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Ventral and Dorsal Striatum Networks in Obesity: Link to Food Craving and Weight Gain

  • Oren Contreras-Rodríguez
    Affiliations
    Red de Trastornos Adictivos, Universidad de Granada, Granada

    Psychiatry Department, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, and Centro de Investigación Biomédica en Red de Salud Mental, Barcelona
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  • Cristina Martín-Pérez
    Affiliations
    Red de Trastornos Adictivos, Universidad de Granada, Granada

    Institute of Neuroscience F. Oloriz, Universidad de Granada, Granada, Spain
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  • Raquel Vilar-López
    Affiliations
    Red de Trastornos Adictivos, Universidad de Granada, Granada

    Mind, Brain and Behavior Research Center, Universidad de Granada, Granada, Spain
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  • Antonio Verdejo-Garcia
    Correspondence
    Address correspondence to: Antonio Verdejo-García, Ph.D., Monash University, School of Psychological Sciences, 18 Innovation Walk (Clayton Campus), Melbourne 3800, Australia.
    Affiliations
    Red de Trastornos Adictivos, Universidad de Granada, Granada

    Institute of Neuroscience F. Oloriz, Universidad de Granada, Granada, Spain

    School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Australia
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Published:December 03, 2015DOI:https://doi.org/10.1016/j.biopsych.2015.11.020

      Abstract

      Background

      The food addiction model proposes that obesity overlaps with addiction in terms of neurobiological alterations in the striatum and related clinical manifestations (i.e., craving and persistence of unhealthy habits). Therefore, we aimed to examine the functional connectivity of the striatum in excess-weight versus normal-weight subjects and to determine the extent of the association between striatum connectivity and individual differences in food craving and changes in body mass index (BMI).

      Methods

      Forty-two excess-weight participants (BMI > 25) and 39 normal-weight participants enrolled in the study. Functional connectivity in the ventral and dorsal striatum was indicated by seed-based analyses on resting-state data. Food craving was indicated with subjective ratings of visual cues of high-calorie food. Changes in BMI between baseline and 12 weeks follow-up were assessed in 28 excess-weight participants. Measures of connectivity in the ventral striatum and dorsal striatum were compared between groups and correlated with craving and BMI change.

      Results

      Participants with excess weight displayed increased functional connectivity between the ventral striatum and the medial prefrontal and parietal cortices and between the dorsal striatum and the somatosensory cortex. Dorsal striatum connectivity correlated with food craving and predicted BMI gains.

      Conclusions

      Obesity is linked to alterations in the functional connectivity of dorsal striatal networks relevant to food craving and weight gain. These neural alterations are associated with habit learning and thus compatible with the food addiction model of obesity.

