Nutrition Society Summer Meeting 2016 held at University College Dublin on 11ndash14 July 2016

Conference on lsquoNew technology in nutrition research and practicersquoSymposium 3 Novel strategies for behaviour changes

The determinants of food choice

Gareth Leng1 Roger A H Adan2 Michele Belot3 Jeffrey M Brunstrom4 Kees de Graaf5Suzanne L Dickson6 Todd Hare7 Silvia Maier7 John Menzies1 Hubert Preissl89 Lucia

A Reisch10 Peter J Rogers4 and Paul A M Smeets5111Centre for Integrative Physiology University of Edinburgh George Square Edinburgh EH8 9XD UK

2Department Translational Neuroscience Brain Center Rudolf Magnus University Medical Center Utrecht UtrechtThe Netherlands

3European University Institute Via dei Roccettini 9 I-50014 San Domenico di Fiesole Italy4Nutrition and Behaviour Unit School of Experimental Psychology University of Bristol 12a Priory Road Bristol

BS8 1TU UK5Division of Human Nutrition Wageningen University amp Research Centre Wageningen Stippeneng 4 6708 WE The

Netherlands6Department PhysiologyEndocrine Institute of Neuroscience and Physiology The Sahlgrenka Academy at the

University of Gothenburg SE-405 30 Gothenburg Sweden7Laboratory for Social and Neural Systems Research Department of Economics University of Zurich

Bluemlisalpstrasse 10 8006 Zurich Switzerland8Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of

Tuumlbingen German Center for Diabetes Research (DZD eV) Tuumlbingen Germany9Institute for Diabetes and Obesity Helmholtz Diabetes Center Helmholtz Zentrum Muumlnchen German Research

Center for Environmental Health (GmbH) Neuherberg Germany10Department of Intercultural Communication and Management Copenhagen Business School Porcelaenshaven 18a

DK ndash 2000 Frederiksberg Denmark11Image Sciences Institute Brain Center Rudolf Magnus University Medical Center Utrecht Heidelberglaan 100

3584 CX Utrecht The Netherlands

Health nudge interventions to steer people into healthier lifestyles are increasingly applied bygovernments worldwide and it is natural to look to such approaches to improve health byaltering what people choose to eat However to produce policy recommendations that arelikely to be effective we need to be able to make valid predictions about the consequencesof proposed interventions and for this we need a better understanding of the determinantsof food choice These determinants include dietary components (eg highly palatable foodsand alcohol) but also diverse cultural and social pressures cognitive-affective factors (per-ceived stress health attitude anxiety and depression) and familial genetic and epigeneticinfluences on personality characteristics In addition our choices are influenced by anarray of physiological mechanisms including signals to the brain from the gastrointestinaltract and adipose tissue which affect not only our hunger and satiety but also our motiv-ation to eat particular nutrients and the reward we experience from eating Thus to developthe evidence base necessary for effective policies we need to build bridges across differentlevels of knowledge and understanding This requires experimental models that can fill inthe gaps in our understanding that are needed to inform policy translational models thatconnect mechanistic understanding from laboratory studies to the real life human conditionand formal models that encapsulate scientific knowledge from diverse disciplines and which

Corresponding author Professor G Leng email garethlengedacukAbbreviation fMRI functional MRI

Proceedings of the Nutrition Society Page 1 of 12 doi101017S002966511600286Xcopy The Authors 2016

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embed understanding in a way that enables policy-relevant predictions to be made Here wereview recent developments in these areas

Appetite Policy Brain imaging Hypothalamus Satiety Food choice

Health nudge interventions to steer people into healthierlifestyles are increasingly applied by governments world-wide(12) Nudges are approaches to law and policy thatmaintain freedom of choice but which steer people incertain directions(3) they consist of small yet relevantbehavioural stimuli such as simplification of informationand choices framing and priming of messages feedbackto onersquos behaviour defaults and reminders and similarbehavioural cues Much of the health burden is causedby modifiable behaviours such as smoking unhealthyfood consumption and sedentary lifestyles but neitherdecades of health information and education norattempts at hard regulation (such as fat taxes or sugartaxes) nor voluntary self-regulation of industry havemarkedly promoted healthier lifestyles or helped tostop the rise of non-communicable diseases At thesame time there is increasing evidence that the purpose-ful design of the living and consumption environmentsthe lsquochoice architecturersquo is a key to changing nutritionaland activity patterns(4) and to maintaining healthier life-styles There is mounting evidence for the usefulness ofWHOrsquos motto lsquomake the healthier choice the easychoicersquo through easier access availability priming andframing(5) More than 150 governments now use behav-ioral science with an emphasis on nudges(67) In thesecountries nudging for health is regarded as an attractiveoption to make health policies more effective andefficient a recent poll in six European countries foundthat health nudges are overwhelmingly approved by thepeople(8) This is the backcloth against which we setout to test nudging tools that might be useful add-onsto traditional health policies

However to produce policy recommendations that arelikely to be effective we need to be able to make validnon-trivial predictions about the consequences of particu-lar behaviours and interventions For this we need a bet-ter understanding of the determinants of food choiceThese determinants include dietary components (eghighly palatable foods and alcohol) but also diverse cul-tural and social pressures cognitive-affective factors (per-ceived stress health attitude anxiety and depression) andfamilial genetic and epigenetic influences on personalitycharacteristics Our choices are influenced by how foodsare marketed and labelled and by economic factors andthey reflect both habits and goals moderated albeitimperfectly by an individual understanding of what con-stitutes healthy eating In addition our choices areinfluenced by physiological mechanisms including signalsto the brain from the gastrointestinal tract and adipose tis-sue which affect not only our hunger and satiety but alsoour motivation to eat particular nutrients and the rewardwe experience from eating

To develop the evidence base necessary for effectivepolicies we need to build bridges across different levels

of knowledge and understanding This requires experi-mental models that can fill in the gaps in our understand-ing that are needed to inform policy translationalmodels that connect mechanistic understanding fromlaboratory studies to the real life human condition andformal models that encapsulate scientific knowledgefrom diverse disciplines and which embed understandingin a way that enables policy-relevant predictions to bemade

State-of-the-art

Although it seems self-evident that changes in bodyweight reflect the choices an individual makes aboutwhat food to eat how much to eat and how much toexercise the long-term balance between energy intakeand energy output is mainly determined by interactingphysiological systems Since the discovery of leptin in1994 and ghrelin in 1999 we have gained a partial mech-anistic understanding of how homeostatic and hedonicinfluences are coded and how they impact on eatingbehaviour and we have an emerging understanding ofthe mechanisms by which particular food constituentsinfluence hunger and satiety The strong evolutionaryconservation of these mechanisms has meant that knowl-edge from animal models translates well into understand-ing of human physiology for example mutations ingenes that affect signalling in these pathways have verysimilar effects in rodents and human subjects

Animal studies and human genetics studies have alsoframed the contributions of genetic and epigeneticinfluences on body weight Body weight in people isestimated (from twin studies) to be about 80 herit-able(9) but the search for the genes responsible has (sofar) revealed associations that account for only about20 of the inter-individual variation(10) This hasfocused attention on other heritable mechanisms and par-ticularly on the consequences of events in uterine and earlypost-natal life Notably stress and impaired nutrition dur-ing gestation and in early post-natal life are now known tohave lifelong programming effects on physiology andmetabolism

