Post-ingestive signals conveying information about the nutritive properties of food are critical for regulating ingestive behavior. Here, using an auction task concomitant to fMRI scanning, we demonstrate that participants are willing to pay more for fat + carbohydrate compared with equally familiar, liked, and caloric fat or carbohydrate foods and that this potentiated reward is associated with response in areas critical for reward valuation, including the dorsal striatum and mediodorsal thalamus. We also show that individuals are better able to estimate the energy density of fat compared with carbohydrate and fat + carbohydrate foods, an effect associated with functional connectivity between visual (fusiform gyrus) and valuation (ventromedial prefrontal cortex) areas. These results provide the first demonstration that foods high in fat and carbohydrate are, calorie for calorie, valued more than foods containing only fat or carbohydrate and that this effect is associated with greater recruitment of central reward circuits.

This enabled us to test the hypothesis that pictures associated with the post-ingestive effects of fat and carbohydrate are more reinforcing than those associated with the post-ingestive effects of primarily fat or carbohydrate. As predicted, we found that participants are willing to pay more for snacks with fat + carbohydrate, compared with fat or carbohydrate alone, and that this effect was reflected by response in the dorsal striatum (caudate and putamen) and mediodorsal thalamus. Unexpectedly, we observed that participants are very accurate at estimating the energy density of fat, but not carbohydrate or fat + carbohydrate foods, an effect that is reflected by response in the fusiform gyrus and its connectivity with ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex, and cerebellum. These results are the first to demonstrate that foods high in fat and carbohydrate are more rewarding, calorie for calorie, than foods high only in fat or carbohydrate and that energy density estimation accuracy differs depending on macronutrient.

With this in mind, we set out to determine if palatable familiar foods high in fat and carbohydrate are more rewarding than similarly caloric, familiar, and liked foods high in only fat or only carbohydrate. To test this we used the Becker-DeGroot-Marshak auction () task, in which participants bid for snacks depicted in photographs, while BOLD response was assessed using fMRI. By using familiar snack items, we ensure that participants have had the opportunity to associate these foods with their nutritional properties via flavor-nutrient conditioning in the past and, based on this conditioning, the food images represent energy-predictive conditioned stimuli.

Since physiology is shaped by natural selection in response to environmental pressures, it is possible that the simultaneous activation of fat and carbohydrate signaling pathways produces a potentiated or perhaps extra-physiologic effect to potentiate reward and render processed foods high in fat and sugar more rewarding, calorie for calorie, than foods high in only fat or sugar. Consistent with this suggestion, rodents tightly regulate total daily caloric intake and body weight () when given access to fat alone or carbohydrate alone; however, when given unrestricted access to fat and carbohydrate they quickly gain weight (), suggesting that it is the combination of these macronutrients that disrupts energy balance.

Short exposure to a diet rich in both fat and sugar or sugar alone impairs place, but not object recognition memory in rats.

The existence of independent sensing pathways for fat and carbohydrate is especially relevant when considering the human obesity epidemic. The modern food environment proffers up nutrients in doses and combinations that do not exist in nature. This contrasts sharply with our ancestors' diet composed mostly of woody plants and raw animal meat (). Moreover, since hunter-gatherer societies eat single dietary components at a time (), even when nuts and seeds, which contain some fat, were available, consuming multiple foods in a single meal would have been a rare occurrence. One exception would be fruits containing seeds high in fat and pulp high in carbohydrate. However, such plants are rare and are very high in fiber, which would have significantly reduced the rate of carbohydrate metabolism (). Opportunities to consume fat and carbohydrate together certainly increased following the domestication of plants and animals and development of grain and dairy production (∼12,000 years ago). However, this is recent in our evolutionary past and still significantly different than the processed foods of today. For example, the nutritional content of oats with a half cup of milk and honey is only 1 g of fat and 27 g of carbohydrates. Compare this with a donut of similar calories, which contains 11 g of fat and 17 g of carbohydrate.

However, very little is understood about the mechanisms behind the generation of post-ingestive signals (in any species) and their regulation of mesolimbic circuits. One overlooked but potentially important factor is the possibility that separate mechanisms evolved for fat and carbohydrate (). Although vagal afferent signals are critical for intra-gastrically administered lipids to increase extracellular striatal dopamine and promote appetitive behavior, dopamine release upon glucose infusion depends upon a yet-unknown metabolic signal thought to be generated during the utilization of glucose as a cellular fuel (). Further, in rodents, vagotomy disrupts the orexigenic effects of nutrient deprivation by blocking fatty acid oxidation, but not by blocking glucose utilization (). There is also evidence for independent gut-brain pathways for fat and sugar (i.e., carbohydrate) reward in humans. People with a genetically derived deficiency in melanocortin-4 receptors (MC4Rs) exhibit increased preference for high-fat but reduced preference for high-sucrose foods (), whereas variants in the hepatokine fibroblast growth factor 21 (FGF21) gene are associated with increased preference for sweet, but not fat, foods ().

