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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Obesity (Silver Spring). Author manuscript; available in PMC 2017 April 1.
Published in final edited form as:
PMCID: PMC4814322

Brain Imaging Demonstrates a Reduced Neural Impact of Eating in Obesity

Nancy Puzziferri, MD, MS,a,b,c Jeffrey M. Zigman, MD, PhD,d,f Binu P. Thomas, PhD,d,e Perry Mihalakos, BS,d Ryan Gallagher, BA,a Michael Lutter, MD, PhD,g Thomas Carmody, PhD,c,d Hanzhang Lu, PhD,d,e and Carol A. Tamminga, MDd



We investigated functional brain response differences to food in women with either BMI <25kg/m2 (lean) or >35kg/m2 (severe obesity).


Thirty women 18-65 years old from academic medical centers participated. Baseline brain perfusion was measured with arterial spin labeling. Brain activity was measured via blood-oxygen-level-dependent functional magnetic resonance imaging (fMRI) in response to food cues, and appeal to cues rated. Subjective hunger/fullness was reported pre- and post-imaging. After a standard meal, measures were repeated.


When fasting, brain perfusion did not differ significantly between groups; and both groups significantly increased activity in the neo- and limbic cortices and midbrain compared to baseline (p<0.05, family-wise-error whole-brain corrected). Once fed, the lean group showed significantly decreased activation in these areas, especially the limbic cortex, while the group with severe obesity showed no such decreases (p<0.05, family-wise-error whole-brain corrected). After eating, appeal ratings of food decreased only in lean women. Within groups, hunger decreased (p<0.001) and fullness increased (p<0.001) fasted to fed.


While fasting, brain response to food cues in women did not differ significantly despite BMI. After eating, brain activity quickly diminished in lean women but remained elevated in women with severe obesity. These brain activation findings confirm previous studies.

Keywords: women, brain, food, imaging, hunger


Even if not hungry, seeing food or thinking about food can stimulate eating (see Smeets, Erkner and de Graaf(1), and Berthoud(2) for recent and broader discussion). The anticipatory or conditioned responses to food cues, rather than hunger, affects us each differently depending on gender, energy state, menstrual status, and previous associations to those foods or food cues, to name a few(3-5). Those with the insight and/or inhibitory control of visual cue urges succeed in maintaining a healthy weight(6). In contrast, individuals with obesity are highly vulnerable to external food-related cues(7, 8) and eat differently than lean individuals in identical environments (9, 10).

Much current research on eating involves characterizing the role of the brain. Central nervous system mechanisms of homeostatic (energy-based) circuits integrate with hedonic (reward/motivation-based) circuits to influence eating(11). Given that homeostatic needs are easily met, differences in eating for pleasure are likely central to overeating. Physiological feedback to our brain upon eating — in the form of changed levels of satiety-related gut hormones, adipokines and/or vagal stimuli — mediate and regulate our intake and activation of key eating-related brain centers (12-14). In turn, these changes to brain region activation further influence behaviors and processes involving eating. If food eaten is pleasurable, its intake is reinforced. Interestingly, while taste-based food choices compel women of all body mass indices (BMI) to eat, when full, lean women will either stop eating or just sample a food they crave rather than eating a large volume as do women with obesity(15).