Against this background of genetics and nurture anindividualrsquos knowledge preferences and behaviours life-style and eating habits are all shaped by their environ-ment In our everyday consumption we are far fromrational agents we do not use only evidence-based infor-mation when deciding which foods to buy but areinfluenced by the wider information environmentwhich is shaped by cultural factors including advertisingand other media and we are strongly influenced by earl-ier decisions and habits even if these have not proven tobe optimal

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Habits are preferences shaped by past choices If diet-ary choices follow habitual patterns then we need tounderstand how these arise Children often have a sayin what they eat (at school they often choose what toeat at lunch) but they may be unable to correctly assessthe costs and benefits of different options In that con-text imitative or impulsive behaviour may dominatemaking them vulnerable to peer pressure and the supplyof food in their direct environment Once habits are inplace they shape preferences and future choices Thehabitual pattern of behaviour has implications for policyinterventions effective interventions must be continuedfor long enough to affect preferences in the longer run

Emotional and environmental cues also have a largerole We are influenced by how product information ispresented even whether the name sounds healthy Atthe point of purchase a number of decision heuristicsand biases undermine rational decision behaviour Theanchor effect leads us to overvalue the information weobtained first the source effect draws greater attentionto the source of information and leads to assumptionsabout its credibility that may be false and herd behav-iour makes us adopt products that others are purchasingFurthermore we are poor at estimating probabilities andobjective risks we overestimate our capacity for self-control and underestimate the health risks associatedwith the choices we make Conversely we cheat in ourmental book-keeping lsquoToday I ate too much but Irsquolljust eat less tomorrowrsquo(3) We tend to select currentenjoyment (ice cream now) over conditions we wishfor later (slim and fit) which behavioural economistsexplain in terms of the temporal discounting of futureconditions(11)

The decision-making situation has a large effect asdemonstrated in human ecology models The triple Afactors (affordability availability and accessibility) havea major impact on decisions(12) and help to explain theattitudendashbehaviour gap(13) Marketers have long under-stood that how a product is positioned in the store (egas a lsquostopperrsquo at eye level) has a major impact Thesame is true for the perception of rapid availability(ready-to-eat dishes) and the brandrsquos potential of rewardIn fact most preferences appear to be less stable thanpostulated in neo-classical models many are formed atthe place where the decision is made This is why behav-ioral economists speak of constructive preferences

Decision heuristics and biases apply in situationsinvolving uncertainty which is true of most realdecision-making In our everyday consumption we arefar from rational (in the sense of following our bestintentions) During the search phase of the consumptionprocess we only perceive selective product characteris-tics and because of our limited processing capacitieswe restrict our search criteria to just a few (lsquoseven plusor minus tworsquo) The presence of many alternatives ismore likely to confuse us than to generate optimal deci-sions (choice overload or hyperchoice) Another keyfinding from behavioural economics is the power ofdefault options such as the standard menu in a cafe-teria People generally follow the default option evenwhen given an opt-out This finding is robust in diverse

decision areas as organ donation purchase of organicapples and the use of green electricity and across awide range of methods (experiments questionnairessecondary evaluations) For this reason a number ofincentive systems have been developed based uponlsquohardrsquo and lsquosoftrsquo defaults(14)

Hedonic processes and reward are important driversfor our decisions and are strong enough to overrulehomeostatic needs Food selection and intake in humansubjects is largely driven by an interaction of homeostaticcontrol and reward signals This interaction involves acomplex involvement of higher cognitive functionsincluding memory learning and evaluation of differentoptions

In summary we need to understand exactly what con-scious and unconscious factors bias our choices and sub-vert our best intentions We need to understand how ourhomeostatic and higher cortical processes supporthealthy eating and how these mechanisms come to beundermined Our policies on healthy eating must beframed in this setting if they are to be effective It isalso crucial to know what real individual responses topolicy instruments and actions can be expected and tocustomise our lsquopolicy toolboxrsquo accordingly

The evidence-based policy approach currently pur-sued at all policy levels is based upon empirical dataand valid models of behaviour and effect(15) It relieson learning policy cycles of testndashlearnndashadaptndashshare thattests policies in pilot applications and assesses theirefficacy and cost-benefits before they are rolled out(16)The most important policy measures are those that relyon optimized information (not more information butmore useful and intuitively understandable information)For an integrated policy-focused understanding of foodchoices we need to optimise information in four keyareas early life experiences environmental factors andimpulsive choice behaviour emotions and decision mak-ing and how choices change with age

Early life experiences

Early life programming can influence stress responsesfood choice and weight gain into adult life The conse-quences of early life events for cardiovascular andweight-related morbidity have been studied in detail inthe Dutch famine birth cohort and are associated withchanges in the methylation of certain genes in peopleconceived during the Hunger Winter of 1944ndash45(17)However even modest differences in food intake orfood choices in early life may have lifelong repercussionsand the metabolic status of the mother during gestationinfluences the brain dynamics of the fetus(18) Obesity ismost prevalent in lower socio-economic groups andthis is likely to reflect genetics (assortative mating) epi-genetics and environmental factors including a child-hood diet of energy-dense foods(19)

Obesity has been rising among European children andit disproportionately affects those in low socio-economicgroups However we do not know the mechanistic linkbetween stress andor poor nutrition in early life and

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obesity in adult life and in particular we do not knowwhether this is mediated by programming effects on thereward systems that affect food choice in adult lifeUnderstanding this is critical for not only are childrenin low socio-economic groups most affected by obesitybut they are also particularly resistant to healthy foodcampaigns In 2004 one London borough after ahealthy food campaign introduced changes in themeals offered in primary schools shifting from low-budget processed meals towards healthier options Theeffect on educational outcomes was analysed using a dif-ference in differences approach using the neighbouringLocal Education Authorities as a control groupOutcomes improved in English and Science andauthorised absences (linked to illness and health) fell by14 (20) However the children that benefited leastwere those from the lowest socio-economic groupsthose most in need of support

Stress in early life is also a concern because it can haveprogramming effects that heighten responsiveness to stressin adult life contributing further to weight gain(21) Stressis a feature of modern life particularly in the workplaceSome people eat less when stressed but most eat moreone large study over 19 years in more than 10 000 partici-pants(22) found that employees experiencing chronic workstress had a 50 increased risk of developing central adi-posityHow stress impacts on appetite andweight gain hasbeen extensively studied in rodent models which appearto mimic the human situation well In rodents whereasacute stress is anorexigenic chronic stress can lead toweight gain(23) Chronic stress is related to chronic stimu-lation of the hypothalamondashpituitary adrenal axis com-prising neuroendocrine neurons in the hypothalamusthat regulate the secretion of adenocorticotrophic hor-mone from the anterior pituitary which in turn regulatesglucocorticoid secretion from the adrenal gland Thehypersecretion of glucocorticoids (cortisol in man cor-ticosterone in rodents) is implicated in obesity at severallevels Intake of high energy foods suppresses the hyper-activity of the hypothalamondashpituitary adrenal axis lead-ing to what has been called comfort eating Theunderlying mechanisms are well established glucocorti-coids stimulate behaviours mediated by the dopaminereward pathway resulting in increased appetite for palat-able foods(24) stress also releases endogenous opioidswhich reinforce palatable food consumption and promotenon-homeostatic eating Conversely comfort food inges-tion decreases hypothalamondashpituitary adrenal axis activ-ity(25) thus if stress becomes chronic then eatingpatterns become a coping strategy Beyond stress whichaffects most of the population at some time about 7 of the European population suffers from depressionevery year A common symptom is an alteration in foodintake and this can result in a vicious circle of weightgain and depression(26)

