Reward, dopamine and the control of food intake: implications for obesity (2011)

Trends Cogn Sci. 2011 Jan;15(1):37-46. doi: 10.1016/j.tics.2010.11.001. Epub 2010 Nov 24.

Volkow ND, Wang GJ, Baler RD.


National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD 20892, USA. [email protected]


The ability to resist the urge to eat requires the proper functioning of neuronal circuits involved in top-down control to oppose the conditioned responses that predict reward from eating the food and the desire to eat the food. Imaging studies show that obese subjects might have impairments in dopaminergic pathways that regulate neuronal systems associated with reward sensitivity, conditioning and control. It is known that the neuropeptides that regulate energy balance (homeostatic processes) through the hypothalamus also modulate the activity of dopamine cells and their projections into regions involved in the rewarding processes underlying food intake. It is postulated that this could also be a mechanism by which overeating and the resultant resistance to homoeostatic signals impairs the function of circuits involved in reward sensitivity, conditioning and cognitive control.


One-third of the US adult population is obese [body mass index (BMI) ≥30 kg m−2] [1]. This fact has far reaching and costly implications, because obesity is strongly associated with serious medical complications (e.g. diabetes, heart disease, fatty liver and some cancers) [2]. Not surprisingly, the health care costs alone owing to obesity in the US have been estimated at close to US$150 billion [3].

Social and cultural factors undoubtedly contribute to this epidemic. Specifically, environments that promote unhealthy eating habits (ubiquitous access to highly processed and junk foods) and physical inactivity are believed to have a fundamental role in the widespread problem of obesity (Overweight and Obesity Website of the Centers for Disease Control and Prevention; However, individual factors also help determine who will (or will not) become obese in these environments. Based on heredity studies, genetic factors are estimated to contribute between 45% and 85% of the variability in BMI [4,5]. Although genetic studies have revealed point mutations that are over-represented among obese individuals [4], for the most part, obesity is thought to be under polygenic control [6,7]. Indeed, the most recent whole genome-wide association analysis study (GWAS) conducted in 249,796 individuals of European descent identified 32 loci associated with BMI. However, these loci explained only 1.5% of the variance in BMI [8]. Moreover, it was estimated that GWAS studies with larger samples should be able to identify 250 extra loci with effects on BMI. However, even with the undiscovered variants, it was estimated that signals from common variant loci would account for only 6–11% of the genetic variation in BMI (based on an estimated heritability of 40–70%). The limited explanation of the variance from these genetic studies is likely to reflect the complex interactions between individual factors (as determined by genetics) and the way in which individuals relate to environments where food is widely available, not only as a source of nutrition, but also as a strong reward that by itself promotes eating [9].

The hypothalamus [via regulatory neuropeptides such as leptin, cholecystokinin (CCK), ghrelin, orexin, insulin, neuropeptide Y (NPY), and through the sensing of nutrients, such as glucose, amino acids and fatty acids] is recognized as the main brain region regulating food intake as it relates to caloric and nutrition requirements [1013]. In particular, the arcuate nucleus through its connections with other hypothalamic nuclei and extra-hypothalamic brain regions, including the nucleus tractus solitarius, regulates homeostatic food intake [12] and is implicated in obesity [1416] (Figure 1a, left panel). However, evidence is accumulating that brain circuits other than those regulating hunger and satiety are involved in food consumption and obesity [17]. Specifically, several limbic [nucleus accumbens (NAc), amygdala and hippocampus] and cortical brain regions [orbitofrontal cortex (OFC), cingulate gyrus (ACC) and insula] and neurotransmitter systems (dopamine, serotonin, opioids and cannabinoids) as well as the hypothalamus are implicated in the rewarding effects of food [18] (Figure 1a, right panel). By contrast, the regulation of food intake by the hypothalamus appears to rely on the reward and motivational neurocircuitry to modify eating behaviors [1921].

Figure 1  

Regulation of food intake relies on multichannel communication between overlapping reward and homeostatic neurocircuits. (a) Schematic diagram of the crosstalk between the homeostatic (hypothalamus, HYP) and reward circuits that control food intake. The

Based on findings from imaging studies, a model of obesity was recently proposed in which overeating reflects an imbalance between circuits that motivate behavior (because of their involvement in reward and conditioning) and circuits that control and inhibit pre-potent responses [22]. This model identifies four main circuits: (i) reward–saliency; (ii) motivation–drive; (iii) learning–conditioning; and (iv) inhibitory control–emotional regulation–executive function. Notably, this model is also applicable to drug addiction.

In vulnerable individuals, the consumption of high quantities of palatable food (or drugs in addiction) can upset the balanced interaction among these circuits, resulting in an enhanced reinforcing value of food (or drugs in addiction) and in a weakening of the control circuits. This perturbation is a consequence of conditioned learning and the resetting of reward thresholds following the consumption of large quantities of high-calorie foods (or drugs in addiction) by at-risk individuals. The undermining of the cortical top-down networks that regulate pre-potent responses results in impulsivity and in compulsive food intake (or compulsive drug intake in addiction).

This paper discusses the evidence that links the neural circuits involved in top-down control with those involved with reward and motivation and their interaction with peripheral signals that regulate homeostatic food intake.

Food is a potent natural reward and conditioning stimulus

Certain foods, particularly those rich in sugars and fat, are potent rewards [23] that promote eating (even in the absence of an energetic requirement) and trigger learned associations between the stimulus and the reward (conditioning). In evolutionary terms, this property of palatable foods used to be advantageous because it ensured that food was eaten when available, enabling energy to be stored in the body (as fat) for future need in environments where food sources were scarce and/or unreliable. However, in modern societies, where food is widely available, this adaptation has become a liability.

