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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Neurosci. Author manuscript; available in PMC Jan 17, 2012.
Published in final edited form as:
PMCID: PMC3260083
NIHMSID: NIHMS282000

Youth at Risk for Obesity Show Greater Activation of Striatal and Somatosensory Regions to Food

Abstract

Obese versus normal-weight humans have less striatal D2 receptors and striatal response to food intake, and weaker striatal response to food predicts weight gain for individuals at genetic risk for reduced dopamine (DA) signaling, consistent with the reward deficit theory of obesity. Yet these may not be initial vulnerability factors, as overeating reduces D2 receptor density, D2 sensitivity, reward sensitivity, and striatal response to food. Obese versus normal-weight humans also show greater striatal, amygdalar, orbitofrontal cortex, and somatosensory region response to food images, which predicts weight gain for those not at genetic risk for compromised dopamine signaling, consonant with the reward surfeit theory of obesity. However, after pairings of palatable food intake and predictive cues, DA signaling increases in response to the cues, implying that eating palatable food contributes to increased responsivity. We tested whether normal-weight adolescents at high- versus low-risk for obesity showed aberrant activation of reward circuitry in response to receipt and anticipated receipt of palatable food and monetary reward using fMRI. High-risk youth showed greater activation in the caudate, parietal operculum, and frontal operculum in response to food intake and in the caudate, putamen, insula, thalamus, and orbitofrontal cortex in response to monetary reward. No differences emerged in response to anticipated food or monetary reward. Data indicate that youth at risk for obesity show elevated reward circuitry responsivity in general coupled with elevated somatosensory region responsivity to food, which may lead to overeating that produces blunted dopamine signaling and elevated responsivity to food cues.

Keywords: obesity, striatum, somatosensory, taste, reward, fMRI

Introduction

Overweight versus normal-weight rats have lower basal DA levels, D2 receptor availability, and induced DA release in the nucleus accumbens, dorsal striatum, and medial prefrontal cortex (Fetissov et al., 2002; Geiger et al.,, 2008). Obese versus normal-weight humans show less striatal D2 receptor availability (Volkow et al., 2008) and striatal response to food intake, which predicts future weight gain for individuals at genetic risk for compromised DA signaling (Stice et al., 2008a, 2008b). These data are consistent with the reward deficit theory, which posits that individuals who show hypo-responsivity of reward circuitry overeat or use psychoactive substances to compensate (Volkow et al., 2008).

Obese versus normal-weight humans also show greater caudate, gustatory region, and oral somatosensory region response to anticipated receipt of food (Stice et al., 2008b) and striatal, insular, orbitofrontal cortex (OFC), and amygdalar response to palatable food images, which predicts weight gain for those not at genetic risk for compromised DA signaling (Rothemund et al., 2007; Stoeckel et al., 2008; Stice et al., 2010b). These data are consistent with the reward surfeit model of obesity, which holds that hyper-responsivity of reward circuitry leads to overeating and substance use (Dawe & Loxton, 2004).

Findings suggest obese humans show less activation of reward regions to food receipt, but greater activation in regions that encode the reward value of food cues. Yet, it is unclear whether the initial vulnerability for obesity is hypo-responsivity to food receipt or hyper-responsivity to food cues because overeating reduces D2 receptors density, D2 sensitivity, striatal response to food, and reward sensitivity (Kelley et al, 2003; Johnson and Kenny, 2010; Stice et al., 2010a) and repeated pairings of food intake and cues predicting food intake cause DA signaling increases in response to food cues (Kiyatkin & Gratton, 1994), suggesting that overeating leads to blunted striatal response to food and greater responsivity to food cues.

Findings are generally consistent with three competing working etiologic theories. One theory is that individuals at risk for obesity initially experience less reward from food intake, leading them to overeat to compensate for this reward deficit and to hyper-responsivity of reward circuitry via conditioning. A second theory is that individuals at risk initially show hyper-responsivity of regions that encode the reward value of food cues, leading to overeating and a reduction in DA signaling in response to food intake. A third theory is that individuals at risk initially experience greater reward from food intake, leading to overeating that reduces DA signaling in response to food intake and hyper-responsivity of reward circuitry to food cues, both of which may drive further overeating.