Collectively, these findings suggest that post-ingestive signals regulate neural circuits in the dopaminergic meso-striato-prefontal system independently of other food characteristics that could influence reward, such as liking, sweetness, perceived energy density, and availability (e.g., portion size). This has important implications for understanding the human obesity epidemic because mesolimbic neural response to food cues correlate with obesity (), genetic risk for obesity (), eating in the absence of hunger (), food choice (), future weight gain (), poorer performance on weight-loss trials (), and overfeeding ().

Post-ingestive signals also act as powerful reinforcers in rodent models (). In rodents, flavor-nutrient conditioning studies demonstrate that intragastric infusion of nutrients, but not saline, produces strong preferences for the flavor of a simultaneously consumed non-caloric flavored liquid () and these signals are both necessary and sufficient to sustain feeding via their effects on dopamine release in the striatum (). Accordingly, in humans, the tasteless and odorless carbohydrate maltodextrin, but not the non-caloric sweetener sucralose, conditions increased intake of sorbet (), while the magnitude of the blood-oxygen-level-dependent (BOLD) signal in dopamine target areas to calorie-predictive flavors depends upon the increase in plasma glucose levels when the flavors are previously consumed with calories (). Notably, neither sorbet intake nor BOLD response correlates with self-reported sorbet or flavor liking. Likewise, willingness to pay for food is associated with actual, but not estimated, caloric density and is reflected by BOLD response in the mesolimbic network ().

Post-ingestive signals conveying information about the nutritive properties of food are critical for regulating ingestive behavior. Rats readily titrate the volume of food (i.e., portion size) they consume to hold daily caloric intake constant, indicating that rats eat for calories rather than portion (). Likewise, in humans, separate neural circuits respond to energy density compared with portion size of foods depicted in images ().

Having confirmed this relationship in the behavioral data, we sought to isolate the neural circuit reflecting the differences in the ability to estimate energy density of fat compared with carbohydrate and fat + carbohydrate by regressing brain response against estimated energy density of fat compared with carbohydrate and fat + carbohydrate foods. We found a negative relationship with estimated energy density in the fusiform cortex when viewing fat pictures, in contrast to a positive response when viewing carbohydrate or fat + carbohydrate pictures ([−26 −72 −8], z = 3.25, p < 0.00001; Figure 5 B). A similar effect was observed in extrastriate cortex ([−20 −98 02], z = 3.84, p < 0.0001). To determine if there was differential connectivity with these visual sensory regions and areas of the meso-striato-prefrontal regions when estimating energy density for fat versus carbohydrate or fat + carbohydrate, we ran a PPI with the extracted time series from a 5 mm sphere around the fusiform gyrus peak ([−26 −72 −8]). This showed that estimated energy density increased fusiform connectivity with the vmPFC ([−10 38 −11], z = 3.02, p < 0.00001; Figure 5 C), anterior cingulate gyrus ([−10 44 8], z = 4.217; Figure 5 C), and cerebellum ([26 −68 −17], z = 4.17; Figure 5 D) when estimating the energy density of fat compared with the energy density of carbohydrate or fat + carbohydrate. This suggests that accurate estimates of energy density are associated with greater coupling of activity between the fusiform gyrus and the vmPFC, cingulate, and cerebellum.

Individual participants’ beta values from each group showing the relationship between true and estimated energy density controlling for portion size (A). BOLD activity associated with energy density differed between carbohydrate and fat groups in the fusiform gyrus (B). A PPI analysis showed connectivity modulated by estimated energy density between the fusiform and the vmPFC (C) and cerebellum (D). Arrows point to the peak voxel of the cluster. PEs are a.u. and are represented as means ± SEM. ∗ p < 0.0001.