Functional magnetic resonance imaging (fMRI) has been used in numerous studies to assess differences in regional brain response to visual food cues between individuals with and without obesity. A common outcome has been differential activation of brain regions mediating reward behaviors. Although space limitations prevent mention of all these works, some key findings from this literature follow: In woman with obesity as compared to those who are lean, the dorsal striatum, which mediates decision-making related to producing reward attainment actions, shows greater activation in the fed state. (16), reward-system-associated regions—the ventral tegmental area, nucleus accumbens, amygdala, orbital frontal cortex (7) and left anterior cingulate(5) — exhibit greater brain activation in the fasted state, and when anticipating ingesting (rather than viewing) a basic sweet taste, brain response is down-regulated in a fed state(4). In both fasted and fed states, women and men with obesity, exhibit significantly greater activity in the medial prefrontal cortex when viewing food cues compared to lean individuals (8), which is important because the prefrontal cortex mediates motivated behaviors aimed at obtaining a reward. Also, individuals with proneness to obesity do not attenuate their neural response after a meal as do women and men with obesity resistance(17). Identifying brain circuit activation pattern differences between individuals with or without obesity may generate functional biomarkers that facilitate the development of obesity treatments. Similarly, identifying brain circuit activation differences predicted to occur with bariatric surgery-induced weight loss may bring opportunities for developing efficacious non-surgical treatments.

We examined brain responses to visual food cues in women with severe obesity or leanness before and after a standard meal. We contrasted alterations in functional brain activation within regions that regulate eating behaviors—both homeostatic and hedonic. We hypothesized that regions important in mediating reward behaviors (ventral tegmental area, nucleus accumbens, hippocampus and prefrontal cortex) would be differentially activated by viewing food cues in the fasted or fed states, contrasting groups with severe obesity or leanness.



Fifteen women with severe obesity and 15 age-matched lean women (18-65 years old) were recruited through two academic medical centers. The University of Texas Southwestern Medical Center and the Veterans Administration North Texas Health Care System Institutional Review Boards approved the protocol and all participants gave informed consent. Participants with severe obesity were BMI 35-50 kg/m2 and pre-bariatric surgery. Lean controls were BMI 18.5-24.9 kg/m2. Exclusion criteria included untreated Axis I psychiatric diagnoses, previous serious head injury, left-handedness, contraindications to MRI, prior bariatric surgery, or untreated severe medical illness.

Study Design

Demographic characteristics were collected (Table 1). Psychiatric diagnoses were determined using the Structured Clinical Interview for the Diagnostic and Statistical Manual-4 (18). Current depressive symptoms were determined using the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16) (19).

Table 1
Group Characteristics

Participants arrived at 9 AM in a fasted state (no food after midnight). Subjective hunger or fullness (visual analog scale) was rated prior to imaging using a scale from -50 (least hunger/fullness ever experienced) to +50 (greatest hunger/fullness ever experienced). Participants were then positioned in the MRI scanner with their heads comfortably immobilized. Baseline brain perfusion, measured by Arterial Spin Labeling (ASL), was acquired prior to fMRI scanning. Participants' brain activities were measured with fMRI blood-oxygen-level-dependent (BOLD) response to seeing food cues interleaved with directional arrows. An appeal rating (scale of 1-3, not appealing to very appealing) for each food shown was recorded. After this imaging session, subjective hunger or fullness was rated again. During the next hour, a standard meal of 337 kcals (52% carbohydrate, 30% fat and 18% protein) was served. Calories consumed were quantified. The meal consisted of: lean beef or chicken, with potato or rice, and green beans; water-packed canned peaches; and iced tea +/-splenda or water. After eating, participants again rated hunger or fullness. Then participants were re-scanned using an fMRI paradigm identical to the first session except with different food cues, avoiding familiarity. Afterward, subjective hunger or fullness was rated again.

fMRI Food Task

Visual food cues (pictures) were similarly sized and presented (Supporting Figure S1). Five fMRI BOLD runs per scan session were acquired in an event-related design. In each run, 40 food pictures were presented for 3 seconds each in a pseudo-randomized sequence over 3.5 minutes. Null “arrow” events (each 1.5 seconds) were randomly interleaved between the food pictures. Ten additional “arrow” events were placed at the run beginning and end to establish a baseline BOLD signal. The pictures included 10 high-calorie savory foods, 10 high-calorie sweet foods, and 20 low-calorie foods. While viewing food pictures, participants were asked to rank foods by appeal (“not appealing”, “appealing” or “very appealing”) using button press. Details on MRI procedures, and parameters are reported in the Supporting Methods.

