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10 Competitive Exam Prep Tips that Actually Work!

I know all of you are hustling to prepare for your competitive exams that are coming up. Let’s make your preparation phase easier with these proven tricks that have helped many students like yourself.

  1. Organize your Schedule
  2. Plan your Timetable
  3. Zero Distractions
  4. Refer standard study material/Previous years paper
  5. Get help online
  6. Adopt shortcut tricks
  7. Read the text, remember the keywords (Color code topics)
  8. Test yourself
  9. Save time for Revision
  10. Take care of yourself

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#1 Organize your Schedule

Look at your schedule and your free time. Mark or dedicate that time specifically for your preparation, which includes syncing your college/working hours into consideration. Without knowing that the number of hours that can be dedicated, the planning would be tough.

It is always better to be ahead of time to schedule and organize rather than cramming on the last minute like your semester exams (because competitive exams don’t work that way).

#2 Plan your Timetable

Know your syllabus and understand your difficult areas among it. Plan your timetable such that you do not cram all your easy/difficult topics on one day. It is not ideal. Focus on studying at least a couple of hours every day, it doesn’t have to be 6 to 7 hours per day. According to scientific studies, it is shown that students absorb certain topics in 6 or more hours have lesser success rate than students who study the topic over a couple of days (2 hours per day).

#3 Zero distractions

Any type of distraction must be avoided during this preparation time to have you focused.

#4 Refer Standard Material

It is always better to walk on the road everyone used rather than the shiny new road never once used. Before you start studying from a random book you bought, it is better to review and refer other choices. You can start with the standard choices that is usually preferred by everyone.

Additionally, you can refer to the previous year’s question papers, which would give you a clearer idea about its blueprint, weightage and type of questions.

#5 Get help online

This is not a surprising idea in the digital era. If you are having trouble following the syllabus and require guidance, you can always get help online. There are many courses or tutors online created focusing these competitive exams.

Choose the right course which offers you the required guidance.

#6 Adopt shortcut tricks

Work smarter, not harder. It is especially important to keep track of time during the examination and preparation. When you are practising your aptitude hard enough, it is necessary that you know the shortcut method for every possible question. Every minute counts during your exam, these shortcuts will be of great help! Trust me.

#7 Read the text but remember the keywords

We all might be able to read the text, but we cannot remember all of it. This suggestion may sound ancient yet works every time. Take notes. Keep track of the important keywords as you read. These will help a lot while revising.

#8 Test yourself

Just studying on and on, does not make sense. You need to challenge your progress constantly at regular intervals. There are many websites online offering practise tests along with the answers, which you can try out. If not, you can try out the previous year paper and have a mock exam for yourself.

#9 Save time for Revision

After you complete covering all the topics, it is important to revise. Skimming through the pages recalls whatever you have learnt and keep in mind to revise your notes.

Note: Do not read anything new two or three days before the exam. The last few days must be dedicated only for revision.

#10 Take care of yourself

As much as studying is important, taking care of yourself is too. Along with a proper diet and sleep, it is essential to have proper mental health. Try not to get stressed about the exam, you are preparing well, and you will do well! Keep thinking about your preparations and take deep breaths whenever you are worried/stressed.

You are ready to tackle your examination with full force. If you have tried any other prep trick which turned out successfully, please do share with us!

Share this to your friend who is prepping for any competitive exam.

Here are some FAQ on how to prepare for competitive exams

Here are the Competitive Exam Prep Tips that Actually Work!
1. Organize your Schedule
2. Plan your Timetable
3. Zero Distractions
4. Refer standard study material/Previous years paper
5. Get help online
6. Adopt shortcut tricks
7. Read the text, remember the keywords (Color code topics)
8. Test yourself
9. Save time for Revision
10. Take care of yourself

Know your syllabus and understand your difficult areas in it. Plan your timetable such that you do not cram all your easy/difficult topics on one day. Take some time for revisions and most important go out for some walk in the morning to keep your mind fresh and healthy.

It doesn’t have to be 6 to 7 hours per day. According to scientific studies, it is shown that students absorb certain topics in 6 or more hours have lesser success rate than students who study the topic over a couple of days (2 hours per day).

Tags examprep Home » English Tips » 10 Competitive Exam Prep Tips that Actually Work!

Neural Vulnerability Factors that Increase Risk for Future Weight Gain

Theorists have proposed several neural vulnerability factors that may increase overeating and consequent weight gain. Early cross-sectional imaging studies could not determine whether aberrant neural responsivity was a precursor or consequence of overeating. However, recent prospective imaging studies examining predictors of future weight gain and response to obesity treatment, and repeated-measures imaging studies before and after weight gain and loss have advanced knowledge of etiologic processes and neural plasticity resulting from weight change. The present article reviews evidence from prospective studies using imaging and behavioral measures reflecting neural function, as well as randomized experiments with humans and animals that are consistent or inconsistent with five neural vulnerability theories for excessive weight gain. Extant data provide strong support for the incentive sensitization theory of obesity and moderate support for the reward surfeit theory, inhibitory control deficit theory, and dynamic vulnerability model of obesity, which attempted to synthesize the former theories into a single etiologic model. However, existing data provide only minimal support for the reward deficit theory. Findings are synthesized into a new working etiologic model that is based on current scientific knowledge. Important directions for future studies, which have the potential to support or refute this working etiologic model, are delineated.

Nearly 70% of US adults are overweight or obese, causing 300,000 deaths and $150 billion in health-related expenses in the US yearly (Finkelstein et al., 2009; Flegal et al., 2012). Indeed, obesity results in 2.8 million premature deaths worldwide annually (World Health Organization, 2013). Yet, treatments almost never result in lasting weight loss (Turk et al., 2009). Although bariatric surgery can produce more persistent weight loss, it is invasive, often contraindicated, and can cost more than $30,000 (Martin et al., 2010). Likewise, virtually all obesity prevention programs have not reduced future obesity onset (Stice et al., 2006). It is important to elucidate risk factors that predict future weight gain, as this should advance knowledge regarding the processes that give rise to obesity and guide the design of more effective preventive programs and treatments. At present, most risk factors that predict future weight gain show small effects. For instance, the predictive effects for parental obesity, a highly replicated risk factor for future weight gain, have only ranged from an r = .18 to .21 in large epidemiologic studies (e.g., Salbe et al., 2004).

Scholars have proposed several neural vulnerability factors that could theoretically increase risk for the positive energy balance that results in excessive weight gain. Given that eating palatable high-fat/high-sugar food increases activation in regions implicated in reward processing, including the striatum, midbrain, amygdala, and orbitofrontal cortex (OFC; Kringelbach et al., 2003; Small et al., 2001; Stice, Burger, & Yokum, 2013) and causes dopamine (dopamine) release in the dorsal striatum, with the amount released correlating with meal pleasantness ratings (Small et al., 2003) and caloric density of the food (Ferreira et al., 2012), etioilogic theories have included a focus on reward regions. Rodent studies document that the oral sensory properties of palatable food consumption (gustatory stimulation) stimulate brain dopamine release (Hajnal et al., 2004; Liang et al., 2006). Intra-gastric infusion of glucose and fat, bypassing the oral cavity and gustatory stimulation, also induces striatal (nucleus accumbens) dopamine release in rodents compared to isocaloric infusion of amino acids (Ren et al., 2010; Tellez et al., 2013). The role of dopamine in food intake is illustrated by the fact that direct pharmacological activation of the striatum prompts hyperphagia in animals, increasing preferential intake of high-calorie foods, even in sated animals (Kelley et al., 2005).

In contrast, anticipated palatable food intake (O’Doherty, Deichman, Critchley, & Dolan, 2002; Small, Veldhuizen, Felsted, Mak, & McGlone, 2008; Stice, Yokum, Burger, Epstein, & Smolen, 2012) and exposure to food images and cues (Frank et al., 2010; Van Meer, van der Laan, Adan, Viergever, & Smeets, 2015) activates regions implicated in incentive valuation, such as the OFC and amygdala. These findings have prompted a focus on incentive valuation regions in etiologic theories for obesity.

In this context, it is important to note that palatable food intake, anticipated intake, and food cues have broad effects, activating regions implicated in visual processing/attention (inferior parietal lobe, posterior cingulate cortex), gustatory processing (insula and overlying operculum), motor response (precentral gyrus, cerebellum), somatosensory processing (postcentral gyrus), and inhibitory behavior (inferior frontal gyrus, ventrolateral prefrontal cortex) (Huerta, Sarkar, Duong, Laird, & Fox, 2014; Stice et al., 2012; Tang et al., 2012; van Meer et al., 2015).