While early life experience has a major impact uponhealth throughout life little is known about how stresspoor nutrition and metabolic challenges like gestationaldiabetes in early life influences later food selection andvaluation and this is key to defining the timing andnature of policy interventions

Environmental factors food reward and impulsive choicebehaviour

Many aspects of modern diet might contribute to theobesity epidemic including the composition and palatabil-ity of modern food its availability and affordability how itis marketed the modern environment contemporary foodculture and genendashenvironment interactions These impacton the reward component of eating that is key to impulsivechoice behaviour the behaviour that governs momentarychoices to eat high or low energy foods The motivationto eat competes with other motivations via a highly con-served neural circuitry the reward circuitry One key partof this is the nucleus accumbens which integrates homeo-static hedonic and cognitive aspects of food intake(2728)and this circuit involves the neurotransmitter dopamineThe nucleus accumbens receives a dense dopamine inputfrom the ventral tegmental area This does not codelsquorewardrsquo in the sense of subjective pleasure rather it med-iates incentive salience (attractiveness) and motivationalproperties of positive stimuli and events(29) The dopaminesystem is regulated by cues that signal the availability ofrewards as well as actual reward dopamine neurons firein a way that reflects the reward value and the dopaminethat is released in the striatum has a key role in habit forma-tion while that released in the orbitofrontal cortex isinvolved in decision-making

Human brain imaging studies using positron emissiontomography and functional MRI (fMRI) confirm thatthese mechanisms function similarly in human subjectsas in rodents Thus the central nervous system responseto palatable foods differs from that to bland foods andresponses of subjects that crave palatable foods differfrom those who do not Importantly cravings for palat-able food activate similar brain regions and involve thesame chemical messengers in human subjects as in ratsIn the striatum the availability of dopamine D2 recep-tors is reduced in severely obese subjects(30) and peoplewho show blunted striatal activation during food intakeare at greater risk of obesity particularly those with com-promised dopamine signalling(31)

Mammals pursue behaviour that is likely to yield themthe greatest reward at that time when fat stores are highthe rewarding power of food is less and they are moremotivated to pursue other rewards Thus hedonic andhomeostatic mechanisms interact and this takes place atdefined brain sites Importantly endocrine signals suchas ghrelin insulin and leptin are not merely regulatorsof energy homeostasis but also influence the reward cir-cuitry to increase the incentive value of food(32ndash34) andimpulsive choice behaviour(35) The consequences arestriking the one intervention of consistent effectivenessfor weight loss in the morbidly obese is bariatric surgeryand this works not by restricting intake or absorptionbut by reducing the incentives to eat via changes in endo-crine signalling to the brain(3637) This shows that morbidobesity is resistant to interventions because of a dysfunc-tion of gut-brain signalling and is important for policyBlame and shame strategies that deny the underlying path-ology are destined to be ineffective and may be counter-productive by promoting comfort eating It is also

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important because these endocrine signals vary with timeof day and according to the timing of meals This opens awindow of opportunity by which changing meal patternswhen we eat rather than how much can influence bothhow we utilise the energy intake and our appetite

Emotions and decision-making

Eating is triggered by many factors including the sightsmell and memory of food and anticipation of food isassociated with activation of well-defined regions of thehypothalamus(38) The sensory characteristics of foodare also important in food choice and these can bewell studied by fMRI(39) Visual attention can be rapidlycued by food items particularly items with high calorificcontent and attentional responding to these is magnifiedin overweight individuals suggesting that heightenedattention to high-energetic food cues promotes greaterintake Animal studies also indicate a major role forlearning associations are formed between the sensorycharacteristics of a food and its post-ingestive effectsOver time these generate flavour preferences and mayalso control meal size

The sight of appetizing food modulates brain activityin consistent ways viewing food items enhances activa-tion both in visually-related brain regions and in regionsassociated with reward (orbitofrontal cortex parahippo-campal gyrus and the insula) in both adults and chil-dren(4041) Visually-driven responses to food are linkedto increased connectivity between the ventral striatumthe amygdala and anterior cingulate in individuals atrisk of obesity hence differences in interactions withinthe appetitive network may determine the risk of obesityObese participants show greater visually-driven responsesto food in reward-sensitive brain regions and for obeseindividuals greater responsiveness in these regions beforeweight-loss treatment predicts treatment outcome Poorweight loss is also predicted by pre-treatment levels ofactivity to food stimuli in brain areas associated with vis-ual attention and memory consistent with the attentionaleffects of food being a predictor of weight loss success(42)

However we have a poor understanding of how valu-ation and selection of food are encoded neuronally Theorbitofrontal cortex dorsolateral prefrontal cortex andventral striatum are all implicated but we have limitedknowledge of what neuronal mechanisms are subservedby these structures If we are to use functional neuroima-ging studies to inform policies that promote healthierfood choices we need a better understanding of howhealth interventions impact on the brain mechanismsthat control food selection and valuation We need toaddress how molecular and cellular events initiated bythe exposure to food translate into changes at the neuronalcircuit level and how these translate to food decisions

Physiological mechanisms of appetite control

In all mammals appetite and energy expenditure areregulated by conserved neuronal circuitry using common

messengers Ghrelin secreted from the empty stomachreaches high levels after a fast and activates neurons inthe hypothalamus that make a potent orexigen neuro-peptide Y Leptin secreted by adipocytes reports onthe bodyrsquos fat reserves it inhibits neuropeptide Y neu-rons while activating others that express anorexigenicfactors notably neurons that express pro-opiomelanocortinPro-opiomelanocortin neurons and neuropeptide Y neu-rons are reciprocally linked and which population is dom-inant determines how much (on average) an animal willeat As an animal eats neural and endocrine signalsfrom the gut report on the volume ingested and on itscomposition including its complement of fat carbohy-drates and protein These signals relayed by satietycentres of the caudal brainstem converge on the ghrelinand leptin sensing circuits of the hypothalamus(43)These in turn project to other limbic sites including theparaventricular nucleus which is the primary regulatorof the sympathetic nervous system and which also regu-lates the hypothalamondashpituitary adrenal axis These path-ways are powerful moderators of energy intake Despitehuge variations in day-by-day food intake in the longterm the body weight of most individuals is remarkablystable However lsquocrash dietingrsquo is an example of an inter-vention that reduces body weight in the short term but asa result of the disruption of normal homeostatic mechan-isms it has counterproductive effects in the long term

