Brain reward system's alterations in response to food and monetary stimuli in overweight and obese individuals

For Peer Review Brain reward system’s alterations in response to food and monetary stimuli in overweight and obese individuals Journal: Human Brain Mapping Manuscript ID HBM-16-0496.R1 Wiley - Manuscript type: Research Article Date Submitted by the Author: 08-Aug-2016 Complete List of Authors: Verdejo-Román, Juan; Universidad de Granada, Vilar-López, Raquel; University of Granada, Centro de Investigación Mente, Cerebro y Comportamiento (CIMCYC) Navas, Juan Francisco; University of Granada, Department of Psychology Soriano-Mas, Carles; IDIBELL, Verdejo-Garcia, Antonio; Monash University, Keywords: Reward, Striatum, Obesity, Overweight John Wiley & Sons, Inc. Human Brain Mapping For Peer Review 1 Title Page Short title: Food and monetary processing in overweight and obesity Brain reward system’s alterations in response to food and monetary stimuli in overweight and obese individuals Juan Verdejo-Román 1 , Raquel Vilar-López 1,2 , Juan F. Navas 1 , Carles Soriano-Mas 3,4,5* , Antonio Verdejo-García 6* 1. Institute of Neuroscience F. Olóriz & Mind, Brain, and Behavior Research Center–CIMCYC, Universidad de Granada, Spain. 2. Red de Trastornos Adictivos, Universidad de Granada, Spain 3. Department of Psychiatry, Bellvitge University Hospital-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain. 4. CIBERSAM, Carlos III Health Institute, Spain. 5. Department of Psychobiology and Methodology in Health Sciences, Universitat Autònoma de Barcelona, Spain. 6. School of Psychological Sciences & Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Australia. Key words: Obesity, Overweight, Reward, Monetary, Food, fMRI *Shared corresponding author: A. Verdejo-García (*) School of Psychological Sciences, Monash University 18 Innovation Walk, 3800, Melbourne, Victoria, Australia E-mail: [email protected]. Ph: +61 (3) 9905 5374 C. Soriano-Mas (*) Department of Psychiatry, Bellvitge University Hospital-IDIBELL Feixa Llarga s/n 08907 L’Hospitalet de Llobregat (Barcelona), Spain E-Mail: [email protected]. Ph: +34932607500 (ext. 2864) Conflict of interest: The authors report no biomedical financial interests or potential conflicts of interests. Page 1 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 2 Abstract The brain’s reward system is crucial to understand obesity in modern society, as increased neural responsivity to reward can fuel the unhealthy food choices that are driving the growing obesity epidemic. We tested brain’s reward system responsivity to food and monetary rewards in individuals with excessive weight (overweight and obese) versus normal weight controls, along with the relationship between this responsivity and body mass index (BMI). The sample comprised 21 adults with obesity (BMI>30), 21 with overweight (BMI between 25 and 30) and 39 with normal weight (BMI<25). Participants underwent a functional magnetic resonance imaging (fMRI) scanner while performing two tasks that involve the processing of food (Willing to Pay) and monetary rewards (Monetary Incentive Delay). Neural activations within the brain reward system were compared across the three groups. Curve fit analyses were conducted to establish the association between BMI and brain reward system’s response. Individuals with obesity had greater food-evoked responsivity in the dorsal and ventral striatum compared to overweight and normal weight groups. There was an inverted U-shape association between BMI and monetary-evoked responsivity in the ventral striatum, medial frontal cortex and amygdala; that is, individuals with BMIs between 27 and 32 had greater responsivity to monetary stimuli. Obesity is associated with greater foodevoked responsivity in the ventral and dorsal striatum, and overweight is associated with greater monetary-evoked responsivity in the ventral striatum, the amygdala and the medial frontal cortex. Findings suggest differential reactivity of the brain’s reward system to food versus monetary rewards in obesity and overweight. Page 2 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 3 Introduction Between 1980 and 2013 the prevalence of overweight and obesity has increased from 857 million to 2.1 billion people worldwide, becoming a major global health challenge [Ng et al., 2014]. Specifically, overweight and obesity are associated with increased risk of cardiovascular disease, stroke, type II diabetes and different types of cancer, being a consistent risk factor for these conditions when Body Mass Index (BMI) is above 23 kg/m 2 [Ng et al., 2014]. In Western societies, cheap availability of high palatable foods is a primary driver of the growing obesity epidemic [Finkelstein et al., 2005]. Foods rich in sugar and fat stimulate the brain reward network, bypassing the homeostatic mechanisms that control food intake, and hence fostering eating, even in the absence of energetic needs [Stice et al., 2013; Volkow et al., 2011]. Current neurobiological theories are advocating for a “food addiction model” of obesity, given overlapping neurobiological alterations between individuals with