself. Post-decision encoding of variables for the chosen option was more widely distributed and especially prominent in the ventromedial prefrontal cortex. Our findings position the human preSMA as central to the implementation of value-based decisions.

Emotion, motivation, decision-making, the orbitofrontal cortex, anterior cingulate cortex, and the amygdala

The orbitofrontal cortex and amygdala are involved in emotion and in motivation, but the relationship between these functions performed by these brain structures is not clear. To address this, a unified theory of emotion and motivation is described in which motivational states are states in which instrumental goal-directed actions are performed to obtain rewards or avoid punishers, and emotional states are states that are elicited when the reward or punisher is or is not received. This greatly simplifies our understanding of emotion and motivation, for the same set of genes and associated brain systems can define the primary or unlearned rewards and punishers such as sweet taste or pain. Recent evidence on the connectivity of human brain systems involved in emotion and motivation indicates that the orbitofrontal cortex is involved in reward value and experienced emotion with outputs to cortical regions including those involved in language, and is a key brain region involved in depression and the associated changes in motivation. The amygdala has weak effective connectivity back to the cortex in humans, and is implicated in brainstem-mediated responses to stimuli such as freezing and autonomic activity, rather than in declarative emotion. The anterior cingulate cortex is involved in learning actions to obtain rewards, and with the orbitofrontal cortex and ventromedial prefrontal cortex in providing the goals for navigation and in reward-related effects on memory consolidation mediated partly via the cholinergic system.

Subspace orthogonalization as a mechanism for binding values to space

When choosing between options, we must solve an important binding problem. The values of the options must be associated with information about the action needed to select them. We hypothesize that the brain solves this binding problem through use of distinct population subspaces. To test this hypothesis, we examined the responses of single neurons in five reward-sensitive regions in rhesus macaques performing a risky choice task. In all areas, neurons encoded the value of the offers presented on both the left and the right side of the display in semi-orthogonal subspaces, which served to bind the values of the two offers to their positions in space. Supporting the idea that this orthogonalization is functionally meaningful, we observed a session-to-session covariation between choice behavior and the orthogonalization of the two value subspaces: trials with less orthogonalized subspaces were associated with greater likelihood of choosing the less valued option. Further inspection revealed that these semi-orthogonal subspaces arose from a combination of linear and nonlinear mixed selectivity in the neural population. We show this combination of selectivity balances reliable binding with an ability to generalize value across different spatial locations. These results support the hypothesis that semi-orthogonal subspaces support reliable binding, which is essential to flexible behavior in the face of multiple options.

Shared and distinct neural activity during anticipation and outcome of win and loss: A meta-analysis of the monetary incentive delay task

Reward and punishment motivate decision making and behavioral changes. Numerous studies have examined regional activities during anticipation and outcome of win and loss in the monetary incentive delay task (MIDT). However, the great majority of studies reported findings of anticipation or outcome and of win or loss alone. It remains unclear how the neural correlates share and differentiate amongst these processes. We conducted an Activation Likelihood Estimation meta-analysis of 81 studies of the MIDT (5,864 subjects), including 24 published since the most recent meta-analysis, to identify and, with conjunction and subtraction, contrast regional responses to win anticipation, loss anticipation, win outcome, and loss outcome. Win and loss anticipation engaged a shared network of bilateral anterior insula (AI), striatum, thalamus, supplementary motor area (SMA), and precentral gyrus. Win and loss outcomes did not share regional activities. Win and loss outcome each engaged higher activity in medial orbitofrontal cortex (mOFC) and dorsal anterior cingulate cortex. Bilateral striatum and right occipital cortex responded to both anticipation and outcome of win, and right AI to both phases of loss. Win anticipation vs. outcome engaged higher activity in bilateral AI, striatum, SMA and precentral gyrus and right thalamus, and lower activity in bilateral mOFC and posterior cingulate cortex as well as right inferior frontal and angular gyri. Loss anticipation relative to outcome involved higher activity in bilateral striatum and left AI. These findings collectively suggest shared and distinct regional responses during monetary wins and losses. Delineating the neural correlates of these component processes may facilitate empirical research of motivated behaviors and dysfunctional approach and avoidance in psychopathology.