It seems that dietary decisions can be regulated by cir-culating metabolic hormones including those that signalto brain areas involved in food intake and appetitivebehaviours One example is ghrelin an orexigenic hor-mone that increases anticipatory(44) and motivatedbehaviour for food notably for fat(45) and sugar(46)Ghrelin enhances the reward value of foods and henceincreases their consumption(32) Recently ghrelin hasbeen shown to guide dietary choice but not entirely asexpected for a reward-promoting hormone For examplerats offered a free choice of lard (100 fat) sucrose andchow increased their lard consumption over 2 weeksghrelin administration changed this food choice andthey started to consume chow Interestingly these effectsof ghrelin diverge from those of fasting after which theconsumption of energy-dense foods is prioritised(47)The pathway from the ventral tegmental area to thenucleus accumbens appears to be engaged by ghrelin tochange food choice(47) and reward-linked behaviour(48)Several other gut- and fat-derived hormones also impacton food reward circuitry Leptin for instance affectsfood reward encoding by dopamine neurons of the ven-tral tegmental area(49)

While morbid obesity is characterised by dysfunc-tional gutndashbrain signalling a key stage in the progres-sion to obesity is the development of leptin resistanceAs a consequence dietary restriction has a limited effecton obesity long term compliance is poor and thosewho lose weight are likely to swiftly regain it and mayeven overshoot after the end of a diet Normally eatingis most rewarding when there is energy deficiency andleast in an energy-replete state but leptin resistancedevelops in both the appetite circuitry and in the rewardcircuitry so food remains rewarding despite a state of

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energy excess Imaging studies have confirmed theimpact of hormones in the recruitment of both hypo-thalamic and reward circuits For example when sub-jects are infused with peptide YY (a postprandialgut-derived satiety factor) the changes in activity inthe caudolateral orbital frontal cortex predict feedingwhereas when levels of peptide YY are low hypothal-amic activation predicts food intake(50) Insulin whichis released in the periphery after food ingestion is alsoa potent modulator of brain activity In recent years ithas become clear that just as peripheral insulin resist-ance develops in association with obesity so does insu-lin resistance in the brain(51)

Thus paradoxically one of the strongest predictors ofweight gain is weight loss dieting One of the biggest stud-ies to demonstrate this was the Growing Up Today Studya prospective study of gt16 000 adolescents(52) At the3-year follow-up adolescents that were frequent or infre-quent dieters had gained significantly more weight thannon-dieters The study controlled for BMI age physicaldevelopment physical activity energy intake and heightchange over the period The longest study that demon-strates this is Project EAT (Eating and Activity in Teensand Young Adults) a population-based study of middleand high school students(53) This study which controlledfor socio-economic status and initial BMI again showedthat the strongest predictors of weight gain were dietingand unhealthy weight control behaviours The behavioursassociated with the largest increases in BMI over a 10-yearperiod were skipping meals eating very little using foodsubstitutes and taking lsquodiet pillsrsquo

This raises the concern that emphasising the healthrisks of obesity may lead to behaviours that exacerbatethe problem This worry is compounded when onelooks at the media response in the UK to recent publi-city where concerns about the effects of excessive weightgain in pregnancy were translated as concern about obes-ity in pregnancy These are very different while excessiveweight gain in pregnancy is detrimental so is weight losseven from a condition of obesity Physiologically dietaryrestriction during pregnancy can lead to starvation of thefetus as homeostatic mechanisms defend maternal bodyweight at the expense of the fetus Thus howadvice relatedto healthy eating and lifestyles is formulated and dissemi-nated needs careful attention There has been littleworkonfood choice in children and this is important to explorebecause of the weaker self-control capacity of childrenwhich is coupled to the maturation of their prefrontalcortex(54) This has a bearing on in-store marketing (andlegislation on that) and the development of interventionsaimed at preventing childhood obesity

The neuroimaging of food choice

Human associational and behavioural studies have manypotential confounding factors so interpreting themdepends on inferences from our understanding of theneurobiology of appetite However there is a disconnectbetween our mechanistic understanding and our lsquosofterrsquoknowledge of individual consumer behaviour which

makes these inferences unsafe We need to create bridgesin our understanding enabling us to integrate behav-ioural and observational studies with neurobiologicalstudies in a way that can be used to educate stakeholdersand inform policy

Human neuroimaging is an emerging technology thatcan be used to define the neural circuits involved in foodvaluation and selection Food decision-making has beenstudied surprisingly little most neuroimaging studies usepassive viewing paradigms in which participants areexposed to food they study food cue reactivity ratherthan the ensuing decision-making processes Combiningdifferent imaging techniques can optimise the temporaland spatial description of the neuronal circuits under-lying food valuation and selection during hunger andsatiety Recent developments in fMRI include (a) com-bining diffusion tensor imaging with resting state analysisto determine network structures and changes during dif-ferent physiological states (b) high-resolution anatom-ical MRI to improve investigation of hypothalamic andmidbrain responses and (c) arterial spin labelling techni-ques to establish a quantitative neural activity measure ofhunger and satiety In addition developments in magne-toencephalography and electroencephalography includeextraction of resting state dynamics with high temporalresolution and combination with diffusion tensorimaging and application of Bayesian-based source local-isation to define the temporal and spatial networkinvolved in food selection Most fMRI studies that linka given brain circuit with cues associated with food orwith the choice for a particular food are based on corre-lations between an event and a recorded brain activityTo determine causality we need to be able to changebrain activity and determine its impact on behaviourIn human subjects defined neuronal structures can bemanipulated using transcranial magnetic stimulation ordirect current stimulation to either facilitate or attenuatecerebral activity

Along with the rise in the number of neuroimagingstudies there have been many neuroimaging data-sharinginitiatives and several databases contain resting fMRIdata and anatomical MRI data from thousands of indi-viduals For functional imaging things are more compli-cated but there are notable efforts of sharing fMRIdatasets (openfmriorg) unthresholded statistical maps(neurovaultorg) and coordinate-based data synthesis(neurosynthorg) However the value of such databasesdepends on the available metadata and existing data-bases lack most or all of the metadata necessary forresearch on food choice such as weight(54) restraint eat-ing status(55) and personality characteristics(56)

For policies to be built on robust evidence it is essen-tial that the evidence is developed in a way that facilitatesmeta-analysis There is great variability in neuroimagingresults and this is especially true for fMRI tasks involv-ing complex stimuli such as food stimuli(4041) Bennett ampMiller(57) showed that the reproducibility of fMRI resultswas only 50 even for the same task and stimuli in thesame participants This was confirmed by a meta-analysisof fMRI studies of responses to food pictures measure-ments for the brain areas that were most consistently

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activated by looking at food v non-food pictures wereonly reported in fewer than half of the studiesincluded(4041) Reproducibility can be improved by stan-dardising measures but there are no standardised fMRIprotocols for assessing food responsivity and food choicefor different food categories To filter out effects due tosubject characteristics rather than methodological differ-ence standardisation of instruments and measures is cru-cial for data sharing and pooling across studies(58)Recently researchers have begun to share (standardised)food images for use in experimental paradigms (eg5960)and tools for standardised collection of food-related sub-ject characteristics(61)

To connect data from human imaging studies withneurophysiological data from rats we must improve andadapt high-field rodent fMRI technology in a settingthat allows to map involvement of neural circuits in foodvaluation and selection Small rodent resting state andpharmacological fMRI is an emerging technology thathas not yet been applied to address how brain activitychanges upon food restriction and food anticipationThus it is not known for example how brain activity ischanged upon food restriction in rodents or how gut pep-tides like leptin and ghrelin affect functional connectivitybetween brain regions Small rodent fMRI bridges thegap between neuronal activity at the cellular level withfMRI measures in human subjects making it possible toconnect molecular and cellular data with fMRI measures