Several neurotransmitters, including dopamine (DA), cannabinoids, opioids and serotonin, as well as neuropetides involved in homeostatic regulation of food intake, such as orexin, leptin and ghrelin, are implicated in the rewarding effects of food [2426]. DA has been the most thoroughly investigated and is the best characterized. It is a key neurotransmitter modulating reward (natural and drug rewards), which it does mainly through its projections from the ventral tegmental area (VTA) into the NAc [27]. Other DA projections are also implicated, including the dorsal striatum (caudate and putamen), cortical (OFC and ACC) and limbic regions (hippocampus and amygdala) and the lateral hypothalamus. Indeed, in humans, ingestion of palatable food has been shown to release DA in the dorsal striatum in proportion to the self-reported level of pleasure derived from eating the food [28]. However, the involvement of DA in reward is more complex than the mere encoding of hedonic value. Upon first exposure to a food reward (or an unexpected reward), the firing of DA neurons in the VTA increases with a resulting increase in DA release in NAc [29]. However, with repeated exposure to the food reward, the DA response habituates and is gradually transferred onto the stimuli associated with the food reward (e.g. the smell of food), which is then processed as a predictor of reward (becoming a cue that is conditioned to the reward) [30,31]; the DA signal in response to the cue then serves to convey a ‘reward prediction error’ [31]. The extensive glutamatergic afferents to DA neurons from regions involved with sensory (insula or primary gustatory cortex), homeostatic (hypothalamus), reward (NAc), emotional (amygdala and hippocampus) and multimodal (OFC for salience attribution) modulate their activity in response to rewards and to conditioned cues [32]. Specifically, projections from the amygdala and the OFC to DA neurons and NAc are involved in conditioned responses to food [33]. Indeed, imaging studies showed that when non-obese male subjects were asked to inhibit their craving for food while being exposed to food cues, they decreased metabolic activity in amygdala and OFC [as well as hippocampus (see also Box 1), insula and striatum]; the decreases in OFC were associated with reductions in food craving [34].

Box 1. The role of the hippocampus in feeding behaviors

The hippocampus is not only central to memory, but is also involved in the regulation of eating behaviors through its processing of mnemonic processes (including remembering whether one ate, remembering conditioning associations, remembering where food is located, identifying interoceptive states of hunger and remembering how to relieve these states). For example, in rodents, selective lesions in the hippocampus impaired their ability to discriminate between the state of hunger and that of satiety [99] and, in female rats, it resulted in hyperphagia [100]. In humans, brain-imaging studies have reported activation of the hippocampus with food craving, a state of hunger, the response to food-conditioned cues and to food tasting [101]. The hippocampus expresses high levels of insulin, ghrelin, glucocorticoids and cannabinoid CB1 receptors, which suggests that this region also regulates food intake by non-mnemonic processes [102,103]. In addition, the hippocampus is implicated in obesity, as evinced by imaging studies showing that in obese but not in lean individuals, the hippocampus shows hyperactivation in response to food stimuli [104].

Conditioned cues can elicit feeding even in sated rats [30] and, in humans, imaging studies have shown that exposure to food cues elicits DA increases in the striatum that are associated with the desire to eat the food [35]. In addition to its involvement with conditioning, DA is also involved with the motivation to perform the behaviors necessary to procure and consume the food. Indeed, the involvement of DA in food reward has been associated with the motivational salience or ‘wanting’ of food as opposed to the ‘liking’ of food [36] (Box 2), an effect that is likely to involve the dorsal striatum and perhaps also the NAc [37]. DA has such a crucial role in this context that transgenic mice that do not synthesize DA die of starvation owing to a lack of motivation to eat [37]. Restoring DA neurotransmission in the dorsal striatum rescues these animals, whereas restoring it in the NAc does not.

Box 2. Wanting versus liking: an important distinction

Brain reward systems involved with food intake distinguish a mechanism involved with motivating the desire for the food, referred to as ‘wanting’, versus a mechanism involved with the hedonic properties of the food, referred to as ‘liking’ [36]. Whereas the dopamine striatal system is predominantly (although not exclusively) implicated in ‘wanting’, the opioid and cannabinoid systems are predominantly (although not exclusively) implicated in food ‘liking’.

Indeed, brain-imaging studies in humans have shown that the dopamine release triggered when humans encounter a food cue correlates with their subjective ratings of wanting the food [35]. Conversely, the activation of endogenous opioid or cannabinoid receptors appears to stimulate appetite in part by enhancing the ‘liking’ of the food (i.e. its palatability). Although these two mechanisms are separate, they act in concert to modulate eating behaviors.

The hedonic (‘liking’) properties of food appear to depend on, among others, opioid, cannabinoid and GABA neurotransmission [36]. These ‘liking’ properties of food are processed in reward regions including lateral hypothalamus, NAc, ventral pallidum, OFC [9,27,38] and insula (primary taste area in the brain) [39].

Opioid signaling in NAc (in the shell) and ventral pallidum appears to mediate food ‘liking’ [40]. By contrast, opioid signaling in the basolateral amygdala is implicated in conveying the affective properties of food, which in turn modulate the incentive value of food and reward-seeking behavior, thus also contributing to food ‘wanting’ [41]. Interestingly, in rodents that have been exposed to diets rich in sugar, a pharmacological challenge with naloxone (opiate antagonist drug devoid of effects in control rats) elicits an opiate withdrawal syndrome similar to that observed in animals that have been chronically exposed to opioid drugs [42]. In addition, exposure of humans or laboratory animals to sugar produces an analgesic response [43], which suggests that sugar (and perhaps other palatable foods) has a direct ability to boost endogenous opioid levels. A research question that emerges from these data is whether, in humans, dieting triggers a mild withdrawal syndrome that could contribute to relapse.