      Keywords

      In rich societies, the ubiquitous availability of appetizing high-calorie foods has increased the relevance of brain reward systems in governing food intake (
      • Volkow N.D.
      • Baler R.D.
      NOW vs LATER brain circuits: Implications for obesity and addiction.
      ). In this context, recent theories have proposed a food addiction model of obesity, by which sensitization of the brain centers that represent reward and habits (i.e., the striatum) can lead to food craving, inability to cut down food intake, and weight gain (
      • Volkow N.D.
      • Wang G.J.
      • Tomasi D.
      • Baler R.D.
      Obesity and addiction: Neurobiological overlaps.
      ,
      • Smith D.G.
      • Robbins T.W.
      The neurobiological underpinnings of obesity and binge eating: A rationale for adopting the food addiction model.
      ,
      • Volkow N.D.
      • Wang G.J.
      • Tomasi D.
      • Baler R.D.
      The addictive dimensionality of obesity.
      ).
      The food addiction model is grounded in neuroimaging evidence showing that obese individuals display increased food cue-evoked activation in cortical-striatal regions that code food-related reward value (i.e., ventral striatum) and hedonic properties (i.e., insula/somatosensory cortices) and food choices (i.e., medial prefrontal/orbitofrontal cortex) (
      • Rothemund Y.
      • Preuschhof C.
      • Bohner G.
      • Bauknecht H.C.
      • Klingebiel R.
      • Flor H.
      • Klapp B.F.
      Differential activation of the dorsal striatum by high-calorie visual food stimuli in obese individuals.
      ,
      • Stice E.
      • Spoor S.
      • Bohon C.
      • Veldhuizen M.G.
      • Small D.M.
      Relation of reward from food intake and anticipated food intake to obesity: A functional magnetic resonance imaging study.
      ,
      • Stice E.
      • Spoor S.
      • Bohon C.
      • Small D.M.
      Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele.
      ,
      • Stoeckel L.E.
      • Weller R.E.
      • Cook 3rd, E.W.
      • Twieg D.B.
      • Knowlton R.C.
      • Cox J.E.
      Widespread reward-system activation in obese women in response to pictures of high-calorie foods.
      ,
      • Potenza M.N.
      Obesity, food, and addiction: Emerging neuroscience and clinical and public health implications.
      ,
      • Fletcher P.C.
      • Napolitano A.
      • Skeggs A.
      • Miller S.R.
      • Delafont B.
      • Cambridge V.C.
      • et al.
      Distinct modulatory effects of satiety and sibutramine on brain responses to food images in humans: A double dissociation across hypothalamus, amygdala, and ventral striatum.
      ,
      • Davids S.
      • Lauffer H.
      • Thoms K.
      • Jagdhuhn M.
      • Hirschfeld H.
      • Domin M.
      • et al.
      Increased dorsolateral prefrontal cortex activation in obese children during observation of food stimuli.
      ). Moreover, food cue-evoked activation in these regions is associated with subjective measures of craving (
      • Potenza M.N.
      Obesity, food, and addiction: Emerging neuroscience and clinical and public health implications.
      ,
      • Fletcher P.C.
      • Napolitano A.
      • Skeggs A.
      • Miller S.R.
      • Delafont B.
      • Cambridge V.C.
      • et al.
      Distinct modulatory effects of satiety and sibutramine on brain responses to food images in humans: A double dissociation across hypothalamus, amygdala, and ventral striatum.
      ,
      • Davids S.
      • Lauffer H.
      • Thoms K.
      • Jagdhuhn M.
      • Hirschfeld H.
      • Domin M.
      • et al.
      Increased dorsolateral prefrontal cortex activation in obese children during observation of food stimuli.
      ,
      • Pelchat M.L.
      • Johnson A.
      • Chan R.
      • Valdez J.
      • Ragland J.D.
      Images of desire: Food craving activation during fMRI.
      ,
      • Hommer R.E.
      • Seo D.
      • Lacadie C.M.
      • Chaplin T.M.
      • Mayes L.C.
      • Sinha R.
      • Potenza M.N.
      Neural correlates of stress and favorite-food cue exposure in adolescents: A functional magnetic resonance imaging study.
      ) and body mass index (BMI) gains (
      • Stice E.
      • Yokum S.
      • Blum K.
      • Bohon C.
      Weight gain is associated with reduced striatal response to palatable food.
      ,
      • Stice E.
      • Yokum S.
      • Bohon C.
      • Marti N.
      • Smolen A.
      Reward circuitry responsivity to food predicts future increases in body mass: Moderating effects of DRD2 and DRD4.
      ,
      • Yokum S.
      • Ng J.
      • Stice E.
      Attentional bias to food images associated with elevated weight and future weight gain: An fMRI study.
      ). These regional alterations are plausibly associated with abnormalities in a broader network between the striatum and prefrontal regions representing food value. For example, neuroimaging studies have shown that functional connectivity between the ventral striatum and the medial prefrontal cortex correlates with external food sensitivity in healthy samples (
      • Passamonti L.
      • Rowe J.B.
      • Schwarzbauer C.
      • Ewbank M.P.
      • von dem Hagen E.
      • Calder A.J.
      Personality predicts the brain’s response to viewing appetizing foods: The neural basis of a risk factor for overeating.
      ). Moreover, positron emission tomography studies have shown that obese individuals, akin to addicts, display reduced dopamine D2 receptors in the striatum (
      • Wang G.J.
      • Volkow N.D.
      • Logan J.
      • Pappas N.R.
      • Wong C.T.
      • Zhu W.
      • et al.
      Brain dopamine and obesity.
      ) linked to lower orbitofrontal cortex metabolism (
      • Volkow N.D.
      • Wang G.J.
      • Telang F.
      • Fowler J.S.
      • Thanos P.K.
      • Logan J.
      • et al.
      Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: Possible contributing factors.
      ).
      The food addiction model also assumes that severity of obesity is associated with neuroadaptations in the dorsal striatum network (
      • Volkow N.D.
      • Wang G.J.
      • Tomasi D.
      • Baler R.D.
      The addictive dimensionality of obesity.
      ). This assumption is based on preclinical studies showing that self-administration of addictive drugs leads to neuroadaptations in the dorsal striatum (
      • Volkow N.D.
      • Wang G.J.
      • Tomasi D.
      • Baler R.D.
      Obesity and addiction: Neurobiological overlaps.
      ,
      • Smith D.G.
      • Robbins T.W.
      The neurobiological underpinnings of obesity and binge eating: A rationale for adopting the food addiction model.
      ,
      • Everitt B.J.
      • Robbins T.W.
      Drug addiction: Updating actions to habits to compulsions ten years on.
      ). This is further illustrated by human neuroimaging studies on drug craving: in severe substance users, drug-related cues activate dorsal striatum regions implicated in habits processing (
      • Volkow N.
      • Wang G.J.
      • Telang F.
      • Fowler J.S.
      • Logan J.
      • Childress A.R.
      • et al.
      Cocaine cues and dopamine in dorsal striatum: Mechanism of craving in cocaine addiction.
      ,
      • Wong D.F.
      • Kuwabara H.
      • Schretlen D.J.
      • Bonson K.R.
      • Zhou Y.
      • Nandi Y.
      • et al.
      Increased occupancy of dopamine receptors in human striatum during cue-elicited cocaine craving.
      ). Therefore, dorsal striatal neuroadaptations have been implicated in the transition between incentive-based and habit-based control of behavior. Hence, greater involvement of the dorsal striatum is predicted as food intake becomes addictive or compulsive (
      • Smith D.G.
      • Robbins T.W.
      The neurobiological underpinnings of obesity and binge eating: A rationale for adopting the food addiction model.
      ,
      • Volkow N.D.
      • Wang G.J.
      • Tomasi D.
      • Baler R.D.
      The addictive dimensionality of obesity.
      ). In obese patients, high-calorie food intake is subjectively perceived as less pleasurable but strongly driven by habits (
      • Neal D.T.
      • Wood W.
      • Wu M.
      • Kurlander D.
      The pull of the past: When do habits persist despite conflict with motives?.
      ,
      • van’t Riet J.
      • Sijtsema S.J.
      • Dagevos H.
      • De Bruijn G.J.
      The importance of habits in eating behaviour. An overview and recommendations for future research.
      ), and they show increased dorsal striatum activation in response to food cues (
      • Stice E.
      • Spoor S.
      • Bohon C.
      • Veldhuizen M.G.
      • Small D.M.
      Relation of reward from food intake and anticipated food intake to obesity: A functional magnetic resonance imaging study.
      ,
      • Stice E.
      • Spoor S.
      • Bohon C.
      • Small D.M.
      Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele.
      ,
      • Nummenmaa L.
      • Hirvonen J.
      • Hannukainen J.C.
      • Immonen H.
      • Lindroos M.M.
      • Salminen P.
      • Nuutila P.
      Dorsal striatum and its limbic connectivity mediate abnormal anticipatory reward processing in obesity.
      ) and reduced activation during food receipt (
      • Stice E.
      • Spoor S.
      • Bohon C.
      • Veldhuizen M.G.
      • Small D.M.
      Relation of reward from food intake and anticipated food intake to obesity: A functional magnetic resonance imaging study.
      ,
      • Stice E.
      • Spoor S.
      • Bohon C.
      • Small D.M.
      Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele.
      ).
      Altogether, ventral and dorsal striatum networks are relevant to the pathophysiology of obesity and to the association between obesity and addiction-related characteristics, such as craving and persistent high-calorie food intake. We aimed to contrast the functional connectivity of ventral and dorsal striatum networks in excess-weight versus normal-weight participants and to determine the association between functional connectivity in the striatum and individual differences in food craving and weight gain. We applied a seed-based resting-state functional connectivity approach to assess ventral and dorsal striatum networks (
      • Di Martino A.D.
      • Scheres A.
      • Margulies D.S.
      Functional connectivity of human striatum: A resting state fMRI study.
      ). Resting-state fluctuations reflect cognitive and emotional biases that contribute to shape individuals’ preferences; thus, striatal connectivity measures may have predictive validity in relation to craving and food intake (
      • Buckner R.L.
      • Vincent J.L.
      Unrest at rest: Default activity and spontaneous network correlations.
      ,
      • Raichle M.E.
      • Snyder A.Z.
      A default mode of brain function: A brief history of an evolving idea.
      ). We hypothesized that excess-weight participants compared with normal-weight control subjects would show increased functional connectivity in the ventral and dorsal striatum. Increased dorsal striatal connectivity would be associated with greater food craving and weight gain.