Hypothalamic interaction with reward-related regions during subjective evaluation of foods

The reward system implemented in the midbrain, ventral striatum, orbitofrontal cortex, and ventromedial prefrontal cortex evaluates and compares various types of rewards given to the organisms. It has been suggested that autonomic factors influence reward-related processing via the hypothalamus, but how the hypothalamus modulates the reward system remains elusive. In this functional magnetic resonance imaging study, the hypothalamus was parcellated into individual hypothalamic nuclei performing different autonomic functions using boundary mapping parcellation analyses. The effective interaction during subjective evaluation of foods in a reward task was then investigated between the human hypothalamic nuclei and the reward-related regions. We found significant brain activity decrease in the paraventricular nucleus (PVH) and lateral nucleus in the hypothalamus in food evaluation compared with monetary evaluation. A psychophysiological interaction analysis revealed dual interactions between the PVH and (1) midbrain region and (2) ventromedial prefrontal cortex, with the former correlated with the stronger tendency of participants toward food-seeking. A dynamic causal modeling analysis further revealed unidirectional interactions from the PVH to the midbrain and ventromedial prefrontal cortex. These results suggest that the PVH in the human hypothalamus interacts with the reward-related regions in the cerebral cortex via multiple pathways (i.e., the midbrain pathway and ventromedial prefrontal pathway) to evaluate rewards for subsequent decision-making.

The PRO model accounts for the anterior cingulate cortex role in risky decision-making and monitoring

The anterior cingulate cortex (ACC) has been implicated in a number of functions, including performance monitoring and decision-making involving effort. The prediction of responses and outcomes (PRO) model has provided a unified account of much human and monkey ACC data involving anatomy, neurophysiology, EEG, fMRI, and behavior. We explored the computational nature of ACC with the PRO model, extending it to account specifically for both human and macaque monkey decision-making under risk, including both behavioral and neural data. We show that the PRO model can account for a number of additional effects related to outcome prediction, decision-making under risk, gambling behavior. In particular, we show that the ACC represents the variance of uncertain outcomes, suggesting a link between ACC function and mean-variance theories of decision making. The PRO model provides a unified account of a large set of data regarding the ACC.

The Promise of Behavioral Tracking Systems for Advancing Primate Animal Welfare

Recent years have witnessed major advances in the ability of computerized systems to track the positions of animals as they move through large and unconstrained environments. These systems have so far been a great boon in the fields of primatology, psychology, neuroscience, and biomedicine. Here, we discuss the promise of these technologies for animal welfare. Their potential benefits include identifying and reducing pain, suffering, and distress in captive populations, improving laboratory animal welfare within the context of the three Rs of animal research (reduction, refinement, and replacement), and applying our understanding of animal behavior to increase the "natural" behaviors in captive and wild populations facing human impact challenges. We note that these benefits are often incidental to the designed purpose of these tracking systems, a reflection of the fact that animal welfare is not inimical to research progress, but instead, that the aligned interests between basic research and welfare hold great promise for improvements to animal well-being.

A structural and functional subdivision in central orbitofrontal cortex

Economic choice requires many cognitive subprocesses, including stimulus detection, valuation, motor output, and outcome monitoring; many of these subprocesses are associated with the central orbitofrontal cortex (cOFC). Prior work has largely assumed that the cOFC is a single region with a single function. Here, we challenge that unified view with convergent anatomical and physiological results from rhesus macaques. Anatomically, we show that the cOFC can be subdivided according to its much stronger (medial) or weaker (lateral) bidirectional anatomical connectivity with the posterior cingulate cortex (PCC). We call these subregions cOFCm and cOFCl, respectively. These two subregions have notable functional differences. Specifically, cOFCm shows enhanced functional connectivity with PCC, as indicated by both spike-field coherence and mutual information. The cOFCm-PCC circuit, but not the cOFCl-PCC circuit, shows signatures of relaying choice signals from a non-spatial comparison framework to a spatially framed organization and shows a putative bidirectional mutually excitatory pattern.