Endocannabinoids, predominantly through cannabinoid CB1 receptor signaling (in contrast to CB2 receptors), are involved with both homeostatic and rewarding mechanisms of food intake and energy expenditure [4446]. Homeostatic regulation is mediated in part through the arcuate and paraventricular nuclei in the hypothalamus and through the nucleus of the solitary tract in the brainstem, and the regulation of reward processes is mediated in part through effects in NAc, hypothalamus and brainstem. Therefore, the cannabinoid system is an important target in medication development for treatment of obesity and metabolic syndrome. Similarly, the modulation by serotonin of feeding behaviors involves both reward and homeostatic regulation and it has also been a target for the development of anti-obesity medications [4750].

In parallel, there is increasing evidence that peripheral homeostatic regulators of energy balance, such as leptin, insulin, orexin, ghrelin and PYY, also regulate behaviors that are non-homeostatic and modulate the rewarding properties of food [50]. These neuropeptides might also be involved with cognitive control over food intake and with conditioning to food stimuli [51]. Specifically, they can interact with cognate receptors in midbrain VTA DA neurons, which not only project to the NAc, but also to prefrontal and limbic regions; in fact, many of them also express receptors in frontal regions and in hippocampus and amygdala [50].

Insulin, which is one of the key hormones involved in the regulation of glucose metabolism, has been shown to attenuate the response of limbic (including brain reward regions) and cortical regions in the human brain to food stimuli. For example, in healthy controls, insulin attenuated the activation of the hippocampus, frontal and visual cortices in response to food pictures [52]. Conversely, insulin-resistant subjects (patients with type 2 diabetes) showed greater activation in limbic regions (amygdala, striatum, OFC and insula) when exposed to food stimuli than did non-diabetic patients [53].

In the human brain, the adipocyte-derived hormone leptin, which acts in part though leptin receptors in hypothalamus (arcuate nucleus) to decrease food intake, has also been shown to attenuate the response of brain reward regions to food stimuli. Specifically, patients with congenital leptin deficiency showed activation of DA mesolimbic targets (NAc and caudate) to visual food stimuli, which was associated with food wanting, even when the subject had just been fed. By contrast, mesolimbic activation did not occur after 1 week of leptin treatment (Figure 2a,b). This was interpreted to suggest that leptin diminished the rewarding responses to food [19]. Another fMRI study, also done with patients with congenital leptin deficiency, showed that leptin treatment reduced the activation of regions involved with hunger (insula, parietal and temporal cortices) whereas it enhanced activation of regions involved in cognitive inhibition [prefrontal cortex (PFC)] upon exposure to food stimuli [20]. Thus, these two studies provide evidence that, in the human brain, leptin modulates the activity of brain regions involved not only with homeostatic processes, but also with rewarding responses and with inhibitory control.

Figure 2   

Leptin decreases whereas ghrelin increases reactivity to food stimuli in brain reward areas. (a, b) Brain images showing areas where leptin reduced the activation (NAc-caudate) in two subjects with leptin deficiency. (b) Histogram for the activation response

Gut hormones also appear to modulate the response of brain reward regions to food stimuli in the human brain. For example, the peptide YY3–36 (PYY), which is released from gut cells post-prandially and reduces food intake, was shown to modulate the transition of the regulation of food intake by homeostatic circuits (i.e. hypothalamus) to its regulation by reward circuits in the transition from hunger to satiety. Specifically, when plasma PYY concentrations were high (as when satiated), activation of the OFC by food stimuli negatively predicted food intake; whereas when plasma PYY levels were low (as when food deprived) hypothalamic activation positively predicted food intake [54]. This was interpreted to reflect that PYY decreases the rewarding aspects of food through its modulation of the OFC. By contrast, ghrelin (a stomach-derived hormone that increases in the fasted state and stimulates food intake) was shown to increase the activation in response to food stimuli in brain reward regions (amygdala, OFC, anterior insula and striatum) and their activation was associated with self-reports of hunger (Figure 2c,d). This was interpreted to reflect an enhancement of the hedonic and incentive responses to food-related cues by ghrelin [55]. Overall, these findings are also consistent with the differential regional brain activation in response to food stimuli in satiated versus fasted individuals; activation of reward regions in response to food stimuli is decreased during the sated when compared to the fasted state [15].

These observations point to an overlap between the neurocircuitry that regulates reward and/or reinforcement and that which regulates energy metabolism (Figure 1b). Peripheral signals that regulate homeostatic signals to food appear to increase the sensitivity of limbic brain regions to food stimuli when they are orexigenic (ghrelin) and to decrease the sensitivity to activation when they are anorexigenic (leptin and insulin). Similarly, the sensitivity of brain reward regions to food stimuli during food deprivation is increased, whereas it is decreased during satiety. Thus, homeostatic and reward circuitry act in concert to promote eating behaviors under conditions of deprivation and to inhibit food intake under conditions of satiety. Disruption of the interaction between homeostatic and reward circuitry might promote overeating and contribute to obesity (Figure 1). Although other peptides [glucagon-like peptide-1 (GLP-1), CKK, bombesin and amylin] also regulate food intake via their hypothalamic actions, their extrahypothalamic effects have received less attention [12]. Thus, much remains to be learned, including the interactions between the homeostatic and the non-homeostatic mechanisms that regulate food intake and their involvement in obesity.