      Methods and Materials

      Participants

      Forty-two subjects with excess weight (BMI >25) and 39 subjects with normal weight enrolled in the study. Participants were recruited via general hospitals and community advertisement (i.e., local press, radio, and social media) and enrolled if they were aged 18 to 45 and had BMI >18. Exclusion criteria were 1) history of brain injury or diseases impacting the central nervous system; 2) history of substance use, major depression, or psychosis; 3) self-reported use of psychotropic medication; and 4) morbid obesity (BMI ≥ 40). The two groups had similar sociodemographic characteristics (Table 1). The Universidad de Granada Human Research Ethics Committee approved the study, and all participants provided informed consent.
      Table 1Demographics and Clinical Characteristics of the Study Groups
      Normal Weight (n = 39)Excess Weight (n = 42)
      Age (Years)33.07 (6.73)33.59 (6.16)
      Education (Years)18.18 (3.75)17.50 (3.77)
      Sex (Men/Women)18 (46.2%)/21 (53.8%)20 (47.6%)/22 (52.4%)
      BMI Baseline (kg/m2)
      p ≤ .01.
      ,
      BMI minimum/maximum values, normal weight 19/24.8, excess weight 25.20/38.30.
      22.09 (1.74)30.51 (3.63)
      BMI Change (kg/m2)
      Data for 24 normal-weight and 28 excess-weight participants at 12 weeks follow-up.
      ,
      Minimum/maximum values, normal weight −1.70/1.30, excess weight −4.60/4.70.
      −.03 (.72)−.60 (1.66)
      Food Craving5.47 (1.36)5.93 (1.39)
      Hunger Before fMRI15.03 (19.07)16.27 (18.72)
      Hunger After fMRI39.59 (28.62)44.20 (25.45)
      Impulsivity (Delay Discounting Area Under the Curve)
      Data from two normal-weight and two excess-weight participants are missing.
      .55 (.19).58 (.23)
      Compulsivity (Reversal Learning Perseveration Error Rate)
      Data from one excess weight are missing.
      1.66 (.69)1.67 (.74)
      Except for sex, all values are mean (± SD).
      BMI, body mass index; fMRI, functional magnetic resonance imaging.
      a p ≤ .01.
      b BMI minimum/maximum values, normal weight 19/24.8, excess weight 25.20/38.30.
      c Data for 24 normal-weight and 28 excess-weight participants at 12 weeks follow-up.
      d Minimum/maximum values, normal weight −1.70/1.30, excess weight −4.60/4.70.
      e Data from two normal-weight and two excess-weight participants are missing.
      f Data from one excess weight are missing.
      Participants were involved in two assessment sessions. At baseline, they 1) were measured and weighed to calculate BMI via an automated scale; 2) underwent a functional magnetic resonance imaging (fMRI) scan; 3) rated their food craving immediately after fMRI scan; and 4) had a 30-minute diet counseling session with a professional dietitian who provided specific strategies to lose weight (i.e., excess-weight group only). At 12-week follow-up, excess-weight participants (n = 28, 67% of the original sample) were reassessed to calculate change in BMI relative to baseline. Twelve weeks is the standard benchmark to assess the outcome of weight loss interventions (
      • Booth H.P.
      • Prevost T.A.
      • Wright A.J.
      • Gulliford M.C.
      Effectiveness of behavioral weight loss interventions delivered in a primary care setting: A systematic review and meta-analysis.
      ).

      Measures

      Imaging Data Acquisition and Preprocessing

      All participants were scanned at the same time of the day (4:00 to 6:00 PM) and after lunch, which is typically between 2:00 and 4:00 PM. Prescanner ratings of hunger (0–100 visual analog scale [VAS]) did not differ between groups (Table 1). Participants underwent a 6-minute resting-state scan. They were instructed to lie still with eyes closed. We used a 3.0 Tesla clinical magnetic resonance imaging scanner, equipped with an eight-channel phased-array head coil (Intera Achieva Philips Medical Systems, Eindhoven, The Netherlands). A T2*-weighted echo-planar imaging was obtained (repetition time = 2000 ms, echo time = 35 ms, field of view = 230 × 230 mm, 96 × 96 pixel matrix; flip angle = 90º, 21 4-mm axial slices, 1-mm gap, 180 whole-brain volumes). The sequence included four initial dummy volumes to allow the magnetization to reach equilibrium.

      Food Craving

      Participants viewed six photographs of highly appetizing foog., cheesecake, chocolate), and were instructed to rate their level of craving using VAS (range 0–100)." is correct as edited. -->d stimuli, all rich in sugar and fat content (e.g., cheesecake, chocolate), and were instructed to rate their level of craving using VAS (range 0–10). The dependent measure was the mean score of the six VAS ratings. To increase the task’s validity, all participants were pre-exposed to these foods in a catered tasting session conducted 1 week before the scan (Supplemental Figure S1).

      BMI Change

      Changes in BMI were computed by subtracting baseline BMI from follow-up BMI, and thus positive values reflected weight gain. This change index is a standard outcome measure in obesity treatment research (
      • Cortés B.
      • Bécker J.
      • Mories Álvarez M.T.
      • Marcos A.I.
      • Molina V.
      Contribution of baseline body mass index and leptin serum level to the prediction of early weight gain with atypical antipsychotics in schizophrenia.
      ,
      • Goyal A.
      • Terry P.D.
      • Superak H.M.
      • Nell-Dybdahl C.L.
      • Chowdhury R.
      • Phillips L.S.
      • Kutner M.H.
      Melatonin supplementation to treat the metabolic syndrome: A randomized controlled trial.
      ).

      Cognitive Measures

      Standard measures of impulsivity (i.e., delay discounting task) (
      • Kirby K.N.
      • Petry N.M.
      • Bickel W.K.
      Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls.
      ) and compulsivity or habit learning (i.e. reversal learning task) (
      • Contreras-Rodriguez O.
      • Albein-Urios N.
      • Perales J.C.
      • Martinez-Gonzalez J.M.
      • Vilar-Lopez R.
      • Fernandez-Serrano M.J.
      • et al.
      Cocaine-specific neuroplasticity in the ventral striatum networks is linked to delay discounting and drug relapse.
      ) were also assessed (see detailed procedures in the Supplement).

      Analyses

      Imaging Analyses and Preprocessing

      The functional imaging data were processed and analyzed using MATLAB version R2008b (The MathWorks Inc., Natick, Massachusetts) and statistical parametric mapping software (SPM8; The Welcome Department of Imaging Neuroscience, London, United Kingdom). Preprocessing steps involved motion correction, spatial normalization, and smoothing using a Gaussian filter (full width at half maximum 8 mm). Data were normalized to the standard SPM echo planar imaging (EPI) template and resliced to a 2-mm isotropic resolution in Montreal Neurological Institute space. We compared both study groups for potential differences in movement for translations and rotations and found no significant differences (mean total movement [SD], normal-weight control subjects = .31 [.18], excess-weight participants = .31 [.29], p = .34).

