relation of reward from food intake and anticipated food intake … · 2011-08-30 · able food or...

Relation of Reward From Food Intake and Anticipated Food Intake toObesity: A Functional Magnetic Resonance Imaging Study

Eric Stice and Sonja SpoorOregon Research Institute

Cara BohonUniversity of Oregon

Marga G. Veldhuizen and Dana M. SmallYale University and John B. Pierce Laboratory

The authors tested the hypothesis that obese individuals experience greater reward from food consump-tion (consummatory food reward) and anticipated consumption (anticipatory food reward) than leanindividuals using functional magnetic resonance imaging (fMRI) with 33 adolescent girls (mean age �15.7, SD � 0.9). Obese relative to lean adolescent girls showed greater activation bilaterally in thegustatory cortex (anterior and mid insula, frontal operculum) and in somatosensory regions (parietaloperculum and Rolandic operculum) in response to anticipated intake of chocolate milkshake (vs. atasteless solution) and to actual consumption of milkshake (vs. a tasteless solution); these brain regionsencode the sensory and hedonic aspects of food. However, obese relative to lean adolescent girls alsoshowed decreased activation in the caudate nucleus in response to consumption of milkshake versus atasteless solution, potentially because they have reduced dopamine receptor availability. Results suggestthat individuals who show greater activation in the gustatory cortex and somatosensory regions inresponse to anticipation and consumption of food, but who show weaker activation in the striatum duringfood intake, may be at risk for overeating and consequent weight gain.

Keywords: obesity, anticipatory food reward, consummatory food reward, fMRI

Obesity is a chronic disease that is credited with over 111,000deaths annually in the United States, which largely result fromatherosclerotic cerebrovascular disease, coronary heart disease,colorectal cancer, hyperlipidemia, hypertension, gallbladder dis-ease, and diabetes mellitus (Flegal, Graubard, Williamson, & Gail,2005). Regrettably, the treatment of choice for obesity only resultsin transitory weight loss (Jeffery et al., 2000), and most obesityprevention programs do not reduce risk for future weight gain(Stice, Shaw, & Marti, 2006). These interventions may have lim-ited efficacy because researchers’ understanding of the etiologicprocesses is still incomplete. Although it has been established thatobesity is the result of a positive energy balance, it is unclear whysome individuals have such a difficult time balancing caloricintake with expenditure.

One possible explanation is that some individuals have abnor-malities in subjective reward from food intake or anticipated intakethat increase risk for obesity. Some scholars hypothesize that obeseindividuals experience greater activation of the mesolimbic rewardsystem in response to food intake (consummatory food reward),

which may increase risk for overeating (Davis, Strachan, & Berk-son, 2004; Dawe & Loxton, 2004). This is akin to the reinforce-ment sensitivity model of substance abuse, which posits that cer-tain people show greater reactivity of reward circuitry topsychoactive drugs (Dawe & Loxton, 2004). In contrast, othershypothesize that obese individuals experience less activation of themesolimbic reward system in response to food intake, which leadsthem to overeat to compensate for this deficiency (Comings &Blum, 2000; Wang, Volkow, & Fowler, 2002). This is similar tothe reward deficiency syndrome thesis, which suggests that peopleturn to alcohol and drug use to stimulate sluggish reward circuitry(Comings & Blum, 2000). A third hypothesis is that greateranticipated reward from food intake (anticipatory food reward)increases risk for overeating (Pelchat, Johnson, Chan, Valdez, &Ragland, 2004; Roefs, Herman, MacLeod, Smulders, & Jansen,2005).

Two lines of evidence imply it may be useful to conceptuallydistinguish between consummatory food reward and anticipatoryfood reward. First, animal studies show that dopamine neuronsshift firing from the consumption of food to the anticipated con-sumption of food after conditioning, wherein cues associated withfood consumption begin to elicit anticipatory food reward. Naıvemonkeys that had not experienced rewards in a setting showedactivation of mesotelencephalic dopamine neurons only in re-sponse to food taste; however, after conditioning, dopaminergicactivity began to precede reward delivery, and eventually maximalactivity was elicited by the conditioned stimuli that predicted theimpending reward rather than by actual food receipt (Schultz,Apicella, & Ljungberg, 1993; Schultz & Romo, 1990). Kiyatkin

Eric Stice and Sonja Spoor, Oregon Research Institute; Cara Bohon,Department of Psychology, University of Oregon; Marga G. Veldhuizenand Dana M. Small, John B. Pierce Laboratory, Yale University.

This study was supported by National Institute of Health Research GrantR1MH64560A. Thanks go to project research assistant Keely Muscatelland the participants who made this study possible.

Correspondence concerning this article should be addressed to EricStice, Oregon Research Institute, 1715 Franklin Boulevard, Eugene, OR97403. E-mail: [email protected]

Journal of Abnormal Psychology Copyright 2008 by the American Psychological Association2008, Vol. 117, No. 4, 924–935 0021-843X/08/$12.00 DOI: 10.1037/a0013600

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and Gratton (1994) found that the greatest dopaminergic activationoccurred in an anticipatory fashion as rats approached and pressedthe bar that produced food reward and activation actually de-creased as the rat received and ate the food. Indeed, Blackburn,Phillips, Jakubovic, and Fibiger (1989) found that dopamine ac-tivity was greater in the nucleus accumbens of rats after presen-tation of a conditioned stimulus that usually signaled food receiptthan after delivery of an unexpected meal. Second, how hardparticipants work to earn snack food in an operant task (which theyare later permitted to consume) is a stronger predictor of ad libcaloric intake than are pleasantness ratings of tastes of the snackfoods (Epstein et al., 2007; Epstein et al., 2004a). These data alsoseem to imply that anticipated reward from food intake is astronger determinant of caloric intake than the reward experiencedwhen the food is actually consumed. Collectively, these data implythat it may be useful to distinguish between consummatory foodreward and anticipatory food reward when examining potentialrisk factors for obesity.