We tested these theories by comparing the activation of reward circuitry in response to receipt and anticipated receipt of food and monetary reward using functional magnetic resonance imaging in normal-weight youth at high- versus low-risk for obesity because there would be no possibility that a history of overeating contributed to anomalous responsivity. We investigated response to monetary reward to determine whether anomalies are specific to food or also apply to other rewards.

Materials and Methods

Participants

Participants were 30 female and 30 male lean adolescents (M age = 15.0, SD = 2.9), of which 35 were high-risk children of two obese or overweight (BMI ≥ 27) parents and 25 were low-risk children of two lean parents (BMI < 25). Participants with two obese or overweight parents (M parental BMIHR = 32.15, SD = 4.35) had a M BMIHR of 20.64 (SD = 1.67). Participants with two lean parents (M parental BMILR = 23.13, SD = 1.16) had a M BMILR of 20.07 (SD = 1.80). Adolescent children of obese versus normal-weight parents show a 4-fold increase in risk for obesity onset (Magarey et al., 2003; Whitaker, 1997). The sample consisted of 5% Hispanic, 2% Asian, 3% African Americans, 85% European Americans, and 5% Native American participants. Parents provided written informed consent and adolescents provided written assent. Individuals who reported binge eating or compensatory behavior in the past 3 months, any use of psychotropic medications or illicit drugs, head injury with a loss of consciousness, or Axis I psychiatric disorder in the past year (including anorexia nervosa, bulimia nervosa, or binge eating disorder) were excluded.

fMRI paradigms

Participants were asked to consume their regular meals, but to refrain from eating or drinking (including caffeinated beverages) for 4-6 hrs immediately preceding their imaging session for standardization purposes. We selected this deprivation period to capture the hunger state that most individuals experience as they approach their next meal, which is a time when individual differences in reward circuitry responsivity would logically impact caloric intake. Because it was necessary to scan youth after school on weekdays, we were unable to feed participants a standardized meal 5 hrs before the scan. Participants were familiarized with the fMRI paradigms before the imaging session and the order of the presentation of the two paradigms (milkshake and money) was counterbalanced. Visual stimuli for both paradigms were presented with a digital projector/reverse screen display system to a screen at the back end of the MRI scanner bore and were visible via a mirror mounted on the head coil.

The food reward paradigm (Fig. 1a) examines response to receipt and anticipated receipt of palatable food. Stimuli were presented in 5 separate randomized scanning runs. The stimuli consisted of 2 images (glasses of milkshake and water) that signaled the delivery of either 0.5 ml of a chocolate milkshake or a tasteless solution. Order of presentation was randomized. The milkshake consisted of 4 scoops of vanilla ice cream, 1.5 cups of 2% milk, and 2 tablespoons of chocolate syrup. The tasteless solution, designed to mimic the natural taste of saliva, consisted of 25 mM KCl and 2.5 mM NaHCO3 in distilled water (O’Doherty et al., 2001). We used artificial saliva because water has a taste that activates the taste cortex (Zald & Pardo, 2000). On 40% of the milkshake and tasteless solution trials the taste was not delivered following the cue to allow the investigation of the neural response to anticipation of a taste that was not confounded with actual receipt of the taste (unpaired trials). There were six events of interest: (1) milkshake cue followed by milkshake receipt, (2) receipt of milkshake, (3) milkshake cue followed by no milkshake receipt, (4) tasteless solution cue followed by tasteless solution receipt, (5) receipt of tasteless solution, and (6) tasteless solution cue followed by no tasteless solution receipt. Cues were presented for 2 seconds and were followed by a jitter of 1-7 seconds during which time the screen was blank. Taste delivery occurred 10 seconds after cue onset and lasted 5 seconds. The trial ended with a second jitter of 1-7 seconds. Each event lasted between 2-5 seconds. Tastes were delivered using programmable syringe pumps to ensure consistent volume, rate, and timing of taste delivery. Sixty ml syringes filled with milkshake and tasteless solution were connected via Tygon tubing through a wave guide to a manifold attached to the sliding table of the scanner. The manifold fit into the participants’ mouths and delivered the taste to a consistent segment of the tongue. This procedure has been successfully used previously (Stice et al., 2008a,b, 2010b). Participants were instructed to swallow when they saw the ‘swallow’ cue. The next cue appeared 1-7 seconds after the ‘swallow’ cue went off. Participants completed a cross-modal visual analogue scale to rate the perceived pleasantness, edibility, and intensity of the milkshake and tasteless solution before the scan.