      Striatal Seed-Based Functional Connectivity Analyses

      Following prior work (
      • Di Martino A.D.
      • Scheres A.
      • Margulies D.S.
      Functional connectivity of human striatum: A resting state fMRI study.
      ,
      • Harrison B.J.
      • Soriano-Mas C.
      • Pujol J.
      • Ortiz H.
      • López-Solà M.
      • Hernández-Ribas R.
      • et al.
      Altered corticostriatal functional connectivity in obsessive-compulsive disorder.
      ), ventral and dorsal striatal subregions were distinguished using z < 7 mm as a marker for the ventral caudate (VC)/nucleus accumbens, z > 7 mm as a marker for dorsal caudate, and z = 2 as the boundary between the dorsal and ventral putamen per hemisphere. Respective seeds of interest were placed in VC (corresponding approximately to the nucleus accumbens) (x = ±9, y = 9, z = −8), ventral rostral putamen (VP) (x = ±20, y = 12, z = −3), dorsal caudate (DC) (x = ±13, y = 15, z = 9), and dorsal caudal putamen (x = ±28, y = 1, z = 3) using 3.5-mm-radius spheres. We included in the model two intermediate seeds (of no interest) located in the ventral caudate superior (x = ±10, y = 15, z = 0) and dorsal rostral putamen (x = ±25, y = 8, z = 6) to replicate the fine striatal parcellation method, and no seed placements were made in the globus pallidus, substantia nigra, or subthalamic nucleus considering the spatial data resolution and the smoothing.
      First-level (single subject) maps were estimated in whole-brain SPM8 linear regression analyses for each striatal seed region (
      • Harrison B.J.
      • Soriano-Mas C.
      • Pujol J.
      • Ortiz H.
      • López-Solà M.
      • Hernández-Ribas R.
      • et al.
      Altered corticostriatal functional connectivity in obsessive-compulsive disorder.
      ) by including its mean activity time courses extracted via the MarsBaR toolbox (http://marsbar.sourceforge.net) (
      • Brett M.
      • Valabregue R.
      • Poline J.
      Region of interest analysis using an SPM toolbox.
      ) together with nuisance signals as predictors of interest and no interest. Nuisance signals included six head-motion parameters (three translations and three rotations) and time courses representing mean signal fluctuations in white matter, cerebrospinal fluid, and the entire brain. Separate first-level analyses were carried out for right and left hemisphere striatal regions. A high-pass filter (128 seconds) was used to remove low-frequency drifts. Contrast images were generated for each subject by estimating the regression coefficient between all brain voxels and each seed’s time series and were then included in separate two-sample models to assess within- and between-group effects. All imaging results were considered significant with a cluster of 1960 mm3 (245 voxels) at a height threshold of p < .005, which satisfied the familywise error rate correction of pfamilywise error < .05 according to Monte Carlo simulations using Alphasim within the REST toolbox (http://www.restfmri.net) (
      • Song X.W.
      • Dong Z.Y.
      • Long X.Y.
      • Li S.F.
      • Zuo X.N.
      • Zhu C.Z.
      • et al.
      REST: A toolkit for resting-state functional magnetic resonance imaging data processing.
      ) with a cluster connection radius of 5 mm, 12-mm full width at half maximum smoothness, and incorporating a gray matter mask volume of 128.190 voxels (2 × 2 × 2 mm). The selected threshold was deemed appropriate to control for type I error, as the targeted striatal networks have been previously defined and have anatomical specificity (
      • Di Martino A.D.
      • Scheres A.
      • Margulies D.S.
      Functional connectivity of human striatum: A resting state fMRI study.
      ,
      • Harrison B.J.
      • Soriano-Mas C.
      • Pujol J.
      • Ortiz H.
      • López-Solà M.
      • Hernández-Ribas R.
      • et al.
      Altered corticostriatal functional connectivity in obsessive-compulsive disorder.
      ).

      Associations With Food Craving and Prediction of BMI Change

      We conducted correlations between the functional connectivity maps of the striatum and food craving in SPM8. We conducted these analyses in each group separately, as we were specifically interested in individual differences in this relationship within the excess-weight group. Analyses were considered significant at a height threshold of p < .005, 1960 mm3 (245 voxels) whole brain. A multiple regression analysis was conducted in SPSS version 22.0 (IBM Corp., Armonk, NY) to examine if functional connectivity associated with craving predicted BMI change. This analysis was thresholded at p ≤ .01 (at least 10% prediction).

      Results

      Functional Connectivity in Ventral and Dorsal Striatum

      Within-group positive and negative functional connectivity maps of the ventral and dorsal striatum networks overlapped with previously described neurofunctional maps of these networks (
      • Di Martino A.D.
      • Scheres A.
      • Margulies D.S.
      Functional connectivity of human striatum: A resting state fMRI study.
      ,
      • Harrison B.J.
      • Soriano-Mas C.
      • Pujol J.
      • Ortiz H.
      • López-Solà M.
      • Hernández-Ribas R.
      • et al.
      Altered corticostriatal functional connectivity in obsessive-compulsive disorder.
      ) (Supplemental Table S1 and Supplemental Figure S2).

      Between-Group Differences in Functional Connectivity

      Results are displayed in Figure 1 and Table 2 and are described below.
      Figure thumbnail gr1
      Figure 1Brain regions showing increased connectivity (red) or increased anticorrelation (blue) with the striatum in excess-weight compared with normal-weight participants. The right hemisphere corresponds to the right side of axial views, and the sagittal images show the right hemisphere. The color bars indicate t values. Results are displayed at p < .005, uncorrected. DC, dorsal caudate; VC, ventral caudate; VP, ventral rostral putamen.
      Table 2Between-Group Differences in Striatal Functional Connectivity
      SeedBrain Regionx, y, ztCSDirection
      VCMedial PFC6, 56, −104.5666Excess > Normal Weight
      PCC−6, −68, 303.7861Excess > Normal Weight
      Angular gyrus−40, −64, 403.9443Excess > Normal Weight
      Occipital cortex6, −90, −123.6438Excess > Normal Weight
      Dorsal ACC-SMA8, 0, 643.8399Normal > Excess Weight
      Posterior insula48, −34, 184.2554Normal > Excess Weight
      VPInsula40, −12, 164.01175Excess > Normal Weight
      −40, −8, 23.71306
      Part of the large cluster.
      Excess > Normal Weight
      Somatosensory cortex56, −10, 244.51175Excess > Normal Weight
      −54, −12, 303.81306
      Part of the large cluster.
      Excess > Normal Weight
      Temporal−58, −10, −84.51306
      Part of the large cluster.
      Excess > Normal Weight
      Lateral OFC−26, 20, −224.2789Normal > Excess Weight
      DCSomatosensory cortex40, −38, 603.5521Excess > Normal Weight
      −18, −32, 564.2692Excess > Normal Weight
      Cerebellum−22, −58, −343.91558Normal > Excess Weight
      Coordinates (x, y, z) are given in Montreal Neurological Institute atlas space.
      ACC, anterior cingulate cortex; CS, cluster size; DC, dorsal caudate; OFC, orbitofrontal cortex; PCC, posterior cingulate cortex; PFC, prefrontal cortex; SMA, supplementary motor area; VC, ventral caudate; VP, ventral putamen.
      a Part of the large cluster.

      Ventral Striatum

      Excess-weight participants compared with normal-weight control subjects showed increased functional connectivity between the VC seed and the medial prefrontal cortex and between both ventral striatum seeds (VC and VP) and the parietal cortex, including posterior cingulate, angular gyrus, and somatosensory regions. Moreover, excess-weight participants showed increased anticorrelation between the VC seed and the dorsal anterior cingulate cortex and the posterior insula and between the VP seed and the lateral orbitofrontal cortex.

      Dorsal Striatum

      Excess-weight participants showed increased functional connectivity between the DC seed and the somatosensory cortex. Moreover, excess-weight participants showed increased anticorrelation between the DC seed and the cerebellum. There were no group differences in the dorsal caudal putamen seed at the selected threshold.