Disruption in reward and conditioning to food in overweight and obese individuals

Preclinical and clinical studies have provided evidence of decreases in DA signaling in striatal regions [decreases in DAD2 (D2R) receptors and in DA release], that are linked with reward (NAc) but also with habits and routines (dorsal striatum) in obesity [5658]. Importantly, decreases in striatal D2R have been linked to compulsive food intake in obese rodents [59] and with decreased metabolic activity in OFC and ACC in obese humans [60] (Figure 3a–c). Given that dysfunction in OFC and ACC results in compulsivity [reviewed 61], this might be the mechanism by which low striatal D2R signaling facilitates hyperphagia [62]. Decreased D2R-related signaling is also likely to reduce the sensitivity to natural rewards, a deficit that obese individuals might strive to compensate temporarily for by overeating [63]. This hypothesis is consistent with preclinical evidence showing that decreased DA activity in the VTA results in a dramatic increase in the consumption of high-fat foods [64].

Figure 3  

Hyperphagia could result from a drive to compensate for a weakened reward circuit (processed through dopamine regulated corticostriatal circuits) combined with a heightened sensitivity to palatability (hedonic properties of food processed in part through …

Indeed, compared with normal-weight individuals, obese individuals who were presented with pictures of high-calorie food (stimuli to which they are conditioned) showed increased neural activation of regions that are part of reward and motivation circuits (NAc, dorsal striatum, OFC, ACC, amygdala, hippocampus and insula) [65]. By contrast, in normal-weight controls, the activation of the ACC and OFC (regions involved in salience attribution that project into the NAc) during presentation of high-calorie food was found to be negatively correlated with their BMI [66]. This suggests a dynamic interaction between the amount of food eaten (reflected in part by the BMI) and the reactivity of reward regions to high-calorie food (reflected in the activation of OFC and ACC) in normal-weight individuals, which is lost in obesity.

Surprisingly, obese individuals, when compared with lean individuals, experienced less activation of reward circuits from the actual food consumption (consummatory food reward), whereas they showed greater activation of somatosensory cortical regions that process palatability when they anticipated consumption [67] (Figure 4). The latter finding is consistent with a study that reported increased baseline glucose metabolic activity (a marker of brain function) in somatosensory regions that process palatability, including insula, in obese as compared with lean subjects [68] (Figure 3d,e). An enhanced activity of regions that process palatability could make obese subjects favor food over other natural reinforcers, whereas decreased activation of dopaminergic targets by the actual food consumption might lead to overconsumption as a means to compensate for the weak DA signals [69].

Figure 4    

Obese subjects have a decreased response in DA target regions when given food compared with that recorded in lean subjects. (a) Coronal section of weaker activation in the left caudate nucleus in response to receiving a milkshake versus a tasteless solution;

These imaging findings are consistent with an enhanced sensitivity of the reward circuitry to conditioned stimuli (viewing high-calorie food) that predict reward, but a decreased sensitivity to the rewarding effects of actual food consumption in dopaminergic pathways in obesity. We hypothesize that, to the extent that there is a mismatch between the expected reward and a delivery that does not fulfill this expectation, this will promote compulsive eating as an attempt to achieve the expected level of reward. Although the failure of an expected reward to arrive is accompanied by a decrease in DA cell firing in laboratory animals [70], the behavioral significance of such a decrease (when a food reward is smaller than expected) has, to our knowledge, not been investigated.

In parallel to these activation changes in the reward circuitry in obese subjects, imaging studies have also documented consistent decreases in the reactivity of the hypothalamus to satiety signals in obese subjects [71,72].

Evidence of cognitive disruption in overweight and obese individuals

There is increasing evidence that obesity is associated with impairment on certain cognitive functions, such as executive function, attention and memory [7375]. Indeed, the ability to inhibit the urges to eat desirable food varies among individuals and might be one of the factors that contribute to their vulnerability for overeating [34]. The adverse influence of obesity on cognition is also reflected in the higher prevalence of attention deficit hyperactivity disorder (ADHD) [76], Alzheimer disease and other dementias [77], cortical atrophy [78] and white matter disease [79] in obese subjects. Although co-morbid medical conditions (e.g. cerebrovascular pathology, hypertension and diabetes) are known to affect cognition adversely, there is also evidence that high BMI, by itself, might impair various cognitive domains, particularly executive function [75].

In spite of some inconsistencies among studies, brain-imaging data have also provided evidence of structural and functional changes associated with high BMI in otherwise healthy controls. For example, an MRI study done in elderly females using voxel-wise morphometry showed a negative correlation between BMI and gray matter volumes (including frontal regions), which, in the OFC, was associated with impaired executive function [80]. Using positron emission tomography (PET) to measure brain glucose metabolism in healthy controls, a negative correlation was also shown between BMI and metabolic activity in PFC (dorsolateral and OFC) and in ACC. In this study, the metabolic activity in PFC predicted the subjects’ performance in tests of executive function [81]. Similarly, an NMR spectroscopic study of healthy middle age and elderly controls showed that BMI was negatively associated with the levels of N-acetyl-aspartate (a marker of neuronal integrity) in frontal cortex and ACC [79,82].

Brain-imaging studies comparing obese and lean individuals have also reported lower gray matter density in frontal regions (frontal operculum and middle frontal gyrus) and in post-central gyrus and putamen [83]. Another study, which found no differences in gray matter volumes between obese and lean subjects, did report a positive correlation between white matter volume in basal brain structures and waist:hip ratio; a trend that was partially reversed by dieting [84].

Finally, the role of DA in inhibitory control is well recognized and its disruption might contribute to behavioral disorders of discontrol, such as obesity. A negative correlation between BMI and striatal D2R has been reported in obese [58] as well as in overweight subjects [85]. As discussed above, the lower-than-normal availability of D2R in the striatum of obese individuals was associated with reduced metabolic activity in PFC and ACC [60]. These findings implicate neuroadaptations in DA signaling as contributors to the disruption of frontal cortical regions associated with overweight and obesity. A better understanding of these disruptions might help guide strategies to ameliorate, or perhaps even reverse, specific impairments in crucial cognitive domains.

