Altered monetary loss processing and reinforcement-based learning in individuals with obesity

How are overweight and obesity associated with reinforcement learning deficits? A systematic review

Reinforcement learning (RL) refers to the ability to learn stimulus-response or response-outcome associations relevant to the acquisition of behavioral repertoire and adaptation to the environment. Research data from correlational and case-control studies have shown that obesity is associated with impairments in RL. The aim of the present study was to systematically review how obesity and overweight are associated with RL performance. More specifically, the relationship between high body mass index (BMI) and task performance was explored through the analysis of specific RL processes associated with different physiological, computational, and behavioral manifestations. Our systematic analyses indicate that obesity might be associated with impairments in the use of aversive outcomes to change ongoing behavior, as revealed by results involving instrumental negative reinforcement and extinction/reversal learning, but further research needs to be conducted to confirm this association. Hypotheses regarding how obesity might be associated with altered RL were discussed.

Food reinforcement architecture: A framework for impulsive and compulsive overeating and food abuse

Few reward-based theories address key drivers of susceptibility to food cues and consumption beyond fullness. Decision-making and habit formation are governed by reinforcement-based learning processes that, when overstimulated, can drive unregulated hedonically motivated overeating. Here, a model food reinforcement architecture is proposed that uses fundamental concepts in reinforcement and decision-making to identify maladaptive eating habits that can lead to obesity. This model is unique in that it identifies metabolic drivers of reward and incorporates neuroscience, computational decision-making, and psychology to map overeating and obesity. Food reinforcement architecture identifies two paths to overeating: a propensity for hedonic targeting of food cues contributing to impulsive overeating and lack of satiation that contributes to compulsive overeating. A combination of those paths will result in a conscious and subconscious drive to overeat independent of negative consequences, leading to food abuse and/or obesity. Use of this model to identify aberrant reinforcement learning processes and decision-making systems that can serve as markers of overeating risk may provide an opportunity for early intervention in obesity.

Reduced sensitivity but intact motivation to monetary rewards and reversal learning in obesity

BACKGROUND

Obesity has been linked to altered reward processing but little is known about which components of reward processing including motivation, sensitivity and learning are impaired in obesity. We examined whether obesity compared to healthy weight controls is associated with differences in distinct subdomains of reward processing. To this end, we used two established paradigms, namely the Effort Expenditure for Rewards task (EEfRT) and the Probabilistic Reversal Learning Task (PRLT).

METHODS

30 individuals with obesity (OBS) and 30 healthy weight control subjects (HC) were included in the study. Generalized estimating equation models were used to analyze EEfRT choice behavior. PRLT data was analyzed using both conventional behavioral variables of choices and computational models.

RESULTS

Our findings from the different tasks speak in favor of a hyposensitivity to non-food rewards in obesity. OBS did not make fewer overall hard task selections compared to HC in the EEfRT suggesting generally intact non-food reward motivation. However, in highly rewarding trials (i.e.,trials with high reward magnitude and high reward probability),OBSmadefewer hard task selections compared to normal weight subjects suggesting decreased sensitivity to highly rewarding non-food reinforcers. Hyposensitivity to non-food rewards was also evident in OBS in the PRLT as evidenced by lower win-stay probability compared to HC. Our computational modelling analyses revealed decreased stochasticity but intact reward and punishment learning rates in OBS.

CONCLUSIONS

Our findings provide evidence for intact reward motivation and learning in OBS but lower reward sensitivity which is linked to stochasticity of choices in a non-food context. These findings might provide further insight into the mechanism underlying dysfunctional choices in obesity.

The human cerebellum in reward anticipation and reward outcome processing: An activation likelihood estimation meta-analysis

The cerebellum generates internal prediction models and actively compares anticipated and actual outcomes in order to reach a desired end state. In this process, reward can serve as a reinforcer that shapes internal prediction models, enabling context-appropriate behavior. While the involvement of the cerebellum in reward processing has been established in animals, there is no detailed account of which cerebellar regions are involved in reward anticipation and reward outcome processing in humans. To this end, an activation likelihood estimation meta-analysis of functional neuroimaging studies was performed to investigate cerebellar functional activity patterns associated with reward anticipation and reward outcome processing in healthy adults. Results showed that reward anticipation (k=31) was associated with regional activity in the bilateral anterior lobe, bilateral lobule VI, left Crus I and the posterior vermis, while reward outcome (k=16) was associated with regional activity in the declive and left lobule VI. The findings of this meta-analysis show distinct involvement of the cerebellum in reward anticipation and reward outcome processing as part of a predictive coding routine.