MRI Data Analysis

Brain images were processed using Statistical Parametric Mapping 5 (20) and fMRI Brain Software Library (FSL; Oxford University, UK) (21). fMRI images were realigned to correct for motion artifacts; runs with motion exceeding one voxel size were excluded. The MPRAGE image was then co-registered to the realigned fMRI images. The Brain Extraction Tool (in FSL) was used to extract the brain from the skull and subcutaneous fat in MPRAGE images, because excess subcutaneous fat was found to generate distortions in the normalized images. Image normalization was done to transform the skull-stripped MPRAGE image into Montreal Neurological Institute template space, and the transformation parameters were used to normalize the fMRI images. Normalized fMRI images were re-sampled into 2 mm cubic voxels and smoothed using an 8mm full-width half-maximum Gaussian kernel to minimize inter- participant anatomic variability. Time and dispersion derivatives of the hemodynamic response function were included to obtain a better model of the data. A 128 seconds high-pass filter removed low-frequency noise and slow signal drifts.

Cerebral blood flow (CBF) data were acquired in the “before-meal” session only. CBF procedures, parameters and analysis are reported in the Supporting Methods.

fMRI Statistical Analysis

fMRI data were analyzed using a general linear model in which stimulus onsets were modeled as events and specified as regressors. These onsets were convolved with the hemodynamic response function to account for lag between event onset and the expected BOLD signal response. To account for variance from head movement, realignment parameters were included as regressors. Flexible factorial design was used for two-group (BMI >35kg/m2, BMI <25kg/m2) and two-condition (fasted, fed) analysis. Predicted activations were considered significant at p<0.05 after correcting for family-wise-error (FWE) across voxels.

Descriptive Statistical Analysis

Continuous demographic and clinical characteristics data were described by means and standard deviations and compared by t-test. Categorical variables were described by number and proportion of participants, and analyzed by Chi-square or Fisher's exact test. A repeated-measures analysis of variance was used to assess subjective hunger and fullness with effects for within-group (fasted versus fed), between-group (lean versus obese) and a within-group by between-group interaction. Appeal ratings of food cues were collapsed to a binary outcome (“very appealing/appealing” versus “not appealing”) before analysis. The outcome was the proportion of very appealing/appealing responses given for the 200 rated pictures. The arc sine transformation was applied to the square root of the proportions to improve the normality of the data, and then the repeated measures analysis of variance was applied. SAS version 9.3 (SAS Institute Inc., Cary, North Carolina) was used for all descriptive statistical analyses.


Group Characteristics

Thirty women entered and completed the protocol. Of the demographic/clinical characteristics and current/lifetime Axis I diagnoses, only weight and BMI differed significantly between groups (p=0.001, Table 1). No participants met criteria for current depression (QIDS-SR16, all <11).

Eating Behavior

There were no significant differences between groups in standard meal calories consumed (BMI <25kg/m2: 301 kcals, (SD 51); BMI >35kg/m2: 302 kcals, (SD 66); p=0.95; table 2). Subjective hunger was significantly greater in the fasted versus fed states for both groups (p<0.001; see absolute hunger values table 2). The subjective hunger group effect was significant (p=0.02), indicating that participants with severe obesity, whether fasted or fed, showed quantitatively less hunger than lean controls.

Table 2
Eating Measures

Subjective fullness was significantly lower in the fasted versus fed states for both groups (p<0.001; see absolute fullness values table 2). Subjective fullness did not differ significantly between groups, at any time point, either before or after eating their standard meal (p=0.40, group effect). The groups did not differ significantly in their fullness ratings in response to the meal (p=0.45, group × fasted-fed effect), suggesting that each group was satiated.