Medial and orbital frontal cortex in decision-making and flexible behavior

The medial frontal cortex and adjacent orbitofrontal cortex have been the focus of investigations of decision-making, behavioral flexibility, and social behavior. We review studies conducted in humans, macaques, and rodents and argue that several regions with different functional roles can be identified in the dorsal anterior cingulate cortex, perigenual anterior cingulate cortex, anterior medial frontal cortex, ventromedial prefrontal cortex, and medial and lateral parts of the orbitofrontal cortex. There is increasing evidence that the manner in which these areas represent the value of the environment and specific choices is different from subcortical brain regions and more complex than previously thought. Although activity in some regions reflects distributions of reward and opportunities across the environment, in other cases, activity reflects the structural relationships between features of the environment that animals can use to infer what decision to take even if they have not encountered identical opportunities in the past.

GRAN3SAT: Creating Flexible Higher-Order Logic Satisfiability in the Discrete Hopfield Neural Network

One of the main problems in representing information in the form of nonsystematic logic is the lack of flexibility, which leads to potential overfitting. Although nonsystematic logic improves the representation of the conventional k Satisfiability, the formulations of the first, second, and third-order logical structures are very predictable. This paper proposed a novel higher-order logical structure, named G-Type Random k Satisfiability, by capitalizing the new random feature of the first, second, and third-order clauses. The proposed logic was implemented into the Discrete Hopfield Neural Network as a symbolic logical rule. The proposed logic in Discrete Hopfield Neural Networks was evaluated using different parameter settings, such as different orders of clauses, different proportions between positive and negative literals, relaxation, and differing numbers of learning trials. Each evaluation utilized various performance metrics, such as learning error, testing error, weight error, energy analysis, and similarity analysis. In addition, the flexibility of the proposed logic was compared with current state-of-the-art logic rules. Based on the simulation, the proposed logic was reported to be more flexible, and produced higher solution diversity.

Logistic analysis of choice data: A primer

Logistic regressions were developed in economics to model individual choice behavior. In recent years, they have become an important tool in decision neuroscience. Here, I describe and discuss different logistic models, emphasizing the underlying assumptions and possible interpretations. Logistic models may be used to quantify a variety of behavioral traits, including the relative subjective value of different goods, the choice accuracy, risk attitudes, and choice biases. More complex logistic models can be used for choices between good bundles, in cases of nonlinear value functions, and for choices between multiple options. Finally, logistic models can quantify the explanatory power of neuronal activity on choices, thus providing a valid alternative to receiver operating characteristic (ROC) analyses.

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

While often seeming to investigate rather different problems, research into value-based decision making and cognitive control have historically offered parallel insights into how people select thoughts and actions. While the former studies how people weigh costs and benefits to make a decision, the latter studies how they adjust information processing to achieve their goals. Recent work has highlighted ways in which decision-making research can inform our understanding of cognitive control. Here, we provide the complementary perspective: how cognitive control research has informed understanding of decision-making. We highlight three particular areas of research where this critical interchange has occurred: (1) how different types of goals shape the evaluation of choice options, (2) how people use control to adjust the ways they make their decisions, and (3) how people monitor decisions to inform adjustments to control at multiple levels and timescales. We show how adopting this alternate viewpoint offers new insight into the determinants of both decisions and control; provides alternative interpretations for common neuroeconomic findings; and generates fruitful directions for future research.

Coexistence of sensory qualities and value representations in human orbitofrontal cortex

Despite the multiple regions and neural networks associated with value-based decision-making, the orbitofrontal cortex (OFC) is possible a particularly important one. Although the role of the OFC in reinforcer devaluation tasks, which assess the ability to represent identity, sensory qualities, and subjective values of the expected outcomes, has been established, the specific aspect represented in this area remains unclear. In this study, using functional magnetic resonance imaging, wherein participants rated the palatability of 128 food items using photographs, we investigated whether the human OFC represents object identity, sensory qualities, or value. Employing many items helped us dissociate object identity from sensory qualities and values; the inferred sensory qualities of identical items were manipulated by a change in metabolic state. Moreover, value differences between items were analytically controlled by employing a technique similar to age adjustment. The palatability ratings for food items significantly decreased after a meal. Using representational similarity analysis, we confirmed that the OFC represents value. Moreover, identical items were represented similarly in the lateral OFC in a given metabolic state; however, these representations were altered post-feeding. Importantly, this change was not explained by subjective value, suggesting that the OFC represents sensory quality and value, but not object identity.