Brain imaging studies have identified regions that appear toencode consummatory food reward in normal weight individuals.Consumption of palatable foods, relative to consumption of un-palatable foods or tasteless foods, results in greater activation ofthe orbitofrontal cortex (OFC) and frontal operculum/insula aswell as greater release of dopamine in the dorsal striatum(O’Doherty, Deichmann, Critchley, & Dolan, 2002; Small, Jones-Gotman, & Dagher, 2003; Volkow et al., 2003). Other brainimaging studies have identified regions that appear to encodeanticipatory food reward in normal weight humans. Anticipatedreceipt of a palatable food, versus anticipated receipt of unpalat-able food or a tasteless food, results in greater activation in theOFC, amygdala, cingulate gyrus, striatum (caudate nucleus andputamen), ventral tegmental area, midbrain, parahippocampal gy-rus, and fusiform gyrus (O’Doherty et al., 2002; Pelchat et al.,2004). These studies suggest that somewhat distinct brain regionsare implicated in anticipatory and consummatory food reward butthat there is some overlap (OFC and striatum). To date, only twostudies have directly compared activation in response to anticipa-tory and consummatory food reward to isolate regions that showgreater activation in response to one phase of food reward versusthe other. Anticipation of a pleasant taste, versus actual taste,resulted in greater activation in the dopaminergic midbrain, nu-cleus accumbens and the posterior right amygdala (O’Doherty etal., 2002). Another study found that anticipation of a pleasant drinkresulted in greater activation in the amygdala and mediodorsalthalamus, whereas the receipt of the drink resulted in greateractivation in the left insula/operculum (Small, Veldhuizen, Felsted,Mak, & McGlone, 2008). These two studies suggest that theamygdala, midbrain, nucleus accumbens, and mediodorsal thala-mus are more responsive to anticipated consumption versus con-sumption of food, whereas the frontal operculum/insula is moreresponsive to consumption versus anticipated consumption offood. Thus, available evidence suggests that distinct brain regionsmay encode anticipatory and consummatory food reward, althoughmore research will be necessary before firm conclusions are pos-sible.

Certain findings appear to be consistent with the thesis thatobese individuals experience greater food reward, though it is notclear whether findings are reflective of disturbances in consum-matory versus anticipatory food reward. Obese relative to lean

individuals recall that high-fat and high-sugar foods are morepleasant tasting and report that eating is more reinforcing (Ris-sanen et al., 2002; Saelens & Epstein, 1996; Westenhoefer &Pudel, 1993). Children at risk for obesity by virtue of parentalobesity rate tastes of high-fat food as more pleasant and show amore avid feeding style than children of lean parents (Stunkard,Berkowitz, Stallings, & Schoeller, 1999; Wardle, Guthrie, Sand-erson, Birch, & Plomin, 2001). Obese children are more likely toeat in the absence of hunger (Fisher & Birch, 2002) and workharder for food than lean children (Temple, Legerski, Giacomelli,& Epstein, in press). Self-reported food cravings correlated posi-tively with body mass and objectively measured caloric intake(Delahanty, Meigs, Hayden, Williamson, & Nathan, 2002; Formanet al., 2007; Franken & Muris, 2005; Nederkoorn, Smulders, &Jansen, 2000). Obese adults report stronger craving of high-fat, high-sugar foods (Drewnowski, Kurth, Holden-Wiltse, & Saari, 1992;White, Whisenhunt, Williamson, Greenway, & Netemeyer, 2002) andwork for more food than lean adults (Epstein et al., 2007; Saelens &Epstein, 1996). Morbidly obese relative to lean individuals showedgreater resting metabolic activity in the oral somatosensory cortex,a region associated with sensation in the mouth, lips, and tongue(Wang, Volkow, Felder et al., 2002), which may render the formermore sensitive to the rewarding properties of food intake andincrease risk for overeating.

To date, few brain imaging studies have compared the brainactivation in response to presentation of pictured food or actualfood among obese versus lean individuals. One study found in-creased activation in the right parietal and temporal cortices afterexposure to pictured food in obese but not lean women and thatthis activation correlated positively with hunger ratings (Karhunen,Lappalainen, Vanninen, Kuikka, & Uusitupa, 1997). Rothemundand associates (2007) found greater dorsal striatum response topictures of high-calorie foods in obese versus lean adults and thatbody mass correlated positively with response in insula, claustrum,cingulate, somatosensory cortex, and lateral OFC. Stoeckel andassociates (2008) found greater activation in the medial and lateralOFC, amygdala, ventral striatum, medial prefrontal cortex, insula,anterior cingulate cortex, ventral pallidum, caudate, and hippocam-pus response to pictures of high-calorie foods (vs. low-caloriefoods) for obese relative to lean individuals. However, activationof the OFC and cingulate in response to viewing pictures ofpalatable foods correlated negatively with body mass index (BMI)among normal weight women (Killgore & Yurgelun-Todd, 2005).Del Parigi et al. (2004) found that the dorsal insula and posteriorhippocampus remain abnormally responsive to consumption offood in previously obese compared with lean individuals, leadingto the conclusion that these abnormal responses may increase riskfor obesity.