Participants with severe obesity and lean participants differed in how they rated the appeal of food cues between the fasted and fed states. The lean group showed a drop of 15% (95% Confidence Interval: 5.0 to 24.1) in their rating of appeal as they moved from fasted to fed (65% versus 50% rated very appealing/appealing). In contrast, the group with severe obesity reported a drop of 4% (95% Confidence Interval: -3.1 to 11.5) (68% versus 64%), which indicates sustained appeal of food cues after eating. The fasted/fed-by-group interaction was not significant (p=0.070).

Lean Group fMRI outcomes (whole brain, Supporting Figure S2)

In fasted lean participants (Figure 1a), diverse brain regions were activated by food cues. Activation of visual/striate cortices was consistent with visual stimuli and the visual cortical activations extended to the cerebellum and parietal cortex. Areas within the prefrontal cortex (including the anterior cingulate, medial prefrontal cortex and dorsolateral prefrontal cortex) were activated, as were regions of the basal ganglia (particularly the caudate nucleus). The medial temporal cortex, including the hippocampus and midbrain, in the region associated with the ventral tegmental area, was also activated. After eating, activation diminished in the anterior cingulate, medial prefrontal cortex, dorsolateral prefrontal cortex, caudate nucleus, and midbrain (Figure 1b). The lean [fasted minus fed] analysis (Figure 1c; Table 3) showed significant activation reductions after eating (p<0.05, FWE corrected) in the prefrontal cortex (anterior cingulate, medial prefrontal cortex and dorsolateral prefrontal cortex), basal ganglia/caudate nucleus, medial temporal cortex and midbrain regions. Activation in the visual cortex and cerebellum were also significantly diminished (p<0.05, FWE corrected), while activations in the ventral cortex were sustained.

Figure 1
Lean Controls
Table 3
Cluster size, t-values, peak coordinates and brain region labels for the lean group, and group with severe obesity, (fasted-fed) contrast, significant at p = 0.05 (family-wise-error corrected).

Group with severe obesity fMRI outcomes (whole brain, Supporting Figure S3)

Fasted participants with severe obesity showed widespread activations by food cues (p<0.05, FWE corrected) as did fasted lean participants (Figure 2a). The severe obesity-fasted group showed brisk activations in the ventral cortex, with extension into the parietal cortex and cerebellum. Prefrontal cortex activations were prominent, including the anterior cingulate, medial prefrontal cortex and dorsolateral prefrontal cortex, extending to the inferior prefrontal cortex and insula. Also prominent were medial temporal cortex activations, including the hippocampus, and brainstem regions, including the ventral tegmental area. In the fed state, many areas remained significantly activated (p<0.05, FWE corrected), unlike the lean group (Figure 2b). The prefrontal, medial temporal, parietal and ventral cortical regions all remained active, making the ‘fed’ activations in the obese brain resemble the ‘fasted’ state. The group with severe obesity [fasted minus fed] analysis showed significantly reduced activations [p<0.05, FWE corrected] only in the insula, inferior prefrontal cortex, and inferior midbrain; with no significant activation diminution within the prefrontal, medial temporal, parietal or ventral cortices (Figure 2c; Table 3).

Figure 2
Severe Obesity

The lean group deactivated brain regions when fed compared to fasted, in contrast to the group with severe obesity (Figure 3; Table 4). Regions in the prefrontal cortex, particularly the anterior cingulate, dorsolateral prefrontal cortex and posterior cingulate cortex, significantly changed going from fasted to fed state in the lean, but not in the group with severe obesity. Additionally, in the severe obesity-fasted group, the hypothalamus activation did not differ statistically (p=0.07) to the lean-fasted group. Both groups showed no meaningful within-group differentiated hypothalamus activation from the fasted to fed states.

Figure 3
Lean-Severe obesity (fasted-fed)
Table 4
Cluster size, t-values, peak coordinates and brain region labels for the lean group versus group with severe obesity ([fasted-fed] × [lean-with obesity]) interaction, significant at p = 0.05 (family-wise-error corrected).