Brain functional and structural magnetic resonance imaging of obesity and weight loss interventions

Obesity has tripled over the past 40 years to become a major public health issue, as it is linked with increased mortality and elevated risk for various physical and neuropsychiatric illnesses. Accumulating evidence from neuroimaging studies suggests that obesity negatively affects brain function and structure, especially within fronto-mesolimbic circuitry. Obese individuals show abnormal neural responses to food cues, taste and smell, resting-state activity and functional connectivity, and cognitive tasks including decision-making, inhibitory-control, learning/memory, and attention. In addition, obesity is associated with altered cortical morphometry, a lowered gray/white matter volume, and impaired white matter integrity. Various interventions and treatments including bariatric surgery, the most effective treatment for obesity in clinical practice, as well as dietary, exercise, pharmacological, and neuromodulation interventions such as transcranial direct current stimulation, transcranial magnetic stimulation and neurofeedback have been employed and achieved promising outcomes. These interventions and treatments appear to normalize hyper- and hypoactivations of brain regions involved with reward processing, food-intake control, and cognitive function, and also promote recovery of brain structural abnormalities. This paper provides a comprehensive literature review of the recent neuroimaging advances on the underlying neural mechanisms of both obesity and interventions, in the hope of guiding development of novel and effective treatments.

Evaluating the neural substrates of effort-expenditure for reward in adults with major depressive disorder and obesity

Converging evidence has suggested that disturbances in monetary reward processing may subserve the shared biosignature between major depressive disorder (MDD) and obesity. However, there remains a paucity of studies that have evaluated the deficits in specific subcomponents of reward functioning in populations with MDD and obesity comorbidity. We evaluated the association between effort-expenditure for monetary reward and neural activation in regions associated with reward-based decision making (i.e., the caudate nucleus, anterior cingulate cortex (ACC) and hippocampus) in people with MDD and obesity comorbidity. We acquired structural and functional magnetic resonance imaging (fMRI) in 12 participants and performed a spherical region-of-interest analysis (ROI) using previously defined peak MNI coordinates. A one-sample t-test was employed to compare ROI-specific blood-oxygen-level-dependent (BOLD) signal change during the task choice selection window (i.e., high-effort vs. low-effort task) of the effort-expenditure for reward task (EEfRT). We observed no change in activation of the caudate nucleus, ACC or hippocampus in participants with increased BMI when contrasting the high effort > low effort reward magnitude condition for the EEfRT. The findings from our exploratory study evaluated the disturbances in fundamental reward processes, including cost-benefit decision making, in people MDD and obesity. Future studies should further investigate this relationship with a larger sample size.

Cerebral 18 F-fluorodeoxyglucose metabolism alteration of reward- and motivation-related regions in groups of different BMI classifications

OBJECTIVE

This study explored the relationship between BMI and regional cerebral glucose metabolism and explicitly detected regions with significant differences in cerebral metabolism using positron emission tomography (PET)/magnetic resonance imaging in the resting state.

METHODS

Corresponding PET images acquired from 220 participants were sorted into four groups according to Asian BMI standards: underweight, normal weight, overweight, and obesity. Pearson correlation coefficient analysis was performed to assess the association between BMI and standard uptake value. The regional cerebral glucose metabolism was measured in the fasted state. The PET images were analyzed using statistical parameter maps. One-way ANOVA was used to explore differences in the standard uptake value as an indicator of regional cerebral glucose metabolism.

RESULTS

This study found that lower cerebral glucose metabolism in reward- and motivation-related regions was accompanied by more severe obesity and that regional cerebral glucose metabolism activities were negatively correlated with BMI. In addition, more severe obesity was accompanied by a larger range of areas with significant differences independent of current dietary status.

CONCLUSIONS

These findings suggest that the reward and motivation circuits may be a factor regulating energy balance and influencing the degree of obesity.