Other findings are more consistent with the notion that obeseindividuals may experience less food reward. Wang et al. (2001)found reduced D2 receptor density in the striatum of obese relativeto lean individuals, suggesting that they may experience decreaseddopamine receptor binding in the mesolimbic system. Obese ratshave lower basal dopamine levels and reduced D2 receptor ex-pression than lean rats (Fetissov, Meguid, Sato, & Zhang, 2002;Hamdi, Porter, & Prasad, 1992; Orosco, Rouch, & Nicolaidis,1996), yet obese rats show more phasic release of dopamine duringfeeding than lean rats (Yang & Meguid, 1995). Furthermore, leanand obese adults with the TaqI A1 allele, which is associated with

925OBESITY AND DISTURBANCES IN FOOD REWARD CIRCUITRY

reduced D2 receptors and weaker dopamine signaling, work moreto earn food in operant paradigms (Epstein et al., 2007, 2004b).These results echo evidence that addictive behaviors such asalcohol, nicotine, marijuana, cocaine, and heroin abuse are asso-ciated with reduced D2 receptor density and blunted sensitivity ofmesolimbic circuitry to reward (Comings & Blum, 2000; Martinezet al., 2005). Wang, Volkow, and Fowler (2002) posit that deficitsin D2 receptors may predispose individuals to use psychoactivedrugs or overeat to boost a sluggish dopamine reward system.However, it is possible that overeating high-fat and high-sugarfood leads to down-regulation of D2 receptors (Davis et al., 2004),paralleling neural response to chronic use of psychoactive drugs(Volkow, Fowler, & Wang, 2002). Indeed, animal studies suggestthat repeated intake of sweet and fatty foods results in down-regulation of D2 receptors and decreased D2 sensitivity (Bello,Lucas, & Hajnal, 2002; Kelley, Will, Steininger, Zhang, & Haber,2003), changes that occur in response to substance abuse.

In summary, there is emerging evidence that obese individualsmay show general abnormalities in food reward relative to leanindividuals. Specifically, obese relative to lean individuals reportgreater craving for high-fat, high-sugar foods, find eating morereinforcing, show greater resting activation of the somatosensorycortex, and show greater reactivity of the gustatory cortex to foodintake and presentation of food or pictured food. Yet, there is alsoevidence that obese individuals show a hypofunctioning striatum,which may prompt them to overeat to boost a sluggish rewardnetwork or may be a result of receptor down-regulation. One factorthat might have contributed to the mixed findings is that self-reportmeasures were used in many studies, which could be misleadingbecause those who struggle with overeating may assume that foodis more rewarding to them, which influences how they completethe scales. Furthermore, self-report scales likely tap anticipatedreward from food intake, or the memory of reward from foodintake, rather than reward experienced during food consumption,as perceived reward during food intake was not measured in thestudies. In addition, findings from self-report and behavioral mea-sures are vulnerable to social desirability biases. Moreover, fewstudies have actually involved food intake or exposure to real food,which may limit the ecological validity of the findings. Perhapsmost importantly, previous studies have not used paradigms thatwere specifically designed to assess individual differences in con-summatory and anticipatory food reward when comparing obesewith lean individuals. Thus, we think it may be useful to useobjective brain imaging paradigms that directly measure activationof reward circuitry in response to food intake and anticipated foodintake. To our knowledge, brain imaging has not been used instudies to test whether obese individuals show differential activa-tion of food reward circuitry during food consumption or antici-pated consumption relative to lean individuals.

We sought to more fully characterize the nature of individualdifferences in neural response to food in the present study usingobjective brain imaging methodology, with the hope that an im-proved understanding of neurological substrates that increase riskfor obesity will advance etiologic models and the design of moreeffective preventive and treatment interventions. We extendedprevious findings by examining activation in response to receipt ofchocolate milkshake versus tasteless solution (consummatory food

reward) and in response to cues signaling impending delivery ofchocolate milkshake versus tasteless solution (anticipatory foodreward) among obese and lean individuals. We hypothesized thatobese relative to lean individuals would show greater activation inthe gustatory cortex and the somatosensory cortex and less acti-vation in the striatum, in response to the anticipation and consump-tion of milkshake. We also hypothesized that the body mass ofparticipants would show linear relations to activation in these brainregions. We studied adolescents because we wanted to reduce therisk that a long history of obesity might result in receptor down-regulation secondary to a chronically rich diet. We studied girlsbecause the primary goal of this study was to test whether foodreward abnormalities correlate with bulimic pathology, which israre in males.

Method

Participants

Participants were 44 healthy adolescent girls (mean age � 15.7;SD � 0.93); 2% Asian/Pacific Islanders, 2% African Americans,86% European Americans, 5% Native Americans, and 5% mixedracial heritage. Participants from a larger study of female highschool students who appeared to meet the inclusion criteria for thepresent imaging study were asked whether they were interested inparticipating in a study on the neural response to presentation offood. Those who reported binge eating or compensatory behaviorsin the past 3 months, any use of psychotropic medications or illicitdrugs, head injury with a loss of consciousness, or current Axis Ipsychiatric disorder were excluded. Data from 11 participants werenot analyzed because they showed excessive head movementduring the scans; 4 showed such pronounced head movement thatthe scans were terminated, and head movement for another 7exceeded 1 mm (M � 2.8 mm, range � 2–8 mm). Becauseexperience indicates that including participants who show headmovement greater than 1 mm introduces excessive error variance,we always exclude such participants from our studies (e.g., Small,Gerber, Mak, & Hummel, 2005; Small et al., 2003). This resultedin a final sample of 33 participants (BMI range � 17.3–38.9). Thelocal Institutional Review Board approved this project. All partic-ipants and parents provided written consent.

Measures

Body mass. The BMI (kg/m2) was used to reflect adiposity(Dietz & Robinson, 1998). After removal of shoes and coats,height was measured to the nearest millimeter using a stadiometer,and weight was assessed to the nearest 0.1 kg using a digital scale.Two measures of height and weight were obtained and averaged.The BMI correlates with direct measures of total body fat such asdual energy x-ray absorptiometry (r � .80–.90) and with healthmeasures including blood pressure, adverse lipoprotein profiles,atherosclerotic lesions, serum insulin levels, and diabetes mellitusin adolescent samples (Dietz & Robinson, 1998). Per convention(Barlow & Dietz, 1998), obesity was defined using the 95thpercentiles of BMI for age and gender, based on historical nation-

926 STICE, SPOOR, BOHON, VELDHUIZEN, AND SMALL

ally representative data because this definition corresponds closelyto the BMI cutpoint that is associated with increased risk forweight-related health problems (Cole, Bellizzi, Flegal, & Dietz,2000). Adolescents with BMI scores below the 50th percentileusing these historical norms were defined as lean. Among the 33participants who provided usable functional magnetic resonanceimaging (fMRI) data, 7 were classified as obese, 11 were classifiedas lean, and the remaining 15 participants fell in between these twoextremes.