Novel technologies to understand the brain mechanismsunderlying food choice

There is a poor understanding of what underlies theresponses quantified in neuroimaging studies By com-bining in vivo electrophysiology with optogenetics orpharmacogenetics it is now possible to record fromand interfere with defined neurons involved in food valu-ation and choice and this is key to unravelling whatunderlies the responses recorded by neuroimagingOptogenetics takes advantage of genes that encode light-sensitive channels and these channels can be expressedconditionally in specific neurons These neurons canthen be either activated or inhibited by shining light onthem This technical approach requires that these neu-rons express the cre recombinase enzyme Targeting crefor instance to tyrosine hydroxylase (the rate limitingenzyme for dopamine production) neurons such as in(germline) tyrosine hydroxylase-cre rats allows theselight-sensitive channels to be expressed only in midbraindopamine neurons To achieve this light-sensitive chan-nels are cloned into a recombinant viral vector such thatonly upon expression of cre the channels are expressed indopamine neurons(6263) This makes it possible to acti-vate precise populations of neurons in rodents and tocompare observations with brain responses observed byneuroimaging Similarly subpopulations of dopamineneurons can be targeted with viruses to express novelreceptors that are not endogenously present these canthen be specifically activated (or inhibited) by systemicallyapplied drugs that act on those novel receptors (eg64)

How the life-long learning process contributes to foodselection and valuation

The sensory characteristics of food are important in foodchoice but learning also has a major role(65) Associationsare formed between the sensory characteristics of a food(the conditioned stimulus) and its post-ingestive effects(the unconditioned stimulus) Over time these flavour-nutrient associations generate flavour preferences andthey also control meal size In human subjects fundamen-tal questions remain about the nature of the uncondi-tioned stimulus and how this is combined with sensorysignalling from the tongue to the brain

In adult human subjects flavour-nutrient learning isnotoriously difficult to observe under controlled labora-tory conditions although in non-human animals thisform of learning is extremely reliable Several examplesof flavour-nutrient learning have been reported in chil-dren and this may be because most dietary learningoccurs in early life By adulthood we have encounteredso many foods and flavours that our capacity to learnnew associations might be saturated If so this reinforcesthe importance of childhood as a critical period duringwhich our dietary behaviours are established A furtherconsideration is the complexity of the modern Westerndietary environment Human subjects are now exposedto a wide range of foods in numerous different brandsand varieties This may limit our opportunity to learnabout individual foods which has the potential to pro-mote overconsumption(66)

Learned beliefs impact our dietary choices directlyTypically we decide how much we are going to eat beforea meal(67) These decisions are often motivated by a concernto avoid hunger between meals and the learned expectedsatiety of individual foods is important in this Low energy-dense foods tend to have greater expected satiety and suchfoods are often selected to avoid hunger between mealsIncreasingly portions are also determined by externalagents such as restaurants or retailers Recently it hasbecome clear that larger portions not only increase ourfood intake but also affect choice This is because largerportions are likely to satisfy our appetite between mealsand in the absence of concerns about satiety decisionstend to be motivated primarily by palatability

A further possibility is that satiation or the absence ofhunger between meals is itself valued(68) The results ofhuman appetite studies suggest that both oral and gastricstimulation are needed for optimal satiety(69ndash71)However the underlying process also involves integra-tion of explicit knowledge about the food and amountthat has been consumed(7273) Consistent with this sev-eral studies show that satiety and satiation are reducedwhen eating occurs in the presence of cognitive distrac-tion(74) Eating lsquoattentivelyrsquo appears to have the oppositeeffect(75) and food properties like viscosity can increaseperceived fullness for otherwise similar foods(76)Despite its importance the process by which interocep-tive signals are integrated remains unclear This meritsattention because some studies indicate that differencesin interoceptive awareness are a predictor of adiposityin human subjects(77)

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How physiological psychological and emotional factorspredispose people to unhealthy eating

One key question in the effects of sensory nutrient andsatiety contributions to reward is whether the initialresponse to certain stimuli remains after repeated expos-ure Does the response to a low-energy beverage withartificial sweeteners stay the same or do people slowlylearn that lsquodietrsquo products contain lower energy contentFor this case it is hard to demonstrate such dietary learn-ing(78) although there is some evidence for detection offood energy content in the mouth(7980) Another import-ant consideration is whether it makes a difference whetherone goes from for example 836middot8 209middot2 kJ (200 50kcal) or from 627middot6 0 kJ (150 0 kcal) In both casesthere is a reduction of 627middot6 kJ (150 kcal) but in the case of836middot8 209middot2 kJ (200 50 kcal) there is still energy leftin the stimulus whereas in the case of 627middot6 0 kJ(150 0 kcal) there is no energy left It has been arguedthat the absence of any energy content will lead to a lowerreward value after repeated exposure Conversely mostlsquolightrsquoproducts still contain energy albeit less than theirregular counterparts with soft drinks a notable exception

In both human subjects and rodents the motivation tochoose one food over another is driven by the emotionalhedonic and metabolic properties of the foods The dopa-mine system is critically involved in this and is essentialfor associating rewards with environmental stimuli thatpredict these rewards Activity of this system is affectedby both metabolic information and emotional and cogni-tive information The hypothalamus amygdala andmedial prefrontal cortex play important roles in respect-ively feeding behaviour emotional processing anddecision-making Manipulation of the dopamine systemcan be achieved by nutritional interventions and reducingdopamine levels in lean and obese subjects leads todecreased activity in the reward system(81)

There is also evidence that incidental emotions can affectfood choices Sadness leads to greater willingness to payfor unnecessary consumer goods(8283) and increasedconsumption of unhealthy food items(84) However thebiological mechanisms linking affective states to foodchoices are unknownRecent work has begun to investigatethe underlying neural mechanisms of dietary choice inhuman subjects using neuroimaging and brain stimulationtechniques together with validated choice paradigms andbehavioural trait measures (eg84ndash88)

A natural assumption would be that the physiologicaland psychological reactions to an affective state use thesame neural pathways to influence food choicesHowever Maier et al(24) have recently shown usingfMRI that experiencing an acute stressor leads tochanges in two separate and dissociable neural pathwaysone associated with the physiological reaction to stressand the other with the conscious perception of beingstressed The physiological response was measured bysampling salivary cortisol the psychological experiencewas recorded using a visual analog scale on which parti-cipants indicated how they felt right after the stressinduction Cortisol was associated with signals aboutthe reward value of food individuals with a higher

cortisol response showed a higher representation oftaste in the ventral striatum and amygdala and amplifiedsignalling between ventral striatumamygdala and theventromedial prefrontal cortex when a tastier food waschosen Yet the subjective perception of being stresseddid not correlate with the strength of this connectionInstead the perceived stress level (but not the cortisolreaction) was associated with the connectivity strengthbetween left dorsolateral prefrontal cortex and theventromedial prefrontal cortex the more stressed partici-pants had felt the weaker was the connectivity betweenthese two regions when self-control was needed to over-come taste temptations in order to choose the healthierfood A series of studies have demonstrated that connect-ivity between dorsolateral prefrontal cortex and ventro-medial prefrontal cortex relates to the degree to whichindividuals use self-control in dietary choice(89ndash92) Thisconnection in the prefrontal cortex may maintain a goalcontext that promotes focusing on long-term outcomessuch as future healthwhereas sensory andmotivational sig-nalling from subcortical areas may promote informationabout more immediate choice outcomes Thus self-controlin dietary choicemay depend on a balance of signalling andinformation exchange in value computation networks anddisruptions to this balance during highly affective statesmay lead to impaired self-control

