Current Opinion in Lipidology:
NUTRITION AND METABOLISM: Edited by Paul Nestel and Ronald P. Mensink
Brain imaging in the context of food perception and eating
Hollmann, Mauricea; Pleger, Burkharda,b,c,d; Villringer, Arnoa,b,c,d; Horstmann, Annettea,b
aDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences
bLeipzig University Medical Center, IFB Adiposity Diseases
cClinic for Cognitive Neurology, University Hospital, Leipzig
dMind and Brain Institute, Berlin School of Mind and Brain, Humboldt-University and Charité, Berlin, Germany
Correspondence to Maurice Hollmann, Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, D-04103 Leipzig, Germany. Tel: +49 (0) 341 9940 2433; fax: +49 (0) 341 9940 2221; e-mail: email@example.com
Purpose of review: Eating behavior depends heavily on brain function. In recent years, brain imaging has proved to be a powerful tool to elucidate brain function and brain structure in the context of eating. In this review, we summarize recent findings in the fast growing body of literature in the field and provide an overview of technical aspects as well as the basic brain mechanisms identified with imaging. Furthermore, we highlight findings linking neural processing of eating-related stimuli with obesity.
Recent findings: The consumption of food is based on a complex interplay between homeostatic and hedonic mechanisms. Several hormones influence brain activity to regulate food intake and interact with the brain's reward circuitry, which is partly mediated by dopamine signaling. Additionally, it was shown that food stimuli trigger cognitive control mechanisms that incorporate internal goals into food choice. The brain mechanisms observed in this context are strongly influenced by genetic factors, sex and personality traits.
Summary: Overall, a complex picture arises from brain-imaging findings, because a multitude of factors influence human food choice. Although several key mechanisms have been identified, there is no comprehensive model that is able to explain the behavioral observations to date. Especially a careful characterization of patients according to genotypes and phenotypes could help to better understand the current and future findings in neuroimaging studies.
This review provides an overview on the current knowledge about brain imaging in the context of food and eating behavior. Food consumption has a tremendous impact on cardiovascular diseases, which mediate an alarming link between obesity and mortality . As a consequence, many of the studies in the context of food and eating behavior are conducted with a focus on obesity. We consider data from healthy normal-weight as well as obese patients. We deliberately left out studies that included patients with either acquired or congenital eating disorders (e.g. Anorexia Nervosa and Prader-Willi-Syndrome).
In the context of this article ‘imaging’ refers to functional MRI (fMRI), PET, and structural MRI. fMRI allows for measuring local disturbances in the magnetic field caused by cerebral blood flow and oxygenation changes in the brain based on the blood oxygen level-dependent (BOLD) effect . Changes in the BOLD signal are suggested to be linked directly to neuronal activity [3,4].
In addition to blood flow and energy consumption PET is also used to explore receptor-binding potentials in the brain. This technique is based on positron emitting radiotracers, which are ligands for specific receptor proteins (e.g. dopamine receptors or serotonin transporters). Structural MRI is a nonfunctional imaging technique that is used to explore differences in gray matter volume (GMV) either between patients (in cross-sectional studies) or within patients (in longitudinal and interventional studies).
In food imaging studies, usually healthy controls with a BMI between 20 and ca. 45 are examined. Typically, BMI is used to categorize patients in food experiments: BMI ranges between 20 and 25 are usually denoted as ‘normal’ and BMI more than 30 as ‘obese’. This categorization implicitly assumes that patients, once reaching a critical cutoff value in terms of BMI, can be treated as a homogeneous group. In contrast to this assumption, data suggest that linear and nonlinear dependencies exist between a multitude of metrics and BMI (e.g. Davis and Fox ). Our own experience suggests that this categorization might be insufficient for food imaging, because the phenotypes are not adequately described using this simple distinction. For example, patients with a BMI of 31 and a high cognitive restraint over eating behavior may prevent themselves from gaining even more weight and fall in a different category than patients with the same BMI and a low cognitive restraint. Accordingly, very different patterns of brain activation can be expected in food experiments for these different individuals. As there are much more factors influencing eating behavior, for example, genetic and behavioral factors, a comprehensive model would be needed to establish correct categorization for experiments, which is not available at the moment. Furthermore, it is challenging to disentangle which pattern of brain functioning is acquired by abnormal high weight and which was the precondition for the weight increase in obese patients. Several longitudinal studies observing weight changes over time try to tackle this issue (see for example [6,7▪,8]).
