Fuel not fun: Reinterpreting attenuated brain responses to reward in obesity

Abstract

There is a well-established literature linking obesity to altered dopamine signaling and brain response to food-related stimuli. Neuroimaging studies frequently report enhanced responses in dopaminergic regions during food anticipation and decreased responses during reward receipt. This has been interpreted as reflecting anticipatory "reward surfeit", and consummatory "reward deficiency". In particular, attenuated response in the dorsal striatum to primary food rewards is proposed to reflect anhedonia, which leads to overeating in an attempt to compensate for the reward deficit. In this paper, we propose an alternative view. We consider brain response to food-related stimuli in a reinforcement-learning framework, which can be employed to separate the contributions of reward sensitivity and reward-related learning that are typically entangled in the brain response to reward. Consequently, we posit that decreased striatal responses to milkshake receipt reflect reduced reward-related learning rather than reward deficiency or anhedonia because reduced reward sensitivity would translate uniformly into reduced anticipatory and consummatory responses to reward. By re-conceptualizing reward deficiency as a shift in learning about subjective value of rewards, we attempt to reconcile neuroimaging findings with the putative role of dopamine in effort, energy expenditure and exploration and suggest that attenuated brain responses to energy dense foods reflect the "fuel", not the fun entailed by the reward.

Keywords: Anhedonia; Dorsal striatum; Food reward; Reinforcement learning; Reward deficiency; fMRI.

Copyright © 2016 Elsevier Inc. All rights reserved.

Figures

Figure 1

Many different designs can be used to study the effects of rewards on the brain response. However, subtle differences in the design may have an impact on the interplay between brain responses at the cue and outcome event. Reinforcement learning models can be used to generate predictions given the choice of design and may help to disambiguate the acquired brain response to reward. Brown = milkshake; blue = rinse; white = tasteless.

Figure 2

Simulated brain responses based on a parametric grid (see Figure 3) of the two parameters learning rate and reward sensitivity. Whereas reward sensitivity has an effect on both predicted cue-value and prediction-error signals, the learning rate mainly influences prediction errors. Since increases in reward sensitivity will lead to corresponding increases in prediction error signals, the two parameters can only be separated by looking at both cue and outcome signals in a conjoint analysis.

Figure 3

Sampling of simulated participants with high reward sensitivity and low learning rates can reproduce the observed pattern of “reward surfeit” during anticipation (cue response) and “reward deficiency” during reward receipt.