FMRI paradigm. Participants were asked to consume theirregular meals but to refrain from eating or drinking (includingcaffeinated beverages) for 4 – 6 hr immediately preceding theirimaging session for standardization purposes. This deprivationperiod was selected to capture the hunger state that most indi-viduals experience as they approach their next meal, which is atime when individual differences in food reward would logi-cally impact caloric intake. Most participants completed theparadigm between 16:00 and 18:00, but a subset completedscans between 11:00 and 13:00. Before the imaging session,participants were familiarized with the fMRI paradigm throughpractice on a separate computer.

The milkshake paradigm was designed to examine consumma-tory and anticipatory food reward. Stimuli were presented in fourseparate scanning runs. The stimuli consisted of three black shapes(diamond, square, circle) that signaled the delivery of either 0.5 mlof a chocolate milkshake (four scoops of Haagen Daz vanilla icecream, 1.5 cups of 2% milk, and 2 tablespoons of Hershey’schocolate syrup), a tasteless solution, or no solution. Although thepairing of cues with stimuli and duration of stimulus presentationwas randomly determined across participants, order of presentationwas not randomized across participants. The tasteless solution,which was designed to mimic the natural taste of saliva, consistedof 25 mM KCl and 2.5 mM NaHCO3 (O’Doherty, Rolls, Francis,Bowtell, & McGlone, 2001). Artificial saliva was used becausewater has a taste that activates the taste cortex (Zald & Pardo,2000). On 50% of the chocolate and tasteless solution trials, thetaste was not delivered as expected to allow the investigation of theneural response to anticipation of a taste that was not confoundedwith actual receipt of the taste (unpaired trials) (see Figure 1).There were six events of interest in the paradigm: (a) chocolatemilkshake cue followed by milkshake taste (paired milkshake cue),(b) receipt of milkshake taste (milkshake delivery), (c) chocolatemilkshake cue followed by no milkshake taste (unpaired milk-

shake cue), (d) tasteless solution cue followed by tasteless solution(paired tasteless cue), (e) receipt of tasteless solution (tastelessdelivery), and (f) tasteless solution cue followed by no tastelesssolution (unpaired tasteless cue). The images were presented for5–12 s (M � 7) using MATLAB run from Windows. Tastedelivery occurred 4–11 s (M � 7) after onset of the cue. As aresult, each event lasted between 4 and 12 s. Each run consisted of16 events. Tastes were delivered using two programmable syringepumps (Braintree Scientific BS-8000, Braintree, MA) controlledby MATLAB to ensure consistent volume, rate, and timing of tastedelivery. Sixty-milliliter syringes filled with the chocolate milk-shake and the tasteless solution were connected via Tygon tubingthrough a wave guide to a manifold attached to the birdcage headcoil in the MRI scanner. The manifold fit into the participants’mouths and delivered the taste to a consistent segment of thetongue. This procedure has been successfully used in the past todeliver liquids in the scanner and has been described in detailelsewhere (e.g., Veldhuizen, Bender, Constable, & Small, 2007).The taste cue remained on the screen for 8.5 s after the taste wasdelivered, and participants were instructed to swallow when theshape disappeared. The next cue appeared 1–5 s after the prior cuewent off. Images were presented with a digital projector/reversescreen display system to a screen at the back end of the MRIscanner bore and were visible via a mirror mounted on the headcoil.

Five lines of evidence from an ongoing fMRI study in whichthis paradigm was used with adolescent girls (N � 46) suggest thatit is a valid measure of individual differences in anticipatory andconsummatory food reward. First, participants rated the milkshakeas significantly, t(45) � 9.79, r � .68, p � .0001, more pleasantthan the tasteless solution per a visual analogue scale, confirmingthat the milkshake was more rewarding to participants than thetasteless solution. Second, pleasantness ratings of the milkshakecorrelated with activation in the anterior insula (r � .70) inresponse to milkshake cues and with activation in the parahip-pocampal gyrus in response to milkshake receipt (r � .72). Third,activation in regions representing anticipatory and consummatoryfood reward (LaBar et al., 2001; O’Doherty et al., 2002; Small etal., 2001) in response to anticipation and receipt of milkshake inthis fMRI paradigm correlated (r � .84–.91) with self-reportedliking and cravings for a variety of foods, as assessed with anadapted version of the Food Craving Inventory (White et al.,

Figure 1. Example of timing and ordering of presentation of pictures and drinks during the run.


2002).1 Fourth, activation in response to anticipatory and consum-matory food reward in this fMRI paradigm correlates (r � .82–.95)with how hard participants work for food and how much food theywork for in an operant behavioral task in which individual differ-ences in food reinforcement are assessed (Saelens & Epstein,1996). Fifth, participants who show relatively greater activation inresponse to anticipatory and consummatory food reward in thisfMRI paradigm showed significantly ( p � .05) more weight gainover a 1-year follow-up than participants who show less activationin this paradigm (r � .54–.65). Collectively, these findings pro-vide evidence for the validity of this fMRI food reward paradigm.