The stimuli frequently used in functional imaging of responses to food and eating behavior are images of food, odor probes (delivered orthonasal or retronasal), or taste probes (e.g. small portions of milkshake). Due to technical requirements, a realistic intake of food (i.e. energy) is usually not possible in measurement environments. The above-mentioned stimuli all represent conditioned cues, which gain their incentive values in everyday life and thereby are always connected to a certain predicted reward value for each individual.
Furthermore, the effects observed in food imaging are always complex combinations of several processes, for example, perception of the physical properties of the cues, valuation and reward prediction signals in the anticipation phase, valuation and reward prediction error signals in receipt phase, and automated cognitive control processes.
CORE NEURAL PROCESSES
This section describes the core neural mechanisms connected to food intake and covers sensory processing, reward and valuation processing, as well as modulation of brain responses by internal and external factors. Please see Table 1 for an overview of functional areas discussed below.
Homeostatic and hedonic influences on food intake
Food intake is controlled by two complementary systems. The homeostatic system, responsible for energy balance, adjusts energy intake or expenditure to meet the body's current needs in response to, for example, blood glucose level (short term) or energy stores (long term). One of the effector organs of this system is the brain, that is, brainstem and hypothalamus, where several hormones (e.g. ghrelin, peptide YY, neuropeptide Y, leptin, insulin) are able to influence brain circuits that regulate food intake and energy expenditure . However, people frequently eat palatable foods beyond their necessary caloric intake. The hedonic system, which mediates this reward-related food consumption, comprises a broader network of brain regions like the amygdala, striatum, insula and orbitofrontal cortex (OFC) . In general, both systems are closely linked and share mutual information but it has been shown repeatedly that they can become virtually independent under certain conditions or in specific patients [11–13].
Sensory processing and integration
The ingestion of food is connected to a multitude of sensory events. Visual, gustatory, oral-somatosensory, and retronasal olfactory signals are integrated in the brain to create a complex percept. Olfactory and gustatory stimuli consistently activate the orbitofrontal cortex, the frontal operculum, and the mid and anterior portions of the insula . There is strong evidence that the integration of the multimodal sensory input during ingestion is achieved mainly in the anterior insula, the primary gustatory cortex [15–17]. The orbitofrontal cortex is repeatedly described as secondary gustatory cortex connected to valuation processes [18,19], which will be further discussed in the following section.
A meta-analysis of fMRI studies using visual food cues additionally identified activation in the fusiform gyrus, a part of the visual association cortex, when presenting food images vs. nonfood images . This activity was not associated with the visual characteristics of the different image categories and implies that food images have a higher salience leading to heightened attentional states.
There is an evidence that obese compared with lean patients show stronger stimulus sensitivity during sensory processing of food stimuli. In a recent fMRI study, Szalay et al. observed increased responses in operculum, insula, and OFC for obese vs. lean patients during the intraorally delivery of palatable and nonpalatable liquids. These observations might be attributed to an additional attention bias towards food cues in overweight and obese patients compared with lean patients (see Nijs and Franken for a review ).
Reward and valuation
Food has a rewarding nature that leads to the incentive motivation to eat, an advantageous mechanism because it increases survival chances in environments with limited food resources. Especially high-caloric foods help to supply the individual with energy and are therefore strong primary reinforcers. Not surprisingly activation in the human reward circuitry is observed in imaging studies of food. Food cues elicit activation in the insula, the amygdala, dopaminergic structures in the brain stem [ventral tegmental area (VTA), substantia nigra], as well as the striatum [nucleus accumbens (NAcc), putamen, nucleus caudatus] [20,23]. Additionally, the OFC and ventromedial prefrontal cortex are evidently connected to assigning incentive motivational value to food stimuli and thereby drive feeding behavior .
