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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.
A common neural scale for the subjective pleasantness of different primary rewards.
When an economic decision is taken, it is between goals with different values, and the values must be on the same scale. Here, we used functional MRI to search for a brain region that represents the subjective pleasantness of two different rewards on the same neural scale. We found activity in the ventral prefrontal cortex that correlated with the subjective pleasantness of two fundamentally different rewards, taste in the mouth and warmth on the hand. The evidence came from two different investigations, a between-group comparison of two independent fMRI studies, and from a within-subject study. In the latter, we showed that neural activity in the same voxels in the ventral prefrontal cortex correlated with the subjective pleasantness of the different rewards. Moreover, the slope and intercept for the regression lines describing the relationship between activations and subjective pleasantness were highly similar for the different rewards. We also provide evidence that the activations did not simply represent multisensory integration or the salience of the rewards. The findings demonstrate the existence of a specific region in the human brain where neural activity scales with the subjective pleasantness of qualitatively different primary rewards. This suggests a principle of brain processing of importance in reward valuation and decision-making.
A Neural Mechanism in the Human Orbitofrontal Cortex for Preferring High-Fat Foods Based on Oral Texture.
Although overconsumption of high-fat foods is a major driver of weight gain, the neural mechanisms that link the oral sensory properties of dietary fat to reward valuation and eating behavior remain unclear. Here we combine novel food-engineering approaches with functional neuroimaging to show that the human orbitofrontal cortex (OFC) translates oral sensations evoked by high-fat foods into subjective economic valuations that guide eating behavior. Male and female volunteers sampled and evaluated nutrient-controlled liquid foods that varied in fat and sugar ("milkshakes"). During oral food processing, OFC activity encoded a specific oral-sensory parameter that mediated the influence of the foods' fat content on reward value: the coefficient of sliding friction. Specifically, OFC responses to foods in the mouth reflected the smooth, oily texture (i.e., mouthfeel) produced by fatty liquids on oral surfaces. Distinct activity patterns in OFC encoded the economic values associated with particular foods, which reflected the subjective integration of sliding friction with other food properties (sugar, fat, viscosity). Critically, neural sensitivity of OFC to oral texture predicted individuals' fat preferences in a naturalistic eating test: individuals whose OFC was more sensitive to fat-related oral texture consumed more fat during ad libitum eating. Our findings suggest that reward systems of the human brain sense dietary fat from oral sliding friction, a mechanical food parameter that likely governs our daily eating experiences by mediating interactions between foods and oral surfaces. These findings identify a specific role for the human OFC in evaluating oral food textures to mediate preference for high-fat foods.SIGNIFICANCE STATEMENT Fat and sugar enhance the reward value of food by imparting a sweet taste and rich mouthfeel but also contribute to overeating and obesity. Here we used a novel food-engineering approach to realistically quantify the physical-mechanical properties of high-fat liquid foods on oral surfaces and used functional neuroimaging while volunteers sampled these foods and placed monetary bids to consume them. We found that a specific area of the brain's reward system, the orbitofrontal cortex, detects the smooth texture of fatty foods in the mouth and links these sensory inputs to economic valuations that guide eating behavior. These findings can inform the design of low-calorie fat-replacement foods that mimic the impact of dietary fat on oral surfaces and neural reward systems.
Primate Amygdala Neurons Simulate Decision Processes of Social Partners.
By observing their social partners, primates learn about reward values of objects. Here, we show that monkeys' amygdala neurons derive object values from observation and use these values to simulate a partner monkey's decision process. While monkeys alternated making reward-based choices, amygdala neurons encoded object-specific values learned from observation. Dynamic activities converted these values to representations of the recorded monkey's own choices. Surprisingly, the same activity patterns unfolded spontaneously before partner's choices in separate neurons, as if these neurons simulated the partner's decision-making. These "simulation neurons" encoded signatures of mutual-inhibitory decision computation, including value comparisons and value-to-choice conversions, resulting in accurate predictions of partner's choices. Population decoding identified differential contributions of amygdala subnuclei. Biophysical modeling of amygdala circuits showed that simulation neurons emerge naturally from convergence between object-value neurons and self-other neurons. By simulating decision computations during observation, these neurons could allow primates to reconstruct their social partners' mental states.
Social Risk Coding by Amygdala Activity and Connectivity with the Dorsal Anterior Cingulate Cortex.