For example, delay discounting, which is the tendency to devalue a reward as a function of the temporal delay of its delivery, is one of the most extensively investigated cognitive operations in relation to disorders associated with impulsivity and compulsivity. Delay discounting has been most comprehensively investigated in drug abusers who prefer small-but-immediate over large-but-delayed rewards [86]. The few studies done in obese individuals have also shown that these individuals display preference for high, immediate rewards, despite an increased chance of suffering higher future losses [87,88]. Moreover, a positive correlation between BMI and hyperbolic discounting, whereby future negative payoffs are discounted less than are future positive payoffs, was recently reported [89]. Delay discounting seems to depend on the function of ventral striatum (where NAc is located) [90,91] and of the PFC, including OFC [92], and is sensitive to DA manipulations [93].

Interestingly, lesions of the OFC in animals can either increase or decrease the preference for immediate small rewards over delayed larger rewards [94,95]. This apparently paradoxical behavioral effect is likely to reflect the fact that at least two operations are processed through the OFC; one is salience attribution, through which a reinforcer acquires incentive motivational value, and the other is control over pre-potent urges [96]. Dysfunction of the OFC is associated with an impaired ability to modify the incentive motivational value of a reinforcer as a function of the context in which it occurs (i.e. decrease the incentive value of food with satiety), which can result in compulsive food consumption [97]. If the stimulus is highly reinforcing (such as food and food cues for an obese subject) the enhanced saliency value of the reinforcer will result in an enhanced motivation to procure it, which could appear as a willingness to delay gratification (such as spending time in long lines to buy ice cream).

However, in contexts where food is readily available, the same enhanced saliency can trigger impulsive behaviors (such as buying and eating the chocolate located next to the cashier even without previous awareness of the desire of such item). Dysfunction of the OFC (and of the ACC) impairs the ability to rein in pre-potent urges, resulting in impulsivity and an exaggerated delayed discount rate.

Food for thought

It would appear, from the collected evidence presented here, that a substantial fraction of obese individuals exhibit an imbalance between an enhanced sensitivity of the reward circuitry to conditioned stimuli linked to energy-dense food and impaired function of the executive control circuitry that weakens inhibitory control over appetitive behaviors. Regardless of whether this imbalance causes, or is caused by, pathological overeating, the phenomenon is reminiscent of the conflict between the reward, conditioning and motivation circuits and the inhibitory control circuit that has been reported in addiction [98].

Knowledge accumulated during the past two decades of the genetic, neural and environmental bases of obesity leaves no doubt that the current crisis has sprouted from the disconnect between the neurobiology that drives food consumption in our species and the richness and diversity of food stimuli driven by our social and economic systems. The good news is that understanding the deep-seated behavioral constructs that sustain the obesity epidemic holds the key to its eventual resolution (see also Boxes 3 and 4).

Box 3. Future basic research directions

  • A better understanding of the interaction at the molecular, cellular, and circuit levels between the homeostatic and reward processes that regulate food intake.
  • Understanding the role of genes in modulating the homeostatic and the reward responses to food.
  • A better understanding of the involvement of other neurotransmitters, such as cannabinoids, opioids, glutamate, serotonin and GABA, in the long-lasting changes that occur in obesity.
  • Investigating the developmental aspects of the neurobiology underlying food intake (homeostatic and rewarding) and its sensitivity to environmental food exposure.
  • Understanding the epigenetic modifications in neuronal circuits involved with the homeostatic and rewarding control of food intake in the fetal brain in response to exposure to food excess and food deprivation during pregnancy.
  • Investigating neuroplastic adaptations in homeostatic and reward circuits associated with chronic exposure to highly palatable foods and/or to high quantities of calorie-dense food.
  • Investigating the relationship between homeostatic and hedonic processes regulating food intake and physical activity.

Box 4. Future clinical research directions

  • Studies to ascertain whether the greater activation of reward-associated areas in response to food-related cues in obese individuals underlies their vulnerability for overeating or reflects a secondary neuroadaptation to overeating.
  • It is suggested that enhanced dopaminergic neurotransmission contributes to improved eating behavior through optimization and/or strengthening of cognitive control mechanisms mediated in part through the PFC; however, further research is needed into the currently ill-defined mechanisms involved.
  • Diet alone is seldom a path to successful (i.e. sustainable) weight loss. It would be instructive to address whether: (i) dieting can trigger a withdrawal syndrome that increases the risk of relapse; and (ii) the decreased leptin levels associated with diet-induced weight loss lead to hyperactivation of reward circuitry and compensatory food seeking behaviors.
  • Research to determine the neurobiology that underlies decreases in food craving and hunger following bariatric surgery.