The learning of stimulus-reinforcement congruencies (conditioning) is strongly based on dopamine signaling in the brain. The idea is that primary reinforcers activate dopamine neurons in VTA, and substantia nigra, projecting to the ventral striatum (mainly NAcc) and thus establish a learning signal. After conditioning, formerly neutral cues (e.g. a smell) linked to the primary reinforcer (e.g. a piece of meat) can acquire reinforcing qualities connected to dopamine release in the reward system during anticipation. Learning is thereby established by signaling of a prediction error in VTA and substantia nigra: dopamine firing increases with unpredicted positive rewards and is suppressed by unpredicted losses [24▪▪]. D’Ardenne et al. showed this mechanism also for food stimuli using fMRI: VTA responses encoded a positive reward prediction error, whereas the ventral striatum encoded positive and negative reward prediction errors, that is, activation for unexpected reward and deactivation for expected but withheld reward, for the receipt of a palatable liquid. A major role in valuation processes is assigned to the OFC, which has been connected to attribution of incentive salience to food [19,26]. This is enabled by projections from the OFC and amygdala to the NAcc, playing also a major role in conditioning of food cues . Importantly, activity in the reward circuit in the food context is modulated by several factors like homeostasis (insulin, leptin, ghrelin), current homeostatic state (hunger and satiety), and cognitive control [23,28,29].
Several recent studies support the notion of elevated cue reactivity of the reward circuitry during food anticipation (e.g. viewing food images) in obese compared with lean patients [30–33]. Additionally, Demos et al.[7▪] could show in an fMRI study that increased activation in NAcc during food anticipation in normal weight patients predicts weight gain in a 6-month follow-up assessment. In contrast, obese patients show decreased activity and dopamine signaling in striatal regions during food consumption [23,30], which is potentially moderated by genetic factors influencing dopamine type 2 (D2) receptor density . This is supported by an observation from animal research: decreased dopamine signaling in the VTA of mice is connected to a significant increase in the consumption of high-caloric foods . Based on these results, the ‘reward-deficiency theory’ was developed that attributes abnormal high weight to increased dopamine responses in striatal regions during reward anticipation and decreased signaling during receipt. This discrepancy is potentially leading to elevated motivation to eat on the one hand, and less reward from eating on the other hand, eventually leading to compensatory overeating.
Importantly, there is strong evidence supporting the view, that dopamine signaling and reward is not the whole story of obesity. Feeding is a complex process incorporating energy-balance signals, reward signaling, emotional information, and cognitive control. For example, recent results obtained with PET show a strong connection between obesity and serotonin receptor density [35▪▪] and serotonin transporter binding . Furthermore, dopamine signaling in striatal regions does not necessarily have to be related to reward signaling per se but may also be connected to cognitive control processes interacting with valuation systems [23,37].
Modulation of brain responses by homeostatic state, stress and food properties
In parallel to a change of homeostatic state between hunger and satiety, brain responses to food stimuli are modulated. OFC as well as visual areas show higher responses to visual food cues during hunger, possibly reflecting a higher behavioral relevance of these cues when energy is needed . Hunger also modulates the explicit reward evaluation of food stimuli, with associated activity changes in the OFC, posterior cingulate cortex (PCC), basal ganglia, visual areas and the insula. In a recent fMRI study, activity in these areas was higher for foods with high calorie content when patients were hungry, and higher for foods with low calorie content when patients were satiated . If patients go beyond satiety, by terms of ingesting more energy than they would usually eat, brain responses to pictures of foods with a high hedonic value are attenuated in visual and parietal areas as well as the hippocampus .
Environmental factors like a high level of chronic stress affect food choice and eating behavior and may contribute to the obesity epidemic . Stress is known to shift behavioral control from being goal-directed to habitual in patients, who are vulnerable to the deleterious effects of stress . A recent fMRI study demonstrates that responses to a milkshake in right amygdala scaled with the amount of chronic stress [42▪]. In the same study, it was shown that acute emotional stress additionally modulates the responses to food receipt in the right amygdala and orbitofrontal cortex of overweight and obese women.