A Neuroeconomics Approach to Obesity

Obesity is a heterogeneous condition that is affected by physiological, behavioral, and environmental factors. Value-based decision making is a useful framework for integrating these factors at the individual level. The disciplines of behavioral economics and reinforcement learning provide tools for identifying specific cognitive and motivational processes that may contribute to the development and maintenance of obesity. Neuroeconomics complements these disciplines by studying the neural mechanisms underlying these processes. We surveyed recent literature on individual decision characteristics that are most frequently implicated in obesity: discounting the value of future outcomes, attitudes toward uncertainty, and learning from rewards and punishments. Our survey highlighted both consistent and inconsistent behavioral findings. These findings underscore the need to examine multiple processes within individuals to identify unique behavioral profiles associated with obesity. Such individual characterization will inform future studies on the neurobiology of obesity as well as the design of effective interventions that are individually tailored.

Dysregulated resting state functional connectivity and obesity: A systematic review

Obesity has been variously linked to differences in brain functional connectivity in regions associated with reward, emotional regulation and cognition, potentially revealing neural mechanisms contributing to its development and maintenance. This systematic review summarizes and critically appraises the existing literature on differences in resting state functional connectivity (Rs-FC) between overweight and individuals with obesity in relation healthy-BMI controls. Twenty-nine studies were identified and the results consistently support the hypothesis that obesity is associated with differences in Rs-FC. Specifically, obesity/overweight was consistently associated with (i) DMN hypoconnectivity and salience network hyperconnectivity; (ii) increased Rs-FC between the hypothalamus and reward, limbic and salience networks, and decreased Rs-FC between the hypothalamus and cognitive regions; (iii) increased power within regions associated with inhibition/emotional reasoning; (iv) decreased nodal efficiency, degree centrality, and global efficiency. Collectively, the results suggest obesity is associated with disrupted connectivity of brain networks responsible for cognition, reward, self-referential processing and emotional regulation.

Is the habit system altered in individuals with obesity? A systematic review

Habit-like eating behavior is repeatedly pointed to as a key cognitive mechanism contributing to the emergence and maintenance of obesity. Here, we conducted a systematic review of the literature to assess the existent behavioral evidence for the Habit Hypothesis for Overeating (HHO) which states that obesity is the consequence of an imbalance between the habit and goal-directed reward learning systems, leading to overconsumption of food. We found a total of 19 studies implementing a variety of experimental protocols (i.e., free operant paradigm, slips-of-action test, two-step task, Pavlovian-to-Instrumental paradigm, probabilistic learning task) and manipulations. Taken together, the studies on clinical (binge eating disorder) and non-clinical individuals with overweight or obesity do not support the HHO conclusively. While the scientific literature on HHO is still in its infancy, the heterogeneity of the extant studies makes it difficult to evaluate the degree of convergence of these findings. Uncovering the role of reward learning systems in eating behaviors might have a transformative impact on public health.

The impact of overweight/obesity on monetary reward processing: A systematic review

OBJECTIVE

Converging evidence suggests abnormalities in monetary reward processing may underlie the shared pathophysiology between major depressive disorder and obesity. As such, there is a need to parse deficits in specific subcomponents of monetary reward functioning (i.e., valuation, learning and anticipation).

METHODS

PsycINFO, Google Scholar and PubMed databases were searched for English-language articles published between database inception to June 6th, 2020. Studies were identified using the following medical search heading (MeSH) terms and search strings: (reward (valuation OR motivation OR anticipation OR learning OR functioning OR decision-making OR reinforcement)) AND ((obesity OR overweight OR obese).

RESULTS

Findings were reviewed from 11 studies evaluating the association between obesity and monetary reward processing. Four studies found significant differences in reward learning in individuals with obesity compared to normal-weight participants. Five studies found body mass index (BMI) to be predictive of willingness to expend effort (i.e., valuation) for a monetary reward. Three studies found changes in neural activations in the ventral striatum during anticipatory phases preceding receipt of a monetary reward in participants with obesity.

CONCLUSIONS

Participants with obesity demonstrated significantly poorer performance in task-based measures of reward learning, valuation, and anticipation, resulting in lower monetary reward outcomes across all studies compared to healthy controls. Notably, participants with obesity and comorbid depression performed worse than participants with no comorbid depression.

LIMITATIONS

There persists heterogeneity between studies with regards to inclusion of mood disorder populations and exclusion of psychiatric comorbidities in groups with obesity.