obesity and substance addictions [Burger and Stice, 2011; Kenny, 2011; Volkow et al., 2013; Volkow and O’Brien, 2007]. Specifically, this model posits that individuals with overweight and obesity display increased responsivity of the brain’s reward system to food stimuli, leading to a loss of control over food intake [Volkow et al., 2013]. In spite of the growing influence of this food addiction model, overweight and obesity are heterogeneous conditions, and more neurobiological research is needed to establish if this notion is relevant across the different manifestations of excessive weight, or to particular phenotypes [Carter et al., 2016]. Currently available functional magnetic resonance imaging (fMRI) studies have shown that sensory cues of high-palatable food evoke increased neural activation in the striatum and related regions of the brain reward network in both overweight and obese individuals versus normal weight controls Page 3 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 4 [Carnell et al., 2014; Fletcher et al., 2010; Jastreboff et al., 2013; Martin et al., 2010; Rothemund et al., 2007; Stoeckel et al., 2008]. Positron Emission Tomography (PET) studies have also shown reduced striatal dopamine D2 binding potential in severely obese individuals (BMI≥40) [Wang et al., 2001]. However, striatal dopamine D2 binding potential is increased in individuals with more moderate degree of excess weight for height [Guo et al., 2014]. Altogether, PET studies suggest that overweight and obesity may have unique neural underpinnings, and it has been proposed that the association between BMI and dopaminergic/reward network activity follows an inverted U-shape curve; that is, the association is positive in overweight individuals, but negative in obese individuals [Horstmann et al., 2015]. This proposed model is clinically significant and needs to be formally tested. If individuals with overweight versus obesity value food and other rewards via different brain mechanisms, delineation of these mechanisms would lead to better understanding of the underlying neurobiology of these disorders and, potentially, to more specific interventions for overweight and/or obesity. General reward sensitivity has been customarily indexed in neuroimaging studies with the Monetary Incentive Delay (MID) task [Costumero et al., 2013]. In normal weight individuals, MID-evoked brain activations in the midbrain, striatum and orbitofrontal cortex have been associated with trait reward sensitivity [Costumero et al., 2013], and the food addiction model would predict a stronger involvement of these regions in people with excess weight. However, currently available studies have yielded contradictory findings. Balodis et al. [2013] showed increased reward system activation during the MID task in obese individuals versus controls, although no differences were found during reward feedback. Conversely, Simon et al. [2015] did not found a significant association between BMI and MID-evoked neural activation. Therefore, Page 4 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 5 existing studies have not yet clearly ascertained the association between excess weight and brain responses to monetary stimuli, or overlapping and/or unique patterns of brain activation related to monetary versus food stimuli. The latter is relevant because the low prices of highly palatable foods have contributed to increase their subjective value, and thus to food choices leading to the obesity epidemic [Rangel, 2013]. In this study, we aimed to compare brain activations evoked by food and monetary rewards in individuals with obesity, overweight and normal weight; and to determine the association between reward-evoked brain activations and BMI. We hypothesized that, in response to high palatable foods, excess weight participants, would display increased activation of key regions of the brain reward system, and particularly the striatum [Simon et al., 2015]. We also hypothesized that in response to monetary rewards, which is a biological index of generalized sensitivity to reward, there would be an inverted U-shape association between brain’s reward system activation and BMI [Horstmann et al., 2015]. Methods and Materials Participants Eighty-one healthy adults, aged between 25 and 45 years old were recruited for this study. They were classified in three groups on the basis of BMI: 39 Normal weights (NW); 21 Overweight (OW) and 21 Obese (OB). Participants’ sociodemographic characteristics, and BMI and fat percentage data are displayed in Table I. The inclusion criteria were defined as follows: (i) BMI falling within the intervals categorized as overweight (BMI between 25 and 30 kg/m 2 ), obesity (BMI over 30 kg/m 2 ) or normal weight (BMI between 19 and 25 kg/m 2 ); (ii) right-handedness. The exclusion criteria Page 5 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 6 were: (i) history or current evidence of medical or psychiatric disorders that co-occur with obesity (e.g., diabetes, hypertension, binge eating, bulimia nervosa, depression) indicated with clinical assessments conducted by professional nurses and