Figure 1
Example of timing and ordering of presentation of A) pictures and drinks during the food reward paradigm and of B) presentation of pictures during the monetary reward paradigm.

The monetary reward paradigm (Fig. 1b) was developed to assess activation in response to receipt and anticipated receipt of monetary reward, based on the Monetary Incentive Delay (MID) paradigm (Knutson et al., 2001a,b). We selected monetary reward because it is a general reinforcer, has been frequently used in behavioral and fMRI paradigms assessing reward sensitivity, and has been used as a standard of comparison for different types of reward (Izuma et al., 2008; Rademacher et al., 2010). Stimuli, consisting of 3 coins (heads or tail), were presented in 2 separate runs. Order of presentation of the runs was randomized. During the run, a coin on the left hand side of the screen would blink 2-4 times (during blinking, the stimulus was presented for 300ms) and would then stay on the screen. After 2 secs, a second coin would blink 4-6 times before remaining in the middle of the screen. After 3 secs this event would be followed by the blinking of a third coin. The third coin would blink 8-10 times and remains on the screen for 4 secs. After the presentation of the coins, a 2-3 secs message appeared saying whether or not the subject has won (“You win $3” or “You don’t win”). Stimulus presentations were jittered. The subject won $3 each time 3 heads or 3 tails were displayed. During each run, the total amount earned was presented below the coins (“Your total is $XX). There were three events of interest: (1) winning $3 indicated by three matching coins (all heads or all tails) (2) a ‘potential win’ indicated by two matching coins (two heads or two tails in a row), and (3) a reward neutral coin display indicated by the first coin (1 head or 1 tail). In total, there were 10 repeats of the experimental event of interest (5 repeats of 3 heads + 5 repeats of 3 tails) and 16 repeats of the other events.

fMRI Data Acquisition, Preprocessing and Statistical Analysis

Scanning was performed by a Siemens Allegra 3 Tesla head-only MRI scanner. A birdcage coil acquired data from the entire brain. A thermo foam vacuum pillow and additional padding restricted head motion. Functional scans used a T2* weighted gradient single-shot echo planar imaging (EPI) sequence (TE=30 ms, TR = 2000 ms, flip angle=80°) with an in plane resolution of 3.0 × 3.0 mm2 (64 × 64 matrix; 192 × 192 mm2 field of view). To cover the whole brain, 32 4mm slices (interleaved acquisition, no skip) were acquired along the AC-PC transverse, oblique plane as determined by the midsagittal section. Structural scans were collected using an inversion recovery T1 weighted sequence (MP-RAGE) in the same orientation as the functional sequences to provide detailed anatomic images aligned to the functional scans. High-resolution structural MRI sequences (FOV = 256 × 256 mm2, 256 × 256 matrix, thickness = 1.0 mm, slice number ≈ 160) were acquired.

Data were pre-processed and analyzed using SPM5 (Wellcome Department of Imaging Neuroscience, London, UK) in MATLAB (Mathworks, Inc., Sherborn, MA; Worsley and Friston, 1995). Images were time-acquisition corrected to the slice obtained at 50% of the TR. Functional images were then realigned to the mean. Images (anatomical and functional) were normalized to the standard Montreal Neurological Institute (MNI) 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 functional images and a voxel size of 1 mm3 for structural images. Functional images were smoothed with a 6 mm FWHM isotropic Gaussian kernel.