Fat and Carbohydrate Interact to Potentiate Food Reward in Healthy Weight but Not in Overweight or Obesity

Prior work suggests that actual, but not estimated, energy density drives the reinforcing value of food and that energy from fat and carbohydrate can interact to potentiate reward. Here we sought to replicate these findings in an American sample and to determine if the effects are influenced by body mass index (BMI). Thirty participants with healthy weight (HW; BMI 21.92 ± 1.77; M ± SD) and 30 participants with overweight/obesity (OW/OB; BMI 29.42 ± 4.44) rated pictures of common American snacks in 120-kcal portions for liking, familiarity, frequency of consumption, expected satiety, healthiness, energy content, energy density, and price. Participants then completed an auction task where they bid for the opportunity to consume each food. Snacks contained either primarily carbohydrate, primarily fat, or roughly equal portions of fat and carbohydrate (combo). Replicating prior work, we found that participants with HW bid the most for combo foods in linear mixed model analyses. This effect was not observed among individuals with OW/OB. Additionally, in contrast with previous reports, our linear regression analyses revealed a negative relationship between the actual energy density of the snacks and bid amount that was mediated by food price. Our findings support altered macronutrient reinforcement in obesity and highlight potential influences of the food environment on the regulation of food reward.

Reliance on model-based and model-free control in obesity

Consuming more energy than is expended may reflect a failure of control over eating behaviour in obesity. Behavioural control arises from a balance between two dissociable strategies of reinforcement learning: model-free and model-based. We hypothesized that weight status relates to an imbalance in reliance on model-based and model-free control, and that it may do so in a linear or quadratic manner. To test this, 90 healthy participants in a wide BMI range [normal-weight (n = 31), overweight (n = 29), obese (n = 30)] performed a sequential decision-making task. The primary analysis indicated that obese participants relied less on model-based control than overweight and normal-weight participants, with no difference between overweight and normal-weight participants. In line, secondary continuous analyses revealed a negative linear, but not quadratic, relationship between BMI and model-based control. Computational modelling of choice behaviour suggested that a mixture of both strategies was shifted towards less model-based control in obese participants. Our findings suggest that obesity may indeed be related to an imbalance in behavioural control as expressed in a phenotype of less model-based control potentially resulting from enhanced reliance on model-free computations.

Preliminary evidence for an association between intake of high-fat high-sugar diet, variations in peripheral dopamine precursor availability and dopamine-dependent cognition in humans

Obesity is associated with alterations in dopaminergic transmission and cognitive function. Rodent studies suggest that diets rich in saturated fat and refined sugars (HFS), as opposed to diets diets low in saturated fat and refined sugars (LFS), change the dopamine system independent of excessive body weight. However, the impact of HFS on the human brain has not been investigated. Here, we compared the effect of dietary dopamine depletion on dopamine-dependent cognitive task performance between two groups differing in habitual intake of dietary fat and sugar. Specifically, we used a double-blind within-subject cross-over design to compare the effect of acute phenylalanine/tyrosine depletion on a reinforcement learning and a working memory task, in two groups that are on opposite ends of the spectrum of self-reported HFS intake (low vs high intake: LFS vs HFS group). We tested 31 healthy young women matched for body mass index (mostly normal weight to overweight) and IQ. Depletion of peripheral precursors of dopamine reduced the working memory specific performance on the operation span task in the LFS, but not in the HFS group (P = 0.016). Learning from positive- and negative-reinforcement (probabilistic selection task) was increased in both diet groups after dopamine depletion (P = 0.049). As a secondary exploratory research question, we measured peripheral dopamine precursor availability (pDAP) at baseline as an estimate for central dopamine levels. The HFS group had a significantly higher pDAP at baseline compared to the LFS group (P = 0.025). Our data provide the first evidence indicating that the intake of HFS is associated with changes in dopamine precursor availability, which is suggestive of changes in central dopamine levels in humans. The observed associations are present in a sample of normal to overweight participants (ie, in the absence of obesity), suggesting that the consumption of a HFS might already be associated with altered behaviours. Alternatively, the effects of HFS diet and obesity might be independent.

A review of the relationship between eating behavior, obesity and functional brain network organization

Obesity is a major public health issue affecting nearly 40% of American adults and is associated with increased mortality and elevated risk for a number of physical and psychological illnesses. Obesity is associated with impairments in executive functions such as decision making and inhibitory control, as well as in reward valuation, which is thought to contribute to difficulty sustaining healthy lifestyle behaviors, including adhering to a healthy diet. Growing evidence indicates that these impairments are accompanied by disruptions in functional brain networks, particularly those that support self-regulation, reward valuation, self-directed thinking and homeostatic control. Weight-related differences in task-evoked and resting-state connectivity have most frequently been noted in the executive control network (ECN), salience network (SN) and default mode network (DMN), with obesity generally being associated with weakened connectivity in the ECN and enhanced connectivity in the SN and DMN. Similar disruptions have been observed in the much smaller literature examining the relationship between diet and disordered eating behaviors on functional network organization. The purpose of this narrative review was to summarize what is currently known about how obesity and eating behavior relate to functional brain networks, describe common patterns and provide recommendations for future research based on the identified gaps in knowledge.