The calorie content of food is another important factor modulating brain responses. In obese women, simple visual stimulation with food stimuli activates regions related to reward anticipation and habit learning (dorsal striatum). Additionally, high-calorie food images yielded BMI-dependent activations in regions associated with taste information processing (anterior insula and lateral orbitofrontal cortex), motivation (orbitofrontal cortex), emotion as well as memory functions (posterior cingulate), independent of the homeostatic state . Furthermore, higher brain reactivity in areas mediating motivational and attentional salience to pictures of high-caloric foods has been shown to be predictive to less success in a weight-loss program [44▪].
THE ROLE OF COGNITIVE CONTROL OVER FEEDING BEHAVIOR
Cognitive control over behavior describes the ability to flexibly adjust behavior to accommodate a specific goal. An important aspect in this framework is the suppression of impulsive or habitual behavior that would interfere with the aspired goal, which is hypothesized to be deficient in obesity [24▪▪]. Additionally, attention has been shown to be a strong modulator of decision-making behavior and stimulus values. An experiment using a food choice task could elicit improved dietary choices when directing patients’ attention to the health aspects of food options . In parallel, valuation signals in ventromedial prefrontal cortex were modulated by activity in dorsolateral prefrontal cortex (DLPFC), a region supporting goal-directed behavior (e.g. in the context of dieting ).
Delay discounting is often used to test cognitive control over behavior. In this task, patients have to choose between smaller but immediate and greater but delayed rewards. It has been shown that the OFC is crucial for being able to delay gratification of rewards, independent of whether the reward is primary (food) or secondary (money, discount voucher) . A recent fMRI study showed that greater activity in prefrontal control regions during a delay discounting task correlated negatively with weight gain in obese women over the subsequent years, suggesting that activity in these areas reflects control activity both within and outside the food context . Furthermore, activation in the DLPFC and the dorsal striatum strongly correlated with the degree of cognitive control over eating behavior in an fMRI experiment asking normal-weight and overweight women to either admit or to suppress their desire for highly palatable food .
THE INFLUENCE OF GENETIC PREDISPOSITION AND GENDER DIFFERENCES
There are already some studies investigating the effects of genetic factors on the brain's response to food. For example, genetic factors that decrease the dopamine type 2 receptor density (i.e. TaqA1 polymorphism) affect body mass and food reinforcement. It also modulates the brain's response to a milkshake receipt: carriers of the variant that decreases D2 receptor density showed increased responses in the midbrain, thalamus and OFC whereas carriers of the variant with increased receptor density showed decreased responses relative to baseline . Furthermore, a study investigating genetic variation in the serotonin transporter gene showed that the PCC, a region implicated in memory and activated by visual food stimuli, of patients, homozygous for the long allele exhibited greater activity in the comparison food vs. nonfood compared with individuals heterozygous or homozygous for the short allele. The short allele is associated with reduced serotonin transporter expression and function. The association between genotype and activation was linear, that is, patients with two copies of the long allele variant having the strongest activation .
The prevalence of obesity and eating disorders is higher in women than in men . This is also reflected in the differential association between brain structure and function and obesity between both sexes. After being kept on a eucaloric diet for a week, women but not men activated prefrontal control regions in response to images of food and ate less when allowed to eat ad libitum again . A PET study reported a significant sex difference in the regional brain responses to cognitive inhibition of hunger while viewing appetitive food stimuli . Women were able to decrease their subjective feeling of hunger without changes in brain metabolism. In contrast, men showed decreases in self-reported hunger that were paralleled by deactivation in limbic and paralimbic brain regions.
Two studies investigating the relationship between brain structure and markers of obesity demonstrated a pronounced interaction effect of sex and obesity on gray matter as well as white matter structure [55,56]. In addition to common gray matter changes in nucleus accumbens and OFC for both sexes, structure of the putamen and DLPFC was altered exclusively in women.
Given the results described above it becomes clear that the brain does not respond uniformly to food cues. These cues are processed differentially depending on the energy content of the presented food, the patient's current physiological state (homeostatic state, stress level) and the amount of cognitive control over eating behavior. Furthermore, patient's brain responses differ substantially depending on their genotype, long-term energy stores, and eating behavior style. Currently, it is challenging to interpret differential brain responses observed in obesity, as it is not clear if these differences are the cause or the effect of excess body weight and corresponding alterations in eating behavior.