Animal experiments suggest that dopamine signaling plays a larger role in reward learning, particularly stimulus-reward learning, and that opioid peptide signaling plays a larger role in hedonic pleasure from food intake, largely on the basis that the effects of each neurotransmitter can be isolated experimentally (Berridge et al., 2010; Flegel et al., 2011). Consistent with this thesis, acute administration of an opioid antagonist reduced response in the caudate, anterior cingulate cortex, and medial frontal gyrus to the sight and taste of palatable food relative to a placebo control condition (Murray et al., 2014). However, reward regions (e.g., the midbrain and striatum) contain both dopamine and opioid receptors (Ambrose, Unterwald, & Bockstaele, 2004; Pollard, Llorens-Cortes, & Schwartz, 1977) and mu opioid and dopamine receptor availability in the striatum and ventral tegmental area (VTA) are highly correlated in humans (Tuominen et al., 2015). Further, the two neurotransmitter systems show crosstalk (Tuominen et al., 2015). Mesolimbic dopamine neurons are under tonic gamma aminobutyric acid-ergic (GABA) inhibition that can be lifted through activation of mu opioid receptors on GABAergic terminals in the VTA (Jalabert et al., 2011). Administration of alfentranil, which is a potent and highly selective μ-opioid receptor agonist, increased PET-assessed dopamine D2 receptor binding potential in the putamen and caudate in humans (Hagelberg et al., 2002). Conversely, amphetamine, which operates by blocking the dopamine transporter that clears dopamine from synapses, thereby increasing dopamine levels, causes the release of opioids in the ventral striatum in humans (Colasanti et al., 2012; Mick et al., 2014). Moreover, blocking striatal opioid receptors attenuates amphetamine-induced locomotion and impulsivity (Gonzalez-Nicolini et al., 2003; Wiskerke et al., 2011), whereas blocking dopamine D2 receptors attenuates the rewarding effects of morphine in opiate-dependent rats (Laviolette, Nader, & Kooy, 2002). This crosstalk is consistent with the notion that dopamine signaling facilitates learning about pleasant experiences.

It is also important to acknowledge that the brain imaging technique that is most widely utilized in this field, blood oxygen dependent level (BOLD) functional magnetic resonance imaging (fMRI) is not able to differentiate neural activation resulting from one neurotransmitter versus other neurotransmitters or signaling agents, and further that no prospective weight gain prediction studies have used imaging techniques, such a positron emission tomography (PET) that can assess individual differences in the availability of receptors for certain neurotransmitters and change in the binding potential of these receptors in response to events such as palatable food intake.

This article reviews the primary theories relating aberrations in responsivity of brain reward and incentive valuation regions, as well as regions that affect activation in these regions (e.g., inhibitory regions), to future weight gain. We begin with three theories that were first introduced to explain neural vulnerability for weight gain, and then turn to an integrative model that was subsequently introduced, which attempted to synthesize the initial theories. We also review evidence that is consistent or inconsistent with neural vulnerability theories for excessive weight gain. We focus predominantly on prospective studies and randomized experiments with humans and animals, rather than cross-sectional studies that do not permit unambiguous conclusions regarding temporal precedence and the directions of effects. The fact that researchers have concluded that lower insula responsivity may contribute to both obesity (Verdejo-García et al., 2015) and anorexia nervosa (Wagner et al., 2008) on the basis of cross-sectional data illustrates the hazards of drawing etiologic inferences from cross-sectional data. We also included high-risk designs that compared individuals at high versus low risk for future weight gain before this phenotype was expressed, as this design should also shed light on initial vulnerability factors for future weight gain. A database search to retrieve published articles was performed on PsychInfo, MedLine, Dissertation Abstracts, and Cumulative Index to Nursing and Allied Health for the years 1980 – 2015 using the following keywords: neural, imaging, fMRI, PET, SPECT, obesity, weight gain, obesity treatment, high-risk, prospective, experimental, repeated measures. We also examined the reference sections of all identified articles and reviews. Further, we contacted investigators who have published these types of studies previously and inquired about unpublished or in press papers (e.g., Dana Small, Kathryn Demos). We used a consensus approach to decide which studies to include in this review, wherein both authors read all potential studies and discussed whether each met the inclusion criteria.

Reward Surfeit Theory of Obesity

It has been theorized that individuals who show greater reward region responsivity to food intake, which is presumably an inborn characteristic, are at elevated risk for overeating and consequent weight gain (Davis, Strachan, & Berkson, 2004; Loxton & Dawe, 2006; Stice et al., 2008b). We refer to this as the reward surfeit model of overeating. Table 1 describes the prospective studies that have examined the relation of individual differences in neural response to palatable food receipt, anticipated receipt, and food cues (e.g., images) to future weight gain and response to obesity treatment, as well as key experimental studies that have manipulated variables relevant to these neural vulnerability theories of weight gain.

Table 1

Overview of experiments, prospective studies, and prospective fMRI studies investigating facets of the Reward Surfeit Theory of Obesity, Incentive Sensitization Theory of Obesity, Reward Deficit Theory of Obesity, Inhibitory Control Deficit Theory of Overeating

