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

Orbitofrontal High-Gamma Reflects Spike-Dissociable Value and Decision Mechanisms

The orbitofrontal cortex (OFC) plays a crucial role in value-based decisions. While much is known about how OFC neurons represent values, far less is known about information encoded in OFC local field potentials (LFPs). LFPs are important because they can reflect subthreshold activity not directly coupled to spiking and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded neural activity in the OFC of male macaques performing a two-option value-based decision task. We compared the value- and decision-coding properties of high-gamma LFPs (HG, 50-150 Hz) to the coding properties of spiking multiunit activity (MUA) recorded concurrently on the same electrodes. HG and MUA both represented the values of decision targets, but HG signals had value-coding features that were distinct from concurrently measured MUA. On average HG amplitude increased monotonically with value, whereas in MUA the value encoding was net neutral on average. HG encoded a signal consistent with a comparison between target values, a signal which was negligible in MUA. In individual channels, HG could predict choice outcomes more accurately than MUA; however, when channels were combined in a population-based decoder, MUA was more accurate than HG. In summary, HG signals reveal value-coding features in OFC that could not be observed from spiking activity, including representation of value comparisons and more accurate behavioral predictions. These results have implications for the role of OFC in value-based decisions and suggest that high-frequency LFPs may be a viable-or even preferable-target for BMIs to assist cognitive function.

Dynamic coding and sequential integration of multiple reward attributes by primate amygdala neurons

The value of visual stimuli guides learning, decision-making, and motivation. Although stimulus values often depend on multiple attributes, how neurons extract and integrate distinct value components from separate cues remains unclear. Here we recorded the activity of amygdala neurons while two male monkeys viewed sequential cues indicating the probability and magnitude of expected rewards. Amygdala neurons frequently signaled reward probability in an abstract, stimulus-independent code that generalized across cue formats. While some probability-coding neurons were insensitive to magnitude information, signaling 'pure' probability rather than value, many neurons showed biphasic responses that signaled probability and magnitude in a dynamic (temporally-patterned) and flexible (reversible) value code. Specific amygdala neurons integrated these reward attributes into risk signals that quantified the variance of expected rewards, distinct from value. Population codes were accurate, mutually transferable between value components, and expressed differently across amygdala nuclei. Our findings identify amygdala neurons as a substrate for the sequential integration of multiple reward attributes into value and risk.

The amygdala and the pursuit of future rewards

The successful pursuit of future rewards requires forming an internal goal, followed by planning, decision-making, and progress-tracking over multiple steps. The initial step-forming goals and the plans for obtaining them-involves the subjective valuation of an anticipated reward, considering both the reward's properties and associated delay and physical-effort costs. Recent findings indicate individuals similarly evaluate cognitive effort over time (Johnson and Most, 2023). Success and failure in these processes have been linked to differential life outcomes and psychiatric conditions. Here we review evidence from single-neuron recordings and neuroimaging studies that implicate the amygdala-a brain structure long associated with cue-reactivity and emotion-in decision-making and the planned pursuit of future rewards (Grabenhorst et al., 2012, 2016, 2019, 2023;Hernadi et al., 2015;Zangemeister et al., 2016). The main findings are that, in behavioral tasks in which future rewards can be pursued through planning and stepwise decision-making, amygdala neurons prospectively encode the value of anticipated rewards and related behavioral plans. Moreover, amygdala neurons predict the stepwise choices to pursue these rewards, signal progress toward goals, and distinguish internally generated (i.e., self-determined) choices from externally imposed actions. Importantly, amygdala neurons integrate the subjective value of a future reward with delay and effort costs inherent in pursuing it. This neural evidence identifies three key computations of the primate amygdala that underlie the pursuit of future rewards: (1) forming a self-determined internal goal based on subjective reward-cost valuations, (2) defining a behavioral plan for obtaining the goal, (3) executing this plan through stepwise decision-making and progress-tracking. Based on this framework, we suggest that amygdala neurons constitute vulnerabilities for dysfunction that contribute to maladaptive reward pursuit in psychiatric and behavioral conditions. Consequently, amygdala neurons may also represent potential targets for behavioral-change interventions that aim to improve individual decision-making.

Attention-dependent attribute comparisons underlie multi-attribute decision-making in orbitofrontal cortex

Economic decisions often require weighing multiple dimensions, or attributes. The orbitofrontal cortex FC) is thought to be important for computing the integrated value of an option from its attributes and comparing lues to make a choice. Although OFC neurons are known to encode integrated values, evidence for value mparison has been limited. Here, we used a multi-attribute choice task for monkeys to investigate how OFC eurons integrate and compare multi-attribute options. Attributes were represented separately and eye tracking as used to measure attention. We found that OFC neurons encode the value of attended attributes, dependent of other attributes in the same option. Encoding was negatively weighted by the value of the same tribute in the other option, consistent with a comparison between the two like attributes. These results indicate at OFC computes comparisons among attributes rather than integrated values, and does so dynamically, ifting with the focus of attention.