As shown here the brain is a key player in homeostatic and hedonic aspects of eating and thus neuroimaging is an important tool in exploring underlying mechanisms if it's results are carefully linked to patients’ behavior and genetic predispositions.
This work was supported by the Max Planck Society, the Federal Ministry of Education and Research [BMBF: Neurocircuits in obesity to AH, AV, BP and IFB AdiposityDiseases (FKZ: 01EO1001) to AH, AV, BP; Bernstein Focus, State Dependencies of Learning (FKZ: 18GL4DW4) to MH and BP].
Conflicts of interest
There are no conflicts of interest.
REFERENCES AND RECOMMENDED READING
Papers of particular interest, published within the annual period of review, have been highlighted as:
▪ of special interest
▪▪ of outstanding interest
Additional references related to this topic can also be found in the Current World Literature section in this issue (pp. 88–89).
1. Berrington de Gonzalez A, Hartge P, Cerhan JR, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med 2010; 363:2211–2219.
2. Ogawa S, Lee TM, Kay a R, et al. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA 1990; 87:9868–9872.
3. Logothetis NK. The underpinnings of the BOLD functional magnetic resonance imaging signal. J Neurosci 2003; 23:3963–3971.
4. Lee JH, Durand R, Gradinaru V, et al. Global and local fMRI signals driven by neurons defined optogenetically by type and wiring. Nature 2010; 465:788–792.
5. Davis C, Fox J. Sensitivity to reward and body mass index (BMI): evidence for a nonlinear relationship. Appetite 2008; 50:43–49.
6. Stice E, Yokum S, Bohon C, et al. Reward circuitry responsivity to food predicts future increases in body mass: moderating effects of DRD2 and DRD4. NeuroImage 2010; 50:1618–1625.
7▪. Demos KE, Heatherton TF, Kelley WM. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci 2012; 32:5549–5552.
The results of Demos et al.[7▪] implicate that increased activity of NAcc, a key component of the reward circuitry, during food anticipation predicts future weight gain in 18–19-year old women.
8. Stice E, Yokum S, Burger KS, et al. Youth at risk for obesity show greater activation of striatal and somatosensory regions to food. J Neurosci 2011; 31:4360–4366.
9. Morton GJ, Cummings DE, Baskin DG, et al. Central nervous system control of food intake and body weight. Nature 2006; 443:289–295.
10. Berridge KC, Robinson TE. Parsing reward. Trends Neurosci 2003; 26:507–513.
11. Kenny PJ. Reward mechanisms in obesity: new insights and future directions. Neuron 2011; 69:664–679.
12. Shomaker LB, Tanofsky-kraff M, Zocca JM, et al. Eating in the absence of hunger in adolescents: intake after a large-array meal compared with that after a standardized meal. Am J Clin Nutr 2010; 92:697–703.
13. Dallman MF. Stress-induced obesity and the emotional nervous system. Trends Endocrinol Metab 2010; 21:159–165.
14. Verhagen JV, Engelen L. The neurocognitive bases of human multimodal food perception: sensory integration. Neurosci Biobehav R 2006; 30:613–650.
15. Small DM. Flavor is in the brain. Physiol Behav 2012 [Epub ahead of print]. doi:10.1016/j.physbeh.2012.04.011
16. Veldhuizen MG, Douglas D, Aschenbrenner K, et al. The anterior insular cortex represents breaches of taste identity expectation. J Neurosci 2011; 31:14735–14744.
17. de Araujo IE, Geha P, Small DM. Orosensory and homeostatic functions of the insular taste cortex. Chemosens Percept 2012; 5:64–79.
18. Rolls ET. Taste, olfactory and food texture reward processing in the brain and obesity. Int J Obesity 2011; 35:550–561.
19. Zald DH. Orbitofrontal cortex contributions to food selection and decision making. Ann Behav Med 2009; 38:18–24.
20. van der Laan LN, de Ridder DTD, Viergever MA, et al.