Baseline Perfusion

To confirm that brain activation findings were not the result of differences between the groups in their fasted (baseline) state, we analyzed baseline CBF data. A mask was obtained from the regions that showed differences between the groups for the [fasted minus fed] state (color voxels, Figure 3) and baseline CBF was obtained for each participant from this mask. Baseline CBFs during the fasted state were: lean group = 70.5±9.8 ml/100g/min, group with severe obesity = 69.7±9.8 ml/100g/min, demonstrating that the groups were not different in the fasted state (p=0.83).


Significant activation of brain regions known to mediate hedonic behaviors occurred in fasted participants, both lean and with severe obesity, in response to visual food cues: the prefrontal cortex (especially the anterior cingulate), basal ganglia (especially the caudate nucleus) and medial temporal cortex, as well as sensory perceptual areas and the parietal cortex. The hypothalamus, viewed as a center for body weight homeostasis, showed no significant change between groups or across feeding states. After eating, the group with severe obesity showed sustained “hungry” activation despite no statistical difference in subjective reports of satiation to the lean group who had diminished “non-hungry” activation.

These brain activation findings confirm previously shown characteristics for lean or groups with obesity studied: without comparators (22, 23), in a different demographic (adolescents, males, BMI 25 - 35kg/m2) (24, 25) or in a single state (fasted or fed, but not both) (7, 26, 27). Few study designs show the clear fed-versus-fasted state differences simultaneously between and within groups. Similar to findings by Cornier et al. in obesity-resistant versus obesity-prone participants(17), this a priori-designed study demonstrates an ongoing brain response to visual food cues in severe obesity—especially in the neo- and limbic cortices, and midbrain—after eating a satisfying meal, distinct from lean controls. These findings suggest participants with severe obesity fail to engage a brain-wide eating-related process associated with satiation. It remains to be clarified whether this brain response to food cues is a trait or state response induced by obesity. We are following the participants with severe obesity after bariatric surgery to test this outcome.

In this study, key aspects of brain response to eating are reflected by objective behavioral response (appeal) and subjective motivation (hunger, fullness)—a translation of brain findings to eating behavior. The brain response when fasted is congruent with behavioral and motivation experience in both groups; brain activation is high, food pictures are appealing and participants are at their hungriest. The brain response when fed remains congruent with behavior and motivation experience in lean participants but is divergent in participants with severe obesity. Unlike leans, despite diminished hunger/peak fullness, the group with severe obesity-fed maintain high brain activation to and appeal ratings of food. Participants with severe obesity reported significantly less absolute hunger than lean participants whether fasted or fed, but group differences between pre- and post-meal hunger/fullness were identical. Taken together, the severe obesity-fed state depicts an ‘uncoupling’ of effective brain response to eating and degree of hunger/fullness. These findings may explain why participants with severe obesity report an underlying drive to eat continually despite not feeling hungry (28), or eat differently, consuming more than lean controls (29, 30).

The lean group decreased regional brain activation to visual food cues after eating, which suggests that the brain withdraws attention from food-related stimuli when satiated. The lack of diminished brain activation in participants with severe obesity once fed, lends support to an appetitive-conditioning psychopathology model (31). Despite satiation, a high brain activation pattern identical to fasting in participants with severe obesity reflects a neuronal pressure around eating which is absent in lean participants. These brain differences may assist clinical weight loss treatment by utilizing brain-based therapies or validating patients' feelings of ‘something being wrong’ with regard to eating. Similar to the variability of genetically driven weight gain and loss (32, 33), clinicians can acknowledge a disparity in response to eating and do so without undermining treatment success.