In our behavioral analysis, we found participants were only able to accurately estimate the energy density of fat snacks, but not carbohydrate or fat + carbohydrate snacks ( Figure 2 A). To further confirm this relationship, we performed regressions such that beta values were generated for each subject for each snack type. Here, we controlled for portion size across groups to ensure it was not driving the effects. We found an overall group difference (Friedman's statistic = 9.1, p = 0.0106; Figure 5 A) using a non-parametric test, as beta values were not normally distributed. Further, participants were better able to estimate the energy density of fat snacks over carbohydrate snacks (p = 0.0144) and over fat + carbohydrate snacks (p = 0.0228).

We next tested whether bid amount modulated connectivity with the striatum differentially as a function of macronutrient category by performing a psychophysiological interaction (PPI) analysis with the seed defined as a 5 mm sphere surrounding the caudate peak ([14 −4 20]). Group difference in bid amount modulated striatal connectivity were observed with insular cortex bilaterally ([38 −12 6], z = 2.719, p < 0.0001; [−40 −8 −10], z = 2.615, p < 0.0001; Figure 4 F) and with the anterior medial temporal lobe (hippocampus and amygdala, [26 −2 −26], z = 2.718, p = 0.00106; Figure 4 G), although this cluster was just above our threshold of p < 0.001. More specifically, striatal connectivity with these regions is stronger when bidding for foods containing fat + carbohydrate compared with when bidding for food containing only fat or only carbohydrate.

Participants are willing to pay more for foods that contain fat and carbohydrate combinations than either macronutrient group alone (A). Foods receiving the highest and lowest bids are depicted in (B). BOLD signal in caudate nucleus (C), putamen (D), and thalamus (E) was more associated with willingness to pay in the fat and carbohydrate group than either alone. A PPI analysis showed that caudate connectivity with the bilateral insula was modulated by willingness to pay (F). The same is true of caudate activity with amygdala and hippocampus, although this bordered on significance at p = 0.00106 (G). Arrows point to the peak voxel in each cluster. Parameter estimates (PE) are a.u. and are represented as means ± SEM. ∗ p < 0.0001.

Next, to test our hypothesis that response in dopamine target areas would be greater for fat + carbohydrate compared with fat or carbohydrate foods during bidding, we created a GLM with estimated calories, true energy density, liking, and bid amount for each item in each group. This was done to control for factors that differed across group to ensure any effects seen were due to the bid amount alone. A contrast of fat + carbohydrate combination (FC) > fat + carbohydrate was modeled on the first level to account for within-subject variance. As predicted, response in the caudate and putamen was more strongly associated with bid amount for fat + carbohydrate compared with fat or carbohydrate alone (caudate, [14 −4 20], z = 3.82, p < 0.001; Table 2 Figure 4 C; putamen, [−28 −2 −2], z = 3.37, p < 0.001; Figure 4 D). A similar effect was also observed in the thalamus ([−10 −12 18], z = 3.87, p < 0.001; Figure 4 E). No significant effects for the reverse analysis (fat + carbohydrate > fat + carbohydrate) were found.

Brain areas whose BOLD activity is associated with willingness to pay, regardless of macronutrient group or true caloric density. ACC, anterior cingulate; OFC, orbitofrontal cortex.

To test for regions sensitive to the rewarding potency of the foods and to determine if we could replicate prior work, we created a general linear model (GLM) in which bid amount for each trial was entered as a parametric modulator controlling for energy density. This analysis produced responses in the right lingual gyrus ([8 −78 −4], z = 4.9, p < 0.00001; Figure 3 ), the anterior cingulate cortex ([−8 44 8], z = 3.66, p < 0.001; Figure 3 ), the orbitofrontal cortex ([−24 40 −26], z = 3.85, p < 0.0005; Figure 3 ), the frontal pole ([−21, 66, 2], z = 3.06, p < 0.001; Figure 3 ), and the anterior insula ([−32, 27, −7], z = 2.6, p < 0.001; Figure 3 ), largely replicating prior work (). Without controlling for energy density, a similar network is observed, including the lingual gyrus ([8 −78 −4], z = 2.7, p < 0.001) and anterior cingulate cortex ([−8 38 8], z = 3.4, p = 0.002 and [6 30 24], z = 3.09, p = 0.002).