      Correlations With Food Craving

      In excess-weight participants, food craving was positively associated with functional connectivity between the DC seed and the somatosensory cortex (x = 46, y = −32, z = 62, t = 3.8, p < .005, 265 voxels). In normal-weight participants, in turn, food craving was positively associated with functional connectivity between the VP seed and the orbitofrontal cortex (x = −32, y = 50, z = −2, t = 4.8, p < .005, 459 voxels) (Figure 2). Fisher’s tests of between-group differences were not significant (DC-somatosensory: F = 1.32, p = .09; VP-orbitofrontal cortex: Fisher’s F = 1.06, p = .14). Additional correlations (i.e., functional connectivity patterns that do not map on between-group differences) are reported in Supplemental Table S2.
      Figure thumbnail gr2
      Figure 2Correlations between food craving and the connectivity between the dorsal caudate (DC) and the somatosensory cortex in excess-weight participants (top panel, MNI coordinates, DC-Somatosensory, x = 46, y = −32, z = 62 mm) and the connectivity between the ventral putamen (VP) and the orbitofrontal cortex in normal-weight controls (bottom panel, MNI coordinates, VP-OFC, x = −32, y = 50, z = −2 mm). In plots, red corresponds to excess-weight participants, green to normal-weight controls. The right hemisphere corresponds to the right side of axial views, and the sagittal image show the left hemisphere. The color bar indicates t values. Results are displayed at p < .005, uncorrected. VP, ventral putamen.
      To further explore the link between BMI-related variation in striatum networks and food craving, we examined this association within subgroups with obesity (BMI ≥ 30, n = 21), overweight (BMI > 25 < 30, n = 21), and normal weight. Thus, we extracted the eigenvariate signal from the peak voxel in the orbitofrontal cortex cluster linked to the VP seed and food craving in normal-weight control subjects and the eigenvariate signal from the peak voxel in the somatosensory cortex cluster linked to the DC seed and food craving in excess-weight participants. We found that the positive association between VP-orbitofrontal functional connectivity and food craving was lower within participants with greater BMI (i.e., normal weight, r = .476, p = .001; overweight, r = .313, p = .044; obese, r = .141, p = .373) (Supplemental Figure S2). Conversely, the positive association between DC-somatosensory cortex functional connectivity and food craving was higher in participants with greater BMI (i.e., normal weight, r = .073, p = .527; overweight, r = .326, p = .035; obese, r = .378, p = .014).

      Prediction of BMI Change

      On average, BMI changed from 30.51 (SD = 3.63) to 29.95 (SD = 3.43) among excess-weight participants (1.96% change). The two networks associated with craving were included in a multiple regression model to predict the change in BMI. The functional connectivity between the VP seed and the orbitofrontal cortex was not associated with BMI change (FChange 1,53 = .001, p = .982, R2 = .000), yet inclusion of the functional connectivity between the DC seed and the somatosensory cortex showed statistically significant effects (FChange 1,53 = 6.787, p = .01, R2 = .114). DC-somatosensory functional connectivity was positively associated with BMI change, accounting for 11% of BMI change (Figure 3).
      Figure thumbnail gr3
      Figure 3Plot showing change in BMI associated with increased connectivity between the dorsal caudate (DC) and the somatosensory cortex (MNI coordinates, x = 46, y = −32, z = 62 mm).

      Sensitivity and Post Hoc Analyses

      To examine if longitudinal associations between functional connectivity and BMI gain were driven by baseline correlations, we conducted bivariate correlations between the functional connectivity associated with food craving and BMI at baseline and BMI change. The functional connectivity between the DC seed and the somatosensory cortex was only associated with BMI change (r = .33, p = .01) but not with baseline BMI (r = −.11, p = .33).
      To better delineate the implications of the connectivity findings, we ran post hoc correlations between the striatal connectivity patterns and cognitive measures of impulsivity and compulsivity. The functional connectivity between the DC seed and the somatosensory cortex was significantly associated with the measure of compulsivity (i.e., reversal learning perseveration error rate) in excess-weight participants (x = 52, y = −24, z = 44, t = 3.6, p < .005, 17,636 voxels). There were no significant correlations with impulsivity. Additional correlations between cognitive measures and functional connectivity patterns that did not overlap with the between-group differences in striatal connectivity are reported in the Supplemental Tables S3 and S4.

