Greater striatopallidal adaptive coding during cue–reward learning and food reward habituation predict future weight gain
Introduction
Animal experiments indicate that firing of dopamine (DA) neurons projecting to the striatopallidal complex initially occurs in response to receipt of palatable food, but after repeated exposures, shifts to occur in response to cues that predict impending food receipt (Day et al., 2007, Schultz et al., 1997, Tindell et al., 2004). Theorists posit this shift during cue–reward learning acts to either update knowledge regarding the predictive cues or attribute reward value to the cues themselves thereby guiding behavior (Balleine et al., 2008, Flagel et al., 2010a, Flagel et al., 2010b, Robinson and Berridge, 1993, Smith et al., 2009). The incentive-sensitization model indicates that greater striatopallidal responsivity to sensitized cues produces food ‘wanting’ and consequent overeating, echoing the processes that maintain habitual drug use (Robinson and Berridge, 1993). In animals that readily exhibit incentive salience toward cues that predict food reward, the cues robustly motivate behavior (Flagel et al., 2010a, Flagel et al., 2010b, Robinson and Flagel, 2009). Cross-sectional support for the incentive-sensitization model of obesity is evident when evaluating blood oxygen level dependent (BOLD) response to food cues in obese versus lean individuals. Compared to their lean counterparts, overweight and obese individuals show significantly greater activation in the striatum, orbitofrontal cortex (OFC), and amygdala in response to images of palatable food (Bruce et al., 2010, Martin et al., 2010, Nummenmaa et al., 2012, Rothemund et al., 2007, Stice et al., 2010, Stoeckel et al., 2008) and to cues that predict impending palatable food receipt (Ng et al., 2011, Stice et al., 2008b). In support of this data, the degree to which lean humans are habitually eating beyond energy needs is related to greater striatal response to cues for impending palatable food receipt (Burger and Stice, 2013). Moreover, animal studies of incentive salience indicate critical individual differences in cue–reward learning (e.g., Flagel et al., 2010a, Flagel et al., 2010b, Robinson and Flagel, 2009). Although elevated reward region responsivity to food cues has predicted future weight gain (Chouinard-Decorte et al., 2010, Demos et al., 2012, Yokum et al., 2011), research has not tested for individual differences in cue–reward learning or whether greater reward-cue learning predicts future weight gain, as implied by the incentive-sensitization model. The prediction of weight gain would represent a rigorous behavioral test of the impact of this potential neural vulnerability factor. Given that virtually all humans consume energy-dense foods on occasion, but only 30% become obese, it is vital to test whether some individuals show elevated reward-cue learning, which may set the stage for incentive sensitization processes that lead to overeating.
Habitation to repeated intake of one food (i.e., sensory specific satiety) is thought to impact weight regulation (e.g., Epstein et al., 2009), however, the vast majority of animal and human studies use acute food intake as the outcome (see Raynor and Epstein, 2001 for review). The mesolimbic neuroadaptive processes associated habitation to reward receipt from food has not been investigated thoroughly. One positron emission tomography study found that over repeated tastes of chocolate, preferences and striatal response food declined in nine ‘chocolate lovers’ (Small et al., 2001). Elucidating this process is vital given prominent theories hypothesizing that reduced sensitivity of reward circuitry increases risk for compensatory overeating and obesity (Johnson and Kenny, 2010, Volkow et al., 2008). Supporting this theory, obese versus lean humans have fewer striatal DA D2 receptors (deWeijer et al., 2011, Volkow et al., 2008) and show reduced striatal response to palatable food intake (Babbs et al., 2013, Green et al., 2011, Stice et al., 2008a, Stice et al., 2008b). Additionally, habitual consumption of sweet foods is inversely related to striatal and amygdala response during intake of similar foods (Burger and Stice, 2012, Green and Murphy, 2012, Rudenga and Small, 2012). There is evidence that this vulnerability may be acquired, as habitual energy-dense food intake that results in weight gain decreases DA D2 receptor density and DA sensitivity in animals (Geiger et al., 2009, Johnson and Kenny, 2010) and reduces striatal response to food receipt in humans (Stice et al., 2010). In previous decision-based reward learning tasks, the reward feedback signal in the caudate associated with monetary gain decreased as learning progressed (Delgado et al., 2005). This indicates habituation of reward feedback during reward-based learning. These data imply that striatal habituation can be observed in an acute setting during a reward-learning task. Although lower reward region responsivity to palatable food receipt has predicted future weight gain for individuals at genetic risk for compromised DA signaling in reward circuitry (Stice et al., 2008a), research has not tested for individual differences in propensity for striatal habituation to palatable food receipt in humans or whether greater habituation propensity predicts weight gain. As most humans consume energy-dense foods at least periodically, but only some become obese, it is vital to investigate individual difference factors that may set the stage for a blunting of reward circuitry responsivity to habitual palatable food intake that may contribute to overeating.
To investigate in vivo individual differences in both cue–reward learning and food receipt reward habituation, we used fMRI during repeated exposures to milkshake and tasteless solution receipt that were paired with unconditioned cues and modeled the data to assess change BOLD response over repeated exposures. We tested the hypotheses that 1) striatopallidal response to cues that predicted impending palatable food receipt would increase after repeated exposures (cue–reward learning); and 2) striatopallidal response to palatable food receipt would decrease after repeated milkshake tastes (food reward habituation). We also assessed BMI at baseline and at 6-month, 1-year, and 2-year follow-ups, which allowed us to test the hypotheses that individuals who show a greater cue–reward learning propensity and a greater food reward habituation propensity showed elevated future increases in BMI.
Section snippets
Participants & procedures
Healthy adolescent girls (n = 35; M age = 15.5 ± 0.94; M BMI = 24.5 ± 5.35, range = 17.3–38.9) underwent an fMRI session while viewing cues (geometric shapes: diamond, square, circle) that predicted impending receipt of a palatable milkshake or a tasteless solution. The sample consisted of: 2% Asian/Pacific Islanders, 2% African Americans, 86% European Americans, 5% Native Americans, and 5% mixed racial heritage. We excluded those who reported binge eating or compensatory behaviors (e.g., vomiting for
Cue–reward learning and food reward habituation
We observed a positive relation between the number of exposures and caudate response to the cue predicting milkshake receipt > cue predicting tasteless solution receipt (r = 0.42; F(1,101) = 6.2; P = 0.014; Figs. 2A, 3A) suggesting cue–reward learning in this region across the sample. Similar activity in the ventral pallidum was also observed, but was only a trend (r = 0.27; F(1,101) = 2.6; P = 0.10; Fig. 1C). There was no significant effect in the putamen (r = 0.08; P = 0.64; Fig. 2B).
When testing for a
Discussion
Results indicated that during exposure to repeated pairings of palatable food receipt and cues that predict impending receipt of the food, caudate response to cues increased, while responses in the putamen and ventral pallidum response to food receipt decreased. The adaptive response to cues seen here, extend findings from animal experiments that indicate that phasic DA release shows a similar dynamic pattern through the course of Pavlovian conditioning. Specifically, animal models indicate
Acknowledgments
Support for this work was provided by the National Institute of Health Research Grant R1MH64560. We thank Lewis Center for Neuroimaging at the University of Oregon for their assistance in data collection in this investigation.
Conflict of interest
The authors report no conflict of interest with respect to the content of this paper.
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