The first taste is always with the eyes: a meta-analysis on the neural correlates of processing visual food cues. NeuroImage 2011; 55:296–303.
21. Szalay C, Aradi M, Schwarcz A, et al.
Gustatory perception alterations in obesity: An fMRI study. Brain research 2012; 1473:131–140.
22. Nijs IMT, Franken IHA. Attentional processing of food cues in overweight and obese individuals. Curr Obes Rep 2012; 1:106–113.
23. Volkow ND, Wang G-J, Baler RD. Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn Sci 2011; 15:37–46.
24▪▪. Volkow ND, Wang GJ, Fowler JS, et al. Food and Drug Reward: Overlapping Circuits in Human Obesity and Addiction Inhibitory control. Curr Top Behav Neurosci 2012; 11:1–24.
Volkow et al.[24▪▪] describe evidence for parallels in neural processing in drug addiction and obesity mainly based on PET imaging. Especially, an increased sensitivity of the reward circuit to conditioned cues and weakened inhibitory control is attributed to both disorders.
25. D’Ardenne K, McClure SM, Nystrom LE, et al. BOLD responses reflecting dopaminergic signals in the human ventral tegmental area. Science 2008; 319:1264–1267.
26. Grabenhorst F, Rolls ET, Bilderbeck A. How cognition modulates affective responses to taste and flavor: top-down influences on the orbitofrontal and pregenual cingulate cortices. Cereb Cortex 2008; 18:1549–1559.
27. Petrovich GD. Forebrain circuits and control of feeding by learned cues. Neurobiol Learn Mem 2011; 95:152–158.
28. Narayanan NS, Guarnieri DJ, DiLeone RJ. Metabolic hormones, dopamine circuits, and feeding. Front Neuroendocrin 2010; 31:104–112.
29. Kober H, Mende-Siedlecki P, Kross EF, et al. Prefrontal-striatal pathway underlies cognitive regulation of craving. Proc Natl Acad Sci USA 2010; 107:14811–14816.
30. Stice E, Spoor S, Bohon C, et al. Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. J Abnorm Psychol 2008; 117:924–935.
31. Grosshans M, Vollmert C, Vollstädt-Klein S, et al. Association of leptin with food cue-induced activation in human reward pathways. Arch Gen Psychiatry 2012; 69:529–537.
32. Stoeckel LE, Weller RE, Cook EW, et al. Widespread reward-system activation in obese women in response to pictures of high-calorie foods. NeuroImage 2008; 41:636–647.
33. Stice E, Yokum S, Zald D, et al. Dopamine-based reward circuitry responsivity, genetics, and overeating. Curr Top Behav Neurosci 2010; 6:81–93.
34. Cordeira JW, Frank L, Sena-Esteves M, et al. Brain-derived neurotrophic factor regulates hedonic feeding by acting on the mesolimbic dopamine system. J Neurosci 2010; 30:2533–2541.
35▪▪. Haahr ME, Rasmussen PM, Madsen K, et al. Obesity is associated with high serotonin 4 receptor availability in the brain reward circuitry. NeuroImage 2012; 61:884–888.
This study highlights the potential importance of the serotonergic transmitter system, in addition to the dopaminergic system, in feeding regulation and obesity. Using PET imaging, Haahr et al.[35▪▪] found a strong positive correlation between serotonin receptor (5-HT4) density and BMI in regions that belong to the reward circuitry (i.e. NAcc, OFC).
36. Erritzoe D, Frokjaer VG, Haahr MT, et al. Cerebral serotonin transporter binding is inversely related to body mass index. NeuroImage 2010; 52:284–289.
37. Volkow ND, Wang G-J, Telang F, et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. NeuroImage 2008; 42:1537–1543.
38. Führer D, Zysset S, Stumvoll M. Brain activity in hunger and satiety: an exploratory visually stimulated FMRI study. Obesity 2008; 16:945–950.
39. Siep N, Roefs A, Roebroeck A, et al. Hunger is the best spice: an fMRI study of the effects of attention, hunger and calorie content on food reward processing in the amygdala and orbitofrontal cortex. Behav Brain Res 2009; 198:149–158.