      Discussion

      We found that individuals with excess weight display increased functional connectivity between the ventral striatum and medial prefrontal and parietal cortices and between the dorsal striatum and the somatosensory cortices. They also show reduced functional connectivity between the ventral striatum and the dorsal anterior cingulate cortex, the insula, and the lateral orbitofrontal cortex. Dorsal striatal connectivity positively correlates with food craving and predicts BMI gains after 12 weeks. These findings are consistent with the food addiction model, which proposes that obesity is associated with neural adaptations in the striatum and particularly within the dorsal striatum network (
      • Smith D.G.
      • Robbins T.W.
      The neurobiological underpinnings of obesity and binge eating: A rationale for adopting the food addiction model.
      ,
      • Volkow N.D.
      • Wang G.J.
      • Tomasi D.
      • Baler R.D.
      The addictive dimensionality of obesity.
      ).
      Ventral striatum findings are consistent with previous studies showing functional connectivity differences between obesity and normal weight via complementary approaches. A recent study using a region-of-interest based approach showed increased functional connectivity between the ventral caudate and the medial frontal cortex in excess-weight individuals (
      • Coveleskie K.
      • Gupta A.
      • Kilpatrick L.A.
      • Mayer E.D.
      • Ashe-McNalley C.
      • Stains J.
      • et al.
      Altered functional connectivity within the central reward network in overweight and obese women.
      ). Increased ventral caudate connectivity is also consistent with our findings in stimulant-addicted individuals (
      • Contreras-Rodriguez O.
      • Albein-Urios N.
      • Perales J.C.
      • Martinez-Gonzalez J.M.
      • Vilar-Lopez R.
      • Fernandez-Serrano M.J.
      • et al.
      Cocaine-specific neuroplasticity in the ventral striatum networks is linked to delay discounting and drug relapse.
      ). Enhanced ventral striatal connectivity aligns with the incentive sensitization theory of addiction, which proposes that addiction leads to increased dopamine excitability in this pathway (
      • Robinson T.E.
      • Berridge K.C.
      The incentive sensitization theory of addiction: Some current issues.
      ,
      • Ma N.
      • Liu Y.
      • Li N.
      • Wang C.X.
      • Zhang H.
      • Jiang X.F.
      • et al.
      Addiction related alteration in resting-state brain connectivity.
      ). However, fMRI studies in heroin addicts have shown decreased connectivity in this network (
      • Zhang Y.
      • Gong J.
      • Xie C.
      • Ye M.E.
      • Jin X.
      • Song H.
      • et al.
      Alterations in brain connectivity in three sub-regions of the anterior cingulate cortex in heroin-dependent individuals: Evidence from resting state fMRI.
      ,
      • Wang Y.
      • Zhu J.
      • Li Q.
      • Li W.
      • Wu N.
      • Zheng Y.
      • et al.
      Altered fronto-striatal and fronto-cerebellar circuits in heroin-dependent individuals: A resting-state FMRI study.
      ), and a recent study has shown that obesity is associated with alterations in opioid, but not dopamine, receptors (
      • Karlsson H.K.
      • Tuominen L.
      • Tuulari J.J.
      • Hirvonen J.
      • Parkkola R.
      • Helin S.
      • et al.
      Obesity is associated with decreased μ-opioid but unaltered dopamine D2 receptor availability in the brain.
      ). Therefore, more studies are needed to establish the neuropharmacologic underpinnings of these findings and the overlap between obesity and different types of addiction (e.g., psychostimulant vs. opiate). Increased connectivity between the ventral striatum and the posterior cingulate cortex and the angular gyrus is consistent with Kullmann et al. (
      • Kullmann S.
      • Heni M.
      • Veit R.
      • Ketterer C.
      • Schick F.
      • Häring H.U.
      • et al.
      The obese brain: Association of body mass index and insulin sensitivity with resting state network functional connectivity.
      ), which suggested that abnormal connectivity in the brain default network may contribute to overeating through an imbalance between reward-affective and cognitive processes. Decreased connectivity between the ventral caudate and the dorsal anterior cingulate cortex/insula accords with previous findings showing abnormal recruitment of brain regions involved in interoception in obese subjects (
      • Mata F.
      • Verdejo-Roman J.
      • Soriano-Mas C.
      • Verdejo-Garcia A.
      Insula tuning towards external eating versus interoceptive input in adolescents with overweight and obesity.
      ).
      Dorsal striatum findings are consistent with the food addiction model in relation to the components of craving and inability to cut down food intake (
      • Smith D.G.
      • Robbins T.W.
      The neurobiological underpinnings of obesity and binge eating: A rationale for adopting the food addiction model.
      ,
      • Volkow N.D.
      • Wang G.J.
      • Tomasi D.
      • Baler R.D.
      The addictive dimensionality of obesity.
      ,
      • Ziauddeen H.
      • Farooqi I.S.
      • Fletcher P.C.
      Obesity and the brain: How convincing is the addiction model?.
      ). The dorsal caudate-somatosensory cortex association with food craving accords with previous studies showing increased dorsal caudate activation in response to palatable food cues in obesity (
      • Rothemund Y.
      • Preuschhof C.
      • Bohner G.
      • Bauknecht H.C.
      • Klingebiel R.
      • Flor H.
      • Klapp B.F.
      Differential activation of the dorsal striatum by high-calorie visual food stimuli in obese individuals.
      ,
      • Nummenmaa L.
      • Hirvonen J.
      • Hannukainen J.C.
      • Immonen H.
      • Lindroos M.M.
      • Salminen P.
      • Nuutila P.
      Dorsal striatum and its limbic connectivity mediate abnormal anticipatory reward processing in obesity.
      ). Given the role of the somatosensory cortex in taste processing (
      • Small D.M.
      • Zald D.H.
      • Jones-Gotman M.
      • Zatorre R.J.
      • Pardo J.V.
      • Frey S.
      Human cortical gustatory areas: A review of functional neuroimaging data.
      ), increased connectivity with the caudate is likely associated with enhanced valuation of high-palatable foods (
      • Small D.M.
      • Jones-Gotman M.
      • Dagher A.
      Feeding-induced dopamine release in dorsal striatum correlates with meal pleasantness ratings in healthy human volunteers.
      ). Moreover, the link between the dorsal striatum network and food craving was significant in the excess-weight group and greater within participants with higher BMI values, in agreement with the notion of an addictive dimensionality of obesity (
      • Volkow N.D.
      • Wang G.J.
      • Tomasi D.
      • Baler R.D.
      The addictive dimensionality of obesity.
      ). The finding that functional connectivity between the dorsal caudate and the somatosensory cortex longitudinally predicts weight gain also accords with previous neuroimaging findings showing that increased dorsal caudate activation is associated with BMI gains in obese subjects (
      • Stice E.
      • Spoor S.
      • Bohon C.
      • Veldhuizen M.G.
      • Small D.M.
      Relation of reward from food intake and anticipated food intake to obesity: A functional magnetic resonance imaging study.
      ,
      • Stice E.
      • Spoor S.
      • Bohon C.
      • Small D.M.
      Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele.
      ). Furthermore, caudate activation during food receipt differentiates obese subjects who gain weight relative to obese subjects who lose weight or remain stable at 6-month follow-up (
      • Stice E.
      • Spoor S.
      • Bohon C.
      • Veldhuizen M.G.
      • Small D.M.
      Relation of reward from food intake and anticipated food intake to obesity: A functional magnetic resonance imaging study.
      ). Our functional connectivity approach extends these findings, demonstrating that a broad network linking the dorsal caudate with sensory/gustatory regions is implicated in future weight gain. Notably, this network is implicated in the coding of the energetic value of foods (
      • Toepel U.
      • Knebel J.F.
      • Hudry J.
      • le Coutre J.
      • Murray M.M.
      The brain tracks the energetic value in food images.
      ,
      • Asmaro D.
      • Liotti M.
      High-caloric and chocolate stimuli processing in healthy humans: An integration of functional imaging and electrophysiological findings.
      ,
      • Ohla K.
      • Toepel U.
      • le Coutre J.
      • Hudry J.
      Visual-gustatory interaction: Orbitofrontal and insular cortices mediate the effect of high-calorie visual food cues on taste pleasantness.
      ) and in habit learning, as indicated by correlations with reversal learning. Therefore, these findings may contribute to explain how ingrained preferences for high-calorie food can trigger eating habits beyond homeostatic needs (
      • Stunkard A.J.
      • Berkowitz R.I.
      • Stallings V.A.
      • Schoeller D.A.
      Energy intake, not energy output, is a determinant of body size in infants.
      ,
      • Salbe A.D.
      • DelParigi A.
      • Pratley R.E.
      • Drewnowski A.
      • Tataranni P.A.
      Taste preferences and body weight changes in an obesity-prone population.
      ). Future studies are needed to formally evaluate this mechanism.
      We conclude that excess weight is associated with striatocortical functional connectivity alterations that are consistent with a food addiction model. Specifically, the associations with food craving and BMI gain during diet resonate with the DSM substance use disorders’ criteria of craving and inability to cut down drug seeking (i.e., in this case food seeking) behavior (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders.
      ). Our results should be appraised in the context of limitations. The cross-sectional design does not allow us to determine if striatum network alterations are a consequence of excessive weight or premorbid vulnerability factors. Further longitudinal research is necessary to understand the causal link between the brain reward system and obesity. The spatial resolution of fMRI did not allow us to differentiate the connectivity of medial versus lateral territories of the dorsal striatum. This dissociation is potentially relevant for human obesity, as preclinical data have shown that habit-based behaviors are supported by the anterior dorsolateral striatum (
      • Balleine B.W.
      • O’Doherty J.P.
      Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action.
      ,
      • Yin H.H.
      • Ostlund S.B.
      • Knowlton B.J.
      • Balleine B.W.
      The role of the dorsomedial striatum in instrumental conditioning.
      ). Our correlation analyses showed significant correlations between dorsal striatal connectivity and food craving within the excess-weight group, but correlations did not significantly differ between groups (i.e., Fisher’s test) and thus the specificity of this finding must be interpreted with caution. Our longitudinal analyses testing the clinical meaningfulness of functional connectivity were conducted in a subgroup representing only 67% of the original sample. Nonetheless, this attrition rate is consistent with that reported in meta-analytic research on obesity interventions (
      • Martin A.
      • Saunders D.H.
      • Shenkin S.D.
      • Sproule J.
      Lifestyle intervention for improving school achievement in overweight or obese children and adolescents.
      ), and participants in the follow-up subgroup did not significantly differ from the main sample in antecedent variables.