Study Study design;
Sample;
Baseline age ± SD;
Baseline BMI ± SD
Follow-up (mo.) Findings Theory: support +;
no support −
Anzman & Birch, 2009 Prospective;
162 females
M age = 7
M BMI = 17
96 Impulsivity (as rated by parents) at age 7 predicted future increases in BMI (r = .18). Inhibitory control deficit: +
Batterink et al., 2010 Prospective fMRI;
35 females
M age = 15.7 ± 0.9
M BMI = 24.5
12 No significant correlations between regions implicated in inhibitory control and change in BMI. Data were analyzed using regions-of-interest analysis. Inhibitory control deficit: −
Burger & Stice, 2014 Prospective fMRI;
35 females
M age = 15.5 ± 0.9
M BMI = 24.5 ± 5.4
24 Greater increase in ventral pallidum BOLD activity to palatable food receipt vs tasteless solution receipt over repeated exposures was associated with future increases in BMI (r = .39). Greater decrease in caudate BOLD activity to food receipt vs tasteless solution receipt over repeated exposures was associated with future increases in BMI (r = −.69). Data were analyzed using regions-of-interest analysis. Incentive sensitization: +
Reward deficit: −
Chouinard-Decorte et al., 2010 Prospective fMRI;
26 subjects
M age and M BMI: not reported
12 Greater BOLD activity in the amygdala to palatable food aromas vs odorless predicted future increases in BMI (effect sizes not reported). Incentive sensitization: +
Reward deficit: −
Clark et al., 2010 Experiment; random assignment to a high-energy-density (HED) or low-energy-density (LED) group. Participants in the HED condition ate 60-g portions of an HED snack food daily for 2 weeks. Participants in the LED group ate 60-g portions of an LED snack over the same time period.
53 females;
M age = 26.9 ± 3.3;
M BMI = 27.8 ± 1.0
0.5 Significant interaction between BMI, time (pre- vs post), and food group (HED vs LED) (r = .25): In the HED group, women with high BMI’s showed an increased willingness to work for their assigned food and women with lower BMI’s showed a decrease in willingness to work for their assigned food relative to women in the LED group. Incentive sensitization: +
Cornier et al., 2012 Prospective repeated-measures fMRI; Participants were scanned before and after completing a 24-week exercise intervention (no control group)
12 females + males;
M age = 38.2 ± 9.5
M BMI = 33.3 ± 4.3
6 Change in insula BOLD activity in response to hedonic food vs nonfood objects from pre- to post intervention positively correlated with changes in fat mass (r = .78) and body weight (r = .76). Data were analyzed using whole-brain analyses. Incentive sensitization: +
Deckersbach et al., 2014 Prospective repeated-measures fMRI; participants were randomized to a 6-month weight-loss intervention vs control group
13 females + males;
M age = 50.4 ± 4.5;
M BMI = 29.6 ± 1.3
6 Participants in the weight loss intervention compared to those in the control group showed increases in putamen BOLD activity in response to low-calorie food images vs high-calorie food images (r = .71) and attenuation in putamen BOLD activity in response to high-calorie food vs low-calorie food images (r = −.54). Data were analyzed using regions-of-interest analysis. Incentive sensitization: +
Demos et al., 2012 Prospective fMRI;
48 females;
M age = 18;
M BMI = not reported
6 Greater BOLD activity in the nucleus accumbens to food images predicted future increases in BMI (r = .37). Data were analyzed using both regions-of-interest and whole-brain analyses. Incentive sensitization: +
Reward deficit: −
Dong et al., 2014 Prospective fMRI;
45 females;
M age = 20.9 ± 1.7;
M BMI = 23.3 ± 1.8
12 Greater resting state activity in the orbitofrontal cortex/ventromedial prefrontal cortex predicted future increases in BMI (r = .57). Data were analyzed using regions-of-interest analysis. Reward surfeit: +
Duckworth et al., 2010 Prospective;
105 females + males;
M age = 10.56 ± 0.4;
M BMI z-score = 0.56 ± 1.1
36 Higher self-reported impulsivity in children predicted future increases in zBMI (r = 0.12). Inhibitory control deficit: +
Epstein et al., 2014 Prospective;
130 females + males;
M age = 15.2 ± 1.0
M BMI = 20.7 ± 2.0
24 Food reinforcement was positively correlated with change in zBMI (r = .18). Incentive sensitization: +
Evans et al., 2012 Prospective;
244 females + males;
M age = 9.23
M BMI percentile = 59.9 ± 29.9
48 Preference for immediate reward as measured by a food-related delay discounting task at baseline was associated with greater increases in BMI (r = .12). Reward surfeit: +
Francis & Susman, 2009 Prospective;
1090 females + males;
M age = 3;
M zBMI = 55.4 ± 28.5
108 Children who exhibited both low self-regulation in a self-control procedure at age 3 and in a food delay of gratification procedure at age 5 showed greater increases in future BMI compared to those who showed high self-regulation on both or 1 of the procedures (r = .15). Inhibitory control deficit: +
Geha et al., 2013 Prospective fMRI;
15 females + males;
M age = 25.1 ± 1.1;
M BMI = 27.1 ± 0.9
12 BOLD activity in the midbrain, hypothalamus, anterior thalamus, ventral pallidum, and nucleus accumbens to palatable food receipt vs tasteless solution receipt correlated positively with change in BMI (M r = 0.83). Data were analyzed using whole-brain analyses. Reward surfeit: +
Reward deficit: −
Hardmann et al., 2012 Experiment; a double-blind, counterbalanced, crossover study in which participants were administered a tyrosine/phenylalanine-free mixture and a balanced amino acid mixture
17 males;
M age = 29.2 ± 2.7;
M BMI = 24.4 ± 0.6
n.a. Dopamine depletion resulted in decreased hunger ratings (r = −.53) and less ad lib caloric intake (r = −.46) relative to the control condition. Reward deficit: −
Jonsson et al. 1986 Prospective;
28 females + males;
M age = 35.5;
M weight = 132 kg
12 Self-reported impulsivity was negatively correlated with weight loss (r = −.21, p>.10). Inhibitory control deficit: −
Kishinevsky et al., 2012 Prospective fMRI;
17 females;
M age = 33.4 ± 9.8;
M BMI = 34.3 ± 3.6
M = 25 Lower BOLD activity in the inferior frontal gyrus (r = −.78), middle frontal gyrus (M r = −.79), and inferior parietal lobe (r = −.74) on hard vs easy trials in a monetary delay discounting task was associated with greater increases in BMI. Data were analyzed using both regions-of-interest and whole-brain analyses. Inhibitory control deficit: +
Murdaugh et al., 2012 Prospective repeated-measures fMRI; Participants scanned before and after completing a 12-week weight loss intervention.
25 females + males;
M age = 48.0 ± 10.9;
M BMI = 32.9 ± 3.8
9 Greater BOLD activity in nucleus accumbens (r = .61) and insula (r = .68) in response to high-calorie food images vs car images at baseline predicted increases in BMI. Data were analyzed using both regions-of-interest and whole-brain analyses. Incentive sensitization: +
Nederkoorn et al., 2007 Prospective;
26 females + males;
M age = 9.3 ± 1.2
M BMI: not reported
12 Poorer response on a stop-signal task at baseline was associated with less weight loss over 6-month follow-up (r = .39) and 1-year follow-up (r = .48). Inhibitory control deficit: +
Rosenbaum et al., 2008 Experiment; a single-blind crossover study in which participants were scanned at baseline, after weight loss and after placebo or leptin administration
6 females + males;
M age = 38 ± 2;
M BMI = 42.1 ± 4.2
1.5 Weight loss was associated with reductions in amygdala, anterior cingulate gyrus, cingulate gyrus, fusiform gyrus, hypothalamus, inferior parietal lobe, middle frontal gyrus, parahippocampal gyrus, precentral gyrus, and supramarginal gyrus in response to visual food cues vs non-food cues (M r = 0.91). Incentive sensitization: +
Schlam et al., 2013 Prospective;
161 females + males;
M age = 4;
M BMI = not reported
360 Poorer performance on a food delay of gratification task predicted increases in BMI (r = −0.20). Inhibitory control deficit: +
Seeyave et al., 2009 Prospective;
805 female + males;
M age = 4;
M zBMI = 0.36 ± 0.99
84 Children with limited food delay gratification were more likely to be overweight at follow-up (r = .33). Inhibitory control deficit: +
Sotak et al., 2005 Experiment;
Mice: dopamine deficient (DD) mice receiving either a control solution, apomorphine (APO), or l-dopa, virally rescued DD (vrDD) mice, and control mice.
L-dopa treatment significantly increased food intake compared with doses of APO and with a control solution (r = .73). NOM (inhibitor of dopamine transporter and norepinephrine transporter) significantly decreased food intake compared with control solution in vrDD mice (r = −.75) and control mice (r = −.80). Reward deficit: −
Stice et al., 2008a Prospective fMRI;
33 females;
M age = 15.5 ± 0.9;
M BMI = 24.3 ± 5.4
12 No significant main effects of BOLD activity in the putamen (r = .19) and caudate (r = .26) to palatable food receipt vs tasteless solution receipt on BMI change. Interactions between TaqIA and BOLD response in the putamen (r = .45) and caudate (r = .42) to palatable food receipt vs tasteless solution receipt were correlated with change in BMI: less activation in these regions was associated with greater BMI increase in subjects with the TaqIA A1 allele and greater activation in these regions was associated with greater BMI increase in those without the TaqIA A1 allele.
Data were analyzed using regions-of-interest analysis.
Reward surfeit: +
Reward deficit: +
Stice et al., 2010a Prospective repeated-measures fMRI
26 females;
M age = 21.0 ± 1.1;
M BMI = 27.8 ± 2.5
6 Females who gained weight showed a reduction in caudate response to palatable food receipt vs tasteless solution relative to weight-stable women. Data were analyzed using regions-of-interest analysis. Reward deficit: −
Stice et al., 2010b Prospective fMRI;
39 females;
M age = 15.5 ± 0.9;
M BMI = 24.6 ± 5.5
12 There were no significant main effects of BOLD activity on increases in BMI over follow-up. Interactions between TaqIA and BOLD response in the putamen (r = 0.33) and orbitofrontal cortex (r = 0.60) in response to palatable food images vs unpalatable food images were correlated with change in BMI: less activation in these regions was associated with greater BMI increase in subjects with the TaqIA A1 allele and greater activation in these regions was associated with greater BMI increase in those without the TaqIA A1 allele. Data were analyzed using both regions-of-interest and whole-brain analyses. Incentive sensitization: +
Reward deficit: +
Stice et al. 2011 High-risk fMRI study;
60 females + males;
M age = 15.0 ± 2.9;
M BMI = 20.4 ±1.7
n.a. Healthy weight adolescents at high (n = 35) versus low risk (n =25) for future weight gain based on parental obesity status showed greater activation in caudate (M r = .41) in response to palatable food receipt vs tasteless solution receipt and greater activation in caudate (r = .42), putamen (M r = .46), and orbitofrontal cortex (r = .72) in response to winning money display vs neutral coin display.
Data were analyzed using both regions-of-interest and whole-brain analyses.
Reward surfeit: +
Reward deficit: −
Stice et al., 2015 Prospective fMRI;
153 males + females;
M age = 15.3 ± 1.1;
M BMI = 20.8 ± 2.0
36 Greater BOLD activity in the orbitofrontal cortex to anticipated palatable food receipt vs anticipated tasteless solution receipt predicted future increases in body fat (r = .32). Interaction between TaqIA and BOLD response in caudate to palatable food receipt vs tasteless solution receipt was correlated with change in body fat (r = 0.24): less activation in these regions was associated with greater BMI increase in subjects with the TaqIA A1 allele and greater activation in these regions was associated with greater BMI increase in those without the TaqIA A1 allele. Data were analyzed using both regions-of-interest and whole-brain analyses. Reward surfeit: +
Incentive sensitization: +
Reward deficit: +
Sun et al., 2015 Prospective fMRI;
32 males + females;
M age = 25.3 ± 5.6;
M BMI = 25.3 ± 4.5
12 No significant main effects of BOLD activity in reward regions in response to palatable food receipt vs tasteless solution receipt and to palatable food cue vs tasteless solution cue on BMI change. Interaction between TaqIA and BOLD response in amygdala to palatable food receipt vs tasteless solution receipt correlated with change in BMI: less activation in this region was associated with greater BMI increase in subjects with the TaqIA A1 allele (r = −.69) and greater activation in this region was related to greater BMI increase in subjects without the A1 allele (r = .68). Data were analyzed using both regions-of-interest and whole-brain analyses. Reward surfeit: +
Incentive sensitization: −
Reward deficit: +
Teegarden et al., 2009 Experiment;
30 mice (early 1 wk exposure to high fat diet n = 14; control n = 16)
2 Mice exposed to a high fat diet immediately prior to weaning consumed a significantly greater proportion of their calories in the form of a high fat diet during adulthood vs. controls (r = .81) Incentive sensitization: +
Tellez et al. 2013 Experiment;
196 male mice (high fat diet n = 74; low fat diet n = 122)
10 days Mice in which reduced striatal dopamine signaling from food intake was experimentally induced through chronic intra-gastric infusion of fat worked less for acute intra-gastric infusion of fat and consumed less rat chow ad lib than control mice (r = −.68) Reward deficit: −
Temple et al. 2009 Experiment;
Participants were randomized to 1 of 3 snack food conditions (0-kcal group, 100-kcal group, and 300-kcal group). 58 females (31 obese and 27 nonobese);
M age = 31.9
M BMI = 30.3 ± 0.8
2 wks After the daily intake phase, obese participants in the high-energy-dense food condition showed an increased willingness to work for their assigned food compared to baseline (r = 0.65) and nonobese females in the high-energy-dense food condition showed a decreased willingness to work for their assigned food compared to baseline (r = −0.44) Incentive sensitization: +
Tey et al., 2012 Experiment; Participants were randomized to 1 of 4 food snack food conditions (263 kcal hazelnuts, 263 kcal chocolate, 263 kcal potato chips or no additional food)
100 females + males
M age = 38
M BMI = 23.7
3 Increase in ad lib energy intake after repeated exposure to assigned snack food across all groups (r = .17) Incentive sensitization: +
Weygandt et al., 2013 Prospective; Participants were scanned before completing a 12-week weight loss intervention (no control group)
16 females + males
M age = 43.0 ± 12.2
M BMI = 34.5 ± 3.2
3 Subjects with limited food delay gratification lost less weight over follow-up (r = −.42). BOLD activity in the ventromedial prefrontal cortex (r = .86) and dorsolateral prefrontal cortex (r = .82 and r = .85) in response to subjective value of delayed meals positively correlated with weight loss. Activation in the anterior insula (r = −.82 and r = −.85) in response to subjective value of delayed meals negatively correlated with weight loss. BOLD activity in a region of the dorsomedial prefrontal cortex in response to subjective value of delayed meals positively correlated with weight loss (r = .87) and BOLD activity in another region was negative correlated with weight loss (r = −.87). Data were analyzed using regions-of-interest analysis. Inhibitory control deficit: +
Weygandt et al., 2015 Prospective; weight loss intervention (no control group)
23 females + males
M age = 46 ± 14
M BMI = 32 ± 4
12 Performance on a food-related delayed gratification task immediately after the weight loss intervention negatively correlated with weight maintenance over follow-up (r = −.36). BOLD activity in the superior frontal gyrus in response to difficult compared to easy food decisions positively correlated with the degree of weight maintenance (effect size not reported). Data were analyzed using regions-of-interest analysis. Inhibitory control deficit: +
Yokum et al., 2011 Prospective fMRI;
35 females;
M age = 15.6 ±1.0;
M BMI = 24.3 ± 4.5
12 Greater BOLD orbitofrontal cortex response to cues signaling the impending presentation of palatable food images predicted future increases in BMI (r = .42). Data were analyzed using regions-of-interest analysis. Incentive sensitization: +
Reward deficit: −
Yokum et al., 2012 Prospective MRI;
83 females
M age = 18.4 ± 2.8
M BMI = 24.3 ± 5.0
12 Less gray matter volume in bilateral superior frontal gyrus (M r = −.41) and middle frontal gyrus (r = −.40) predicted future increases in BMI. Data were analyzed using both regions-of-interest and whole-brain analyses. Inhibitory control deficit: +
Yokum et al., 2014 Prospective fMRI;
30 females + males;
M age = 15.2 ± 1.1
M BMI = 26.9 ± 5.4
12 Greater BOLD caudate response to food commercials – nonfood commercials (r = .57) and to food commercials – television show (r = .51) predicted future increases in BMI Incentive sensitization: +
Reward deficit: −
Yokum et al., 2015 Prospective;
Study 1: 30 females
M age = 15.2 ± 1.1
M BMI = 26.9 ± 5.4.
Study 2: 34 females
M age = 20.9 ± 1.2
M BMI = 28.2 ± 3.0.
Study 3: 162 females + males
M age = 15.3 ± 1.1
M BMI = 20.8 ± 1.9
24 The multilocus genetic composite risk score, defined by the total number of genotypes putatively associated with greater dopamine signaling capacity, was positively correlated with future increases in BMI in all 3 studies (M r = 0.24) Reward surfeit: +