Modeling the interactions between physiologicalpsychological and emotional factors related to feeding

behaviour

An ultimate ambition must be to generate formal modelsthat encapsulate scientific knowledge from diverse disci-plines and which embed understanding in a way thatenables policy-relevant predictions to be made Modellingis a natural way of working together to provide addedvalue it expresses intrinsically the need to make linksbetween levels of understanding Most importantly ittakes seriously the issue of how to generate policy guidelinesthat have a robust scientific basis by providing a commonframework of understanding across disciplines

Modelling provides a logically coherent framework fora multi-level analysis of food choice integrating mea-sures of the neural components of the appetitive networkwith whole-system output (behavioural experiments) in aframework consistent with the neural homeostatic andhedonic mechanisms and providing a test-bed for studiesof behavioural interventions The first phase in modellingis a scheme that embodies constructs that explain behav-ior by describing a causal chain of events A computa-tional model expresses these mathematically usually asdifferential equations Typically such differential equa-tions are (a) coupled (expressing interdependencebetween factors) and (b) non-linear (expressing complexdependencies between variables) To be useful a modelmust be developed at a level of detail appropriate forthe data it is informed by and the type of predictionthat it is called upon to make It must be complex enoughto satisfy the former but simple enough to satisfy the

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latter making models over-complex is counterproduct-ive as such models are not predictive(93)

For example oxytocin neurons are well established asplaying an important role in satiety(9495) and accordingto recent studies in food choice(9697) These neuronsrespond to signals from the gut that control meal sizeand exactly how they respond has been analysed at thesingle-cell level Their behaviour can be captured in detailby biophysical (HodgkinndashHuxley style) models that canthen be approximated by simpler models that capturethe essential behaviour while being better suited for mod-elling networks of neurons(98) Decision making at thelevel of the neuron networks that oxytocin engages canbe modelled by biologically realistic lsquowinner-takes-allrsquo net-works which provide predictive models of how continuousvariables lead to categorical decisionmaking and such net-work models can be fit to human brain imaging data bymean field approximation(99) Such models can link brainimaging data with experimental behavioural data in a pre-dictive way as in the spiking search over time amp spacemodel that has been developed to analyse attentional pro-cesses(100) Relatively simple mathematical models can cap-ture important features of value-baseddecisionswell and ina similar way for food-based decisions as for social deci-sions indicating that there is a common computationalframework by which different types of value-based deci-sions are made(101) At a high level the aimmust be to gen-erate agent-based models that describe by a set of explicitrules all the factors that influence food choice validatingeach rule by a mechanistic understanding of the neurobio-logical and physiological mechanisms that implementthese rules It is a long goal butworking towards it providesa unified framework for multi-disciplinary research

Conclusions

Clearly we need a more sophisticated understanding ofthe determinants of food choice an understanding con-sistent with many different types of evidence To trans-late this into policy recommendations will involvefurther challenges we must be aware of the potentialfor unintended consequences of the likely need for pol-icies tailored to specific populations and of the difficul-ties in achieving compliance and measuring outcomesThe nudge approach to behavioural change appears atpresent to be most likely to be fruitful small interven-tions that can be trialled for effectiveness in controlledsettings To develop these policy tools we need to identifya set of specific proposed interventions that are aimed atparticular target groups We must identify the evidencethat suggests that these will be effective and identifythe gaps in our knowledge that may make our predic-tions uncertain before deciding which interventions totrial and exactly how to implement them

Financial Support

The research leading to these results has received fundingfrom the European Unionrsquos Seventh Framework

programme for research technological development anddemonstration under grant agreement no 607310(Nudge-it)

Conflict of Interest

Peter Rogers has received funding for research on foodand behaviour from industry including from SugarNutrition UK He has also provided consultancy servicesfor Coca-Cola Great Britain and received speakerrsquos feesfrom the International Sweeteners Association

Authorship

All authors participated in writing this review

References

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2 Matjasko JL Cawley J Baker-Goering MM et al (2016)Applying behavioral economics to public health policyillustrative examples and promising directions Am JPreventive Med 50 S13ndashS19

3 Sunstein CS amp Thaler RH (2008) Nudge ImprovingDecisions About Health Wealth and Happiness p 312New Haven and London Yale University Press

4 Bucher T Collins C Rollo ME et al (2016) Nudging con-sumers towards healthier choices a systematic review ofpositional influences on food choice Br J Nutr 1152252ndash2263

5 Wilson AL Buckley E Buckley JD et al (2016) Nudginghealthier foodandbeverage choices throughsalienceandprim-ing Evidence from a systematic review Food Quality andPreference 51 47ndash64

6 Ly K amp Soman D (2013) Nudging Around the World(Research Report Series) Retrieved from the RotmanSchool of Management University of Toronto httpinsiderotmanutorontocabehaviouraleconomicsinactionfiles201312Nudging-Around-The-World_Sep2013pdf

7 Sunstein CR (2016b) The council of psychological advisersAnn Rev Psychol 67 713ndash737

8 Reisch LA amp Sunstein CR (2016) Do Europeans likenudges J Judgment Decision Making 11 310ndash325

9 Wardle J Carnell S Haworth CM et al (2008) Evidencefor a strong genetic influence on childhood adiposity des-pite the force of the obesogenic environment Am J ClinNutr 87 398ndash404

10 Locke AE Kahali B Berndt SI et al (2015) Genetic studiesof body mass index yield new insights for obesity biologyNature 518 197ndash206

11 Benabou R amp Tirole J (2005) Incentives and prosocialbehavior Am Economic Rev 96 1652ndash1678

12 Maas J de Ridder DT de Vet E et al (2012) Do distantfoods decrease intake The effect of food accessibility onconsumption Psychol Health 27 Suppl 2 59ndash73

13 Reisch LA amp Gwozdz W (2013) Smart defaults and softnudges How insights from behavioral economics caninform effective nutrition policy In Marketing food andthe consumer Festschrift in Honour of Klaus Grunert pp189ndash200 [K Brunsoslash amp J Scholderer editors] New JerseyPearson Publisher

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ciety

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14 Sunstein CR amp Reisch LA (2014) Automatically greenbehavioral economics and environmental protectionHarvard Environ Law Rev 38 127ndash158

15 Bogenschneider K amp Corbett TJ (2010) Evidence-BasedPolicymaking Insights from Policy-Minded Researchers andrEsearch-Minded Policymakers p 368 Oxford Routledge

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17 Tobi EW Goeman JJ Monajemi R et al (2014) DNAmethylation signatures link prenatal famine exposure togrowth and metabolism Nat Commun 5 5592

18 Linder K Schleger F Kiefer-Schmidt I et al (2015)Gestational diabetes impairs human fetal postprandialbrain activity J Clin Endocrinol Metab 100 4029ndash4036