Although we controlled for liking in all analyses, fat + carbohydrate items were liked slightly, but not significantly, more, and liking and bid amount were highly correlated. We therefore took an additional step to verify that effects were not related to liking. More specifically, we re-ran all analyses after removing the two least liked items from the carbohydrate and fat categories and the two most liked items from the fat + carbohydrate category. In so doing, the fat category became most liked (carbohydrate = 7.655 ± 3.342, fat = 17.12 ± 3.267, fat + carbohydrate = 14.59 ± 2.261), but there were still no significant differences in liking (F= 2.674, p = 0.0854; Figure S2 C). Categories were also still matched for calories shown (F= 0.06, p = 0.9417; Figure S2 A) and familiarity (F= 1.354, p = 0.273; Figure S2 B). Just as previously, fat + carbohydrate items were rated as more energy dense (F= 8.235, p = 0.0014; fat + carbohydrate > C, t= 1.89, p < 0.05; Figure S2 D) and actually were more energy dense (kcal/g) (F= 6.12, p = 0.0055; fat + carbohydrate > C, t= 2.82, p < 0.05; fat + carbohydrate > F, t= 3.12, p < 0.01; Figure S2 E). Reaction time did not differ significantly between groups in the subset (F= 2.71, p = 0.078; Figure S2 F). Again, a linear mixed effects model identical to the one described above (and including energy density as a covariate) revealed that participants were willing to bid more for fat + carbohydrate items overall (F= 8.161, p = 0.0003; fat + carbohydrate > C, F= 15.322, p < 0.0001; fat + carbohydrate > F, F= 10.9797, p < 0.0001; Figure S2 G). The supra-additive effect was also still present (F= 15.771, p < 0.0001).

Next, we generated a linear mixed effects model with bid as the outcome variable; subject as a random effect; and macronutrient group, true energy density, estimated energy density, liking, estimated portion calories, portion size, and calories shown in each picture as fixed effects to test our prediction that participants would pay more for the fat + carbohydrate foods compared with the fat or carbohydrate foods. As predicted, participants were willing to pay significantly more for foods with fat + carbohydrate compared with fat (t= −5.024, p < 0.0001; Figure 4 A) or carbohydrate (t= −4.9021, p < 0.0001; Figure 4 A) foods. Importantly, liking, actual energy density, and estimated energy density were in this model and therefore were adjusted for. To test if the effect was supra-additive, we generated a second model in which macronutrient categories were coded as containing fat or carbohydrate, with the other random and fixed factors identical to the previous model. The interaction term was significant (F= 20.383, p < 0.0001), showing that bids for fat + carbohydrate were greater than would be expected from summing the bids for fat and carbohydrate foods.

Unlike in prior work, where willingness to pay was unrelated to ratings of liking (), we observed a strong positive correlation between liking and willingness to pay for all foods (r= 0.69, p < 0.0001; Figure S1 C) and in fat (r= 0.89, p = 0.00003) and fat + carbohydrate (r= 0.70, p = 0.011) categories, but not carbohydrate (r= 0.58, p = 0.077). To test if energy density and liking are independent predictors of willingness to pay, we performed a mediation analysis by constructing models with both variables entered as predictors. Significance improved with both variables entered (p = 0.002 to <0.001), but each contributed uniquely to the effect, with energy density remaining a significant predictor when liking was included (β = 0.207, p = 0.03) and liking remaining a significant predictor when energy density was included (β = 0.763, p < 0.001), indicating both predictors contribute independently.

Willingness to pay was not related to portion size (r= 0.0009, p > 0.9; Figure S1 A). Participants could not estimate the number of calories shown in each snack portion (r= 0.211, p = 0.82; Figure S1 B). Liking ratings were also associated with energy density overall (r= 0.122, p = 0.01; Figure S1 D), but this was not significant in any individual group. Liking was not related to estimated energy density for the whole group (r= 0.0003, p = 0.92; Figure S1 E) or any individual group.

The relationship of willingness to pay with true (A) and estimated (B) caloric density is shown, with the all three macronutrient groups combined on the right and separated on the left. Estimated calories and true caloric density are shown to be related overall (C) and in the fat macronutrient group on the right. All tests are Bonferroni corrected for the total number of tests performed in this series (32). See also Figure S2 p > 0.001.

We next examined correlations between willingness to pay, energy density, estimated energy density, and liking using linear regressions. All regression p values are Bonferroni corrected for the total number of tests run (32). Replicating prior work by, willingness to pay was highly associated with energy density (r= 0.225, p < 0.001; Figure 2 A) across all snacks. Importantly, willingness to pay was not correlated with estimated energy density in the full group (r= 0.070, p > 0.9; Figure 2 B) or in any subgroup. Taken together, this indicates that actual energy density is a better predictor of food reward than estimated energy density. Interestingly, participants could very accurately estimate the energy density for fat snacks (r= 0.688, p = 0.016; Figure 2 C). In contrast to fat snacks, participants were unable to accurately estimate energy density of carbohydrate (r= 0.012, p > 0.9) and fat + carbohydrate snacks (r= 0.22, p > 0.9; group × estimated energy density F= 13.132, p = 0.0008).