A study that used a high-risk design generated findings consistent with the thesis that elevated reward region response to palatable food receipt constitutes an initial vulnerability factor that increases risk for initial overeating. Specifically, healthy weight adolescents at high- versus low-risk for future weight gain based on parental obesity status showed greater activation of regions implicated in reward (caudate, putamen, OFC) in response to receipt of high-calorie food and monetary reward (Stice et al., 2011; Table 1 ). The latter finding converges with evidence that obese versus lean individuals showed elevated responsivity in the insula, striatum, and OFC to monetary reward (Opel et al., 2015) and anticipated monetary reward (Balodis et al., 2013), suggesting that this reward region hyper-responsivity is general, rather than specific to palatable food reward. Elevated midbrain and medial OFC response to high-calorie food receipt also predicted higher subsequent ad libitum milkshake consumption (Nolan-Poupart, et al., 2013). These results converge with evidence that individuals who rate high-calorie foods as high versus low in pleasantness show elevated future weight gain (e.g., Salbe et al., 2004).

Critically, prospective research has examined the relation between neural response to receipt of high-calorie foods and future weight gain. One study found that elevated response to high-calorie milkshake tastes in the midbrain, thalamus, hypothalamus, ventral pallidum, and nucleus accumbens predicted elevated weight gain over 1-year follow-up (Geha et al. 2013; Table 1 ). These results appear to dovetail with evidence that elevated resting state activation in reward regions (e.g., vmPFC) predicted future weight gain (Dong et al., 2014; Table 1 ). However, three other studies did not find a main effect between reward region response to high-calorie food receipt and future weight gain (Stice, Spoor, Bohon, & Small, 2008a; Stice, Burger, & Yokum, 2015; Sun et al., 2015; Table 1 ). There is also evidence that individuals who show greater recruitment of reward regions (e.g., striatum) in response to tastes of high-calorie milkshakes show greater future weight variability, operationalized as greater observed deviation of participants BMI over time around their average BMI (Winter et al., 2015). Interestingly, reward region response to high-calorie food intake showed stronger relations to future weight variability than to future linear weight gain in the same sample (Stice et a., 2015; Winter et al., 2015), potentially because weight variability better captures the cumulative effects of repetitive periods of weight gain, which are often countered by periods of weight loss.

In this context it is important to note that elevated reward region responsivity has also been theorized to increase risk for other appetitive disorders, such as substance abuse (Davis & Claridge, 1998). Consistent with this thesis, non-substance using adolescents at high- versus low-risk for future substance use disorders, based on parental substance use disorder, showed greater activation of a key reward region (midbrain) in response to receipt of high-calorie food (Stice & Yokum, 2014) and elevated reward region responsivity (caudate, putamen) in response to monetary reward predicted future substance use onset (Stice, Yokum, & Burger, 2013). These findings suggest that reward region hyper-responsivity may increase risk for a range of appetitive problems and that there may be parallels in neural vulnerability factors that increase risk for obesity and substance use.

Of note, two studies found significant interactions wherein elevated caudate response to milkshake receipt predicted future weight gain for adolescents with a genetic propensity for greater dopamine signaling capacity by virtue of possessing the TaqIA A2/A2 allele, but lower caudate response predicted weight gain for adolescents with a genetic propensity for lower dopamine signaling capacity by virtue of possessing one or more TaqIA A1 allele (Stice et al., 2008a; Stice et al., 2015), though the interaction was only marginally significant in the latter study. A third study found a significant interaction wherein elevated amygdala response to milkshake receipt predicted future weight gain for adults with a genetic propensity for greater dopamine signaling capacity by virtue of possessing the TaqIA A2/A2 allele, but lower amygdala response predicted weight gain for adults with a genetic propensity for lower dopamine signaling capacity by virtue of possessing one or more TaqIA A1 allele (Sun et al., 2015). However, this study did not replicate the significant interaction between TaqIA allele status and caudate response to milkshake receipt in the prediction of future weight gain. The evidence that elevated reward region response to high-calorie food receipt predicted future weight gain for individuals with a genetic propensity for greater dopamine signaling appears consistent with the reward surfeit theory of obesity.