      Acknowledgments and Disclosures

      This study was funded by the Project Grant NEUROCOBE (Grant No. HUM-6635) from the Andalusian Council of Innovation, Science and Industry (Principal Investigator: Antonio Verdejo-Garcia). Dr. Contreras-Rodriguez is currently funded by a Sara Borrell postdoctoral fellowship (Grant No. CD14/00246) from the Carlos III Health Institute.
      The authors report no biomedical financial interests or potential conflicts of interests.

      Appendix A. Supplementary Materials

      References

        • Volkow N.D.
        • Baler R.D.
        NOW vs LATER brain circuits: Implications for obesity and addiction.
        Trends Neurosci. 2015; 38: 345-352
        • Volkow N.D.
        • Wang G.J.
        • Tomasi D.
        • Baler R.D.
        Obesity and addiction: Neurobiological overlaps.
        Obes Rev. 2013; 14: 2-18
        • Smith D.G.
        • Robbins T.W.
        The neurobiological underpinnings of obesity and binge eating: A rationale for adopting the food addiction model.
        Biol Psychiatry. 2013; 73: 804-810
        • Volkow N.D.
        • Wang G.J.
        • Tomasi D.
        • Baler R.D.
        The addictive dimensionality of obesity.
        Biol Psychiatry. 2013; 73: 811-818
        • Rothemund Y.
        • Preuschhof C.
        • Bohner G.
        • Bauknecht H.C.
        • Klingebiel R.
        • Flor H.
        • Klapp B.F.
        Differential activation of the dorsal striatum by high-calorie visual food stimuli in obese individuals.
        Neuroimage. 2007; 37: 410-421
        • Stice E.
        • Spoor S.
        • Bohon C.
        • Veldhuizen M.G.
        • Small D.M.
        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
        • Stice E.
        • Spoor S.
        • Bohon C.
        • Small D.M.
        Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele.
        Science. 2008; 322: 449-452
        • Stoeckel L.E.
        • Weller R.E.
        • Cook 3rd, E.W.
        • Twieg D.B.
        • Knowlton R.C.
        • Cox J.E.
        Widespread reward-system activation in obese women in response to pictures of high-calorie foods.
        Neuroimage. 2008; 41: 636-647
        • Potenza M.N.
        Obesity, food, and addiction: Emerging neuroscience and clinical and public health implications.
        Neuropsychopharmacology. 2014; 39: 249-250
        • Fletcher P.C.
        • Napolitano A.
        • Skeggs A.
        • Miller S.R.
        • Delafont B.
        • Cambridge V.C.
        • et al.
        Distinct modulatory effects of satiety and sibutramine on brain responses to food images in humans: A double dissociation across hypothalamus, amygdala, and ventral striatum.
        J Neurosci. 2010; 30: 14346-14355
        • Davids S.
        • Lauffer H.
        • Thoms K.
        • Jagdhuhn M.
        • Hirschfeld H.
        • Domin M.
        • et al.
        Increased dorsolateral prefrontal cortex activation in obese children during observation of food stimuli.
        Int J Obes (Lond). 2010; 34: 94-104
        • Pelchat M.L.
        • Johnson A.
        • Chan R.
        • Valdez J.
        • Ragland J.D.
        Images of desire: Food craving activation during fMRI.
        Neuroimage. 2004; 23: 1486-1493
        • Hommer R.E.
        • Seo D.
        • Lacadie C.M.
        • Chaplin T.M.
        • Mayes L.C.
        • Sinha R.
        • Potenza M.N.
        Neural correlates of stress and favorite-food cue exposure in adolescents: A functional magnetic resonance imaging study.
        Hum Brain Mapp. 2013; 34: 2561-2573
        • Stice E.
        • Yokum S.
        • Blum K.
        • Bohon C.
        Weight gain is associated with reduced striatal response to palatable food.
        J Neurosci. 2010; 30: 13105-13109
        • Stice E.
        • Yokum S.
        • Bohon C.
        • Marti N.
        • Smolen A.
        Reward circuitry responsivity to food predicts future increases in body mass: Moderating effects of DRD2 and DRD4.
        Neuroimage. 2010; 50: 1618-1625
        • Yokum S.
        • Ng J.
        • Stice E.
        Attentional bias to food images associated with elevated weight and future weight gain: An fMRI study.
        Obesity (Silver Spring). 2011; 19: 1775-1783
        • Passamonti L.
        • Rowe J.B.
        • Schwarzbauer C.
        • Ewbank M.P.
        • von dem Hagen E.
        • Calder A.J.
        Personality predicts the brain’s response to viewing appetizing foods: The neural basis of a risk factor for overeating.
        J Neurosci. 2009; 29: 43-51
        • Wang G.J.
        • Volkow N.D.
        • Logan J.
        • Pappas N.R.
        • Wong C.T.
        • Zhu W.
        • et al.
        Brain dopamine and obesity.
        Lancet. 2001; 357: 354-357
        • Volkow N.D.
        • Wang G.J.
        • Telang F.
        • Fowler J.S.
        • Thanos P.K.
        • Logan J.
        • et al.
        Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: Possible contributing factors.
        Neuroimage. 2008; 42: 1537-1543
        • Everitt B.J.
        • Robbins T.W.
        Drug addiction: Updating actions to habits to compulsions ten years on.
        Annu Rev Psychol. 2016; 67: 23-50
        • Volkow N.
        • Wang G.J.
        • Telang F.
        • Fowler J.S.
        • Logan J.
        • Childress A.R.
        • et al.
        Cocaine cues and dopamine in dorsal striatum: Mechanism of craving in cocaine addiction.
        J Neurosci. 2006; 26: 6583-6588
        • Wong D.F.
        • Kuwabara H.
        • Schretlen D.J.
        • Bonson K.R.
        • Zhou Y.
        • Nandi Y.
        • et al.
        Increased occupancy of dopamine receptors in human striatum during cue-elicited cocaine craving.
        Neuropsychopharmacology. 2006; 31: 2716-2727
        • Neal D.T.
        • Wood W.
        • Wu M.
        • Kurlander D.
        The pull of the past: When do habits persist despite conflict with motives?.
        Pers Soc Psychol Bull. 2011; 37: 1428-1437
        • van’t Riet J.
        • Sijtsema S.J.
        • Dagevos H.
        • De Bruijn G.J.
        The importance of habits in eating behaviour. An overview and recommendations for future research.
        Appetite. 2011; 57: 585-596
        • Nummenmaa L.
        • Hirvonen J.
        • Hannukainen J.C.
        • Immonen H.
        • Lindroos M.M.
        • Salminen P.
        • Nuutila P.
        Dorsal striatum and its limbic connectivity mediate abnormal anticipatory reward processing in obesity.
        PLoS One. 2012; 7: e31089
        • Di Martino A.D.
        • Scheres A.
        • Margulies D.S.
        Functional connectivity of human striatum: A resting state fMRI study.
        Cereb Cortex. 2008; 18: 2735-2747
        • Buckner R.L.
        • Vincent J.L.
        Unrest at rest: Default activity and spontaneous network correlations.
        Neuroimage. 2007; 37: 1091-1096
        • Raichle M.E.
        • Snyder A.Z.
        A default mode of brain function: A brief history of an evolving idea.