We first compared the snack macronutrient categories to test for differences in liking and estimated and actual energy density using one-way ANOVAs. By design, no differences were observed in caloric content (F< 0.001, p = 0.9; Figure 1 B), familiarity (F= 0.08, p = 0.92; Figure 1 C), or liking (F= 1.94, p = 0.073; Figure 1 D). Fat + carbohydrate snacks were higher in energy density (F= 9.99, p = 0.0004; Figure 1 E) and were accurately estimated by participants as being more calorically dense (F= 8.73, p = 0.0008; Figure 1 F). Participants also bid slightly faster for these snacks (F= 4.286, p = 0.0214; Figure 1 G).

Methods and analyses are described in detail in the STAR Methods section. In brief, all participants in the main study first rated these snacks for liking, familiarity, estimated energy density, and total calories shown. On a subsequent day, they arrived fasted to the laboratory and were fed a standard breakfast of 426 kcal from orange juice, cheddar cheese, whole-wheat toast, white toast, strawberry jam, and butter (described in). They began the fMRI session 3 hr later. Prior to scanning, participants were given €5 and told they could bid between €0 and €5 against the computer to purchase snacks depicted in pictures presented during scanning. They were also told that one item will be selected at random for auction at the end of scanning. If the participant's bid was higher than the computer's bid, he or she was able purchase the item and receive the remainder of the €5 in cash. Otherwise, the participant received the entire €5 but did not get the item. Participants remained in a supervised setting following scanning, during which time they consumed the item, if one had been won. Under these circumstances the optimum strategy is to bid what one believes is the value of the item ().

Our first step was to create a set of pictures of small snacks falling into one of three categories of macronutrient content: those with most of their calories coming from (1) fat, (2) carbohydrate, or (3) fat + carbohydrate, but varying equally within category for liking, familiarity, and caloric content ( Table 1 ). Testing was performed in a pilot study (n = 56) that did not include participants from the main study ( STAR Methods ). Examples from each group are depicted in Figure 1 A. Portion sizes were adjusted across macronutrient groups to equate caloric content across snacks so that there were examples of low, medium, and high caloric values in each category with overall mean calories across categories similar ( Table 1 ). Portion size was not significantly different across groups and pictures were chosen to have equal object size, intensity, and complexity, although they differed slightly in contrast ( Table 1 ). This resulted in 39 stimuli with 13 in each group.

Examples of each macronutrient group are displayed in (A). Averages across macronutrient groups for the scanned participants in calories shown (B), familiarity (C), liking (D), energy density (E), estimated energy density (F), and reaction time (G). Following pre-testing on a separate day, participants viewed a fixation cross for approximately 9 s (H). A picture of a food item to be bid on was displayed for 5 s. Participants then had 5 s to make a bid on the item. They moved a trackball inside the scanner to move a cursor back and forth between 0 and 5 euros. After they submitted their response it remained on screen for the remainder of the 5 s. Data are represented as means ± SEM. C, carbohydrate; F, fat. ∗ p < 0.05.

For the snack images: object size is the proportion of non-white pixels, brightness is the differences between the mean luminance of all non-white pixels and the white background, contrast is the SD of the luminance of the non-white pixels, and complexity is the proportion of outline pixels to object pixels (). Means (n = 39 pictures) with SEM are given with the range below each mean. C, carbohydrate; F, fat; FC, fat + carbohydrate.

Discussion

Our study produced two novel findings that are relevant for understanding food choice. First, we demonstrate for the first time that foods containing both fat and carbohydrate are more rewarding, calorie for calorie, than those containing only fat or only carbohydrate, and we further describe a network of brain regions (caudate, putamen, and mediodorsal thalamus) underlying this effect. Second, we discovered, unexpectedly, that individuals are better able to estimate the energy density of fat compared with carbohydrate and fat + carbohydrate foods, with accurate estimations of energy density depicted in pictures of fatty foods associated with increased coupling of visual sensory areas with the vmPFC and cerebellum. Both findings support and extend work from animal models indicating that these two energy sources have distinct pathways for conveying nutritive value to the CNS to ultimately guide food choice and highlight the need to further understand the mechanisms driving the interaction of macronutrients on circuits regulating ingestive behavior.