This study has limitations. The sample size, while sufficient for fMRI data stability, is not large per usual clinical study size. Our study meal (337 kcals) was set at the recommended lunch caloric level for a 1200 kcal diet(34) and is less than the average 626 kcals meal consumed by U.S. women (35); brain activation differences between groups may change after a larger meal. While our focus on women is essential as the experience of obesity in women differs in many respects from that in men—including heightened perceived physical impairments (36), weight-related social stigma and discrimination (37), risk of depression (38) and distinct risk factors (39)—our results may not be generalizable to men. Though age-matched, with equal numbers of postmenopausal women per group, we could not control for menstrual cycles, oral contraceptives or hormone replacement therapy. Sex hormones are known to modulate appetite(40) and may affect these results. Finally, neither molecular markers nor gut hormones known to affect brain function were analyzed; such correlations are forthcoming. This study's strengths include its use of a longitudinal within- and between-group design of absolute and change in brain activation, coupled with behavior measures, and post-bariatric surgery follow-up in the obese group. This study contributes to the high priority of developing effective obesity targets and treatment strategies.


This study's comparison between lean and participants with severe obesity's brain responses to food cues demonstrates how brain activation patterns vary in a fasted versus a fed state. In response to food cues, the brains of both lean and participants with severe obesity when fasted show fMRI BOLD activation patterns widely distributed in the neo- and limbic cortices. Once lean participants are fed, their brain BOLD activations to food pictures are broadly reduced. In contrast, after participants with severe obesity are fed, they fail to show remarkably altered patterns of brain activation to food cues. In particular, after eating, participants with severe obesity maintain activation in the midbrain, one of the most potent reward centers. Thus, once satiated after eating, participants with severe obesity continue to perceive food as appealing and their brains continue to be activated by visual food cues as though they were hungry. Future experiments will determine whether the observed participants with severe obesity's brain activation patterns will change following bariatric surgery, correlate with changes in body weight-related gut hormones, or differ between high- versus low-caloric density visual food cues.

Study Importance Questions

What is already known about this subject?

  • Differences in brain activity response to food and eating exist between people with versus without obesity.
  • Studies showing differences in brain activity response to food, generally focus on one state (fasted or fed), and rarely include a group with BMI >35m/kg2 (severe obesity).

What does our study add?

  • Our study uncovers a critical difference in the brain response to eating between women with or without severe obesity. We uniquely establish no significant difference in baseline brain perfusion, a surrogate of neural activity, between groups at study onset.
  • The difference, a failure in obesity of brain reward center activity to diminish after a meal, uniquely augments previous findings by studying brain activity across states (fasted and fed), providing within- and between-group comparisons, and focusing on women with severe obesity.

Supplementary Material

Supp Info


We would like to acknowledge the editorial support of Jon Kilner, MS, MA (Pittsburgh, PA), the dietary-recall assessments by Rosemary Son, PA-C, RD (Dallas, TX), and the preliminary fMRI BOLD analyses by Yan Fang, PhD (Dallas, TX).

Sources of financial funding: 1. Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX 75390 provided funding for all aspects of the research.

2. NIH/NCATS Grant Number UL1TR000451 provided pilot research funding.

3. Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390 provided pilot research funding.


All authors claim no potential conflicts of interest.