psychologists; (iv) abnormalities on Magnetic Resonance Imaging (MRI) or any contraindications to MRI scanning (including claustrophobia and implanted ferromagnetic objects). All participants had normal or corrected-to-normal vision. They were recruited through media advertisements and received a financial compensation. The study was approved by the Ethics Committee for Research in Humans of the University of Granada (Spain) and was conducted in accordance with the Declaration of Helsinki. All participants signed written informed consent. Experimental Procedure Participants underwent two reward related tasks during an fMRI session. Each of these tasks involved the processing of different rewards: food and money. To ensure that every subject knew all the food stimuli to be used in the food reward fMRI task, two weeks before scanning participants attended to a catered tasting session. During that session subjects were gathered in a room and allowed to eat 18 different foods. These products had been previously classified based in their palatability: high palatable food, including sweet and fatty food (e.g., chocolate, cheese cake, burger) and plain food (e.g., yoghurt, omelet, orange). These sessions were conducted at 6:00 pm, and each participant had to taste each food and rate how much they liked these foods in a numerical scale of 1 to 10. All groups showed higher linking ratings for high palatable food compared to plain food (all p<0.05). Page 6 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 7 All the fMRI sessions were conducted between one and three hours after lunch. At the beginning of this session BMI and fat percentage were obtained using a body composition analyzer TANITA BC-420 (GP Supplies Ltd., London, UK). To control the satiety level, participants rated their subjective degree of appetite on a 10-cm visual analog scale (VAS) three times along the fMRI session: prior to scan, immediately before the food-stimuli task and immediately after leaving the MRI room. fMRI Tasks Food reward: We used a modified version of the Willingness to pay task [Plassmann et al., 2007]. Participants watched each of the 18 previously tasted foods once. Each stimulus was presented in the screen for 2 seconds and after that, they had 4 seconds to answer: “How much would you pay for it?” They could choose between four prices, ranging from 20 cents to 10 euros. Each selection was followed by a variable time between 3 and 5 seconds of baseline during which a cross fixation was presented on the screen (see Figure 1-A). Stimuli were presented in a pseudorandomized sequence to ensure that no more than two images of the same category appeared consecutively (i.e., high palatable food, plain food). Our main interest was to contrast group differences between high palatable and plain food trials. Monetary reward: We used an adaptation of the Monetary Incentive Delay task [Nestor et al., 2010], based on the original task employed by Knutson et al. [2001]. At the beginning of each trial, participants were shown one of two cues (green or blue square) indicating potential winnings or no financial outcome at the end of the trial. The incentive value of each trial was signaled by means of the number of horizontal lines crossing the square (one line for 0.2€, two for 1€ and three for 5€). Each cue was Page 7 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 8 presented for a fixed duration of 750msec. Subsequently, a cross-fixation was shown during a variable period of 3 to 5 sec, and after this interval participants had to perform a reaction-time task: respond to a white target star appearing for a variable length of time (150–450 ms) with a button press. Then participants received feedback (hit/miss) about the accuracy of their response for 750ms, together with the information about the amount of money won in that trial (when adequate, i.e., correct responses in reward cued trials) and their cumulative total at that point of the experiment. Finally, another fixation period (750 ms) was included before the next trial. Therefore, total trial duration ranged between 5700 and 7000 ms. Participants performed 24 trials of each type of cue yielding a total of 96 trials (see Figure 1-B). Imaging analyses explored brain activity changes during two periods, the rewardanticipatory period, which included the cue presentation, the variable waiting delay and the actual response period, and the reward-feedback period, involving the presentation of visual feedback (hit/miss). For the anticipatory period we defined four events of interest: (i) No outcome (0€); (ii) Low reward (0.2€); (iii) Medium reward (1€); (iv) High reward (5€). For the feedback period we defined two events of interest: (i) Win trials; (ii) Miss trials, pooling together the different gains. Specifically, a linear contrast (High reward > Medium reward > Low reward > No outcome trials) was defined at the first level (within-subject) to explore brain activation during rewardanticipation, while a Win vs. Miss contrast was used for the reward-feedback period. Therefore, this task yields two main conditions of interest: reward anticipation (High vs. Medium