Risk is a fundamental factor affecting individual and social economic decisions, but its neural correlates are largely unexplored in the social domain. The amygdala, together with the dorsal anterior cingulate cortex (dACC), is thought to play a central role in risk-taking. Here, we investigated in human volunteers (n = 20; 11 females) how risk (defined as the variance of reward probability distributions) in a social situation affects decisions and concomitant neural activity as measured with fMRI. We found separate variance-risk signals for social and nonsocial outcomes in the amygdala. Specifically, amygdala activity increased parametrically with social reward variance of presented choice options and on separate trials with nonsocial reward variance. Behaviorally, 75% of participants were averse to social risk as estimated in a Becker-DeGroot-Marschak auction-like procedure. The stronger this aversion, the more negative the coupling between risk-related amygdala regions and dACC. This negative relation was significant for social risk attitude but not for the attitude toward variance-risk in juice outcomes. Our results indicate that the amygdala and its coupling with dACC process objective and subjectively evaluated social risk. Moreover, while social risk can be captured with a framework originally established by finance theory for nonsocial risk, the amygdala appears to process social risk largely separately from nonsocial risk.
Nutrient-Sensitive Reinforcement Learning in Monkeys.
In reinforcement learning (RL), animals choose by assigning values to options and learn by updating these values from reward outcomes. This framework has been instrumental in identifying fundamental learning variables and their neuronal implementations. However, canonical RL models do not explain how reward values are constructed from biologically critical intrinsic reward components, such as nutrients. From an ecological perspective, animals should adapt their foraging choices in dynamic environments to acquire nutrients that are essential for survival. Here, to advance the biological and ecological validity of RL models, we investigated how (male) monkeys adapt their choices to obtain preferred nutrient rewards under varying reward probabilities. We found that the nutrient composition of rewards strongly influenced learning and choices. Preferences of the animals for specific nutrients (sugar, fat) affected how they adapted to changing reward probabilities; the history of recent rewards influenced choices of the monkeys more strongly if these rewards contained the their preferred nutrients (nutrient-specific reward history). The monkeys also chose preferred nutrients even when they were associated with lower reward probability. A nutrient-sensitive RL model captured these processes; it updated the values of individual sugar and fat components of expected rewards based on experience and integrated them into subjective values that explained the choices of the monkeys. Nutrient-specific reward prediction errors guided this value-updating process. Our results identify nutrients as important reward components that guide learning and choice by influencing the subjective value of choice options. Extending RL models with nutrient-value functions may enhance their biological validity and uncover nutrient-specific learning and decision variables.SIGNIFICANCE STATEMENT RL is an influential framework that formalizes how animals learn from experienced rewards. Although reward is a foundational concept in RL theory, canonical RL models cannot explain how learning depends on specific reward properties, such as nutrients. Intuitively, learning should be sensitive to the nutrient components of the reward to benefit health and survival. Here, we show that the nutrient (fat, sugar) composition of rewards affects how the monkeys choose and learn in an RL paradigm and that key learning variables including reward history and reward prediction error should be modified with nutrient-specific components to account for the choice behavior observed in the monkeys. By incorporating biologically critical nutrient rewards into the RL framework, our findings help advance the ecological validity of RL models.
Focusing attention in working and long-term memory through dissociable mechanisms.
We developed an experimental approach to compare how attentional orienting facilitates retrieval from spatial working memory (WM) and long-term memory (LTM), and how selective attention within these two memory types impacts incoming sensory information processing. In three experiments with healthy young adults, retrospective attention cues prioritize an item represented in WM or LTM. Participants then retrieve a memory item or perform a perceptual task. The retrocue is informative for the retrieval task but not for the perceptual task. We show that attentional orienting benefits performance for both WM and LTM, with stronger effects for WM. Eye-tracking reveals significant gaze shifts and microsaccades correlated with attention in WM, but no statistically significant gaze biases were found for LTM. Visual discrimination of unrelated visual stimuli is consistently improved for items matching attended WM locations. Similar effects occur at LTM locations but less consistently. The findings suggest at least partly dissociable attention-orienting processes for different memory types. Although our conclusions are necessarily constrained to the type of WM and LTM representations relevant to our task, they suggest that, under certain conditions, attentional prioritization in LTM can operate independently from WM. Future research should explore whether similar dissociations extend to non-spatial or more complex forms of LTM.