Neurocomputational mechanisms underlying the subjective value of information

Humans have a striking desire to actively seek new information, even when it is devoid of any instrumental utility. However, the mechanisms that drive individuals' subjective preference for information remain unclear. Here, we used fMRI to examine the processing of subjective information value, by having participants decide how much effort they were willing to trade-off for non-instrumental information. We showed that choices were best described by a model that accounted for: (1) the variability in individuals' estimates of uncertainty, (2) their desire to reduce that uncertainty, and (3) their subjective preference for positively valenced information. Model-based analyses revealed the anterior cingulate as a key node that encodes the subjective value of information across multiple stages of decision-making - including when information was prospectively valued, and when the outcome was definitively delivered. These findings emphasise the multidimensionality of information value, and reveal the neurocomputational mechanisms underlying the variability in individuals' desire to physically pursue informative outcomes.

Magnitude-sensitivity: rethinking decision-making

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

Neural underpinnings of food choice and consumption in obesity

BACKGROUND/OBJECTIVES

Obesity is associated with unhealthy food choices. Food selection is driven by the subjective valuation of available options, and the perceived and actual rewards accompanying consumption. These cognitive operations are mediated by brain regions including the ventromedial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), and ventral striatum (vStr). This study investigated the relationship between body mass index (BMI) and functional activations in the vmPFC, dACC, and vStr during food selection and consumption.

SUBJECTS/METHODS

After overnight fasting, 26 individuals (BMI: 18-40 kg/m²) performed a food choice task while being scanned with functional magnetic resonance imaging (fMRI). Each trial involved selecting one beverage from a pair of presented options, followed by delivery of a 3 mL aliquot of the selected option using an MR-compatible gustometer. We also tracked subjective preference for each beverage throughout the experiment.

RESULTS

During food choice, individuals with greater BMI had less activation in the dorsolateral prefrontal cortex when selecting a high-value option and less vmPFC activation upon its consumption. Independent of BMI, during food choice the dACC and anterior insula elicited higher activation when a less preferred beverage was selected. Activation of the dACC and a broader frontoparietal network was also observed when deciding between options more similar in value. During consumption, receipt of a more preferred beverage was associated with greater vmPFC response, and attenuation of the dACC.

CONCLUSIONS

An individual's preference for a food option modulates the brain activity associated with choosing and consuming it. The relationship between food preference and underlying brain activity is altered in obesity, with reduced engagement of cognition-related regions when presented with a highly valued option, but a blunted response in reward-related regions upon consumption.

Coherent theta activity in the medial and orbital frontal cortices encodes reward value

This study examined how the medial frontal (MFC) and orbital frontal (OFC) cortices process reward information. We simultaneously recorded local field potentials in the two areas as rats consumed liquid sucrose rewards. Both areas exhibited a 4-8 Hz 'theta' rhythm that was phase-locked to the lick cycle. The rhythm tracked shifts in sucrose concentrations and fluid volumes, demonstrating that it is sensitive to differences in reward magnitude. The coupling between the rhythm and licking was stronger in MFC than OFC and varied with response vigor and absolute reward value in the MFC. Spectral analysis revealed zero-lag coherence between the cortical areas, and found evidence for a directionality of the rhythm, with MFC leading OFC. Our findings suggest that consummatory behavior generates simultaneous theta range activity in the MFC and OFC that encodes the value of consumed fluids, with the MFC having a top-down role in the control of consumption.