vs. Low vs. No reward) and reward feedback (Win vs. Miss). Imaging data acquisition and preprocessing Page 8 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 15 Group comparisons showed that OW individuals displayed significantly increased activation in the anterior cingulate cortex/supplementary motor area in comparison with both OB and NW groups. Likewise, OW individuals (but not OB individuals) showed a significantly increased activation in the ventral tegmental area, the ventral putamen, the lateral orbitofrontal cortex and the hippocampus-amygdala complex in comparison with NW participants (Table SII, Figure 3). Curve fit analyses of the association between BMI and peak activations from the above analyses showed inverted-U associations for the supplementary motor area (R 2 = 0.240, P < 0.001), dorsal anterior cingulate (R 2 = 0.144, P = 0.003), ventral tegmental area (R 2 = 0.103, P = 0.016), ventral putamen (right: R 2 = 0.137, P = 0.004; left: R 2 = 0.079, P = 0.043), hippocampus (R 2 = 0.135, P = 0.004) and amygdala (R 2 = 0.115, P = 0.009). Post-hoc analyses showed that the peaks of the inverted U ranged between 27 and 32 Kg/m 2 . Reward feedback contrast In win versus miss trials participants significantly activated the bilateral ventral and dorsal striatum, the amygdala-hippocampal complex, the orbitofrontal cortex, the middle frontal gyrus, the posterior cingulate, and the intraparietal cortices. Miss compared to win trials evoked activations including the anterior insula, the dorsal anterior cingulate cortex and the supplementary motor area. (Table SIII, Figure 4). We found no significant correlations with sensitivity to reward. Group comparisons in Win versus Miss trials showed that OB individuals compared to NW had increased activation in the rostral-ventral pons. Likewise, OB individuals compared to OW had increased activation in the nucleus accumbens. Curve fit analyses showed a linear and positive association between nucleus accumbens and pons Page 15 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 16 activations and BMI scores (r = 0.363, R 2 = 0.132, P = 0.001, r = 0.276, R 2 = 0.076, P = 0.014). (Table SIII, Figure 4). In addition, we conducted a whole brain analysis to ascertain between-group differences in brain activation that were outside the reward system identified by Neurosynth. We only found two significant clusters of activation. In the food task, one cluster comprising the left frontal operculum extending to the anterior insula was more activated in obese versus healthy weight participants. In the anticipation phase of the monetary task, a cluster located in the intraparietal cortex was more activated in overweight versus healthy weight participants. Discussion We found that individuals with obesity and overweight have unique patterns of brain activation in response to food and monetary rewards. Specifically, individuals with obesity display enhanced food-evoked ventral and dorsal striatal activations compared to individuals with overweight and normal weight. Conversely, individuals with overweight display increased monetary-reward anticipation activations in widespread regions across the brain reward network. Monetary reward feedback, however, evoked greater responses in the rostral-ventral pons and nucleus accumbens in obese individuals versus normal weight and overweight subjects, respectively. Food and monetaryfeedback evoked neural activations showed a linear positive relationship with BMI, whereas monetary-reward-anticipation evoked neural activations showed an inverted Ushape association with BMI. Behavioural measures showed that individuals with overweight and obesity had greater sensitivity to high palatable versus plain food. This pattern indicates that individuals with excess weight have increased reward sensitivity in relation to Page 16 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 17 palatable food, which is consistent with greater striatal activation in the food task [Passamonti et al., 2009]. In addition, individuals with obesity showed slower reaction times in low monetary incentive trials, suggesting reduced reward sensitivity and/or weaker reward learning. Based on recent theoretical work, reduced reward learning may contribute to explain their decreased brain responsivity during the cue phase, coupled with increased responsivity during the feedback phase [Kroemer and Small, 2016]. The increased responsivity of the ventral and dorsal striatum to high-palatable food in obese individuals is consistent with previous fMRI studies showing increased striatal activation in response to food cues [Rothemund et al., 2007; Simon et al., 2014]. Critically, we show that these alterations are specific to individuals with obesity (relative to overweight), and therefore they may reflect severity related neuroadaptations. This notion is consistent with food addiction models of obesity, which propose that this disorder is associated with ventral striatal neuroadaptations leading to incentive sensitization of food, and dorsal striatal neuroadaptations leading to food-related habits [Tomasi and Volkow, 2013]. Our