Obesity is associated with reduced orbitofrontal cortex volume: A coordinate-based meta-analysis

Neural models of obesity vary in their focus upon prefrontal and striatal differences. Animal and human studies suggest that differential functioning of the orbitofrontal cortex is associated with obesity. However, meta-analyses of functional neuroimaging studies have not found a clear relationship between the orbitofrontal cortex and obesity. Meta-analyses of structural imaging studies of obesity have shown mixed findings with regards to an association with reduced orbitofrontal cortex gray matter volume. To clarify these findings, we conducted a meta-analysis of 25 voxel-based morphometry studies, and found that greater body mass index is associated with decreased gray matter volume in the right orbitofrontal cortex (Brodmanns' areas 10 and 11), where family-wise corrected p < .05, N = 7,612. Use of the right orbitofrontal cortex as a seed in a Neurosynth Network Coactivation analysis showed that this region is associated with activity in the left frontal medial cortex, left temporal lobe, right precuneus cortex, posterior division of the left middle temporal gyrus, and right frontal pole. When Neurosynth Network Coactivation results were submitted as regions of interest in the Human Connectome Project data, we found that greater body mass index was associated with greater activity in left frontal medial cortex response to the Gambling Task, where p < .05, although this did not survive Bonferroni-correction. Our findings highlight the importance of the orbitofrontal cortex structure and functioning in neural models of obesity. Exploratory analyses suggest more studies are needed that examine the functional significance of reduced orbitofrontal cortex gray matter volume in obesity, and the effect of age and weight changes on this relationship using longitudinal designs.

Assessing cognitive control and the reward system in overweight young adults using sensitivity to incentives and white matter integrity

Cognitive control and incentive sensitivity are related to overeating and obesity. Optimal white matter integrity is relevant for an efficient interaction among reward-related brain regions. However, its relationship with sensitivity to incentives remains controversial. The aim of this study was to assess the incentive sensitivity and its relationship to white matter integrity in normal-weight and overweight groups. Seventy-six young adults participated in this study: 31 were normal-weight (body mass index [BMI] 18.5 to < 25.0 kg/m², 14 females) and 45 were overweight (BMI ≥ 25.0 kg/m², 22 females). Incentive sensitivity was assessed using an antisaccade task that evaluates the effect of incentives (neutral, reward, and loss avoidance) on cognitive control performance. Diffusion tensor imaging studies were performed to assess white matter integrity. The relationship between white matter microstructure and incentive sensitivity was investigated through tract-based spatial statistics. Behavioral antisaccade results showed that normal-weight participants presented higher accuracy (78.0 vs. 66.7%, p = 0.01) for loss avoidance incentive compared to overweight participants. Diffusion tensor imaging analysis revealed a positive relationship between fractional anisotropy and loss avoidance accuracy in the normal-weight group (p < 0.05). No relationship reached significance in the overweight group. These results support the hypothesis that white matter integrity is relevant for performance in an incentivized antisaccade task.

Behavioral and physiological characteristics associated with learning performance on an appetitive probabilistic selection task

Individuals show meaningful variability in food choices. Choices are affected by individual differences in sensitivity to food reward and punishment, so understanding correlates of response to food reinforcement can help characterize food choices. Here, we examined behavioral and physiological correlates of individual differences in how individuals learn from food reward and punishment, as measured by performance on an appetitive probabilistic selection task that used sweet and bitter tastes as reinforcement. Sensitivity to food reward, sensitivity to food punishment, and overall learning performance were measured in 89 adults. Multivariate linear regressions were used to test if variables including body mass index (BMI), external eating, emotional eating, behavioral inhibition/behavioral activation scales (BIS/BAS), and perceived sensitivity to reward and punishment (SPQ/SRQ) were associated with measures of learning performance. External eating (β=-.035, p=.019), BIS (β=-.066, p=.004), and SPQ (β=.003, p=.023) were associated with overall learning performance. BMI (β=-.000, p=.012), emotional eating (β=.055, p=.006), and external eating (β=-.062, p=.004) were associated with sensitivity to food reward. No variables were associated with sensitivity to food punishment. In post hoc analyses, the interaction of sex and SPQ was associated with overall performance (β=-.005, p=.025), such that the relationship was positive in women only (β=.006, p=0.002). Results support that, controlling for key individual characteristics, BMI and susceptibility to food cues are associated with lower sensitivity to food reward, which may affect future food choices and eating behavior.