40. Cornier M-A, Von Kaenel SS, Bessesen DH, et al. Effects of overfeeding on the neuronal response to visual food cues. Am J Clin Nutr 2007; 86:965–971.
41. Gibson EL. Emotional influences on food choice: sensory, physiological and psychological pathways. Physiol Behav 2006; 89:53–61.
42▪. Rudenga KJ, Sinha R, Small DM. Acute stress potentiates brain response to milkshake as a function of body weight and chronic stress. Int J Obesity 2012 [Epub ahead of print]. doi:10.1038/ijo.2012.39
Rudenga et al. [42▪] show an important influence of acute and chronic stress on the activation of the amygdala and OFC during the receipt of palatable food. This links brain activity to stress-related eating which is known to be a major factor in obesity.
43. Rothemund Y, Preuschhof C, Bohner G, et al. Differential activation of the dorsal striatum by high-calorie visual food stimuli in obese individuals. NeuroImage 2007; 37:410–421.
44▪. Murdaugh DL, Cox JE, Cook EW, et al. fMRI reactivity to high-calorie food pictures predicts short- and long-term outcome in a weight-loss program. NeuroImage 2012; 59:2709–2721.
Here it could be shown that reactivity of components of the reward circuitry when viewing food pictures was negatively correlated with the success of obese patients in a 12-week weight-loss program.
45. Hare TA, Malmaud J, Rangel A. Focusing attention on the health aspects of foods changes value signals in vmPFC and improves dietary choice. J Neurosci 2011; 31:11077–11087.
46. Hare TA, Camerer CF, Rangel A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science 2009; 324:646–648.
47. Sellitto M, Ciaramelli E, di Pellegrino G. Myopic discounting of future rewards after medial orbitofrontal damage in humans. J Neurosci 2010; 30:16429–16436.
48. Kishinevsky FI, Cox JE, Murdaugh DL, et al. fMRI reactivity on a delay discounting task predicts weight gain in obese women. Appetite 2012; 58:582–592.
49. Hollmann M, Hellrung L, Pleger B, et al. Neural correlates of the volitional regulation of the desire for food. Int J Obesity 2012; 36:648–655.
50. Felsted J a, Ren X, Chouinard-Decorte F, et al.
Genetically determined differences in brain response to a primary food reward. J Neurosci 2010; 30:2428–2432.
51. Kaurijoki S, Kuikka JT, Niskanen E, et al. Association of serotonin transporter promoter regulatory region polymorphism and cerebral activity to visual presentation of food. Clin Physiol Funct Imaging 2008; 28:270–276.
53. Cornier M-A, Salzberg AK, Endly DC, et al. Sex-based differences in the behavioral and neuronal responses to food. Physiol Behav 2010; 99:538–543.
54. Wang G-J, Volkow ND, Telang F, et al. Evidence of gender differences in the ability to inhibit brain activation elicited by food stimulation. Proc Natl Acad Sci USA 2009; 106:1249–1254.
55. Horstmann A, Busse FP, Mathar D, et al. Obesity-related differences between women and men in brain structure and goal-directed behavior. Front Hum Neurosci 2011; 5:1–9.
56. Mueller K, Anwander A, Möller HE, et al. Sex-dependent influences of obesity on cerebral white matter investigated by diffusion-tensor imaging. PloS One 2011; 6:1–9.
This article has been cited 1 time(s).
Plos OneSomato-Motor Haptic Processing in Posterior Inner Perisylvian Region (SII/pIC) of the Macaque MonkeyPlos One
brain imaging; cognitive control; eating; obesity; reward
© 2013 Lippincott Williams & Wilkins, Inc.
What does "Remember me" mean?
By checking this box, you'll stay logged in until you logout. You'll get easier access to your articles, collections,
media, and all your other content, even if you close your browser or shut down your
To protect your most sensitive data and activities (like changing your password),
we'll ask you to re-enter your password when you access these services.
What if I'm on a computer that I share with others?
If you're using a public computer or you share this computer with others, we recommend
that you uncheck the "Remember me" box.
Highlight selected keywords in the article text.
Data is temporarily unavailable. Please try again soon.
Readers Of this Article Also Read