        Neuroimage. 2007; 37: 1083-1090
        • Booth H.P.
        • Prevost T.A.
        • Wright A.J.
        • Gulliford M.C.
        Effectiveness of behavioral weight loss interventions delivered in a primary care setting: A systematic review and meta-analysis.
        Fam Pract. 2014; 31: 643-653
        • Cortés B.
        • Bécker J.
        • Mories Álvarez M.T.
        • Marcos A.I.
        • Molina V.
        Contribution of baseline body mass index and leptin serum level to the prediction of early weight gain with atypical antipsychotics in schizophrenia.
        Psychiatry Clin Neurosci. 2014; 68: 127-132
        • Goyal A.
        • Terry P.D.
        • Superak H.M.
        • Nell-Dybdahl C.L.
        • Chowdhury R.
        • Phillips L.S.
        • Kutner M.H.
        Melatonin supplementation to treat the metabolic syndrome: A randomized controlled trial.
        Diabetol Metab Syndr. 2014; 6: 124
        • Kirby K.N.
        • Petry N.M.
        • Bickel W.K.
        Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls.
        J Exp Psychol Gen. 1999; 128: 78-87
        • Contreras-Rodriguez O.
        • Albein-Urios N.
        • Perales J.C.
        • Martinez-Gonzalez J.M.
        • Vilar-Lopez R.
        • Fernandez-Serrano M.J.
        • et al.
        Cocaine-specific neuroplasticity in the ventral striatum networks is linked to delay discounting and drug relapse.
        Addiction. 2015; 110: 1953-1962
        • Harrison B.J.
        • Soriano-Mas C.
        • Pujol J.
        • Ortiz H.
        • López-Solà M.
        • Hernández-Ribas R.
        • et al.
        Altered corticostriatal functional connectivity in obsessive-compulsive disorder.
        Arch Gen Psychiatry. 2009; 66: 1189-1200
        • Brett M.
        • Valabregue R.
        • Poline J.
        Region of interest analysis using an SPM toolbox.
        Neuroimage 16:Supplement 1. 2003;
        • Song X.W.
        • Dong Z.Y.
        • Long X.Y.
        • Li S.F.
        • Zuo X.N.
        • Zhu C.Z.
        • et al.
        REST: A toolkit for resting-state functional magnetic resonance imaging data processing.
        PLoS One. 2011; 6: e25031
        • Coveleskie K.
        • Gupta A.
        • Kilpatrick L.A.
        • Mayer E.D.
        • Ashe-McNalley C.
        • Stains J.
        • et al.
        Altered functional connectivity within the central reward network in overweight and obese women.
        Nutr Diabetes. 2015; 5: e148
        • Robinson T.E.
        • Berridge K.C.
        The incentive sensitization theory of addiction: Some current issues.
        Philos Trans R Soc Lond B Biol Sci. 2008; 363: 3137-3146
        • Ma N.
        • Liu Y.
        • Li N.
        • Wang C.X.
        • Zhang H.
        • Jiang X.F.
        • et al.
        Addiction related alteration in resting-state brain connectivity.
        Neuroimage. 2010; 46: 738-744
        • Zhang Y.
        • Gong J.
        • Xie C.
        • Ye M.E.
        • Jin X.
        • Song H.
        • et al.
        Alterations in brain connectivity in three sub-regions of the anterior cingulate cortex in heroin-dependent individuals: Evidence from resting state fMRI.
        Neuroscience. 2015; 284: 998-1010
        • Wang Y.
        • Zhu J.
        • Li Q.
        • Li W.
        • Wu N.
        • Zheng Y.
        • et al.
        Altered fronto-striatal and fronto-cerebellar circuits in heroin-dependent individuals: A resting-state FMRI study.
        PLoS One. 2013; 8: e58098
        • Karlsson H.K.
        • Tuominen L.
        • Tuulari J.J.
        • Hirvonen J.
        • Parkkola R.
        • Helin S.
        • et al.
        Obesity is associated with decreased μ-opioid but unaltered dopamine D2 receptor availability in the brain.
        J Neurosci. 2015; 35: 3959-3965
        • Kullmann S.
        • Heni M.
        • Veit R.
        • Ketterer C.
        • Schick F.
        • Häring H.U.
        • et al.
        The obese brain: Association of body mass index and insulin sensitivity with resting state network functional connectivity.
        Hum Brain Mapp. 2012; 33: 1052-1061
        • Mata F.
        • Verdejo-Roman J.
        • Soriano-Mas C.
        • Verdejo-Garcia A.
        Insula tuning towards external eating versus interoceptive input in adolescents with overweight and obesity.
        Appetite. 2015; 93: 24-30
        • Ziauddeen H.
        • Farooqi I.S.
        • Fletcher P.C.
        Obesity and the brain: How convincing is the addiction model?.
        Nat Rev Neuroscience. 2012; 13: 279-286
        • Small D.M.
        • Zald D.H.
        • Jones-Gotman M.
        • Zatorre R.J.
        • Pardo J.V.
        • Frey S.
        Human cortical gustatory areas: A review of functional neuroimaging data.
        Neuroreport. 1999; 10: 7-14
        • Small D.M.
        • Jones-Gotman M.
        • Dagher A.
        Feeding-induced dopamine release in dorsal striatum correlates with meal pleasantness ratings in healthy human volunteers.
        Neuroimage. 2003; 19: 1709-1715
        • Toepel U.
        • Knebel J.F.
        • Hudry J.
        • le Coutre J.
        • Murray M.M.
        The brain tracks the energetic value in food images.
        Neuroimage. 2009; 44: 967-974
        • Asmaro D.
        • Liotti M.
        High-caloric and chocolate stimuli processing in healthy humans: An integration of functional imaging and electrophysiological findings.
        Nutrients. 2014; 6: 319-341
        • Ohla K.
        • Toepel U.
        • le Coutre J.
        • Hudry J.
        Visual-gustatory interaction: Orbitofrontal and insular cortices mediate the effect of high-calorie visual food cues on taste pleasantness.
        PLoS One. 2012; 7: e32434
        • Stunkard A.J.
        • Berkowitz R.I.
        • Stallings V.A.
        • Schoeller D.A.
        Energy intake, not energy output, is a determinant of body size in infants.
        Am J Clin Nutr. 1999; 69: 524-530
        • Salbe A.D.
        • DelParigi A.
        • Pratley R.E.
        • Drewnowski A.
        • Tataranni P.A.
        Taste preferences and body weight changes in an obesity-prone population.
        Am J Clin Nutr. 2004; 79: 372-378
        • American Psychiatric Association
        Diagnostic and Statistical Manual of Mental Disorders.
        5th ed. American Psychiatric Press, Washington, DC2013
        • Balleine B.W.
        • O’Doherty J.P.
        Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action.
        Neuropsychopharmacology. 2010; 35: 48-69
        • Yin H.H.
        • Ostlund S.B.
        • Knowlton B.J.
        • Balleine B.W.
        The role of the dorsomedial striatum in instrumental conditioning.
        Eur J Neurosci. 2005; 22: 513-523
        • Martin A.
        • Saunders D.H.
        • Shenkin S.D.
        • Sproule J.
        Lifestyle intervention for improving school achievement in overweight or obese children and adolescents.
        Cochrane Database Syst Rev. 2014; 3 (CD009728)

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