1. Ogden CL, et al. Prevalence of overweight and obesity in the United States, 1999 to 2004. JAMA. 2006;295:1549–1555. [PubMed]
2. Flegal KM, et al. Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010;303:235–241. [PubMed]
3. Finkelstein EA, et al. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff. 2009;28:w822–w831.
4. Baessler A, et al. Genetic linkage and association of the growth hormone secretagogue receptor (ghrelin receptor) gene in human obesity. Diabetes. 2005;54:259–267. [PMC free article] [PubMed]
5. Silventoinen K, Kaprio J. Genetics of tracking of body mass index from birth to late middle age: evidence from twin and family studies. Obes. Facts. 2009;2:196–202. [PubMed]
6. Speliotes E, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 2010;42:937–948. [PMC free article] [PubMed]
7. Thorleifsson G, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 2009;41:18–24. [PubMed]
8. Naukkarinen J, et al. Use of genome-wide expression data to mine the ‘Gray Zone’ of GWA studies leads to novel candidate obesity genes. PLoS Genet. 2010;6 e1000976.
9. Gosnell B, Levine A. Reward systems and food intake: role of opioids. Int. J. Obes. 2009;33 Suppl. 2:S54, S58.
10. van Vliet-Ostaptchouk JV, et al. Genetic variation in the hypothalamic pathways and its role on obesity. Obes. Rev. 2009;10:593–609. [PubMed]
11. Blouet C, Schwartz GJ. Hypothalamic nutrient sensing in the control of energy homeostasis. Behav. Brain Res. 2010;209:1–12. [PubMed]
12. Coll AP, et al. The hormonal control of food intake. Cell. 2007;129:251–262. [PMC free article] [PubMed]
13. Dietrich M, Horvath T. Feeding signals and brain circuitry. Eur. J. Neurosci. 2009;30:1688–1696. [PubMed]
14. Belgardt B, et al. Hormone and glucose signalling in POMC and AgRP neurons. J. Physiol. 2009;587(Pt 22):5305–5314. [PMC free article] [PubMed]
15. Goldstone AP. The hypothalamus, hormones, and hunger: alterations in human obesity and illness. Prog. Brain Res. 2006;153:57–73. [PubMed]
16. Rolls E. Taste, olfactory, and food texture reward processing in the brain and obesity. Int. J. Obes. 2005;85:45–56.
17. Rolls ET. Functions of the orbitofrontal and pregenual cingulate cortex in taste, olfaction, appetite and emotion. Acta Physiol. Hung. 2008;95:131–164. [PubMed]
18. Petrovich GD, et al. Amygdalar and prefrontal pathways to the lateral hypothalamus are activated by a learned cue that stimulates eating. J. Neurosci. 2005;25:8295–8302. [PubMed]
19. Farooqi IS, et al. Leptin regulates striatal regions and human eating behavior. Science. 2007;317:1355. [PubMed]
20. Baicy K, et al. Leptin replacement alters brain response to food cues in genetically leptin-deficient adults. Proc. Natl. Acad. Sci. U. S. A. 2007;104:18276–18279. [PMC free article] [PubMed]
21. Passamonti L, et al. Personality predicts the brain’s response to viewing appetizing foods: the neural basis of a risk factor for overeating. J. Neurosci. 2009;29:43–51. [PubMed]
22. Volkow ND, et al. Overlapping neuronal circuits in addiction and obesity: evidence of systems pathology. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2008;363:3191–3200. [PMC free article] [PubMed]
23. Lenoir M, et al. Intense sweetness surpasses cocaine reward. PLoS One. 2007;2:e698. [PMC free article] [PubMed]
24. Cason AM, et al. Role of orexin/hypocretin in reward-seeking and addiction: implications for obesity. Physiol. Behav. 2010;100:419–428. [PMC free article] [PubMed]
25. Cota D, et al. Cannabinoids, opioids and eating behavior: the molecular face of hedonism? Brain Res. Rev. 2006;51:85–107. [PubMed]
26. Atkinson T. Central and peripheral neuroendocrine peptides and signalling in appetite regulation: considerations for obesity pharmacotherapy. Obes. Rev. 2008;9:108–120. [PubMed]
27. Wise R. Role of brain dopamine in food reward and reinforcement. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2006;361:1149–1158. [PMC free article] [PubMed]
28. Small DM, et al. Feeding-induced dopamine release in dorsal striatum correlates with meal pleasantness ratings in healthy human volunteers. Neuroimage. 2003;19:1709–1715. [PubMed]
29. Norgren R, et al. Gustatory reward and the nucleus accumbens. Physiol. Behav. 2006;89:531–535. [PMC free article] [PubMed]
30. Epstein L, et al. Habituation as a determinant of human food intake. Psychol. Rev. 2009;116:384–407. [PMC free article] [PubMed]
31. Schultz W. Dopamine signals for reward value and risk: basic and recent data. Behav. Brain Funct. 2010;6:24. [PMC free article] [PubMed]
32. Geisler S, Wise R. Functional implications of glutamatergic projections to the ventral tegmental area. Rev. Neurosci. 2008;19:227–244. [PMC free article] [PubMed]
33. Petrovich G. Forebrain circuits and control of feeding by learned cues. Neurobiol. Learn. Mem. 2010 Oct 19; [Epub ahead of print]
34. Wang GJ, et al. Evidence of gender differences in the ability to inhibit brain activation elicited by food stimulation. Proc. Natl. Acad. Sci. U. S. A. 2009;106:1249–1254. [PMC free article] [PubMed]
35. Volkow ND, et al. ‘Nonhedonic’ food motivation in humans involves dopamine in the dorsal striatum and methylphenidate amplifies this effect. Synapse. 2002;44:175–180. [PubMed]
36. Berridge K. ‘Liking’ and ‘wanting’ food rewards: brain substrates and roles in eating disorders. Physiol. Behav. 2009;97:537–550. [PMC free article] [PubMed]
37. Szczypka MS, et al. Dopamine production in the caudate putamen restores feeding in dopamine-deficient mice. Neuron. 2001;30:819–828. [PubMed]