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First, our data suggest that the association between reward and energy density differs for fat and carbohydrate ( Figure 2 ). For fatty foods, willingness to pay (our measure of reward) is tightly coupled to energy density and self-reported food liking. Although both food liking and energy density are associated with willingness to pay, mediation analyses indicate that these factors contribute independently to reward value. In contrast, for carbohydrate-containing foods, willingness to pay is positively related to self-reported food liking, but not energy density. Moreover, although individuals are very accurate at estimating the energy density of fatty foods, they are poor at estimating the energy density of carbohydrate-containing foods, which our fMRI data suggest results from differential engagement of the fusiform gyrus.

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de Graaf C. Taste-nutrient relationships in commonly consumed foods. The mechanism by which striatal dopamine influences fusiform activity is unclear; however, a striatum to vmPFC to fusiform circuit is possible. Accordingly, we observe preferential connectivity between the fusiform gyrus and vmPFC when bidding for fat versus carbohydrate or fat + carbohydrate foods. The vmPFC plays a critical role in determining food choices based on comparing taste and health information () and the fusiform gyrus to vmPFC neural circuit is thought to integrate visual features of objects with the generation of value signals to drive choice (). In a prior study, vmPFC response correlated with bid amount and actual, but not estimated, energy density (). The pattern of activations we observe suggests that the fusiform gyrus-vmPFC circuit plays an important role in accurately estimating energy density from visual information. More specifically, estimates are accurate when there is a negative association between energy density and fusiform response (lower responses for higher energy density; Figure 5 ) coupled with increased functional connectivity with the vmPFC (and cerebellum). Collectively, these data suggest that the fusiform has access to the value of the nutritional properties of foods conveyed by visual information and that interactions between the fusiform and vmPFC are important in enabling more accurate estimates of energy density for fat compared with carbohydrate-containing foods. One possibility is that the post-ingestive signal conveying information about the nutritive value of fat is successfully integrated with visual information about foods to determine value, whereas the value of carbohydrates may derive primarily from other features, such as flavor or visual presentation; however, further research is needed to confirm this hypothesis. An alternative possibility is that the ubiquity of artificial sweeteners in modern diets degrades the association between carbohydrate-containing foods and energy density, resulting in an impaired ability to estimate calories (). For example, in the gustatory modality, intensity ratings of sweetness are positively related to sugar content, but the association is significantly stronger for raw and moderately processed foods compared with highly processed foods ().

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Keller K.L. Food portion size and energy density evoke different patterns of brain activation in children. We also show, for the first time, that fat and carbohydrate interact to potentiate reward, by demonstrating greater brain response to, and increased willingness to pay for, equally caloric foods containing fat and carbohydrate compared with fat or carbohydrate alone. We further show that these effects are not accounted for by differences in food liking or energy density since the interactive effects of fat and carbohydrate survive the inclusion of these factors as covariates in statistical models. Finally, we rule out portion size as a contributing factor because portion size is unrelated to willingness to pay, which is consistent with a recent report showing different patterns of evoked brain response to food portion size and energy density ().

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Everitt B.J. Parallel and interactive learning processes within the basal ganglia: relevance for the understanding of addiction. Replicating prior work (), irrespective of macronutrient category bid amount was associated with BOLD response in the anterior cingulate cortex and orbitofrontal cortex, which has also been shown to code food attributes (). However,also reported associations between bid amount and energy density in the vmPFC and striatum, which we did not observe when considering all food items. Rather, we found that striatum (caudate and putamen/globus pallidus) was selectively engaged when bidding for foods that contain fat and carbohydrate, compared with fat or carbohydrate alone. This is of note because this area is thought to play an important role in the shift from goal-directed to habitual control over behavior, which is a fundamental characteristic of addiction ().

Study Limitations Burger and Stice, 2012 Burger K.S.

Stice E. Frequent ice cream consumption is associated with reduced striatal response to receipt of an ice cream-based milkshake. Schultz et al., 1993 Schultz W.

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Ljungberg T. Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task. Although we ensured all stimuli were equally familiar across macronutrient groups, we did not explicitly measure frequency of consumption of each item. This distinction could be relevant given striatal activity of people who frequently consume ice cream is blunted in response to an ice cream-like milkshake () and repeated exposure to food decreases activity in dopamine neurons that project to striatal regions in response to that food (). Our effects were in the opposite direction: increases in striatal activity were associated with willingness to pay, but future studies should include a measure of frequency of consumption to control for this effect on striatal activity. Although our sample size was sufficient here, a future study should include more participants and expand the effect and food items to other regions of the world, outside of western Europe.