Neural Substrates of Food Valuation and Its Relationship With BMI and Healthy Eating in Higher BMI Individuals

Abstract

Considerable evidence points to a link between body mass index (BMI), eating behavior, and the brain's reward system. However, much of this research focuses on food cue reactivity without examining the subjective valuation process as a potential mechanism driving individual differences in BMI and eating behavior. The current pre-registered study (https://osf.io/n4c95/) examined the relationship between BMI, healthy eating, and subjective valuation of healthy and unhealthy foods in a community sample of individuals with higher BMI who intended to eat more healthily. Particularly, we examined: (1) alterations in neurocognitive measures of subjective valuation related to BMI and healthy eating; (2) differences in the neurocognitive valuation for healthy and unhealthy foods and their relation to BMI and healthy eating; (3) and whether we could conceptually replicate prior findings demonstrating differences in neural reactivity to palatable vs. plain foods. To this end, we scanned 105 participants with BMIs ranging from 23 to 42 using fMRI during a willingness-to-pay task that quantifies trial-by-trial valuation of 30 healthy and 30 unhealthy food items. We measured out of lab eating behavior via the Automated Self-Administered 24 H Dietary Assessment Tool, which allowed us to calculate a Healthy Eating Index (HEI). We found that our sample exhibited robust, positive linear relationships between self-reported value and neural responses in regions previously implicated in studies of subjective value, suggesting an intact valuation system. However, we found no relationship between valuation and BMI nor HEI, with Bayes Factor indicating moderate evidence for a null relationship. Separating the food types revealed that healthy eating, as measured by the HEI, was inversely related to subjective valuation of unhealthy foods. Imaging data further revealed a stronger linkage between valuation of healthy (compared to unhealthy) foods and corresponding response in the ventromedial prefrontal cortex (vmPFC), and that the interaction between healthy and unhealthy food valuation in this region is related to HEI. Finally, our results did not replicate reactivity differences demonstrated in prior work, likely due to differences in the mapping between food healthiness and palatability. Together, our findings point to disruptions in the valuation of unhealthy foods in the vmPFC as a potential mechanism influencing healthy eating.

Keywords: BMI—body mass index; fMRI; food-cue reactivity; healthy eating; subjective valuation; willingness-to-pay.

Figure 1

Task schematic for the willingness-to-pay task. On each trial, a snack food was presented for 4 s, followed by a 4 s bid period, and jittered fixation period between trials (M = 4.38 s). Snack foods were healthy (e.g., fruit) or unhealthy (e.g., candy) items.

Figure 2

Density plots and correlations between the healthy eating index (HEI), average bid amounts for healthy foods, and average bid amounts for unhealthy foods.

Figure 3

vmPFC and aVS ROIs used in analysis pictured in center image. Parameter estimates in arbitrary units (AU) of overall valuation of all foods, healthy foods, and unhealthy foods from (A) vmPFC and (B) aVS. The interaction between valuation for healthy and unhealthy foods, and its relation to healthy eating (C) plotted using median split of the vmPFC + VS combined parameter estimates of healthy and unhealthy foods (even though data are continuous).

Figure 4

(A) Whole-brain search of subjective valuation for all foods with positive (hot colors) and negative (cold colors) coupling between bid value and corresponding BOLD response (cluster corrected p < 0.05). vmPFC cluster (green) showing greater coupling for healthy than unhealthy foods (p < 0.005, k = 300, uncorrected). (B) Whole-brain contrast between reactivity for healthy (hot colors) and unhealthy foods (cold colors) not accounting for bid value (cluster corrected p < 0.05). Cluster correction of p < 0.001, k = 119 to achieve a whole-brain familywise error rate of α = 0.05.