To identify brain regions activated in response to food receipt we contrasted BOLD activation during receipt of milkshake versus receipt of tasteless solution. To identify regions activated in response to anticipated food receipt, BOLD activation during presentation of the cue signaling impending delivery of the milkshake was contrasted with response during presentation of the cue signaling impending delivery of the tasteless solution. We analyzed data from trials in which the tastes were not actually delivered to ensure that taste receipt would not influence our operationalisation of anticipatory brain activation. To identify regions activated in response to receipt of monetary reward we contrasted BOLD activation at the time a participant “won” (third coin stopped blinking and matched previous two) versus a reward neutral coin display (the time the first coin stopped blinking [1 head or 1 tail], which conveyed no information about possible monetary reward). The neutral comparison was used because of evidence that losing in monetary paradigms activates similar areas to winning money (Knutson et al., 2001a; Elliott et al., 2003; Small et al., 2005). To identify regions activated in response to anticipation of monetary receipt, BOLD activation during presentation of the cue signaling a potential win (i.e., 2 heads or 2 tails in a row) was contrasted with the reward neutral coin display (i.e., 1 head or 1 tail). Condition-specific effects at each voxel were estimated using general linear models. Vectors of the onsets for each event of interest were compiled and entered into the design matrix so that event-related responses could be modeled by the canonical hemodynamic response function (HRF), as implemented in SPM5, consisting of a mixture of 2 gamma functions that emulate the early peak at 5 seconds and the subsequent undershoot.

To account for variance induced by swallowing the solutions during the food reward paradigm, we included the time of the swallow cue as a covariate. We also included temporal derivatives of the hemodynamic function to obtain a better model of the data (Henson et al., 2002). A 128 second high-pass filter (per SPM5 convention) was used to remove low-frequency noise and slow drifts in the signal.

Individual maps were constructed to compare the activations within each participant for the contrasts: milkshake receipt – tasteless receipt, milkshake cue – tasteless cue, time of win – reward neutral coin display, and time of a potential win – reward neutral coin display. Between-group comparisons were then performed using random effect models to account for inter-participant variability. For the food reward paradigm parameter estimate images were entered into a mixed between and within-subjects second-level 2 × 2 ANOVA (high-risk vs. low-risk) by (milkshake receipt– tasteless receipt or unpaired milkshake cue – unpaired tasteless cue). For analysis of monetary reward paradigm parameter estimate images were entered into a second level mixed between and within-subjects 2 × 2 ANOVA (high-risk vs. low-risk) by (win – neutral coin display or potential win – neutral coin display). Data presented in figures 2 and and33 represent parameter estimates of the indicated peak voxel ± SEM.

Figure 2
Greater activation in the right caudate (A: 6, 9, 24, Z = 3.14, pFDR = .04, k = 3; B: 6, 9, 30, Z = 3.23, pFDR = .04, k = 3), C right frontal operculum (39, 21, 21, Z = 3.44, pFDR = .02, k = 5) and D left parietal operculum (−54, 15, 21, Z = 3.36, ...
Figure 3
Greater activation in high-risk versus low-risk group in response to the win – neutral display in the A) right putamen (square, 18, 0, 9, Z = 3.44, pFDR = .018, k = 3), left putamen (circle, −18, 0, 12, Z = 3.74, pFDR [whole brain] = .007, ...

We performed small volume correction analyses (SVC) on regions of interest in areas associated with food and monetary reward (caudate, putamen, insula, orbitofrontal cortex and thalamus), somatosensory regions (operculuar regions) and attention (visual cortex). A priori defined small volumes were based on activation peaks found in previous fMRI studies with similar food (Stice et al., 2008a; Stice et al., 2008b) and monetary reward (Knutson et al., 2001b; Elliott et al., 2003; Small et al., 2005; Rademacher et al., 2010) paradigms as centroids to define 10-mm diameter spheres. T-map threshold was set at P uncorrected = 0.001 and a 3-voxel cluster size. Predicted activations were considered to be significant at p<0.05 after correcting for multiple comparisons (pFDR) across the voxels within the a priori defined small volumes. Peaks outside these regions were considered to be significant at p<0.05 FDR corrected across the whole brain. All stereotactic coordinates are presented in MNI space (Internet: http://mni.mcgill.ca/).