1. Smeets PA, Erkner A, de Graaf C. Cephalic phase responses and appetite. Nutr Rev. 2010;68(11):643–55. [PubMed]
2. Berthoud HR. The neurobiology of food intake in an obesogenic environment. Proc Nutr Soc. 2012;71(4):478–87. [PMC free article] [PubMed]
3. Legget KT, Cornier MA, Rojas DC, Lawful B, Tregellas JR. Harnessing the power of disgust: a randomized trial to reduce high-calorie food appeal through implicit priming. Am J Clin Nutr. 2015;102(2):249–55. [PubMed]
4. Cornier MA, Shott ME, Thomas EA, Bechtell JL, Bessesen DH, Tregellas JR, et al. The effects of energy balance, obesity-proneness and sex on the neuronal response to sweet taste. Behav Brain Res. 2015;278:446–52. [PMC free article] [PubMed]
5. Martens MJ, Born JM, Lemmens SG, Karhunen L, Heinecke A, Goebel R, et al. Increased sensitivity to food cues in the fasted state and decreased inhibitory control in the satiated state in the overweight. Am J Clin Nutr. 2013;97(3):471–9. [PubMed]
6. Sweet LH, Hassenstab JJ, McCaffery JM, Raynor HA, Bond DS, Demos KE, et al. Brain response to food stimulation in obese, normal weight, and successful weight loss maintainers. Obesity (Silver Spring) 2012;20(11):2220–5. [PMC free article] [PubMed]
7. Stoeckel LE, Weller RE, Cook EW, 3rd, Twieg DB, Knowlton RC, Cox JE. Widespread reward-system activation in obese women in response to pictures of high-calorie foods. Neuroimage. 2008;41(2):636–47. [PubMed]
8. Martin LE, Holsen LM, Chambers RJ, Bruce AS, Brooks WM, Zarcone JR, et al. Neural mechanisms associated with food motivation in obese and healthy weight adults. Obesity (Silver Spring) 2010;18(2):254–60. [PubMed]
9. Schachter S. Obesity and eating. Internal and external cues differentially affect the eating behavior of obese and normal subjects. Science. 1968;161(3843):751–6. [PubMed]
10. Lawrence NS, Hinton EC, Parkinson JA, Lawrence AD. Nucleus accumbens response to food cues predicts subsequent snack consumption in women and increased body mass index in those with reduced self-control. Neuroimage. 2012;63(1):415–22. [PubMed]
11. Williams KW, Elmquist JK. From neuroanatomy to behavior: central integration of peripheral signals regulating feeding behavior. Nat Neurosci. 2012;15(10):1350–5. [PMC free article] [PubMed]
12. Batterham RL, ffytche DH, Rosenthal JM, Zelaya FO, Barker GJ, Withers DJ, et al. PYY modulation of cortical and hypothalamic brain areas predicts feeding behaviour in humans. Nature. 2007;450(7166):106–9. [PubMed]
13. Farooqi IS, Bullmore E, Keogh J, Gillard J, O'Rahilly S, Fletcher PC. Leptin regulates striatal regions and human eating behavior. Science. 2007;317(5843):1355. [PMC free article] [PubMed]
14. Schwartz GJ. The role of gastrointestinal vagal afferents in the control of food intake: current prospects. Nutrition. 2000;16(10):866–73. [PubMed]
15. Dressler H, Smith C. Food choice, eating behavior, and food liking differs between lean/normal and overweight/obese, low-income women. Appetite. 2013;65:145–52. [PubMed]
16. Rothemund Y, Preuschhof C, Bohner G, Bauknecht HC, Klingebiel R, Flor H, et al. Differential activation of the dorsal striatum by high-calorie visual food stimuli in obese individuals. Neuroimage. 2007;37(2):410–21. [PubMed]
17. Cornier MA, McFadden KL, Thomas EA, Bechtell JL, Eichman LS, Bessesen DH, et al. Differences in the neuronal response to food in obesity-resistant as compared to obesity-prone individuals. Physiol Behav. 2013;110-111:122–8. [PMC free article] [PubMed]
18. Spitzer RL, Williams JB, Gibbon M, First MB. The Structured Clinical Interview for DSM-III-R (SCID). I: History, rationale, and description. Arch Gen Psychiatry. 1992;49(8):624–9. [PubMed]
19. Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, et al. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54(5):573–83. [PubMed]