Current and prospective roles of magnetic resonance imaging in mild traumatic brain injury
There is unmet clinical need for biomarkers to predict recovery or the development of long-term sequelae of mild traumatic brain injury, a highly prevalent condition causing a constellation of disabling symptoms. A substantial proportion of patients live with long-lasting sequelae affecting their quality of life and ability to work. At present, symptoms can be assessed through clinical tests; however, there are no imaging or laboratory tests fully reflective of pathophysiology routinely used by clinicians to characterize post-concussive symptoms. Magnetic resonance imaging has potential to link subtle pathophysiological alterations to clinical outcomes. Here, we review the state of the art of MRI research in adults with mild traumatic brain injury and provide recommendations to facilitate transition into clinical practice. Studies utilizing MRI can inform on pathophysiology of mild traumatic brain injury. They suggest presence of early cytotoxic and vasogenic oedema. They also show that mild traumatic brain injury results in cellular injury and microbleeds affecting the integrity of myelin and white matter tracts, all processes that appear to induce delayed vascular reactions and functional changes. Crucially, correlates between MRI parameters and post-concussive symptoms are emerging. Clinical sequences such as T1-weighted MRI, susceptibility-weighted MRI or fluid attenuation inversion recovery could be easily implementable in clinical practice, but are not sufficient, in isolation for prognostication. Diffusion sequences have shown promises and, although in need of analysis standardization, are a research priority. Lastly, arterial spin labelling is emerging as a high-utility research as it could become useful to assess delayed neurovascular response and possible long-term symptoms.
Contributed Talks I: Information integration in early visual processing revealed by Vernier thresholds.
Vernier acuity thresholds represent minimal detectable spatial offset between two closely placed targets. We previously showed that Vernier thresholds for a Poisson-limited ideal observer with access to the cone excitations are determined jointly by duration and contrast through the quantity duration x contrast squared. Here we measured thresholds in 7 human observers for combinations of stimulus contrast (100%, 50%, 25%, and 12.5%) and duration (16.7 ms, 66.7 ms, 266.7 ms and 1066.7 ms), while fixing other stimulus properties (foveal viewing; two achromatic vertical bars; length 10.98 arcmin; width 4.39 arcmin; vertical gap 0.878 arcmin). The combinations of duration and contrast were chosen to form four groups of constant duration x contrast squared. Thresholds were a decreasing function of duration x contrast squared. A one-way between observers ANOVA does not reject the hypothesis threshold duration and contrast are integrated through the quantity duration x contrast squared, but the residuals obtained by predicting threshold within each of the four groups by its mean varied systematically with duration, indicating that duration x contrast squared does not fully summarize the information integration. This difference between ideal and human performance indicates that post-receptoral factors not included in the ideal observer model, such as temporal filtering, affect human performance. These factors will be included in future modeling.
Contributed Talks I: Fixational eye movements and retinal adaptation: optimizing drift to maximize information acquisition.
Fixational eye movements (FEMs) are small, fluctuating eye motions when fixating on a target. Given our visual system is evolved, we may ask why FEMs are beneficial and whether they are optimal. A possible reason for FEMs is overcoming retinal adaptation (fading perception of a fixed image). We present a simple model system allowing theoretical investigation of FEM influence on information about an external stimulus. The model incorporates temporal stimulus modulation, retinal image motion due to the drift component of FEMs, blurring due to optics and receptor size, uniform sampling by the receptor array, adaptation via a bandpass temporal filter, and added noise. We investigate how elements of the model mediate the information transmitted, via: i) mutual information between visual system response and external stimulus, ii) direct estimation of stimulus from the system response, and iii) contrast threshold for signal detection. For all these we find a common quantity that must be maximized. For each spatial frequency this quantity is a summed power transmitted due to stimulus temporal modulation and phase shifts from FEMs, when passed through the temporal filter. We demonstrate that the information transmitted can be increased by adding local persistence to an underlying diffusive process. We also quantify the contribution of FEMs to signal detection for targets of different size and duration; such predictions provide a qualitative account of human psychophysical performance.
Poster Session: The effect of fixational eye-movements on the temporal summation at detection threshold: A simulation study.
We explored how fixational eye movements (FEMs) affect threshold temporal summation of increment pulses using realistic simulations of early visual processing. Using the Image Systems Engineering Toolbox for Biology, we assessed performance in a spatial 2AFC increment detection task, where the observer identified whether a stimulus appeared on the left or right. The signal-known-exactly ideal observer was trained on the noise-free photocurrent output of the cone mosaic for both stimulus alternatives, with performance calculated using noisy instances of photocurrents, given FEMs knowledge. The stimuli, modelled as 0.24x2.2 arcmin increments of 543 nm light presented via an AOSLO, included both a single 2 ms flash and pairs of flashes separated by interstimulus intervals (ISI) of 17 ms, 33 ms, 100 ms, or 300 ms. Detection thresholds, defined as the stimulus contrast corresponding to 75% correct, were assessed with and without FEMs. Without FEMs, thresholds for detecting two flashes separated by 17-100 ms slightly increased with ISI but remained lower than those for a single flash. With FEMs, the modelled differences between single- and two-flash thresholds were less pronounced, suggesting that, at the level of photocurrent signals, FEMs reduce the benefits of temporal summation for detection. Future work will quantify this reduction by simulating FEMs with varying velocities and explore if adding a temporal adaptation stage improves effect of FEMs' on performance.