38. Faure A, et al. Mesolimbic dopamine in desire and dread: enabling motivation to be generated by localized glutamate disruptions in nucleus accumbens. J. Neurosci. 2008;28:7148–7192.
39. Saddoris M, et al. Associatively learned representations of taste outcomes activate taste-encoding neural ensembles in gustatory cortex. J. Neurosci. 2009;29:15386–15396. [PMC free article] [PubMed]
40. Smith KS, Berridge KC. Opioid limbic circuit for reward: interaction between hedonic hotspots of nucleus accumbens and ventral pallidum. J. Neurosci. 2007;27:1594–1605. [PubMed]
41. Wassum KM, et al. Distinct opioid circuits determine the palatability and the desirability of rewarding events. Proc. Natl. Acad. Sci. U. S. A. 2009;106:12512–12517. [PMC free article] [PubMed]
42. Avena NM, et al. Evidence for sugar addiction: behavioral and neurochemical effects of intermittent, excessive sugar intake. Neurosci. Biobehav. Rev. 2008;32:20–39. [PMC free article] [PubMed]
43. Graillon A, et al. Differential response to intraoral sucrose, quinine and corn oil in crying human newborns. Physiol. Behav. 1997;62:317–325. [PubMed]
44. Richard D, et al. The brain endocannabinoid system in the regulation of energy balance. Best Pract. Res. Clin. Endocrinol. Metab. 2009;23:17–32. [PubMed]
45. Di Marzo V, et al. The endocannabinoid system as a link between homoeostatic and hedonic pathways involved in energy balance regulation. Int. J. Obes. 2009;33 Suppl. 2:S18–S24.
46. Matias I, Di Marzo V. Endocannabinoids and the control of energy balance. Trends Endocrinol. Metab. 2007;18:27–37. [PubMed]
47. Garfield A, Heisler L. Pharmacological targeting of the serotonergic system for the treatment of obesity. J. Physiol. 2009;587:48–60.
48. Halford J, et al. Pharmacological management of appetite expression in obesity. Nat. Rev. Endocrinol. 2010;6:255–269. [PubMed]
49. Lam D, et al. Brain serotonin system in the coordination of food intake and body weight. Pharmacol. Biochem. Behav. 2010;97:84–91. [PubMed]
50. Lattemann D. Endocrine links between food reward and caloric homeostasis. Appetite. 2008;51:452–455. [PMC free article] [PubMed]
51. Rosenbaum M, et al. Leptin reverses weight loss-induced changes in regional neural activity responses to visual food stimuli. J. Clin. Invest. 2008;118:2583–2591. [PMC free article] [PubMed]
52. Guthoff M, et al. Insulin modulates food-related activity in the central nervous system. J. Clin. Endocrinol. Metab. 2010;95:748–755. [PubMed]
53. Chechlacz M, et al. Diabetes dietary management alters responses to food pictures in brain regions associated with motivation and emotion: a functional magnetic resonance imaging study. Diabetologia. 2009;52:524–533. [PubMed]
54. Batterham RL, et al. PYY modulation of cortical and hypothalamic brain areas predicts feeding behaviour in humans. Nature. 2007;450:106–109. [PubMed]
55. Malik S, et al. Ghrelin modulates brain activity in areas that control appetitive behavior. Cell Metab. 2008;7:400–409. [PubMed]
56. Fulton S, et al. Leptin regulation of the mesoaccumbens dopamine pathway. Neuron. 2006;51:811–822. [PubMed]
57. Geiger BM, et al. Deficits of mesolimbic dopamine neurotransmission in rat dietary obesity. Neuroscience. 2009;159:1193–1199. [PMC free article] [PubMed]
58. Wang GJ, et al. Brain dopamine and obesity. Lancet. 2001;357:354–357. [PubMed]
59. Johnson PM, Kenny PJ. Dopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats. Nat. Neurosci. 2010;13:635–641. [PMC free article] [PubMed]
60. Volkow ND, et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage. 2008;42:1537–1543. [PMC free article] [PubMed]
61. Fineberg NA, et al. Probing compulsive and impulsive behaviors, from animal models to endophenotypes: a narrative review. Neuropsychopharmacology. 2010;35:591–604. [PMC free article] [PubMed]
62. Davis LM, et al. Bromocriptine administration reduces hyperphagia and adiposity and differentially affects dopamine D2 receptor and transporter binding in leptin-receptor-deficient Zucker rats and rats with diet-induced obesity. Neuroendocrinology. 2009;89:152–162. [PMC free article] [PubMed]
63. Geiger BM, et al. Evidence for defective mesolimbic dopamine exocytosis in obesity-prone rats. FASEB J. 2008;22:2740–2746. [PMC free article] [PubMed]
64. Cordeira JW, et al. Brain-derived neurotrophic factor regulates hedonic feeding by acting on the mesolimbic dopamine system. J. Neurosci. 2010;30:2533–2541. [PMC free article] [PubMed]
65. Stoeckel L, et al. Widespread reward-system activation in obese women in response to pictures of high-calorie foods. Neuroimage. 2008;41:636–647. [PubMed]
66. Killgore W, Yurgelun-Todd D. Body mass predicts orbitofrontal activity during visual presentations of high-calorie foods. Neuroreport. 2005;31:859–863. [PubMed]
67. Stice E, et al. Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. J. Abnorm. Psychol. 2008;117:924–935. [PMC free article] [PubMed]
68. Wang G, et al. Enhanced resting activity of the oral somatosensory cortex in obese subjects. Neuroreport. 2002;13:1151–1155. [PubMed]
69. Stice E, et al. Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele. Science. 2008;322:449–452. [PMC free article] [PubMed]
70. Schultz W. Getting formal with dopamine and reward. Neuron. 2002;36:241–263. [PubMed]
71. Cornier MA, et al. The effects of overfeeding on the neuronal response to visual food cues in thin and reduced-obese individuals. PLoS One. 2009;4:e6310. [PMC free article] [PubMed]