Imaging and statistical analysis. Scanning was performedwith a Siemens Allegra 3 Tesla head-only MRI scanner (Erlangen,Germany). A standard birdcage coil was used to acquire data fromthe entire brain. A thermo foam vacuum pillow and additionalpadding were used to restrict head motion. In total, 152 scans werecollected during each of four functional runs. Functional scansused a T2*-weighted gradient single-shot echo planar imaging(EPI) sequence (echo time [TE] � 30 ms, return time [TR] �2,000 ms, flip angle � 80°) with an in-plane resolution of 3.0 �3.0 mm2 (64 � 64 matrix; 192 � 192 mm2 field of view [FOV]).To cover the whole brain, thirty-two 4-mm slices (interleavedacquisition, no skip) were acquired along the AC–PC transverse,oblique plane as determined by the midsagittal section. Structuralscans were collected using an inversion recovery T1-weightedsequence (MP-RAGE) in the same orientation as the functionalsequences to provide detailed anatomic images aligned to thefunctional scans. High-resolution structural MRI sequences(FOV � 256 � 256 mm2, 256 � 256 matrix, thickness � 1.0 mm,slice number � 160) were acquired.

Data were preprocessed and analyzed using statistical paramet-ric mapping (SPM)5 software (Wellcome Department of ImagingNeuroscience, London, England) in MATLAB (Mathworks, Inc.,Sherborn, MA) (Worsley & Friston, 1995). The images were timeacquisition corrected to the slice obtained at 50% of the TR. Allfunctional images were then realigned to the mean. The images(anatomical and functional) were normalized to the standard Mon-treal Neurological Institute template brain implemented in SPM5(ICBM152, based on an average of 152 normal MRI scans).Normalization resulted in a voxel size of 3 mm3 for functionalimages and a voxel size of 1 mm3 for structural images. Functionalimages were smoothed with a 6-mm full-width half-maximum(FWHM) isotropic Gaussian kernel.

To identify brain regions activated in response to consummatoryreward, blood oxygen level-dependent (BOLD) response was con-strated during receipt of milkshake versus during receipt of taste-less solution. The arrival of a taste in the mouth was considered tobe consummatory reward, rather than when the taste was swal-lowed; however, postingestive effects were also acknowledged ascontributing to the reward value of food (O’Doherty et al., 2002).To identify brain regions activated in response to anticipatoryreward in the milkshake paradigm, BOLD response during pre-sentation of the cue signaling impending delivery of the milkshakewas contrasted with response during presentation of the cue sig-naling impending delivery of the tasteless solution. Data fromunpaired cue presentation in which the tastes were not actuallydelivered were analyzed to ensure that receipt of the actual tasteswould not influence the operational definition of anticipatory brainactivation. Condition-specific effects at each voxel were estimated

using general linear models. Vectors of the onsets for each eventof interest were compiled and entered into the design matrix so thatevent-related responses could be modeled by the canonical hemo-dynamic response function (HRF), as implemented in SPM5,consisting of a mixture of two gamma functions that emulate theearly peak at 5 s and the subsequent undershoot. To account for thevariance induced by swallowing the solutions, the time of disap-pearance of the cue (participants were trained to swallow at thistime) was included as a variable of no interest. Temporal deriva-tives of the HRF were also included to obtain a better model of thedata (Henson, Price, Rugg, Turner, & Friston, 2002). A 128-shigh-pass filter (per SPM5 convention) was used to remove low-frequency noise and slow drifts in the signal.

Individual contrast maps were constructed to compare the acti-vations within each participant for the aforementioned contrasts inSPM5. Between-group comparisons were then performed usingrandom effects models to account for interparticipant variability.For analysis of consummatory food reward, parameter estimateimages from milkshake–tasteless contrast were entered into asecond-level 2 � 2 analysis of variance (ANOVA) (obese vs. lean)by (milkshake receipt–tasteless receipt). For analysis of anticipa-tory food reward, the parameter estimate images from unpairedmilkshake–unpaired tasteless contrast (i.e., milkshake cue notfollowed by milkshake receipt–tasteless cue not followed by taste-less receipt) were entered into the second-level 2 � 2 ANOVA(obese vs. lean) by (unpaired milkshake–unpaired tasteless). Thus,ANOVA models were used to specifically test whether obeseparticipants showed significantly greater food reward abnormali-ties than lean participants.

Individual SPM contrast maps were also entered into regressionmodels, with BMI scores entered as a covariate. This model testedwhether participants with higher BMI scores showed greater acti-vation believed to reflect consummatory and anticipatory foodreward relative to participants with lower BMI scores. Theseregression models were estimated to provide a more sensitive testof these relations using data from all participants in the sample (theANOVA models only included the obese and lean participants).

The significance of BOLD activation is determined by consid-ering both the maximum intensity of a response as well as theextent of the response. SPM relies primarily on the maximumintensity to determine significance, with users generally setting astrict intensity criterion with t maps thresholded at p � .001(uncorrected) per voxel and a more liberal extent criterion (clustercriterion of three voxels). Following convention, this criterion wasused to determine significance for the activations for both theregression models and the ANOVA models. Activation clusterswere considered significant at p � .05 (with respect to clusters),corrected for multiple comparisons across the entire brain. On thebasis of previous studies, directed searches were performed in

1 The Food Craving Inventory (FCI; White et al., 2002) assesses thedegree of craving for a variety of foods. We adapted this scale by alsorequesting ratings of how palatable participants find each food. The orig-inal FCI has shown internal consistency (� � .93), 2-week test–retestreliability (r � .86), and sensitivity to detecting intervention effects (Mar-tin, O’Neil, & Pawlow, 2006; White et al., 2002). In a pilot study (n � 27),the craving score and the palatability score showed internal consistency(� � .91 and .89, respectively).


areas activated by consummatory and anticipatory food reward:striatum, amygdala, midbrain regions, OFC, dorsolateral prefrontalcortex, insula, anterior cingulate gyrus, parahippocampal gyrus,and fusiform gyrus.