Additional genetic findings appear consistent with the reward surfeit model of obesity. Specifically, individuals with a genetic propensity for elevated dopamine signaling capacity in reward circuitry showed elevated future weight gain in three samples, as well as significantly less weight loss in response to obesity treatment (Yokum, Marti, Smolen, & Stice, 2015; Table 1 ). That study examined a multilocus score because it relates more strongly to reward region responsivity than the individual alleles used to calculate the composite genetic risk score (Nikolova, Ferrel, Manuck, & Hariri, 2011; Stice et al., 2012). Theoretically, this is because the greater number of these genotypes, regardless of the particular combination, the greater the dopamine signaling capacity. The multilocus composite was scored as follows: TaqIA A1/A1, DRD2-141C Ins/Ins, DRD4-L, DAT1 10R/10R, and COMT Met/Met genotypes were scored 0 (‘low’); TaqIA A2/A2, DRD2-141C Ins/Del and Del/Del, DRD4-S, DAT1 9R, and COMT Val/Val genotypes were scored 1 (‘high’), and TaqIA A1/A2 and COMT Met/Val genotypes were scored 0.5 (scores were summed to create the composite). Humans with the A2/A2 allele versus an A1 allele of the Taq1A polymorphism and the Del allele versus Ins/Ins genotype of the DRD2-141C Ins/Del polymorphism show more D2 receptors (Jönsson et al., 1999). Humans with the shorter than 7 allele (DRD4-S) versus 7-repeat or longer allele (DRD4-L) of the DRD4 genotype show greater in vitro dopamine functioning and stronger response to dopamine agonists (Asghari et al., 1995; Seeger et al., 2001). Humans with the 9-repeat allele (DAT1-S) versus homozygous for the 10-repeat allele (DAT1-L) of the DAT1 show lower DAT1 expression (Heinz et al., 2000), theoretically increasing synaptic dopamine clearance, producing lower basal dopamine levels and increasing phasic dopamine release (van Dyck et al., 2005). Val homozygotes versus Met homozygotes of the Catechol-O-methyltransferase (COMT val 158 met) gene putatively have lower basal striatal dopamine levels and greater phasic dopamine release (Lachman et al., 1996).

In sum, healthy weight adolescents at high-risk for future weight gain by virtue of parental obesity showed greater reward region responsivity to palatable food receipt and monetary reward than their low-risk counterparts (Stice et al., 2011), individuals who evidenced elevated reward region responsivity to palatable food receipt showed greater future weight gain (Geha et al., 2013), though this finding did not replicate in other studies (Stice et al., 2008a; Stice et al., 2015; Sun et al., 2015), greater resting state activation in a network including a region implicated in reward processing predicted future weight gain (Dong et al., 2014), and youth with a genetic propensity for greater dopamine signaling showed greater future weight gain in three samples in a multi-study report (Yokum et al, 2015). Further, three out of three studies that tested for interactions between reward region responsivity and the TaqIA genotype found that individuals who showed greater reward region response to palatable food receipt and who had a genetic propensity for elevated dopamine signaling showed greater future weight gain (Stice et al., 2008a; Stice et al., 2015; Sun et al., 2015), which also appears consistent with the reward surfeit theory. Thus, six out of the nine high-risk and prospective studies that investigated facets of the reward surfeit theory generated supportive findings, providing moderate support for this etiologic theory of weight gain.

Incentive Sensitization Theory of Obesity

The incentive sensitization model posits that repeated intake of high-calorie palatable foods results in an elevated responsivity of regions involved in incentive valuation to cues that are associated with palatable food intake via conditioning, which prompts craving and overeating when these cues are encountered (Berridge et al., 2010). Animal experiments indicate that firing of striatal and ventral pallidum dopamine neurons initially occurs in response to receipt of a novel palatable food, but that after repeated pairings of palatable food intake and cues that signal impending receipt of that food, dopamine neurons begin to fire in response to food-predictive cues and no longer fire in response to food receipt (Schultz et al., 1997; Tindell et al., 2004; Tobler et al., 2005). Theorists posit that this shift during cue-reward learning serves to either update knowledge regarding the predictive cues or attribute reward value to the cues themselves thereby guiding behavior (Balleine et al., 2008; Robinson & Berridge 1993). This theory implies that a period of overeating palatable foods may be necessary for the conditioning process that gives rise to hyper-responsivity of reward regions to food cues, suggesting that this might be better viewed as a maintenance model of overeating, rather than a process that contributes to the initial emergence of overeating.

Apparently consistent with the incentive sensitization theory, obese versus lean humans show greater responsivity of brain regions associated with reward and motivation (striatum, amygdala, OFC) to pictures of high-calorie foods versus low-calorie foods and control images (e.g., Bruce et al. 2010; Dimitropoulos, Tkach, Ho, & Kennedy, 2012; Frankort et al., 2012; Holsen et al., 2012; Martin et al., 2010; Rothemund et al., 2007; Stice, Yokum, Bohon, Marti, & Smolen, 2010b; Stoeckel et al., 2008). Similarly, humans with versus without a range of various substance use disorders show greater activation of regions implicated in reward and motivation to substance use images (e.g., Due, Huettel, Hall, & Rubin, 2002; Myrick et al., 2004; Tapert et al., 2003). Elevated responsivity in the ventral striatum (Lawrence, Hinton, Parkinson, & Lawrence, 2012) and amygdala (Mehta et al., 2012) during exposure to food images also predicted greater subsequent ad lib high-calorie food intake. Interestingly, healthy weight adolescents who were eating beyond objectively measured basal metabolic needs showed greater response during cues predicting impending palatable food receipt in regions that encode visual processing and attention (visual and anterior cingulate cortices), salience (precuneus; Frohlich, 1994), and reward and motivation (striatum), as well as a region in the primary gustatory cortex (frontal operculum; Burger & Stice, 2013), suggesting that overeating, even if it has not yet resulted in excess weight gain, may be accompanied by elevated responsivity of reward, attentional, and gustatory regions to food predictive cues.

Of note, obese versus lean individuals also show greater recruitment of motor response regions when exposed to high-calorie food images (Brooks et al., 2013; Jastreboff et al., 2013), suggesting an elevated motor approach tendency. Obese versus lean individuals likewise show attentional bias for high-calorie food images according to the Stroop test (Braet & Crombez, 2003; Nijs et al., 2010a) and eye tracking (Castellanos et al., 2009; Graham et al., 2011).

Critically, prospective fMRI studies have found that elevated nucleus accumbens response to high-calorie palatable food images, elevated amygdala response to palatable food odors, elevated OFC response to cues that signal impending presentation of palatable food images, and elevated striatal response to commercials for high-calorie foods predicted future weight gain (Demos et al., 2012; Chouinard-Decorte et al., 2010; Yokum et al., 2011; Yokum, Gearhardt, Harris, Brownell, & Stice, 2014; Table 1 ). However, one study did not find any relation between reward region response to palatable food smells, which can be construed as another type of food cue, and future weight gain (Sun et al., 2015). Obese individuals who evidenced greater reward and attention region response to high-calorie food images also showed poorer response to behavioral weight loss treatment (Murdaugh et al., 2012; Table 1 ), consistent with the notion that hyper-responsivity of these regions may maintain overeating. However, because each of the samples from those studies included overweight individuals, it is possible that a period of overeating might have caused the elevated reward region responsivity to palatable food images. One study recruited healthy weight adolescents to test the thesis that youth who show greater reward region response to palatable food tastes and cues that signal impending palatable food tastes are at risk for initial excessive weight gain; elevated OFC response to cues signaling impending milkshake receipt predicted initial excessive body fat gain (Stice et al., 2015), an effect that replicated in split halves of the sample. The average predictive relation between elevated reward region responsivity to food cues and future weight gain from the six prospective studies corresponds to a medium effect size (M r = .37; Table 1 ). These results converge with behavioral evidence indicating that individuals who work longer to earn high-fat/high-sugar snack foods, which presumably reflects greater anticipatory food reward, also show elevated future weight gain (Epstein, Yokum, Feda, & Stice, 2014; Table 1 ).