findings also extend available evidence by showing alterations in a food choice task, with greater ecological validity than passive observation of food cues [Fletcher et al., 2010]. In fact, imaging findings were paralleled by behavioral results, which show that obese individuals assign less value to standard food, which may bias their food choices towards highly palatable unhealthy food [Rangel, 2013]. The increased responsivity of the VTA/striatum, amygdala, orbitofrontal cortex, and medial prefrontal cortex in overweight individuals to anticipation of monetary rewards, and the inverted U-shaped relationship between activation of these regions and BMI is consistent with findings of dopamine-PET studies [Horstmann et al., 2015]. Indeed, Page 17 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 18 brain activation in the MID task is regarded as a biological index of general sensitivity of the brain reward system [Costumero et al., 2013]. Our findings clearly indicate that brain response to monetary-reward anticipation is increased in individuals with overweight, and comparatively decreased in individuals with obesity. This finding is relevant, as it indicates that strategies to prevent overweight might need to focus on downplaying general hyper-reactivity of the brain reward system, whereas strategies to prevent obesity might need to stimulate the brain reward system’s responsivity to alternative reinforcers that can compete with food. It remains to be determined if overweight-specific reward system hyper-reactivity represents a different biological phenotype, or an “en-route” state leading to obesity. In any case, our results have theoretical implications for the understanding and prevention of overweight versus obesity. The increased responsivity of the nucleus accumbens to monetary reward feedback in obese individuals is also consistent with the incentive sensitization model, although in this case with the “liking” or hedonic aspects of reward (and not the “wanting” or anticipation aspects) [Robinson and Berridge, 2003]. The nucleus accumbens is the key “liking” hotspot of the brain, which is involved among other functions in amplifying the taste of food [Berridge et al., 2010]. Alternatively, it may be explained by reinforcement learning theory, as individuals with obesity may have weaker learning signals linked to monetary reward (cue phase) and, subsequently, greater responsivity when the reward value is updated (feedback phase) [Kroemer and Small, 2016]. Likewise, our finding is similar to previous results in cocaine dependent users, which have greater activation of the nucleus accumbens during feedback processing in the MID task [Bustamante et al., 2014; Jia et al., 2011]. Therefore, our Page 18 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 19 findings indicate that obese individuals have similar alterations in reward feedback processing to those observed among addiction populations. Whole-brain results identified two additional clusters that showed between-group differences. These clusters were consistent with the main findings, as they involve brain regions that have been previously associated with reward processing which showed increased activation in the obese and overweight groups compared to normal weight participants. Increased activation of the frontal operculum/anterior insula has been previously found in obese participants in response to visual stimuli of high calorie food [Rothemund et al., 2007]. Both regions are involved in the processing of the gustatory aspects of food [Ziaudeen et al., 2012]. The greater activation of the intraparietal cortex in overweight individuals during the monetary task is consistent with the key role of this region on subjective valuation of reward, as shown in monkey studies [Kubanek and Snyder, 2015; Louie and Glimcher, 2010]. This study has important strengths. The groups were well matched in key sociodemographic characteristics, such as age, years of education and socioeconomicstatus. We applied strict eligibility criteria, which ruled out the presence of obesity related comorbid conditions, including medical comorbidities (i.e., diabetes, hypertension) and mental health problems (i.e., depression or eating disorders, such as binge eating or bulimia nervosa). We also maximized the ecological validity of assessments by pre-exposing participants to the food products of the neuroimaging task in a pre-scanner buffet session. Nevertheless, our findings also need to be understood in the context of some limitations. First, we used different tasks to assess food-related reward (Willingness to Pay) and monetary reward (Monetary Incentive Delay), and therefore we could not analyze interaction effects of food and monetary rewards on the Page 19 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 20 brain reward system. Nonetheless, both tasks are well-validated measures of reward processing in relation to food and money stimuli. Moreover, the number of participants in each group was unequal: Obese and overweight groups were smaller than the normal weight group. We addressed this limitation by performing post-hoc tests of