19 Wang Y amp Lim H (2012) The global childhood obesityepidemic and the association between socio-economicstatus and childhood obesity Int Rev Psychiatry 24176ndash188

20 Belot M amp James J (2011) Healthy school meals and edu-cational outcomes J Health Econ 30 489ndash504

21 Brunton PJ (2015) Programming the brain and behaviourby early-life stress a focus on neuroactive steroids JNeuroendocrinol 27 468ndash480

22 Brunner EJ Chandola T amp Marmot MG (2007)Prospective effect of job strain on general and centralobesity in the Whitehall II study Am J Epidemiol 165828ndash837

23 Rabasa C amp Dickson SL (2016) Impact of stress onmetabolism and energy balance Curr Opin Behavl Sci 971ndash77

24 Maier SU Makwana AB amp Hare TA (2015) Acute stressimpairs self-control in goal-directed choice by altering mul-tiple functional connections within the brainrsquos decision cir-cuits Neuron 87 621ndash631

25 Dallman MF Pecoraro N Akana SF et al (2003) Chronicstress and obesity a new view of lsquocomfort foodrsquo Proc NatlAcad Sci USA 100 11696ndash11701

26 Stunkard AJ Faith MS amp Allison KC (2003) Depressionand obesity Biol Psychiatry 54 330ndash337

27 Kelley AE Baldo BA Pratt WE et al (2005)Corticostriatal-hypothalamic circuitry and food motiv-ation integration of energy action and reward PhysiolBehav 86 773ndash795

28 Adan RA Vanderschuren LJ amp la Fleur SE (2008)Anti-obesity drugs and neural circuits of feeding TrendsPharmacol Sci 29 208ndash217

29 Berridge KC (2007) The debate over dopaminersquos role inreward Psychopharmacology 191 391ndash431

30 Benton D amp Young HA (2016) A meta-analysis of the rela-tionship between brain dopamine receptors and obesity amatter of changes in behavior rather than food addictionInt J Obes (Lond) 40 S12ndashS21

31 Stice E Spoor S Bohon C et al (2008) Relation betweenobesity and blunted striatal response to food is moderatedby TaqIA A1 allele Science 322 449ndash452

32 Egecioglu E Jerlhag E Salomeacute N et al (2010) Ghrelinincreases intake of rewarding food in rodents Addict Biol15 304ndash311

33 Figlewicz DP MacDonald Naleid A amp Sipols AJ (2007)Modulation of food reward by adiposity signals PhysiolBehav 91 473ndash478

34 Kullmann S Heni M Veit R et al (2015) Selective insulinresistance in homeostatic and cognitive control brain areasin overweight and obese adults Diabetes Care 38 1044ndash1050

35 Anderberg RH Hansson C Fenander M et al (2016) Thestomach-derived hormone ghrelin increases impulsivebehavior Neuropsychopharmacology 41 1199ndash1209

36 Leng G (2014) Gut instinct body weight homeostasis inhealth and obesity Exp Physiol 99 1101ndash1103

37 Frank GK Oberndorfer TA Simmons AN et al (2008)Sucrose activates human taste pathways differently fromartificial sweetener Neuroimage 39 1559ndash1569

38 Johnstone LE Fong TM amp Leng G (2006) Neuronal acti-vation in the hypothalamus and brainstem during feedingin rats Cell Metab 4 313ndash321

39 Lundstroumlm JN Boesveldt S amp Albrecht J (2011) Centralprocessing of the chemical senses an overview ACSChem Neurosci 2 5ndash16

40 Van Meer F van der Laan LN Adan RA et al (2015)What you see is what you eat an ALE meta-analysis ofthe neural correlates of food viewing in children and ado-lescents Neuroimage 104 35ndash43

41 van der Laan LN de Ridder DT Viergever MA et al(2011) The first taste is always with the eyes ameta-analysis on the neural correlates of processing visualfood cues NeuroImage 55 296ndash303

42 Hege MA Stingl KT Ketterer C et al (2013) Workingmemory-related brain activity is associated with outcomeof lifestyle intervention Obesity (Silver Spring) 21 2488ndash2494

43 Murphy KG amp Bloom SR (2006) Gut hormones and theregulation of energy homeostasis Nature 444 854ndash859

44 Verhagen LA Egecioglu E Luijendijk MC et al (2011)Acute and chronic suppression of the central ghrelin signal-ing system reveals a role in food anticipatory activity EurNeuropsychopharmacol 21 384ndash392

45 Perello M amp Dickson SL (2015) Ghrelin signalling on foodreward a salient link between the gut and the mesolimbicsystem J Neuroendocrinol 27 424ndash434

46 Skibicka KP Hansson C Alvarez-Crespo M et al (2011)Ghrelin directly targets the ventral tegmental area toincrease food motivation Neuroscience 180 129ndash137

47 Scheacutele E Bake T Rabasa C et al (2016) Centrally adminis-tered ghrelin acutely influences food choice in rodentsPLoS ONE 11 e0149456

48 Skibicka KP Shirazi RH Rabasa-Papio C et al (2013)Divergent circuitry underlying food reward and intakeeffects of ghrelin dopaminergic VTA-accumbens projec-tion mediates ghrelinrsquos effect on food reward but notfood intake Neuropharmacology 73 274ndash283

49 van der Plasse G van Zessen R Luijendijk MC et al(2015) Modulation of cue-induced firing of ventral tegmen-tal area dopamine neurons by leptin and ghrelin Int J Obes(Lond) 39 1742ndash1749

50 Batterham RL Ffytche DH Rosenthal JM et al (2007)PYY modulation of cortical and hypothalamic brainareas predicts feeding behavior in humans Nature 450106ndash109

51 Kullmann S Heni M Hallschmid M et al (2016) Braininsulin resistance at the crossroads of metabolic and cogni-tive disorders in humans Physiol Rev 96 1169ndash1209

52 Field AE Austin SB Taylor CB et al (2003) Relationbetween dieting and weight change among preadolescentsand adolescents Pediatrics 112 900ndash906

53 Neumark-Sztainer D Wall M Story M et al (2012)Dieting and unhealthy weight control behaviors duringadolescence associations with 10-year changes in bodymass index J Adolescent Health 50 80ndash86

54 van Meer F Charbonnier L amp Smeets PA (2016) Fooddecision-making effects of weight status and age CurrDiab Rep 16 84

55 van der Laan LN Charbonnier L Griffioen-Roose S et al(2016) Supersize my brain a cross-sectional voxel-basedmorphometry study on the association between self-

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reported dietary restraint and regional grey mattervolumes Biol Psychol 117 108ndash116

56 van der Laan LN amp Smeets PA (2015) You are what youeat a neuroscience perspective on consumersrsquo personalitycharacteristics as determinants of eating behavior CurrOp Food Sci 3 11ndash18

57 Bennett CM amp Miller MB (2010) How reliable are theresults from functional magnetic resonance imaging AnnNY Acad Sci 1191 133ndash155

58 Smeets PA Charbonnier L van Meer F et al (2012)Food-induced brain responses and eating behavior ProcNutr Soc 71 511ndash520

59 Charbonnier L van Meer F van der Laan LN et al (2016)Standardized food images a photographing protocol andimage database Appetite 96 166ndash173

60 Blechert J Meule A Busch NA et al (2014) Food-pics animage database for experimental research on eating andappetite Front Psychol 5 617