Results

There was no significant difference between high-risk (M = 13.89, SD = 1.47) and low-risk participants (M = 14.5, SD = 2.02) in pleasantness ratings of the milkshake (t(58) = −1.40, p = .17). High-risk versus low-risk participants showed greater activation in the right caudate (η2 = .11, η2 = .16 [Fig. 2A,B]), right frontal operculum 2 = .12 [Fig. 2C]), and left parietal operculum 2 = .15 [Fig. 2D]) during milkshake receipt – tasteless solution receipt. No significant differences emerged in response to the unpaired cue predicting impending chocolate milkshake receipt – the unpaired cue predicting impending tasteless solution receipt. When we conducted the reverse low-risk versus high-risk contrast there were no significant differences in activation during the milkshake receipt – tasteless solution receipt contrast or the unpaired cue predicting milkshake – unpaired cue predicting tasteless solution contrast.

High-risk versus low-risk participants showed greater activation in the right putamen (η2 = .06 [Fig. 3A,B]), left putamen (η2 = .08 [Fig. 3A,C]), and the right OFC (η2 = .13 [Fig. 3D]) and left caudate boundary (Table 1) in response to winning money – the reward neutral display. Significant activation also emerged in the bilateral anterior insula, bilateral thalamus, and bilateral visual cortex in response to monetary reward (Table 1). No significant differences emerged in response to anticipation of winning money – the reward neutral display or for the reverse contrasts during receipt or anticipated receipt of monetary reward.

Table 1
Greater regional brain activity in high-risk for obesity relative to low-risk participants in response to monetary reward (win – neutral contrast).

Discussion

The finding that normal-weight adolescents at high-risk versus low-risk for future obesity showed greater activation in the dorsal striatum in response to palatable food receipt is novel. This suggests that the initial vulnerability that gives rise to obesity may be elevated rather than blunted sensitivity of the dorsal striatum to food reward. It is thus possible that lower dorsal striatal response to food receipt (Stice et al., 2008a, 2008b) and reduced D2 receptor availability (Wang et al., 2001; Volkow et al., 2008) for obese versus normal-weight individuals is a consequence rather than a cause of overeating. This possibility aligns with evidence that overeating leads to down regulation of post-synaptic D2 receptors, decreased D2 sensitivity and reduced reward sensitivity in rodents (Colantuoni, 2001; Bello, 2002; Kelley, 2003; Johnson, 2010) and that weight gain results in reduced striatal response to palatable food intake in humans (Stice et al., 2010a). It is important to note that some of the significant interactions conform to the expected pattern of effects better than others. The findings shown in Fig. 2 A, B, and C indicate that high-risk youth showed greater activation in reward regions in response to milkshake receipt relative to tasteless solution receipt compared to low-risk youth. However, the interaction shown in Fig. 2 D emerged in part because the activation pattern was reversed for the low-risk relative to the high-risk group, even though the parietal operculum activation was still greater for high-risk versus low-risk youth. This is because the group-by-reward receipt interactions expressly identified peaks for which the activation pattern for reward receipt (vs. the contrast condition) differed for high-risk versus low-risk groups. In this context it is also important to note that post hoc analyses confirmed that both the low-risk and the high risk youth showed significantly greater activation of reward circuitry in response to receipt of milkshake versus tasteless solution, including the caudate, putamen, amygdala, and medial orbitofrontal cortex (Tables 2 and and3),3), partially validating the paradigm.