homogeneity of variance for all significant findings, which showed non-significant results (i.e., homogenous variances across groups) in all cases. Another potential limitation is the use of BMI as the main independent variable. Recent evidence has shown that measures of body fat, particulary visceral fat, are more sensitive to brain health specifically among adolescents [Schwartz et al., 2014]. We chose BMI over body fat because our measure of fat (bioelectrical impedance) does not allow reliable estimations of visceral versus subcutaneous fat, and BMI was more adequate than total body fat to classify adult participants of both sexes. Furthermore, BMI is regarded as a reliable index of weight-to-height ratio and is the key indicator of overweight and obesity in population-based studies [Ng et al., 2014]. An additional limitation is the nonsignificant curvilinear relationship between BMI and the behavioral measure of sensitivity to reward (SPSRQ). This negative finding can be explained by methodological differences between self-report and biological (neuroimaging) measures –the latter more objective and sensitive, and/or by the strict inclusion/exclusion criteria, which resulted in a narrow BMI range. This relationship has been previously demonstrated in a behavioral study with a broader BMI range (17 to 51 kg/m2) relative to ours (19 to 38 kg/m2) [Davis and Fox, 2008]. Finally, we analyzed neuroimaging activations within discrete regions of the brain reward system, although these regions are known to be part of an integrated network. Therefore, future studies performing functional connectivity assessments of the reward system during food and monetary reward processing will probably be a relevant add-on to present findings. Page 20 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 21 In conclusion, our results support the food addiction model and previous evidence showing an increased food-cue reactivity in striatal areas and a greater subjective value of high palatable foods in excess weight adults. Conversely, a different pattern of activation was found during monetary reward anticipation, with an inverted U-shape relationship between brain reward system activation and BMI. These reinforcementdependent differential processing should be confirmed using other natural reinforces, and further studies in overweight populations should also investigate whether overweight-specific reward system alterations represents a distinctive feature of this group or an “en route” state to obesity. Acknowledgments This study has been funded by project grant P10-HUM-6635 (NEUROECOBE) from the Andalusian Council of Innovation, Science and Industry, program gran RETICS from the Institute of Health Carlos III, Spanish of Ministry of Health, co-funded by FEDER funds of the European Union – a way to build Europe – (RD12/0028/0017) and a medical project grant of the Ian Potter Foundation (Victoria, Australia) to A.V.G. J.V.R is funded by an FPI scholarship from the Junta de Andalucía, associated with the NEUROECOBE grant. J.F.N. is funded by a predoctoral fellowship of the Spanish Ministry of Education, Culture and Sports, FPU Program, (FPU13/00669). C.S.M. is funded by a ‘Miguel Servet’ contract from the Carlos III Health Institute (CP10/00604). Conflict of interest The authors report no biomedical financial interests or potential conflicts of interests. Page 21 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 22 Page 22 of 32 John Wiley & Sons, Inc. 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Normal weight (n=39) Mean (SD) Overweight (n=21) Mean (SD) Obese (n=21) Mean (SD) P-value Age 33.08 (6.73) 35.00 (6.31) 32.19 (5.81) 0.345 Sex (male/female) 18 / 21 10 / 11 10 / 11 0.992 Years of education 18.18 (3.75) 17.86 (3.58) 17.14 (3.75) 0.599 Monthly income <600€ 20.5% 9.5% 10.0% 601-1000€ 10.3% 9.5% 15.0% 1001-1500€ 20.5% 28.6% 25.0% 0.650 1501-2000€ 17.9% 14.3% 15.0% 2001-2499€ 10.3% 9.5% 30.0% >2500€ 20.5% 28.6% 5% BMI (kg/m 2 ) 22.20 (1.76) 27.35* (1.59) 33.43* (2.56) <0.001 Fat (%) 19.66 (5.96) 28.23* (7.56) 33.99* (8.97) <0.001 BMI, Body mass index; *P<0.05 compared to Normal Weight group. Page 31 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Table II: Behavioral data on trait sensitivity to reward and performance on fMRI tasks. Normal weight (n=39) Mean (SD) Overweight (n=21) Mean (SD) Obese (n=21) Mean (SD) ANOVA P-value Sensitivity to reward 10.31 (3.89) 10.14 (3.81) 9.76 (4.00) 0.875 Taste High-palatable food 7.28 (1.57) 7.74 (1.03) 8.01 (0.79) 0.138 Plain food 6.92 (1.38) 7.33 (1.00) 7.30 (0.91) 0.397 Willingness to Pay: Money paid (€) High-palatable food 2.63 (1.75) 3.03 (2.26) 2.49 (1.25) 0.605 Plain food 2.36 (1.63) 1.81 (1.27) 1.42* (1.01) 0.045 Monetary Incentive Delay: Response Time (s) Neutral 0.246 (0.038) 0.252 (0.052) 0.279* (0.059) 0.042 Low 0.227 (0.033) 0.233 (0.048) 0.249* (0.046) 0.137 Medium 0.231 (0.037) 0.234 (0.043) 0.242 (0.047) 0.612 High 0.219 (0.032) 0.222 (0.036) 0.230 (0.040) 0.507 *P<0.05 in relation to Normal Weight group. Page 32 of 32 John Wiley & Sons, Inc. Human Brain Mapping 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60