Subjective value, not a gridlike code, describes neural activity in ventromedial prefrontal cortex during value-based decision-making

Across many studies, ventromedial prefrontal cortex (vmPFC) activity has been found to correlate with subjective value during value-based decision-making. Recently, however, vmPFC has also been shown to reflect a hexagonal gridlike code during navigation through physical and conceptual space, and such gridlike codes have been proposed to enable value-based choices between novel options. Here, we first show that, in theory, a hexagonal gridlike code can in some cases mimic vmPFC activity previously attributed to subjective value, raising the possibility that the subjective value correlates previously observed in vmPFC may have actually been a misconstrued gridlike signal. We then compare the two accounts empirically, using fMRI data from a large number of subjects performing an intertemporal choice task. We find clear and unambiguous evidence that subjective value is a better description of vmPFC activity in this task than a hexagonal gridlike code. In fact, we find no significant evidence at all for a hexagonal gridlike code in vmPFC activity during intertemporal choice. This result limits the generality of gridlike modulation as description of vmPFC activity. We suggest that vmPFC may flexibly switch representational schemes so as to encode the most relevant information for the current task.

The neuroscience of emotional disorders

Emotions can be defined as states elicited by rewards or punishments, and indeed the neurology of emotional disorders can be understood in terms of this foundation. The orbitofrontal cortex in humans and other primates is a critical area in emotion processing, determining the value of stimuli and whether they are rewarding or nonrewarding. The cortical processing that occurs before the orbitofrontal cortex primarily involves defining the identity of stimuli, i.e., "what" is present and not reward value. There is evidence that this holds true for taste, visual, somatosensory, and olfactory stimuli. The human medial orbitofrontal cortex is important in processing many different types of reward, and the lateral orbitofrontal cortex in processing nonreward and punishment. Humans with damage to the orbitofrontal cortex have an impaired ability to identify facial and voice expressions of emotions, and impaired subjective experience of emotion. They can have an altered personality and be impulsive because they are impaired at processing failures to receive expected rewards and at processing punishments. In humans, the role of the amygdala in the processing of emotions is reduced because of the great evolutionary development of the orbitofrontal cortex: amygdala damage has much less effect on emotion than does orbitofrontal cortex damage. The orbitofrontal cortex projects reward value information to the anterior cingulate cortex, which is involved in learning those actions required to obtain rewards and avoid punishments. The cingulate cortex thus provides an output route for emotional behavior. In depression, the medial orbitofrontal cortex has decreased connectivity and sensitivity to reward, and the lateral orbitofrontal cortex has increased connectivity and sensitivity to nonreward. The orbitofrontal cortex has major projections to the anterior cingulate cortex, including its subcommissural region, and the anterior cingulate cortex is also implicated in depression.

Choice-relevant information transformation along a ventrodorsal axis in the medial prefrontal cortex

Choice-relevant brain regions in prefrontal cortex may progressively transform information about options into choices. Here, we examine responses of neurons in four regions of the medial prefrontal cortex as macaques performed two-option risky choices. All four regions encode economic variables in similar proportions and show similar putative signatures of key choice-related computations. We provide evidence to support a gradient of function that proceeds from areas 14 to 25 to 32 to 24. Specifically, we show that decodability of twelve distinct task variables increases along that path, consistent with the idea that regions that are higher in the anatomical hierarchy make choice-relevant variables more separable. We also show progressively longer intrinsic timescales in the same series. Together these results highlight the importance of the medial wall in choice, endorse a specific gradient-based organization, and argue against a modular functional neuroanatomy of choice.

Responses to Heartbeats in Ventromedial Prefrontal Cortex Contribute to Subjective Preference-Based Decisions