Interestingly, there is also evidence that attentional bias for high-calorie food predicts greater ad lib food intake (Nijs, Muris, Euser, & Franken, 2010b; Werthmann, Field, Roefs, Nederkoorn, & Jansen, 2014) and future weight gain (Calitri, Pothos, Tapper, Brunstrom, & Rogers, 2010). However, these samples contained overweight individuals, raising the possibility that a period of overeating may be necessary to give rise to these predictive effects.

The evidence that elevated reward and attention region responsivity predicts future weight gain dovetails with evidence from controlled trials that weight loss reduces reward region (e.g., parahippocampal gyrus, parietal cortices, putamen, insula, visual cortex) responsivity to high-calorie food images (Cornier, Melanson, Salzberg, Bechtell, & Tregellas, 2012; Deckersbach et al., 2014; Rosenbaum, Pavlovich, Leibel, & Hirsch, 2008; Table 1 ). Weight loss has also been associated with concurrent reductions in food preference ratings for high-calorie foods relative to changes observed in waitlist controls (Deckersbach et al., 2014).

Echoing evidence that elevated dopamine signaling capacity amplified the predictive relation between elevated reward region response to palatable food receipt and future weight gain, one study found that the relation of reward region response to food images and future weight gain was significantly moderated by a genetic propensity for greater dopamine signaling capacity in reward regions. Specifically, adolescents who show elevated striatal and OFC response to palatable food images and who had a genetic propensity for greater dopamine signaling due to possessing an A2/A2 TaqIA allele, showed elevated future weight gain (Stice et al., 2010b; Table 1 ).

Experiments have also generated findings that appear consistent with the incentive sensitization theory of obesity. Specifically, young adults randomly assigned to consume high-calorie foods daily over 2–3 week periods show an increased willingness to work for their assigned food relative to controls (Clark et al., 2010; Temple et al., 2009; Table 1 ), echoing findings with rodents (Teegarden et al., 2009; Table 1 ), as well as increased ad lib consumption of the snack foods after consuming the snack food on a daily basis (Tey et al., 2012; Table 1 ).

The above findings imply that some individuals may show an elevated propensity to associate reward from palatable food intake with cues repeatedly paired with such food reward, which drives elevated responsivity of reward regions to food cues. As noted, animal experiments indicate that after repeated pairings of palatable food receipt and cues that predict palatable food receipt, dopamine signaling increases in response to predictive cues but decreases in response to food receipt (Schultz et al., 1997; Tindell et al., 2004; Tobler et al., 2005). Using functional MRI, one study documented an increase in caudate response to cues predicting impending milkshake receipt over repeated pairings of the predictive cues and milkshake receipt, demonstrating a direct measure of in vivo cue-reward learning in humans (Burger & Stice, 2014; Table 1 ). Further, that study observed a simultaneous decrease in putamen and ventral pallidum response during milkshake receipt that occurred over repeated pairings of the cue and milkshake receipt, mirroring the reduction in dopamine release in response to food reward after it is repeatedly paired with a cue that signals impending food receipt (Zellner & Ranaldi, 2010). The reduction in putamen and ventral pallidum signal may reflect reinforcer satiation. Most importantly, participants who exhibited the greatest escalation in ventral pallidum responsivity to cues and those individuals that show the greatest decrease in caudate response to milkshake receipt showed significantly larger increases in BMI over 2-year follow-up (r = .39 and −.69 respectively); the average predictive effect (M r = .54) was also large in magnitude. Interestingly, participants who showed the strongest initial caudate response to the first few tastes of milkshake showed the greatest reduction in caudate response over time, suggesting that the food reinforcer satiation propensity may be at least partially rooted in elevated reward region response to novel palatable foods. Also of note, there was no relation between cue-reward learning propensity and food reinforcer satiation propensity, implying that there may be two qualitatively distinct vulnerability pathways to weight gain. These results provide preliminary evidence that there are important individual differences in food cue-reward learning and food reinforcer satiation that may give rise to elevated reward region responsivity that underlies the incentive sensitization process. These individual difference factors may explain why certain people have shown obesity onset in response to the current obesogenic environment in western cultures, whereas others have not.

In sum, heightened reward region responsivity to food cues or anticipated receipt predicted future weight gain in five out of six prospective studies (Chouinard-Decorte et al., 2010; Demos et al., 2012; Stice et al., 2015; Yokum et al., 2011; Yokum et al., 2014) and poorer response to a weight loss intervention in one study (Murdaugh et al., 2012). Another study found that individuals who worked longer to earn snack foods showed elevated future weight gain (Epstein et al., 2014). Three studies found that weight loss is associated with a reduction in reward region responsivity to high-calorie food images (Cornier et al., 2012; Deckersbach et al., 2014; Rosenbaum et al., 2008). In addition, one study found that the predictive effects between reward region response to food images to future weight gain is stronger for individuals with a genetic propensity for elevated dopamine signaling (Stice et al., 2010b). Four experiments with humans and animals also indicated that habitual intake of high-calorie snack foods resulted in greater subsequent intake of the snack food and a greater willingness to work for the snack foods (Clark et al., 2010; Teegarden et al., 2009; Temple et al., 2009; Tey et al., 2012). Another study found that there are individual differences in cue-reward learning and food reinforcer satiation, and that individuals who show the most potent reward-cue learning and food reinforcer satiation show elevated future weight gain (Burger & Stice, 2014). Findings from these 16 prospective studies and randomized experiments provide strong support for the incentive sensitization theory of obesity.

Reward Deficit Theory of Obesity

The reward deficit model of obesity posits that individuals with lower sensitivity of dopamine-based reward regions overeat to compensate for this reward deficiency (Wang et al., 2002). This theory was advanced largely based on evidence that drugs that block dopamine D2 receptors increase appetite and result in weight gain, whereas drugs that increase brain dopamine concentrations reduce appetite and produce weight loss (Wang et al., 2001). However, there are some questionable aspects of this line of reasoning. First, all classes of drugs that produce euphoria, including stimulants, barbiturates, benzodiazepines, opioids, and marijuana, increase dopamine signaling in reward circuitry (Wise & Rompre, 1989), but only stimulants have been associated with weight loss. Second, “dopaminergic” drugs, such as amphetamine, increase neurotransmission of dopamine, serotonin, norepinephrine, epinephrine, histamine, acetylcholine, opioids, and glutamate (Eiden & Weihe, 2011; Loseth, Ellingsen, & Leknes, 2014; Miller, 2011), making it difficult to conclude that it is the increase in dopaminergic signaling in particular that causes weight loss. Third, “antidopaminergic” drugs, also known as antipsychotics, affect neurotransmission of dopamine and serotonin, and also show affinity for adrenergic, opioidergic, and glutamate receptors (Meltzer, 2002; Miller, 2009), making it difficult to conclude that it is the decrease in dopamine signaling that cause weight gain. Indeed, a randomized trial found that directly compared the effects of haloperidol, an antipsychotic with very high affinity for dopamine D2 receptors, to clozapine and olanzapine, which are atypical antipsychotic medications with lower affinity for dopamine D2 receptors, found that only the atypical antipsychotics resulted in significant weight gain; haloperidol did not (Krakowski, Czobor, & Citrome, 2009).

Seemingly consistent with the reward deficit theory, obese versus lean humans showed lower striatal dopamine D2 receptor availability than lean humans (de Weijer et al., 2011; Haltia et al., 2007; Kessler et al., 2014; Volkow et al., 2008), though other studies have not replicated this finding (Eisenstein et al., 2013; Haltia et al., 2008; Karlsson et al., 2015; Steele et al., 2010). Obese humans also have lower μ-opioid receptor availability in the ventral striatum, dorsal caudate, orbitofrontal cortex, anterior cingulate cortex, insula, and thalamus than their lean counterparts (Karlsson et al., 2015). Further, obese versus lean humans show lower capacity of nigrostriatal neurons to synthesize dopamine (Wilcox et al., 2010). Obese versus lean humans also show less striatal responsivity to tastes of high-calorie beverages (Babbs et al., 2013; Frank et al., 2012; Green et al., 2011; Stice et al., 2008a, b). Obese versus lean rats likewise have lower basal dopamine levels and D2 receptor availability and less ex vivo dopamine release in response to electrical stimulation in nucleus accumbens and dorsal striatum tissue (Fetissov et al., 2002; Geiger et al., 2008; Huang et al., 2006; Thanos et al., 2008). A human study found a positive correlation between BMI and dopamine release in the dorsal striatum and substantia nigra in response to amphetamine (Kessler et al., 2014), suggesting that D2 receptor availability may not be closely coupled with degree of dopamine response from rewarding experiences, or at least may not show a linear relation. Another study found that obese versus lean individuals showed greater tyrosine and phenylalanine availability, which are amino acid precursors used in the production of dopamine (Frank et al., 2015), which likewise implies that obese individuals may have greater endogenous dopamine availability.