Table 2
Within-group comparisons for the low-risk group contrasting differences in the food reward paradigm and the monetary reward paradigm
Table 3
Within-group comparisons for the high-risk group contrasting differences in the food reward paradigm and the monetary reward paradigm

The finding that youth at high-risk versus low-risk for future obesity showed greater striatal response to monetary reward is also novel. This suggests that the initial vulnerability that increases risk for obesity may be a general hyper-responsivity of the dorsal striatum to various reward types, rather than specific to food reward, with this hyper-responsivity applying to both primary (food) and conditioned (money) rewards. However, it is important to note that the interactions shown in Fig 3 did not conform to the expected pattern as well as the food reward interactions; although the high-risk youth did show greater activation in response to winning money then the control contrast, each of these interactions were driven in part by the fact that the activation pattern was qualitatively different for the low-risk youth, such as Fig. 3 D. Thus, the conclusion that high-risk youth showed greater reward circuitry responsivity to monetary reward should be interpreted in this light. Nonetheless post hoc analyses confirmed that both the low-risk and the high-risk youth showed greater activation of reward circuitry in response to winning money versus the contrast condition (e.g., in the anterior cingulate cortex) (Tables 2 and and33).

As would be expected, the elevated oral somatosensory response was only observed in response to food but not in response to monetary reward. This finding extends evidence that obese versus normal-weight individuals show greater activation of oral somatosensory regions in response to anticipated palatable food receipt (Stice et al., 2008b), and images of palatable foods (Stice et al., 2010a), greater regional blood flow in somatosensory regions in response to images of palatable foods (Karhunen et al., 1997), and greater resting glucose metabolism in the oral somatosensory cortex (Wang et al., 2002). Moreover, since fat is detected primarily by the oral somatosensory system via cues about creaminess, viscosity, and texture (de Araujo and Rolls, 2004), these findings also accord with evidence that obese versus normal-weight humans rate high-fat foods as more pleasant (Drewnowski et al., 1985; McGloin et al., 2002; Rissanen et al., 2002), that children at risk for obesity by virtue of parental obesity prefer the taste of high-fat foods relative to children of normal-weight parents (Fisher & Birch, 1995; Stunkard et al., 1999; Wardle et al., 2001), and that preferences for high-fat foods predict elevated future weight gain (Salbe et al., 2004; Stunkard et al., 1999). This suggests that individuals at risk for obesity exhibit a hyper-responsivity to reward in general, but that this may need to be coupled with a hyper-responsivity of somatosensory regions to convey specific risk for obesity, versus other appetitive behavioral problems (e.g., drug abuse, gambling, compulsive sexuality).

We did not observe significant differential response to anticipation of either food or monetary reward as a function of obesity risk, suggesting that elevated sensitivity of regions that encode the reward value of food cues may not be an initial vulnerability factor for obesity. The evidence that conditioning leads to an increased responsiveness of reward circuitry to food cues (Schultz et al., 1993; Kiyatkin & Gratton, 1994), putatively via a learning mechanism, implies that recurrent overeating may produce the elevated activation of the dorsal striatum and other reward regions in response to images of palatable foods and anticipated food receipt observed in obese versus normal-weight individuals (Rothemund et al., 2007; Stice et al., 2008b; Stoeckel et al., 2008; Stice et al., 2010a).

Collectively, extant findings suggest the possibility of a dynamic vulnerability model for obesity that may evolve and change over time in response to overeating. Data suggest that individuals at risk for obesity initially show hyper-responsivity of the striatum to reward in general and somatosensory regions in response to palatable, energy dense foods, which may increase risk for overeating. We posit that the oral somatosensory responses reflect altered sensitivity for fat and/or enhanced preference for fat foods. We further submit that overeating may in turn result in a down-regulation of DA-based reward regions, producing a blunted striatal response to food intake, which may lead people to overeating in an effort to achieve the same subjective reward from palatable food they initially experienced (i.e., chasing the high) in a feed forward manner. The overeating may also result in greater responsivity of regions that encode the reward value of food cues, making people more vulnerable to food cues in our obesogenic environment, which also may increase risk for escalation of overeating in a feed forward fashion.

Acknowledgment

Support for this work was provided by National Institutes of Health grant DK-080760.

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