Forrest Gump or The Matrix? Preference-based decisions are subjective and entail self-reflection. However, these self-related features are unaccounted for by known neural mechanisms of valuation and choice. Self-related processes have been linked to a basic interoceptive biological mechanism, the neural monitoring of heartbeats, in particular in ventromedial prefrontal cortex (vmPFC), a region also involved in value encoding. We thus hypothesized a functional coupling between the neural monitoring of heartbeats and the precision of value encoding in vmPFC. Human participants of both sexes were presented with pairs of movie titles. They indicated either which movie they preferred or performed a control objective visual discrimination that did not require self-reflection. Using magnetoencephalography, we measured heartbeat-evoked responses (HERs) before option presentation and confirmed that HERs in vmPFC were larger when preparing for the subjective, self-related task. We retrieved the expected cortical value network during choice with time-resolved statistical modeling. Crucially, we show that larger HERs before option presentation are followed by stronger value encoding during choice in vmPFC. This effect is independent of overall vmPFC baseline activity. The neural interaction between HERs and value encoding predicted preference-based choice consistency over time, accounting for both interindividual differences and trial-to-trial fluctuations within individuals. Neither cardiac activity nor arousal fluctuations could account for any of the effects. HERs did not interact with the encoding of perceptual evidence in the discrimination task. Our results show that the self-reflection underlying preference-based decisions involves HERs, and that HER integration to subjective value encoding in vmPFC contributes to preference stability.SIGNIFICANCE STATEMENT Deciding whether you prefer Forrest Gump or The Matrix is based on subjective values, which only you, the decision-maker, can estimate and compare, by asking yourself. Yet, how self-reflection is biologically implemented and its contribution to subjective valuation are not known. We show that in ventromedial prefrontal cortex, the neural response to heartbeats, an interoceptive self-related process, influences the cortical representation of subjective value. The neural interaction between the cortical monitoring of heartbeats and value encoding predicts choice consistency (i.e., whether you consistently prefer Forrest Gump over Matrix over time. Our results pave the way for the quantification of self-related processes in decision-making and may shed new light on the relationship between maladaptive decisions and impaired interoception.

Frontal circuit specialisations for decision making

There is widespread consensus that distributed circuits across prefrontal and anterior cingulate cortex (PFC/ACC) are critical for reward-based decision making. The circuit specialisations of these areas in primates were likely shaped by their foraging niche, in which decision making is typically sequential, attention-guided and temporally extended. Here, I argue that in humans and other primates, PFC/ACC circuits are functionally specialised in two ways. First, microcircuits found across PFC/ACC are highly recurrent in nature and have synaptic properties that support persistent activity across temporally extended cognitive tasks. These properties provide the basis of a computational account of time-varying neural activity within PFC/ACC as a decision is being made. Second, the macrocircuit connections (to other brain areas) differ between distinct PFC/ACC cytoarchitectonic subregions. This variation in macrocircuit connections explains why PFC/ACC subregions make unique contributions to reward-based decision tasks and how these contributions are shaped by attention. They predict dissociable neural representations to emerge in orbitofrontal, anterior cingulate and dorsolateral prefrontal cortex during sequential attention-guided choice, as recently confirmed in neurophysiological recordings.

Reduced adaptation of glutamatergic stress response is associated with pessimistic expectations in depression

Stress is a significant risk factor for the development of major depressive disorder (MDD), yet the underlying mechanisms remain unclear. Preclinically, adaptive and maladaptive stress-induced changes in glutamatergic function have been observed in the medial prefrontal cortex (mPFC). Here, we examine stress-induced changes in human mPFC glutamate using magnetic resonance spectroscopy (MRS) in two healthy control samples and a third sample of unmedicated participants with MDD who completed the Maastricht acute stress task, and one sample of healthy control participants who completed a no-stress control manipulation. In healthy controls, we find that the magnitude of mPFC glutamate response to the acute stressor decreases as individual levels of perceived stress increase. This adaptative glutamate response is absent in individuals with MDD and is associated with pessimistic expectations during a 1-month follow-up period. Together, this work shows evidence for glutamatergic adaptation to stress that is significantly disrupted in MDD.