72. Matsuda M, et al. Altered hypothalamic function in response to glucose ingestion in obese humans. Diabetes. 1999;48:1801–1806. [PubMed]
73. Bruce-Keller AJ, et al. Obesity and vulnerability of the CNS. Biochim. Biophys. Acta. 2009;1792:395–400. [PMC free article] [PubMed]
74. Bruehl H, et al. Modifiers of cognitive function and brain structure in middle-aged and elderly individuals with type 2 diabetes mellitus. Brain Res. 2009;1280:186–194. [PMC free article] [PubMed]
75. Gunstad J, et al. Elevated body mass index is associated with executive dysfunction in otherwise healthy adults. Compr. Psychiatry. 2007;48:57–61. [PubMed]
76. Cortese S, et al. Attention-deficit/hyperactivity disorder (ADHD) and obesity: a systematic review of the literature. Crit. Rev. Food Sci. Nutr. 2008;48:524–537. [PubMed]
77. Fotuhi M, et al. Changing perspectives regarding late-life dementia. Nat. Rev. Neurol. 2009;5:649–658. [PubMed]
78. Raji CA, et al. Brain structure and obesity. Hum. Brain Mapp. 2010;31:353–364. [PMC free article] [PubMed]
79. Gazdzinski S, et al. Body mass index and magnetic resonance markers of brain integrity in adults. Ann. Neurol. 2008;63:652–657. [PMC free article] [PubMed]
80. Walther K, et al. Structural brain differences and cognitive functioning related to body mass index in older females. Hum. Brain Mapp. 2010;31:1052–1064. [PubMed]
81. Volkow ND, et al. Inverse association between BMI and prefrontal metabolic activity in healthy adults. Obesity. 2008;17:60–65. [PMC free article] [PubMed]
82. Gazdzinski S, et al. BMI and neuronal integrity in healthy, cognitively normal elderly: a proton magnetic resonance spectroscopy study. Obesity. 2009;18:743–748. [PMC free article] [PubMed]
83. Pannacciulli N, et al. Brain abnormalities in human obesity: a voxel-based morphometric study. Neuroimage. 2006;31:1419–1425. [PubMed]
84. Haltia LT, et al. Brain white matter expansion in human obesity and the recovering effect of dieting. J. Clin. Endocrinol. Metab. 2007;92:3278–3284. [PubMed]
85. Haltia LT, et al. Effects of intravenous glucose on dopaminergic function in the human brain in vivo. Synapse. 2007;61:748–756. [PubMed]
86. Bickel WK, et al. Behavioral and neuroeconomics of drug addiction: competing neural systems and temporal discounting processes. Drug Alcohol. Depend. 2007;90 Suppl. 1:S85–S91. [PMC free article] [PubMed]
87. Brogan A, et al. Anorexia, bulimia, and obesity: shared decision making deficits on the Iowa Gambling Task (IGT) J. Int. Neuropsychol. Soc. 2010:1–5.
88. Weller RE, et al. Obese women show greater delay discounting than healthy-weight women. Appetite. 2008;51:563–569. [PubMed]
89. Ikeda S, et al. Hyperbolic discounting, the sign effect, and the body mass index. J. Health Econ. 2010;29:268–284. [PubMed]
90. Cardinal RN. Neural systems implicated in delayed and probabilistic reinforcement. Neural Netw. 2006;19:1277–1301. [PubMed]
91. Gregorios-Pippas L, et al. Short-term temporal discounting of reward value in human ventral striatum. J. Neurophysiol. 2009;101:1507–1523. [PMC free article] [PubMed]
92. Bjork JM, et al. Delay discounting correlates with proportional lateral frontal cortex volumes. Biol. Psychiatry. 2009;65:710–713. [PubMed]
93. Pine A, et al. Dopamine, time, and impulsivity in humans. J. Neurosci. 2010;30:8888–8896. [PMC free article] [PubMed]
94. Mobini S, et al. Effects of lesions of the orbitofrontal cortex on sensitivity to delayed and probabilistic reinforcement. Psychopharmacology. 2002;160:290–298. [PubMed]
95. Roesch MR, et al. Should I stay or should I go? Transformation of time-discounted rewards in orbitofrontal cortex and associated brain circuits. Ann. N. Y. Acad. Sci. 2007;1104:21–34. [PMC free article] [PubMed]
96. Schoenbaum G, et al. A new perspective on the role of the orbitofrontal cortex in adaptive behaviour. Nat. Rev. Neurosci. 2009;10:885–892. [PMC free article] [PubMed]
97. Schilman EA, et al. The role of the striatum in compulsive behavior in intact and orbitofrontal-cortex-lesioned rats: possible involvement of the serotonergic system. Neuropsychopharmacology. 2010;35:1026–1039. [PMC free article] [PubMed]
98. Volkow ND, et al. Imaging dopamine’s role in drug abuse and addiction. Neuropharmacology. 2009;56 Suppl. 1:3–8. [PMC free article] [PubMed]
99. Davidson T, et al. Contributions of the hippocampus and medial prefrontal cortex to energy and body weight regulation. Hippocampus. 2009;19:235–252. [PMC free article] [PubMed]
100. Forloni G, et al. Role of the hippocampus in the sex-dependent regulation of eating behavior: studies with kainic acid. Physiol. Behav. 1986;38:321–326. [PubMed]
101. Haase L, et al. Cortical activation in response to pure taste stimuli during the physiological states of hunger and satiety. Neuroimage. 2009;44:1008–1021. [PMC free article] [PubMed]
102. Massa F, et al. Alterations in the hippocampal endocannabinoid system in diet-induced obese mice. J. Neurosci. 2010;30:6273–6281. [PubMed]
103. McNay EC. Insulin and ghrelin: peripheral hormones modulating memory and hippocampal function. Curr. Opin. Pharmacol. 2007;7:628–632. [PubMed]
104. Bragulat V, et al. Food-related odor probes of brain reward circuits during hunger: a pilot FMRI study. Obesity. 2010;18:1566–1571. [PubMed]
105. Benarroch E. Neural control of feeding behavior: overview and clinical correlations. Neurology. 2010;74:1643–1650. [PubMed]
106. Olszewski P, et al. Analysis of the network of feeding neuroregulators using the Allen Brain Atlas. Neurosci. Biobehav. Rev. 2008;32:945–956. [PMC free article] [PubMed]