However, certain prospective and experimental findings indicate that overeating contributes to reward region hypo-responsivity. Young women who gained weight over a 6-month period showed a reduction in striatal responsivity to palatable food receipt relative to women who remained weight stable (Stice, Yokum, Blum, & Bohon, 2010a; Table 1 ). This finding converges with numerous experimental overfeeding experiments with animals; rats randomized to overeating conditions that result in weight gain versus control conditions show down-regulation of post-synaptic D2 receptors, and reduced D2 sensitivity, extracellular dopamine levels in the nucleus accumbens and dopamine turnover, and lower sensitivity of dopamine reward circuitry to food intake, electrical stimulation, amphetamine administration, and potassium administration (Bello et al., 2002; Davis et al., 2008; Geiger et al., 2009; Kelley et al., 2003; Johnson & Kenny et al., 2010; Thanos et al., 2008). One experiment randomized rats to a 40-day period of unlimited access to a high-fat/sugar diet, to limited access to a high-fat/sugar diet, or unlimited access to rat chow; they subsequently randomized rats in each of the three conditions to exposure to a light cue that was associated with a foot shock or the light cue only, finding that on a subsequent test day, exposure to the light cue reduced caloric intake in rats that had experienced limited access to the high-fat/sugar diet or unlimited access to the chow diet, but not in those that had previously had unlimited access to the high-fat/sugar diet (Johnson & Kenny, 2010). The authors interpreted this pattern of findings as suggesting that habitual intake of energy dense diets may induce a compulsive-style of eating that is resistant to subsequent punishment learning. Pigs randomized to a weight gain intervention versus a stable weight condition showed reduced resting activity in the midbrain and nucleus accumbens (Val-Laillet et al., 2011). The reduced dopamine signaling capacity appears to occur because habitual intake of high-fat diets decreases synthesis of oleoylethanolamine, a gastrointestinal lipid messenger (Tellez et al., 2013; Table 1 ). People who report elevated intake of particular foods show reduced striatal response during intake of that food, independent of BMI (Burger & Stice, 2012; Green & Murphy, 2012; Rudenga & Small, 2012). Converging with these results, experiments indicate that young adults randomly assigned to consume high-calorie foods daily over 2–12 week periods report reduced “liking” of the foods relative to baseline and control high-calorie foods not consumed daily (Clark et al., 2010; Hetherington et al., 2000; 2002; Temple et al., 2009; Tey et al., 2012; Table 1 ).

The evidence that weight gain is associated with down-regulation of dopamine-based reward circuitry dovetails with evidence that weight loss increases D2 receptor availability in humans (Steele et al., 2010) and rats (Thanos et al., 2008), and responsivity of reward circuitry to food cues (Cornier et al., 2012; Deckersbach et al., 2014; Rosenbaum et al., 2008), though one study reported that weight loss was associated with a reduction in D2 receptor availability (Dunn et al., 2010). For the most part, this literature also seems consistent with the thesis that habitual overeating results in down-regulation of reward circuitry and that reducing overeating can reverse this process.

Interestingly, one experiment found that intake of high-fat/high-sugar food resulted in down regulation of striatal D1 and D2 receptors in rats relative to isocaloric intake of low-fat/low-sugar rat chow (Aliso et al., 2010), implying that it is intake of energy dense foods versus a positive energy balance per se that causes plasticity of reward circuitry. Another study found that mice that received chronic intra-gastric infusion of fat showed reduced striatal dopamine signaling from food intake relative to chow fed weight-matched control mice (Tellez et al., 2013), providing further evidence that habitual consumption of fat can reduce dopamine response to food intake, independent of weight gain. These results prompted us to test whether habitual ice cream intake is associated with reduced reward region responsivity to ice cream-based chocolate milkshake (Burger & Stice, 2012). Ice cream intake was inversely related to activation in the striatum (bilateral putamen: right r = −.31; left r = −.30; caudate: r = −.28) and insula (r = −.35) in response to milkshake receipt. Yet, total kcal intake over the past 2-weeks did not correlate with striatal or insula response to milkshake receipt, providing additional evidence that it may be intake of energy dense food, rather than overall caloric intake that reduces responsivity of reward circuitry.

The growing evidence that overeating results in down-regulation of dopamine-based reward circuitry seems to converge with data suggesting that habitual substance use, which also causes acute increases in dopamine-signaling, likewise eventually leads to down-regulated reward circuitry. For instance, lower dopamine release in the nucleus accumbens in response to methylphenidate has been observed in cocaine-dependent and alcohol-dependent individuals relative to healthy controls (Volkow et al., 1997, 2007). Indeed, even adolescents with a relatively short history of substance use showed less caudate response to monetary reward relative to adolescents who had not initiated substance use (Stice et al., 2013).

Given that animals that have shown down-regulation of reward circuitry because of habitual drugs use will work to keep dopamine levels in the nucleus accumbens above a certain level (Wise et al., 1995a,b; Ranaldi et al., 1999), Geiger and associates (2009) speculate that rats that have experienced diet-induced down-regulation of dopamine circuitry may similarly overeat to increase dopamine signaling. However, a study found that mice in which reduced striatal dopamine signaling from food intake was experimentally induced through chronic intra-gastric infusion of fat worked less for acute intra-gastric infusion of fat and consumed less rat chow ad lib than control mice (Tellez et al., 2013). These animal findings converge with evidence that experimentally induced dopamine depletion resulted in decreased hunger ratings and less ad lib caloric intake relative to the control condition, though the later effect was only marginal because of the small sample size (Hardman, Herbert, Brunstrom, Munafo, & Rogers, 2012; Table 1 ). Further, genetically engineered dopamine-deficient mice are unable to sustain appropriate levels of feeding and dysregulation of dopamine signaling in the dorsal striatum in particular is sufficient to induce hypophagia (Sotak et al., 2005; Zhou & Palmiter, 1995; Table 1 ). These data converge with the finding that experimental administration of 6-hydroxydopamine, a neurotoxin that selectively destroys dopaminergic and noradrenergic neurons, at any of several points along the nigrostriatal dopamine pathway between the substantia nigra and the caudate-putamen results in severe aphasia (Robbins & Everitt, 1999). These findings seem incompatible with the notion that an induced down-regulation of dopamine reward circuitry leads to compensatory overeating.

Prospective fMRI studies that have examined neural responsivity that predicts future weight gain have also produced little support for the reward deficit theory. None of the 9 prospective studies that examined the relation of BOLD response to high-calorie palatable food images/cues, anticipated palatable food receipt, and palatable food receipt to future weight gain reviewed above found a main effect between reduced reward region responsivity to these food stimuli and greater future weight gain (Chouinard-Decorte et al., 2010; Demos et al., 2012; Geha et al. 2013; Stice et al., 2008a, 2010b, 2015; Sun et al., 2015; Yokum et al., 2011, 2014). Further, lean youth at risk for future obesity by virtue of parental obesity show hyper-responsivity of reward regions to palatable food receipt and monetary reward, and no evidence of hypo-responsivity or reward regions (Stice et al., 2011).

However, two studies found significant interactions wherein a weaker striatal response to receipt of high-calorie chocolate milkshake predicted future weight gain for participants with a genetic propensity for lower dopamine signaling in reward circuitry, by virtue of possessing the TaqIA A1 allele (Stice et al., 2008a, 2015), though this interaction was only marginally significant in the latter study. A third study found a significant interaction wherein a weaker amygdala response to milkshake receipt predicted future weight gain for adults with a genetic propensity for lower dopamine signaling capacity by virtue of possessing a TaqIA A1 allele (Sun et al., 2015). However, this latter study did not replicate the significant interaction between TaqIA allele status and caudate response to milkshake receipt in the prediction of future weight gain. Further, weaker putamen and OFC response to palatable food images predicted future weight gain for adolescents at genetic risk for lower dopamine signaling by virtue of possessing the TaqIA A1 allele (Stice et al., 2010b). The interactive effects observed in four studies (Stice et al., 2008a, 2010b, 2015; Sun et al., 2015) suggest the possibility of qualitatively distinct reward surfeit and reward deficit pathways to obesity. Specifically, it appears that the reward surfeit model may apply to individuals with a genetic propensity for greater dopamine signaling capacity and that the reward deficit model may apply to those with a genetic propensity for weaker dopamine signaling. These findings may imply that too much or too little dopamine signaling capacity and reward region responsivity may both increase risk for overeating, potentially because each perturbs homeostatic processes that maintain a balance between caloric intake and caloric expenditure. There are other examples of such inverted U-shaped relations between neurotransmitters and neural function, such as the evidence that too little or too much epinephrine and norepinephrine impair memory formation (Eichenbaum et al., 1999). It will be important for future studies to test whether genotypes that affect dopamine signaling moderate the relations between reward region responsivity and future weight gain. And given that opioid signaling plays a critical role in conveying hedonic reward from palatable food intake, it would also be potentially profitable to test whether genotypes associated with opioid signaling likewise moderate these predictive relations.