Network organization during probabilistic learning via taste outcomes

Reinforcement learning guides food decisions, yet how the brain learns from taste in humans is not fully understood. Existing research examines reinforcement learning from taste using passive condition paradigms, but response-dependent instrumental conditioning better reflects natural eating behavior. Here, we examined brain response during a taste-motivated reinforcement learning task and how measures of task-based network structure were related to behavioral outcomes. During a functional MRI scan, 85 participants completed a probabilistic selection task with feedback via sweet taste or bitter taste. Whole brain response and functional network topology measures, including identification of communities and community segregation, were examined during choice, sweet taste, and bitter taste conditions. Relative to the bitter taste, sweet taste was associated with increased whole brain response in the hippocampus, oral somatosensory cortex, and orbitofrontal cortex. Sweet taste was also related to differential community assignment of the ventromedial prefrontal cortex and ventrolateral prefrontal cortex compared to bitter taste. During choice, increasing segregation of a community containing the amygdala, hippocampus, and right fusiform gyrus was associated with increased sensitivity to punishment on the task's posttest. Further, normal BMI was associated with differential community structure compared to overweight and obese BMI, where high BMI reflected increased connectivity of visual regions. Together, results demonstrate that network topology of learning and memory regions during choice is related to avoiding a bitter taste, and that BMI is associated with increased connectivity of area involved in processing external stimuli. Network organization and topology provide unique insight into individual differences in brain response to instrumental conditioning via taste reinforcers.

Effects of hunger on mood and affect reactivity to monetary reward in women with obesity - A pilot study

Worldwide, nearly 3 million people die every year because of being overweight or obese. Although obesity is a metabolic disease, behavioral aspects are important in its etiology. Hunger changes the rewarding potential of food in normal-weight controls. In obesity, impairments related to reward processing are present, but it is not clear whether these are due to mental disorders more common among this population. Therefore, in this pilot study, we aimed at investigating whether fasting influence mood reactivity to reward in people with obesity. Women with obesity (n = 11, all mentally healthy) and normal weight controls (n = 17) were compared on a computerized monetary reward task (the wheel of fortune), using self-reports of mood and affect (e.g., PANAS and mood evaluation during the task) as dependent variables. This task was done in 2 satiety conditions, during fasting and after eating. Partially, in line with our expectation of a reduced affect and mood reactivity to monetary reward in participants with obesity accentuated by fasting, our results indicated a significant within-group difference across time (before and after the task), with monetary gains significantly improving positive affect in healthy controls (p>0.001), but not in individuals with obesity (p = 0.32). There were no significant between-group differences in positive affect before (p = 0.328) and after (p = 0.70) the task. In addition, women with obesity, compared to controls, reported more negative affect in general (p < 0.05) and less mood reactivity during the task in response to risky gains (p < 0.001) than healthy controls. The latter was independent of the level of satiety. These preliminary results suggest an impairment in mood reactivity to monetary reward in women with obesity which is not connected to the fasting state. Increasing the reinforcing potential of rewards other than food in obesity may be one target of intervention in order to verify if that could reduce overeating.