Results

Test of Whether Obese Participants Showed Differencesin Anticipatory Food Reward Relative to LeanParticipants (Milkshake Cue vs. Tasteless Cue)

We conducted analyses that compared the brain responses inobese adolescent girls (n � 7, mean BMI � 33, SD � 4.25) withlean adolescent girls (n � 11, mean BMI � 19.6, SD � 1.08) usinga group ANOVA model. We located 13 activation clusters withinthe insula; the Rolandic region; and the temporal, frontal, andparietal opercular regions; obese participants showed greater acti-vation in these areas compared with lean participants (see Figure 2A–B and Table 1). Of these 13 activation clusters, 9 fell in the leftand 4 in the right hemisphere. Obese participants also showedgreater activation in the left-anterior cingulate cortex (ventralBrodmann area � 24) than lean participants. Table 1 reportscoordinates, voxel size, uncorrected p values, and effect sizes (�2).Several p values were significant at p � .05, whole brain correctedat the cluster level. The effect sizes from these analyses rangedfrom small (�2 � .01) to large (�2 � .17), with an average effectof .05, which represents a medium effect size per Cohen’s (1988)criteria.2

Test of Whether Participants’ BMI Showed LinearRelations to Anticipatory Food Reward

We entered individual SPM contrasts maps into regressionmodels, with BMI scores as a covariate to test whether BMI islinearly related to activation in response to anticipatory foodreward. These analyses are theoretically more sensitive becausethey involved all participants rather than just obese and leanparticipants. We found a positive correlation of BMI with activa-tions in the ventral lateral and dorsal lateral prefrontal cortex andtemporal operculum in response to anticipatory food reward (seeFigure 3A and Table 2). However, none of the effects were signif-icant at p � .05, whole brain corrected at the cluster level. Theeffect sizes from these analyses were all large per Cohen’s (1988)criteria (r � .48–.68), with a mean r of .56.

Test of Whether Obese Participants Showed Differencesin Consummatory Food Reward Relative to LeanParticipants (Receipt of Milkshake vs. Receiptof Tasteless)

Comparable to the results with regard to anticipatory foodreward, we found that obese adolescent girls showed greater acti-vation in the Rolandic operculum and left-frontal operculum inresponse to consummatory food reward compared with lean par-ticipants (see Figure 2C–D and Table 1). The activation cluster in

2 Whereas some software packages, like the Analysis of FunctionalNeuroImages, focus primarily on volume and thus use a larger clustercriterion, SPM focuses primarily on intensity and uses a smaller clustercriterion (but higher intensity requirements). Using an intensity require-ment of t � 0.001 and a contiguous three-voxel minimum cluster criterionto threshold t maps is standard for SPM and is the approach we have usedin prior studies. Within this context, it is important to note that all of theclusters we report are larger than three voxels (see Tables 1 and 2).

Figure 2. A: Saggital section of greater activation in the left-anterior insula(�36, 6, 6, Z � 3.92, p uncorrected � .001) in response to anticipatory foodreward in obese compared with lean participants, with the bar graphs ofparameter estimates from that region (B). C: Coronal section of greateractivation in the Rolandic operculum (54, �12, 33, Z � 5.95, p uncor-rected � .001) in response to consummatory food reward in obese com-pared with lean particpants, with the bar graphs of parameter estimatesfrom that region (D). � p � .05. �� p � .001.


the Rolandic operculum was significant at p � .05, whole braincorrected at the cluster level (see Table 1). The effect sizes fromthese analyses ranged from small (�2 � .03) to medium (�2 �.08), with an average effect of .06, which represents a mediumeffect size per Cohen’s (1988) criteria.

Test of Whether Participants’ BMI Showed LinearRelations to Consummatory Food Reward

We also entered individual SPM contrasts maps into regressionmodels, with BMI scores as a covariate to test whether BMI islinearly related to activation in response to consummatory foodreward. We found a positive relation between BMI and activationin the insula and several regions of the operculum (see Figure3B–C and Table 2). BMI was also negatively correlated withactivation in the caudate nucleus in response to consummatoryfood reward in this more sensitive model, indicating that high-BMIparticipants showed decreased response in this area compared withlow-BMI participants (see Figure 3D–E and Table 2). None of thep values were significant at p � .05, whole brain corrected at thecluster level. The effect sizes from these analyses were medium(r � .35) to large (r � .58) per Cohen’s (1988) criteria, with anaverage effect that was large (r � .48).3

Discussion

In the present study, we tested the hypothesis that obese ado-lescent girls would show differential activation in reward circuitryin response to food consumption and anticipated consumptionrelative to lean adolescent girls and that activation would belinearly related to the BMI of participants. Brain responses were

examined during the receipt of chocolate milkshake versus taste-less solution (consummatory food reward) and in response to cuessignaling impending delivery of chocolate milkshake versus taste-less solution (anticipatory food reward). On the basis of the find-ings of previous studies (e.g., Wang, Volkow, Felder, et al., 2002),we expected abnormalities in consummatory and anticipatory foodreward among obese participants relative to their lean counterparts.

As hypothesized, the responses to consummatory and anticipa-tory food reward in the predicted regions were different in theobese adolescent girls compared with their lean counterparts.Obese participants showed greater activation in the primary gus-tatory cortex (anterior insula/frontal operculum) and in the somato-sensory cortex (the Rolandic operculum, temporal operculum, andparietal operculum) and anterior cingulate in response to ourmeasure of anticipatory food reward compared with lean partici-pants. These effect sizes were small to large in magnitude, with anaverage effect size that was medium. The insula has been shown toplay a role in anticipatory food reward (Gottfried, O’Doherty, &Dolan, 2003; Small et al., 2005; Yamamoto, 2006) and foodcraving (Pelchat et al., 2004). Furthermore, Balleine and Dickin-son (2001) showed that animals with resection of the insula fail tolearn that behavior responding to food is devalued, also suggesting

3 On the basis of the evidence that reward-related neural function inwomen is heightened during the mid-follicular phase (Dreher et al., 2007),we created a dichotomous variable that reflected whether participantscompleted the fMRI scans during the midfollicular phase (Days 4–8 afteronset of menses; n � 2) or not (n � 31). When we controlled for thisvariable in all analyses, the activation in the reported regions remainedsignificant.