Partial integration of the components of value in anterior cingulate cortex

Evaluation often involves integrating multiple determinants of value, such as the different possible outcomes in risky choice. A brain region can be placed either before or after a presumed evaluation stage by measuring how responses of its neurons depend on multiple determinants of value. A brain region could also, in principle, show partial integration, which would indicate that it occupies a middle position between (preevaluative) nonintegration and (postevaluative) full integration. Existing mathematical techniques cannot distinguish full from partial integration and therefore risk misidentifying regional function. Here we use a new Bayesian regression-based approach to analyze responses of neurons in dorsal anterior cingulate cortex (dACC) to risky offers. We find that dACC neurons only partially integrate across outcome dimensions, indicating that dACC cannot be assigned to either a pre- or postevaluative position. Neurons in dACC also show putative signatures of value comparison, thereby demonstrating that comparison does not require complete evaluation before proceeding. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Is value-based choice repetition susceptible to medial frontal transcranial direct current stimulation (tDCS)? A preregistered study

In value-based decision making, people have to weigh different options based on their subjective value. This process, however, also is influenced by choice biases, such as choice repetition: in a series of choices, people are more likely to repeat their decision than to switch to a different choice. Previously, it was shown that transcranial direct current stimulation (tDCS) can affect such choice biases. We applied tDCS over the medial prefrontal cortex to investigate whether tDCS can alter choice repetition in value-based decision making. In a preregistered study, we applied anodal, cathodal, and sham tDCS stimulation to 52 participants. While we found robust choice repetition effects, we did not find support for an effect of tDCS stimulation. We discuss these findings within the larger scope of the tDCS literature and highlight the potential roles of interindividual variability and current density strength.

Electroencephalographic Theta-Band Oscillatory Dynamics Represent Attentional Bias to Subjective Preferences in Value-Based Decisions

Background

Previous studies have shown that people always pay more attention to highly preferred items of choice, which is well defined by behavioral measurements and eye-tracking. However, less is known about the neural dynamics underlying the role that visual attention plays in value-based decisions, especially in those characterized by the “relative value” (ie, value difference) between two items displayed simultaneously in a binary choice.

Purpose

This study examined the neural temporal and neural oscillatory features underlying selective attention to subjective preferences in value-based decision making.

Methods

In this study, we recorded electroencephalography (EEG) measurements while participants performed a binary choice task in which they were instructed to respond to their preferred snack in high value difference (HVD) or low value difference (LVD) conditions.

Results

Behaviorally, participants showed faster responses and lower error rates in the HVD condition than in the LVD condition. In parallel, participants exerted a reduced prefrontal N2 component and attenuated frontal theta-band synchronization in the HVD condition as opposed to the LVD condition. Crucially, participants showed greater N2pc component and theta-band synchronization over the human posterior cortex in the HVD condition than in the LVD condition. Moreover, there was a direct correlation between frontal and posterior theta-band synchronization.

Conclusion

The results show that theta-band oscillatory dynamics may represent attentional bias to subjective preferences, and this effect can be modulated by the level attentional bias to subjective preferences, and this effect can be modulated by the level of value difference. Our research provides insights into a new avenue via which the processing of selective attention and value representation in the value-based decisions can be implicated in an integrative neural oscillatory mechanism.

Dissociable roles of cortical excitation-inhibition balance during patch-leaving versus value-guided decisions

In a dynamic world, it is essential to decide when to leave an exploited resource. Such patch-leaving decisions involve balancing the cost of moving against the gain expected from the alternative patch. This contrasts with value-guided decisions that typically involve maximizing reward by selecting the current best option. Patterns of neuronal activity pertaining to patch-leaving decisions have been reported in dorsal anterior cingulate cortex (dACC), whereas competition via mutual inhibition in ventromedial prefrontal cortex (vmPFC) is thought to underlie value-guided choice. Here, we show that the balance between cortical excitation and inhibition (E/I balance), measured by the ratio of GABA and glutamate concentrations, plays a dissociable role for the two kinds of decisions. Patch-leaving decision behaviour relates to E/I balance in dACC. In contrast, value-guided decision-making relates to E/I balance in vmPFC. These results support mechanistic accounts of value-guided choice and provide evidence for a role of dACC E/I balance in patch-leaving decisions.

Neural Computations of Threat

A host of learning, memory, and decision-making processes form the individual's response to threat and may be disrupted in anxiety and post-trauma psychopathology. Here we review the neural computations of threat, from the first encounter with a dangerous situation, through learning, storing, and updating cues that predict it, to making decisions about the optimal course of action. The overview highlights the interconnected nature of these processes and their reliance on shared neural and computational mechanisms. We propose an integrative approach to the study of threat-related processes, in which specific computations are studied across the various stages of threat experience rather than in isolation. This approach can generate new insights about the evolution, diagnosis, and treatment of threat-related psychopathology.