A cognitive map for value-guided choice in the ventromedial prefrontal cortex.
The prefrontal cortex (PFC) is crucial for economic decision-making. However, how PFC value representations facilitate flexible decisions remains unknown. We reframe economic decision-making as a navigation process through a cognitive map of choice values. We found rhesus macaques represented choices as navigation trajectories in a value space using a grid-like code. This occurred in ventromedial PFC (vmPFC) local field potential theta frequency across two datasets. vmPFC neurons deployed the same grid-like code and encoded chosen value. However, both signals depended on theta phase: occurring on theta troughs but on separate theta cycles. Finally, we found sharp-wave ripples-a key signature of planning and flexible behavior-in vmPFC. Thus, vmPFC utilizes cognitive map-based computations to organize and compare values, suggesting an alternative architecture for economic choice in PFC.
Thermal constraints on Middle Pleistocene hominin brain evolution and cognition
High latitude habitats are subject to thermally-driven energetic constraints that make their occupation challenging. This is likely to have had a particularly significant impact on energy-expensive tissue like the brain, especially during periods of lower global temperatures during the Mid-Pleistocene Ice Ages. I analyse data on endocranial volumes for archaic humans (Homo heidelbergensis, H. neanderthalensis and allies) to show (1) that cranial volumes were typically smaller at high latitudes than in the tropics and (2) that they declined during cold phases and increased during warm phases of the Middle Pleistocene Ice Ages. Within this broad pattern, there is a significant uplift in cranial volumes after 400 ka that seems to coincide with widespread presence of hearths at high latitudes, suggesting that hominin populations might have gained at least partial release from this constraint through cultural control over fire. While this might pinpoint the time at which hominins first began to cook on a regular basis, fire offers other important benefits (notably warmth and extending the length of the working day) that might have played an equally important role in buffering populations against thermal stresses. The larger brain sizes that this made possible have implications for social cognitive capacities like mentalising, that in turn have implications for language skills, cultural behaviour and social group size.
Multimodal population study reveals the neurobiological underpinnings of chronotype.
The rapid shifts in society have altered human behavioural patterns, with increased evening activities, increased screen time and changed sleep schedules. As an explicit manifestation of circadian rhythms, chronotype is closely intertwined with physical and mental health. Night owls often exhibit unhealthier lifestyle habits, are more susceptible to mood disorders and have poorer physical fitness compared with early risers. Although individual differences in chronotype yield varying consequences, their neurobiological underpinnings remain elusive. Here we conducted a pattern-learning analysis with three brain-imaging modalities (grey matter volume, white-matter integrity and functional connectivity) and capitalized on 976 phenotypes in 27,030 UK Biobank participants. The resulting multilevel analysis reveals convergence on the basal ganglia, limbic system, hippocampus and cerebellum. The pattern derived from modelling actigraphy wearables data of daily movement further highlighted these key brain features. Overall, our population-level study comprehensively investigates chronotype, emphasizing its close connections with habit formation, reward processing and emotional regulation.
The fNIRS glossary project: a consensus-based resource for functional near-infrared spectroscopy terminology.
SIGNIFICANCE: A shared understanding of terminology is essential for clear scientific communication and minimizing misconceptions. This is particularly challenging in rapidly expanding, interdisciplinary domains that utilize functional near-infrared spectroscopy (fNIRS), where researchers come from diverse backgrounds and apply their expertise in fields such as engineering, neuroscience, and psychology. AIM: The fNIRS Glossary Project was established to develop a community-sourced glossary covering key fNIRS terms, including those related to the continuous-wave (CW), frequency-domain (FD), and time-domain (TD) NIRS techniques. APPROACH: The glossary was collaboratively developed by a diverse group of 76 fNIRS researchers, representing a wide range of career stages (from PhD students to experts) and disciplines. This collaborative process, structured across five phases, ensured the glossary's depth and comprehensiveness. RESULTS: The glossary features over 300 terms categorized into six key domains: analysis, experimental design, hardware, neuroscience, mathematics, and physics. It also includes abbreviations, symbols, synonyms, references, alternative definitions, and figures where relevant. CONCLUSIONS: The fNIRS glossary provides a community-sourced resource that facilitates education and effective scientific communication within the fNIRS community and related fields. By lowering barriers to learning and engaging with fNIRS, the glossary is poised to benefit a broad spectrum of researchers, including those with limited access to educational resources.