Table 1Regions Showing Increased Activation During Anticipatory Food Reward and Consummatory Food Reward in Obese AdolescentGirls (n � 7) Compared With Lean Adolescent Girls (n � 11)

Variable

MNI coordinatesNumber of voxels

in clusterActivationcluster Z p uncorrected Effect size �2x y z

Anticipatory food rewardRolandic operculum 54 �3 27 142 5.75 �.001� .06

�54 �9 30 168 4.71 �.001 .01�39 �18 39 168 4.60 �.001 .10

51 �12 33 142 4.36 �.001 .01Temporal operculum �48 �6 3 168 5.19 �.001� .06Frontal operculum 51 3 18 17 4.47 �.001� .01

�54 �3 18 42 3.92 �.001 .02Parietal operculum �60 �42 30 6 3.91 �.001 .08

60 �36 42 7 3.56 �.001 .01Anterior insula �36 6 6 6 3.92 �.001 .01Posterior insula �39 �15 18 4 3.79 �.001 .07Ventral anterior cingulate (BA 24) �6 3 45 8 3.65 �.001� .17

Consummatory food rewardRolandic operculum 54 �6 27 284 5.95 �.001� .06

54 �12 33 284 5.68 �.001 .08�48 �12 33 216 5.29 �.001 .03

Frontal operculum �48 �12 12 216 4.01 �.001 .0551 3 18 6 3.33 �.001 .07

Note. BA � Brodmann area.� p � .05, whole brain corrected at the cluster levels.


Figure 3. A: Axial section of greater activation in the left-temporal operculum (TOp; �54, �3, 3, Z � 3.41, puncorrected � .001) and in the right-ventrolateral prefrontal cortex (VLPFC; 45, 45, 0, Z � 3.57, p uncorrected �.001) in response to anticipatory food reward as a function of body mass index (BMI). B: Coronal section of greateractivation in the parietal operculum (�54, �3, 3, Z � 3.49, p uncorrected � .001) in response to consummatory foodreward as a function of BMI, with the graph of estimates from that region (C). D: Less activation in the caudatenucleus (�12, 3, 27, Z � 3.57, p uncorrected � .001) in response to food receipt as a function of BMI, with the graphof parameter estimates from that region (E).


a role of the insula in anticipatory food reward. The ventralanterior cingulate region has been found to be involved in thecoding of energy content and palatability of foods (de Araujo &Rolls, 2004). As a result, our findings may suggest that the obeseindividuals experienced increased anticipation of the palatabilityof the milkshake compared with lean individuals. It will be im-portant for future studies to rule out the possibility that condition-ing that occurs as a result of overeating high-fat and high-sugarfoods does not contribute to the elevated anticipatory food rewardshown by obese participants.

Also as hypothesized, there was evidence that obese participantsshowed differential activation in response to consummatory foodreward relative to lean participants. The former showed increasedactivation in the Rolandic operculum, frontal operculum, posteriorinsula, and cingulate gyrus in response to consummatory foodreward compared with the latter. Effect sizes were small to me-dium in magnitude, with an average effect size that was medium.These results converge with those from previous studies; DelParigi et al. (2004) found that percentage of body fat correlatedwith increased activation in the insula during the sensory experi-ence of a meal, and Wang, Volkow, Felder, et al. (2002) foundgreater activation in the somatosensory cortex while resting as afunction of BMI. Given that the insula and overlying operculumhave been associated with subjective reward from food intake(Small et al., 2003; Yamamoto, 2006), these findings may implythat obese individuals experience greater food reward relative tolean individuals, which might correspond to behavioral data fromother studies, as outlined in the introduction.

We also tested whether BMI is linearly related to activation inresponse to anticipatory and consummatory food reward withregression models to provide a more sensitive test of the hypoth-esized relations. Comparable to the results found in the ANOVAmodels, we found increased activation in the temporal operculumto anticipatory food reward as a function of BMI. Furthermore,greater responses were found in the dorsolateral prefrontal cortexin response to anticipatory food reward as a function of BMI. Alsocomparable to the findings from the ANOVA models was theincreased activation in the insula/frontoparietal operculum in re-sponse to consummatory food reward as a function of BMI.Overall, the results of the regression models generally convergedwith the findings from the ANOVA models, even though the latteranalyses only involved obese and lean participants, providing

further findings consistent with our hypotheses. The relationsidentified in the regression models were typically large effects.

Interestingly, the regression models suggested that BMI wasinversely related to activation in the caudate nucleus in response toconsummatory food reward, as hypothesized on the basis of earlierfindings (Wang et al., 2001). This was a large effect size. Ourfunctional finding corroborates and extends the results reported inthe study conducted by Wang et al. (2001), in which they foundthat morbidly obese participants showed decreased D2 receptoravailability at rest in the putamen in proportion to their BMI. Thesefindings may reflect lower dopamine receptor availability. It ispossible that individuals overeat to stimulate a sluggish and long-standing dopamine-based reward system (Wang et al., 2001).Alternatively, elevated intake of high-fat and high-sugar foodsmay result in receptor down-regulation, as has been observedamong substance users (Davis et al., 2004). As noted, animalstudies suggest that repeated intake of sweet and fatty foods resultsin down-regulation of D2 receptors and decreased D2 sensitivity(Bello et al., 2002; Kelley et al., 2003). Another possible interpre-tation is that obese individuals show hypofunctioning of foodreward circuitry while resting but hyperfunctioning when exposedto food or food cues. This interpretation accords with the evidencethat obese and postobese individuals show greater responsivity inthe dorsal insula and posterior hippocampus after food intakerelative to lean individuals (Del Parigi et al., 2004); that exposureto food cues results in greater activation in the right parietal andtemporal cortices in obese but not lean individuals (Karhunen etal., 1997; Stoeckel et al., 2008); that obese individuals showgreater activation in dorsal striatum, insula, claustrum, and so-matosensory cortex in response to food cues than lean individuals(Rothemund et al., 2007); that obese rats have lower basal dopa-mine levels and reduced D2 receptor expression than lean rats(Fetissov et al., 2002; Hamdi et al., 1992; Orosco et al., 1996); andthat obese rats show more phasic release of dopamine duringfeeding than do lean rats (Yang & Meguid, 1995). However, thisinterpretation does not accord with the evidence that obese relativeto lean individuals showed greater resting metabolic activity in theoral somatosensory cortex (Wang, Volkow, Felder, et al., 2002)and that activation of the OFC and cingulate in response toviewing pictures of palatable foods correlated negatively with BMIamong normal weight women (Killgore & Yurgelun-Todd, 2005).It will be useful for future research to determine which interpre-