From affective to cognitive processing: Functional organization of the medial frontal cortex

The medial wall of the primate frontal lobe encompasses multiple anatomical subregions. Based on distinct neurophysiological correlates and effects of lesions, individual areas are thought to play unique roles in behavior. Further, evidence suggests that dysfunction localized to specific subregions is commonly found in different neuropsychiatric disorders. The neurobiological underpinnings of these disorders, however, remain far from clear. Here, to better understand the functions of medial frontal cortex (MFC) and its role in psychiatric disease, we focus on its functional organization. We describe the emerging pattern in which more dorsal regions subserve temporally extended cognitive functions and more ventral regions predominantly subserve affective functions. We focus on two specific domains, decision-making and social cognition, that require integration across emotion and cognition. In each case, we discuss the current understanding of the functions believed to depend on subregions of MFC as a stepping-stone to speculate on how they might work in unison. We conclude with an overview of how symptoms of certain psychiatric disorders relate to our understanding of MFC functional organization and how further discovery could fuel advances in circuit-based therapies.

Oscillations as a window into neuronal mechanisms underlying dorsal anterior cingulate cortex function

The function of dorsal Anterior Cingulate Cortex (dACC) remains poorly understood. While many methods, spanning bottom-up and top-down approaches, have been deployed, the view they offer is often conflicting. Integrating bottom-up and top-down approaches requires an intermediary with sufficient explanatory power, theoretical development, and empirical support. Oscillations in the local field potential (LFP) provide such a link. LFP oscillations arise from empirically well-characterized neuronal circuit motifs. Synchronizing the firing of individual units has appealing properties to bind disparate brain regions and propagate information, including gating, routing, and coding. Moreover, the LFP, rather than single unit activity, more closely relates to macro-scale recordings, such as the electroencephalogram and functional magnetic resonance imaging. Thus, LFP oscillations are a critical link that allow for the inference of neuronal micro-circuitry underlying macroscopic brain recordings.

The orbitofrontal cortex: reward, emotion and depression

The orbitofrontal cortex in primates including humans is the key brain area in emotion, and in the representation of reward value and in non-reward, that is not obtaining an expected reward. Cortical processing before the orbitofrontal cortex is about the identity of stimuli, i.e. 'what' is present, and not about reward value. There is evidence that this holds for taste, visual, somatosensory and olfactory stimuli. The human medial orbitofrontal cortex represents many different types of reward, and the lateral orbitofrontal cortex represents non-reward and punishment. Not obtaining an expected reward can lead to sadness, and feeling depressed. The concept is advanced that an important brain region in depression is the orbitofrontal cortex, with depression related to over-responsiveness and over-connectedness of the non-reward-related lateral orbitofrontal cortex, and to under-responsiveness and under-connectivity of the reward-related medial orbitofrontal cortex. Evidence from large-scale voxel-level studies and supported by an activation study is described that provides support for this hypothesis. Increased functional connectivity of the lateral orbitofrontal cortex with brain areas that include the precuneus, posterior cingulate cortex and angular gyrus is found in patients with depression and is reduced towards the levels in controls when treated with medication. Decreased functional connectivity of the medial orbitofrontal cortex with medial temporal lobe areas involved in memory is found in patients with depression. Some treatments for depression may act by reducing activity or connectivity of the lateral orbitofrontal cortex. New treatments that increase the activity or connectivity of the medial orbitofrontal cortex may be useful for depression. These concepts, and that of increased activity in non-reward attractor networks, have potential for advancing our understanding and treatment of depression. The focus is on the orbitofrontal cortex in primates including humans, because of differences of operation of the orbitofrontal cortex, and indeed of reward systems, in rodents. Finally, the hypothesis is developed that the orbitofrontal cortex has a special role in emotion and decision-making in part because as a cortical area it can implement attractor networks useful in maintaining reward and emotional states online, and in decision-making.