Enhanced Go and NoGo Learning in Individuals With Obesity

Overeating in individuals with obesity is hypothesized to be partly caused by automatic action tendencies to food cues that have the potential to override goal-directed dietary restriction. Individuals with obesity are often characterized by alterations in the processing of such rewarding food, but also of non-food stimuli, and previous research has suggested a stronger impact on the execution of goal-directed actions in obesity. Here, we investigated whether Pavlovian cues can also corrupt the learning of new approach or withdrawal behavior in individuals with obesity. We employed a probabilistic Pavlovian-instrumental learning paradigm in which participants (29 normal-weight and 29 obese) learned to actively respond (Go learning) or withhold a response (NoGo learning) in order to gain monetary rewards or avoid losses. Participants were better at learning active approach responses (Go) in the light of anticipated rewards and at learning to withhold a response (NoGo) in the light of imminent punishments. Importantly, there was no evidence for a stronger corruption of instrumental learning in individuals with obesity. Instead, they showed better learning across conditions than normal-weight participants. Using a computational reinforcement learning model, we additionally found an increased learning rate in individuals with obesity. Previous studies have mostly reported a lower reinforcement learning performance in individuals with obesity. Our results contradict this and suggest that their performance is not universally impaired: Instead, while previous studies found reduced stimulus-value learning, individuals with obesity may show better action-value learning. Our findings highlight the need for a broader investigation of behavioral adaptation in obesity across different task designs and types of reinforcement learning.

Reward-related brain activity and behavior are associated with peripheral ghrelin levels in obesity

BACKGROUND/OBJECTIVES

While excessive food consumption represents a key factor in the development of obesity, the underlying mechanisms are still unclear. Ghrelin, a gut-brain hormone involved in the regulation of appetite, is impaired in obesity. In addition to its role in eating behavior, this hormone was shown to affect brain regions controlling reward, including the striatum and prefrontal cortex, and there is strong evidence of impaired reward processing in obesity. The present study investigated the possibility that disrupted reward-related brain activity in obesity relates to ghrelin deficiency.

SUBJECTS/METHODS

Fifteen severely obese subjects (BMI > 35 kg/m²) and fifteen healthy non-obese control subjects (BMI < 30 kg/m²) were recruited. A guessing-task paradigm, previously shown to activate the ventral striatum, was used to assess reward-related brain neural activity by functional magnetic resonance imaging (fMRI). Fasting blood samples were collected for the measurement of circulating ghrelin.

RESULTS

Significant activations in the ventral striatum, ventromedial prefrontal cortex and extrastriate visual cortex were elicited by the fMRI task in both obese and control subjects. In addition, greater reward-related activations were present in the dorsolateral prefrontal cortex, and precuneus/posterior cingulate of obese subjects compared to controls. Obese subjects exhibited longer choice times after repeated reward and lower circulating ghrelin levels than lean controls. Reduced ghrelin levels significantly predicted slower post-reward choices and reward-related hyperactivity in dorsolateral prefrontal cortices in obese subjects.

CONCLUSION

This study provides evidence of association between circulating ghrelin and reward-related brain activity in obesity and encourages further exploration of the role of ghrelin system in altered eating behavior in obesity.

The impact of elevated body mass on brain responses during appetitive prediction error in postpartum women

Repeated exposure to highly palatable foods and elevated weight promote: 1) insensitivity to punishment in striatal regions and, 2) increased willingness to work for food. We hypothesized that BMI would be positively associated with negative prediction error BOLD response in the occipital cortex. Additionally, we postulated that food reinforcement value would be negatively associated with negative prediction error BOLD response in the orbital frontal cortex and amygdala. Postpartum women (n = 47; BMI = 25.5 ± 5.1) were 'trained' to associate specific cues paired to either a highly palatable milkshake or a sub-palatable milkshake. We then violated these cue-taste pairings in 40% of the trials by showing a palatable cue followed by the sub-palatable taste (negative prediction error). Contrary to our hypotheses, during negative prediction error (mismatched cue-taste) versus matched palatable cue-taste, women showed increased BOLD response in the central operculum (pFWE = 0.002; k = 1680; MNI: -57, -7,14) and postcentral gyrus (pFWE = 0.006, k = 1219; MNI: 62, -8,18). When comparing the matched sub-palatable cue-taste to the negative prediction error trials, BOLD response increased in the postcentral gyrus (r = -0.60, pFWE = 0.008), putamen (r = -0.55, pFWE = 0.02), and insula (r = -0.50, pFWE = 0.01). Similarly, viewing the palatable cue vs sub-palatable cue was related to BOLD response in the putamen (pFWE = 0.025, k = 53; MNI: -20, 6, -8) and the insula (pFWE = 0.04, k = 19, MNI:38, -12, -6). Neither BMI at 6-month postpartum nor food reinforcement value was related to BOLD response. The insula and putamen appear to encode for visual food cue processing, and the gustatory and somatosensory cortices appear to encode negative prediction errors. Differential response in the somatosensory cortex to the matched cue-taste pairs to negative prediction error may indicate that a palatable cue may dull aversive qualities in the stimulus.