Table 2Regions Responding During Anticipatory Food Reward and Consummatory Food Reward as a Function of Body Mass Index (N � 33)

Variable

MNI coordinatesNumber of voxels

in cluster Activation cluster Z p uncorrected Effect size rx y z

Anticipatory food rewardVentrolateral prefrontal cortex 45 45 0 5 3.57 �.001 .59Dorsal lateral prefrontal cortex �39 30 36 10 3.59 �.001 .68

�45 33 30 10 3.57 �.001 .54Temporal operculum �54 �3 3 12 3.51 �.001 .48

�57 �6 6 12 3.41 �.001 .49Consummatory food reward

Insula/frontoparietal operculum �54 �9 12 4 3.33 �.001 .35Parietal operculum 63 �12 12 6 3.43 �.001 .51Caudate nucleus �12 3 27 4 �3.57 �.001 .58


tation explains the seemingly inconsistent findings, as it wouldsignificantly advance researchers’ understanding of etiologic andmaintenance processes that contribute to obesity.

Collectively, the present findings suggest that different brainregions are activated by anticipatory versus consummatory foodreward, which is an important contribution because only a fewstudies have attempted to identify the neural substrates of antici-patory and consummatory food reward. In the ANOVA modelscomparing obese with lean participants (see Table 1), the Rolandicoperculum and frontal operculum were activated by both antici-pation and consumption of milkshake, but the temporal operculum,parietal operculum, anterior insula, posterior insula, and ventralanterior cingulate were activated only in response to anticipatedreceipt of milkshake. In the regression models in which the relationof BMI to regions of activation were examined (see Table 2), therewas no overlap in activated regions: Whereas the ventrolateralprefrontal cortex, dorsal lateral prefrontal cortex, and temporaloperculum were activated in response to anticipated receipt ofmilkshake, the insula, frontoparietal operculum, parietal opercu-lum, and caudate nucleus were activated in response to receipt ofmilkshake. These findings largely converge with those from pre-vious studies in which brain regions specific to consummatory andanticipatory food reward have been investigated (O’Doherty et al.,2002; Pelchat et al., 2004; Small et al., 2003, 2008; Volkow et al.,2003).

This study is novel in that it is one of the first in which relationsbetween BMI and neural response to anticipatory and consumma-tory food reward have been tested using a paradigm involvingdelivery of food in the scanner. However, there were severallimitations of the present study that should be noted. First, we hada moderate sample size to test between-group effects, although itwas larger than most previously published fMRI studies of foodreward published to date. Second, we used only one palatableflavor. Perhaps other tastes are more rewarding to participants andwould have resulted in greater reward response in the brain. Third,because the receipt of the milkshake was always preceded by thecue (i.e., never delivered without the cue), participants alwaysknew about the taste before it was delivered. Differential responseto taste and flavors as a function of whether they are expected orunexpected have been found in past studies (e.g., Berns, McClure,Pagoni, & Montague, 2001). Therefore, investigators should con-sider including a measure of response to the receipt of unexpectedfood reward in future studies. Fourth, the cues used for the milk-shake paradigm were geometric shapes, which might not holdenough reward meaning to participants and therefore may haveproduced blunted anticipatory sensations and brain activation.Fifth, we collected limited behavioral data to validate the fMRIparadigm with participants in our study. Nonetheless, validity datafrom ongoing studies using this paradigm suggest it is a validmeasure of individual differences in food reward.

In conclusion, our results suggest differential neural responseduring anticipatory and consummatory food reward as a functionof obesity status and BMI, although it will be important to replicatethese relations in independent samples. Because there was greaterresponse in many regions that have been shown to encode foodreward in obese participants, the pattern of response is consistentwith behavioral studies suggesting that obese individuals antici-pate more reward from food intake and experience greater sensorypleasure when eating. However, we also found that participants

with a higher BMI showed less activation in the striatum inresponse to food consumption relative to those with a lower BMI,which is consistent with the proposal that obese individuals mayexperience less phasic dopamine release when consuming foodrelative to lean individuals. It is biologically possible that individ-uals may anticipate more reward from food intake and experiencegreater somatosensory pleasure when eating yet experience lessphasic release of dopamine when food is consumed, as eachinvolves separate neural circuitry. However, it is also possible thatsome of these abnormalities predate obesity, whereas others are aconsequence of overeating. For example, the former two effectsmay increase risk for hyperphagia that results in a positive energybalance, and the latter effect may be a product of receptor down-regulation secondary to consumption of a high-fat and high-sugardiet. Alternatively, hypofunctioning of dopamine-mediated rewardcircuitry may cause individuals to overeat to compensate for thisreward deficit, which through conditioning produces greater an-ticipatory food reward and heightened development of the somato-sensotry cortex. It will be vital for researchers to investigate whichof these abnormalities precedes obesity onset and which are aproduct of chronic overeating. It is our hope that a systematic studyof the abnormalities that predate obesity onset may allow thedesign of more effective prevention and treatment interventions.

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Received December 12, 2007Revision received July 3, 2008

Accepted July 11, 2008 �


relation of reward from food intake and anticipated food intake … · 2011-08-30 · able food or...

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