Comparative basolateral amygdala connectomics reveals dissociable single-neuron projection patterns to frontal cortex in macaques and mice

Basolateral amygdala (BLA) is a key hub for affect in the brain,1,2,3 and dysfunction within this area contributes to a host of psychiatric disorders.4,5 BLA is extensively and reciprocally interconnected with frontal cortex,6,7,8,9 and some aspects of its function are evolutionarily conserved across rodents, anthropoid primates, and humans.10 Neuron density in BLA is substantially lower in primates compared to murine rodents,11 and frontal cortex (FC) is dramatically expanded in primates, particularly the more anterior granular and dysgranular areas.12,13,14 Yet, how these anatomical differences influence the projection patterns of single BLA neurons to frontal cortex across rodents and primates is unknown. Using a barcoded connectomic approach, we assessed the single BLA neuron connections to frontal cortex in mice and macaques. We found that BLA neurons are more likely to project to multiple distinct parts of FC in mice than in macaques. Further, while single BLA neuron projections to nucleus accumbens were similarly organized in mice and macaques, BLA-FC connections differed substantially. Notably, BLA connections to subcallosal anterior cingulate cortex (scACC) in macaques were least likely to branch to other medial frontal cortex areas compared to perigenual ACC (pgACC). This pattern of connections was reversed in the mouse homologues of these areas, infralimbic and prelimbic cortex (IL and PL), mirroring functional differences between rodents and non-human primates. Taken together, these results indicate that BLA connections to FC are not linearly scaled from mice to macaques and instead the organization of single-neuron BLA connections is distinct between these species.

Reward Circuit Local Field Potential Modulations Precede Risk Taking

Risk taking behavior is a symptom of multiple neuropsychiatric disorders and often lacks effective treatments. Reward circuitry regions including the amygdala, orbitofrontal cortex, insula, and anterior cingulate have been implicated in risk-taking by neuroimaging studies. Electrophysiological activity associated with risk taking in these regions is not well understood in humans. Further characterizing the neural signalling that underlies risk-taking may provide therapeutic insight into disorders associated with risk-taking. Eleven patients with pharmacoresistant epilepsy who underwent stereotactic electroencephalography with electrodes in the amygdala, orbitofrontal cortex, insula, and/or anterior cingulate participated. Patients participated in a gambling task where they wagered on a visible playing card being higher than a hidden card, betting $5 or $20 on this outcome, while local field potentials were recorded from implanted electrodes. We used cluster-based permutation testing to identify reward prediction error signals by comparing oscillatory power following unexpected and expected rewards. We also used cluster-based permutation testing to compare power preceding high and low bets in high-risk (<50% chance of winning) trials and two-way ANOVA with bet and risk level to identify signals associated with risky, risk averse, and optimized decisions. We used linear mixed effects models to evaluate the relationship between reward prediction error and risky decision signals across trials, and a linear regression model for associations between risky decision signal power and Barratt Impulsiveness Scale scores for each patient. Reward prediction error signals were identified in the amygdala (p=0.0066), anterior cingulate (p=0.0092), and orbitofrontal cortex (p=6.0E-4, p=4.0E-4). Risky decisions were predicted by increased oscillatory power in high-gamma frequency range during card presentation in the orbitofrontal cortex (p=0.0022), and by increased power following bet cue presentation across the theta-to-beta range in the orbitofrontal cortex ( p =0.0022), high-gamma in the anterior cingulate ( p =0.0004), and high-gamma in the insula ( p =0.0014). Risk averse decisions were predicted by decreased orbitofrontal cortex gamma power ( p =2.0E-4). Optimized decisions that maximized earnings were preceded by decreases within the theta to beta range in orbitofrontal cortex ( p =2.0E-4), broad frequencies in amygdala ( p =2.0E-4), and theta to low-gamma in insula ( p =4.0E-4). Insula risky decision power was associated with orbitofrontal cortex high-gamma reward prediction error signal ( p =0.0048) and with patient impulsivity ( p =0.00478). Our findings identify and help characterize reward circuitry activity predictive of risk-taking in humans. These findings may serve as potential biomarkers to inform the development of novel treatment strategies such as closed loop neuromodulation for disorders of risk taking.