61 Nutritional Neuroscience Laboratory (2015) httpnutri-tionalneuroscienceeuresourcesforc-toolbox

62 Witten IB Steinberg EE Lee SY et al (2011)Recombinase-driver rat lines tools techniques and opto-genetic application to dopamine-mediated reinforcementNeuron 72 721ndash733

63 de Backer MW Garner KM Luijendijk MC et al (2011)Recombinant adeno-associated viral vectors MethodsMol Biol 789 357ndash376

64 Boender AJ de Jong JW Boekhoudt L et al (2014)Combined use of the canine adenovirus-2 andDREADD-technology to activate specific neural pathwaysin vivo PLoS ONE 9 e95392

65 Brunstrom JM (2007) Associative learning and the controlof human dietary behavior Appetite 49 268ndash271

66 Hardman CA Ferriday D Kyle L et al (2015) So manybrands and varieties to choose from does this compromisethe control of food intake in humans PLoS ONE 10e0125869

67 Fay SH Ferriday D Hinton EC et al (2011) What deter-mines real-world meal size Evidence for pre-meal plan-ning Appetite 56 284ndash289

68 Brunstrom JMamp Shakeshaft NG (2009) Measuring affective(liking) and non-affective (expected satiety) determinants ofportion size and food reward Appetite 52 108ndash114

69 Wijlens AG Erkner A Alexander E et al (2012) Effects oforal and gastric stimulation on appetite and energy intakeObesity (Silver Spring) 20 2226ndash2232

70 Spetter MS Mars M Viergever MA et al (2014) Tastematters ndash effects of bypassing oral stimulation on hormoneand appetite responses Physiol Behav 137 9ndash17

71 Spetter MS de Graaf C Mars M et al (2014) The sum ofits partsndasheffects of gastric distention nutrient content andsensory stimulation on brain activation PLoS ONE 9e90872

72 Brunstrom JM Burn JF Sell NR et al (2012) Episodicmemory and appetite regulation in humans PLoS ONE7 e50707

73 Cassady BA Considine RV amp Mattes RD (2012) Beverageconsumption appetite and energy intake what did youexpect Am J Clin Nutr 95 587ndash593

74 Brunstrom JM amp Mitchell GL (2006) Effects of distractionon the development of satiety Br J Nutr 96 761ndash769

75 Higgs S amp Donohoe JE (2011) Focusing on food duringlunch enhances lunch memory and decreases later snackintake Appetite 57 202ndash206

76 Camps G Mars M de Graaf C et al (2016) Empty caloriesand phantom fullness a randomized trial studying the rela-tive effects of energy density and viscosity on gastric

emptying determined by MRI and satiety Am J ClinNutr 104 73ndash80

77 Herbert BM Blechert J Hautzinger M et al (2013)Intuitive eating is associated with interoceptive sensitivityEffects on body mass index Appetite 70 22ndash30

78 Griffioen-Roose S Smeets PA Weijzen PL et al (2013)Effect of replacing sugar with non-caloric sweeteners inbeverages on the reward value after repeated exposurePLoS ONE 8 e81924

79 Smeets PA Weijzen P de Graaf C et al (2011)Consumption of caloric and non-caloric versions of a softdrink differentially affects brain activation during tastingNeuroimage 54 1367ndash1374

80 van Rijn I de Graaf C amp Smeets PA (2015) Tasting caloriesdifferentially affects brain activation during hunger andsatiety Behav Brain Res 279 139ndash147

81 Frank S Veit R Sauer H et al (2016) Dopamine depletionreduces food-related reward activity independent of BMINeuropsychopharmacology 41 1551ndash1559

82 Lerner JS Small DA amp Loewenstein G (2004) Heart stringsand purse strings carry‐over effects of emotions on eco-nomic transactions Psychol Sci 15 337ndash341

83 Cryder CE Lerner JS Gross JJ et al (2008) Misery is notmiserly sad and self‐focused individuals spend morePsychol Sci 19 525ndash530

84 Garg N Wansink B amp Inman JJ (2007) The influence ofincidental affect on consumersrsquo food intake J Marketing71 194ndash206

85 Pogoda L Holzer M Mormann F et al (2016)Multivariate representation of food preferences in thehuman brain Brain Cogn 110 43ndash52

86 Val-Laillet D Aarts E Weber B et al (2015)Neuroimaging and neuromodulation approaches to studyeating behavior and prevent and treat eating disordersand obesity NeuroImage 8 1ndash31

87 van der Laan LN de Ridder DT ViergeverMA et al (2014)Activation in inhibitory brain regions during food choicecorrelates with temptation strength and self-regulatory suc-cess in weight-concerned women Front Neurosci 8 308

88 van der Laan LN Barendse ME Viergever MA et al(2016) Subtypes of trait impulsivity differentially correlatewith neural responses to food choices Behav Brain Res296 442ndash450

89 Hare TA Camerer CF amp Rangel A (2009) Self-control indecision-making involves modulation of the vmPFC valu-ation system Science 324 646ndash648

90 Hare TA Malmaud J amp Rangel A (2011) Focusing atten-tion on the health aspects of foods changes value signalsin vmPFC and improves dietary choice J Neurosci 3111077ndash11087

91 Harris A Hare T amp Rangel A (2013) Temporally dissoci-able mechanisms of self-control early attentional filteringversus late value modulation J Neurosci 33 18917ndash18931

92 Lim SL Cherry JBC Davis AM et al (2016) The childbrain computes and utilizes internalized maternal choicesNature Comm 7 1700

93 Leng G amp MacGregor DJ (2008) Mathematicalmodelling in neuroendocrinology J Neuroendocrinol 20713ndash718

94 Leng G Onaka T Caquineau C et al (2008) Oxytocin andappetite Prog Brain Res 170 137ndash151

95 Blevins JE amp Ho JM (2013) Role of oxytocin signaling inthe regulation of body weight Rev Endocr Metab Disord14 311ndash329

96 Olszewski PK Klockars A amp Levine AS (2016)Oxytocin a conditional anorexigen whose effects onappetite depend on the physiological behavioural and

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social contexts J Neuroendocrinol 28 Available at httpswwwncbinlmnihgovpmcarticlesPMC4879516

97 Olszewski PK Klockars A Olszewska AM et al (2010)Molecular immunohistochemical and pharmacologicalevidence of oxytocinrsquos role as inhibitor of carbohydratebut not fat intake Endocrinology 151 4736ndash4744

98 Maiacutecas Royo J Brown CH Leng G et al (2016)Oxytocin neurones intrinsic mechanisms governing theregularity of spiking activity J Neuroendocrinol 28Available at httponlinelibrarywileycomdoi101111jne12376abstract

99 Deco G Jirsa VK Robinson PA et al (2008) The dynamicbrain from spiking neurons to neural masses and corticalfields PLoS Comput Biol 4 e1000092

100 Mavritsaki E Allen HA amp Humphreys GW (2010)Decomposing the neural mechanisms of visual searchthrough model-based analysis of fMRI top-down excita-tion active ignoring and the use of saliency by the rightTPJ Neuroimage 52 934ndash946

101 Krajbich I Hare T Bartling B et al (2015) A commonmechanism underlying food choice and social decisionsPLoS Comput Biol 11 e1004371

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