20. Wise RA. Drug-activation of brain reward pathways. Drug Alcohol Depend. 1998;51(1-2):13–22. [PubMed]
22. Ochner CN, Kwok Y, Conceicao E, Pantazatos SP, Puma LM, Carnell S, et al. Selective reduction in neural responses to high calorie foods following gastric bypass surgery. Ann Surg. 2011;253(3):502–7. [PMC free article] [PubMed]
23. Frank S, Laharnar N, Kullmann S, Veit R, Canova C, Hegner YL, et al. Processing of food pictures: influence of hunger, gender and calorie content. Brain Res. 2010;1350:159–66. [PubMed]
24. Dimitropoulos A, Tkach J, Ho A, Kennedy J. Greater corticolimbic activation to high-calorie food cues after eating in obese vs. normal-weight adults. Appetite. 2012;58(1):303–12. [PMC free article] [PubMed]
25. Bruce AS, Holsen LM, Chambers RJ, Martin LE, Brooks WM, Zarcone JR, et al. Obese children show hyperactivation to food pictures in brain networks linked to motivation, reward and cognitive control. Int J Obes (Lond) 2010;34(10):1494–500. [PubMed]
26. Stoeckel LE, Kim J, Weller RE, Cox JE, Cook EW, 3rd, Horwitz B. Effective connectivity of a reward network in obese women. Brain Res Bull. 2009;79(6):388–95. [PMC free article] [PubMed]
27. Cornier MA, Von Kaenel SS, Bessesen DH, Tregellas JR. Effects of overfeeding on the neuronal response to visual food cues. Am J Clin Nutr. 2007;86(4):965–71. [PubMed]
28. Zunker C, Karr T, Saunders R, Mitchell JE. Eating behaviors post-bariatric surgery: a qualitative study of grazing. Obes Surg. 2012;22(8):1225–31. [PubMed]
29. Ernst B, Thurnheer M, Wilms B, Schultes B. Differential changes in dietary habits after gastric bypass versus gastric banding operations. Obes Surg. 2009;19(3):274–80. [PubMed]
30. Spiegel TA, Shrager EE, Stellar E. Responses of lean and obese subjects to preloads, deprivation, and palatability. Appetite. 1989;13(1):45–69. [PubMed]
31. Martin-Soelch C, Linthicum J, Ernst M. Appetitive conditioning: neural bases and implications for psychopathology. Neurosci Biobehav Rev. 2007;31(3):426–40. [PMC free article] [PubMed]
32. Levine JA, Eberhardt NL, Jensen MD. Role of nonexercise activity thermogenesis in resistance to fat gain in humans. Science. 1999;283(5399):212–4. [PubMed]
33. Stunkard AJ, Harris JR, Pedersen NL, McClearn GE. The body-mass index of twins who have been reared apart. N Engl J Med. 1990;322(21):1483–7. [PubMed]
34. Naleid AM, Grace MK, Cummings DE, Levine AS. Ghrelin induces feeding in the mesolimbic reward pathway between the ventral tegmental area and the nucleus accumbens. Peptides. 2005;26(11):2274–9. [PubMed]
35. Wright JD, Wang CY, Kennedy-Stephenson J, Ervin RB. Advance data from vital and health statistics. 334. Hyattsville, Maryland: National Center for Health Statistics; 2003. Dietary intake of ten key nutrients for public health, United States: 1999-2000. [PubMed]
36. Larsson U, Karlsson J, Sullivan M. Impact of overweight and obesity on health-related quality of life--a Swedish population study. Int J Obes Relat Metab Disord. 2002;26(3):417–24. [PubMed]
37. Carr D, Friedman MA. Is obesity stigmatizing? Body weight, perceived discrimination, and psychological well-being in the United States. J Health Soc Behav. 2005;46(3):244–59. [PubMed]
38. Onyike CU, Crum RM, Lee HB, Lyketsos CG, Eaton WW. Is obesity associated with major depression? Results from the Third National Health and Nutrition Examination Survey. Am J Epidemiol. 2003;158(12):1139–47. [PubMed]
39. Azarbad L, Gonder-Frederick L. Obesity in women. Psychiatr Clin North Am. 2010;33(2):423–40. [PubMed]
40. Xu Y, Nedungadi TP, Zhu L, Sobhani N, Irani BG, Davis KE, et al. Distinct hypothalamic neurons mediate estrogenic effects on energy homeostasis and reproduction. Cell Metab. 2011;14(4):453–65. [PMC free article] [PubMed]