In sum, research has provided little prospective or experimental support for the thesis that individuals who show reduced responsivity of reward circuitry to food stimuli overeat to compensate for this deficit. Most critically, adolescents at high- versus low-risk for future weight gain showed elevated reward region responsivity to food and no evidence of blunted reward region response (Stice et al., 2011) and none of the nine prospective studies found a main effect wherein lower reward region response to food stimuli predicted future weight gain (Chouinard-Decorte et al., 2010; Demos et al., 2012; Geha et al. 2013; Stice et al., 2008a, 2010b, 2015; Sun et al., 2015; Yokum et al., 2011, 2014). Indeed, most of these prospective studies found that elevated responsivity of reward circuitry, including the amygdala, midbrain, ventral pallidum, nucleus accumbens, and striatum, to food images/cues, anticipated palatable food receipt, and palatable food receipt predicted future weight gain. Moreover, experimentally induced down-regulation of dopamine response to fat intake in mice reduced caloric intake and the motivational value of high-calorie food compared to control mice (Tellez et al., 2013), experimentally-induced dopamine depletion was associated with less ad lib food intake in humans (Hardman et al., 2012), and dopamine-deficient mice are unable to sustain appropriate levels of feeding (Zhou & Palmiter, 1995). Yet, four studies found interactions that suggest that the reward surfeit model might operate for individuals with a genetic propensity for greater dopamine signaling capacity and the reward deficit model might operate for individuals with a genetic propensity for lower dopamine signaling capacity (Stice et a., 2008a, 2010b, 2015; Sun et al., 2015). Thus, although the majority of findings from these 13 prospective and experimental studies provided results that do not support the reward deficit theory, select result from four of these studies can be interpreted as providing support for this etiologic theory. This pattern of findings suggest that it would be useful if additional independent labs tested whether the TaqIA polymorphism moderates the relation between reward region response and future weight gain before the reward deficit theory if completely set aside because of insufficient support from prospective and experimental studies.

Inhibitory Control Deficit Theory of Overeating

It has also been proposed that individuals with inhibitory control deficits, and by extension lower responsivity of brain regions implicated in inhibitory control, are more sensitive to food cues and more vulnerable to the pervasive temptation of appetizing foods in our environment, which increases overeating (Francis & Susman, 2009; Nederkoorn et al., 2006a; Sutin, Ferrucci, Zonderman, & Terracciano, 2011). Trait impulsivity is thought to result in greater sensitivity to reward-predictive cues, which may contribute to compulsive food intake (Diergaarde et al., 2009).

Consistent with the inhibitory control deficit theory of overeating, obese versus lean individuals show response inhibition deficits on go/no-go and stop-signal tasks (Bonato & Boland, 1983a; Nederkoorn et al., 2006a; Nederkoorn, Jansen, Mulkens, & Jansen, 2006b). Response inhibition deficits on a stop-signal task also correlate positively with unobtrusively measured caloric intake among adults (Guerrieri et al., 2007). Research using speeded responses to the Matching Familiar Figure Test found that obese versus lean individuals respond more quickly, but make more false-positive response errors (Braet et al., 2007). Rats that showed behavioral disinhibition in response to food reward on a serial reaction time task exhibited greater future sucrose seeking behaviors and enhanced sensitivity to sucrose-associated stimuli after extinction, relative to rats that exhibited behavioral inhibition (Diergaarde et al., 2009). Obese versus lean individuals have shown a preference for immediate monetary reward versus a larger delayed monetary reward (Epstein, Dearing, Temple & Cavanaugh, 2008; Jasinska et al., 2012; Weller, Cook, Avsar & Cox, 2008), though this finding has not always replicated (Bonato & Boland, 1983a; Nederkoorn et al., 2006b). Obese versus lean individuals have also shown a preference for immediate food reward versus a larger delayed food reward (Bonato & Boland, 1983a; Epstein et al., 2008; Sobhany & Rodgers, 1985), though not in all studies (Bourget & White, 1984).

Most critically, inhibitory control deficits in response to high-calorie foods in delay discounting tasks, which reflects an immediate reward bias, has reliably predicted future weight gain (Evans, Fuller-Rowell, & Doan, 2012; Francis & Susman, 2009; Schlam, Wilson Shoda, Mischel, & Ayduk, 2013; Seeyave et al., 2009; Table 1 ). Similar results have emerged from studies that examined the relation of self-report measures of inhibitory control to future weight gain (Anzman & Birch, 2009; Duckworth, Tsukayama, & Geier, 2010; Sutin et al., 2011; Table 1 ). The average predictive relation between baseline impulsivity and future weight gain was an r = .18, which corresponds to a small effect size. Further, individuals with inhibitory control deficits show poorer response to weight loss treatment and poorer weight loss maintenance (Nederkoorn et al., 2007; Weygandt et al., 2013; Weygandt et al., 2015; Table 1 ), though the former effect did not emerge in one study (Jonsson, Bjorvell, Levander & Rossner, 1986; Table 1 ). The average predictive relation between baseline impulsivity and response to weight loss treatment was an r = .37, which corresponds to a medium effect size.

In terms of neuroimaging findings, obese versus lean teens showed less activation of prefrontal regions (dorsolateral prefrontal cortex [dlPFC], ventral lateral prefrontal cortex [vlPFC]) when trying to inhibit responses to high-calorie food images and behavioral evidence of reduced inhibitory control (Batterink et al., 2010; Table 1 ), though there was no evidence that participants who showed less recruitment of inhibitory regions showed elevated future weight gain. Another study found that participants who showed less recruitment of inhibitory control regions (inferior, middle, and superior frontal gyri) during difficult versus easy choices on a delay-discounting task showed elevated future weight gain (r = .71; Kishinevsky et al., 2012; Table 1 ), which represented a large effect. Further, individuals that showed less recruitment of inhibitory control regions (dorsolateral prefrontal cortex) during a delay discounting task showed significantly less weight loss in response to weight loss treatment (Weygandt et al., 2013) and less weight loss maintenance over a 1-year follow-up (Weygandt et al., 2015). These results converge with evidence that obese versus lean adults showed less grey mater volume in the prefrontal cortex (Pannacciulli et al., 2006), a region that modulates inhibitory control, and with a marginal trend for reduced grey matter volume in the prefrontal cortex to predict weight gain over 1-year follow-up (Yokum, Ng, & Stice, 2012; Table 1 ). Interestingly, obese versus lean humans also showed less recruitment of inhibitory regions (ventral medial prefrontal cortex [vmPFC]) in response to high-calorie food images (Silvers et al., 2014) and high-calorie food TV commercials (Gearhardt et al., 2014). Further, lower dlPFC response to high-calorie food images predicted greater ad lib food intake over the next 3 days (Cornier et al., 2010) and individuals reporting chronic stress showed less recruitment of frontal regions in response to images of high-calories foods and showed greater ad lib caloric intake (Tryon, Carter, DeCant, & Laugero, 2013). The findings from the latter four studies are noteworthy because they emerged in paradigms lacking a behavioral response component. These findings may be explained by the fact that the primary motor area received a very dense innervation from dopamine-containing fibers originating in the midbrain (Berger, Gaspar, & Verney, 1991). Indeed, participants have shown activation of motor regions, as assessed via electromyography, in response to palatable food images (Gupta & Aron, 2011).

It is important to acknowledge that inhibitory control deficits increase risk for a wide range of negative outcomes, including substance use, school dropout, crime, incarceration, and poverty (Diamond, Barnett, Thomas, & Munro, 2007; Sher & Trull, 1994), putatively because such individuals are more likely to act upon temptation and less likely to consider negative consequences from their actions. For instance, elevated impulsivity has predicted future onset of (Ernst et al., 2006; Leeuwen et al., 2011; Malmberg et al., 2012; McGue et al., 2001) and increases in substance use (Krank et al., 2011; Stice et al., 1998). Further, adolescents who showed less prefrontal inhibitory region recruitment during a go/no-go task were more likely to show onset of heavy alcohol use (Norman et al., 2011). The fact that deficits in inhibitory control is a more general risk factor for negative outcomes than is reward circuitry sensitivity, implies that the former may have a broader impact on weight gain, which may be mediated by additional processes. For instance, other negative outcomes resulting from impulsivity, such as incarceration and poverty may result in increased stress, which has also been found to increase the reward value of food (Lemmens, Rutters, Born, & Westerterp-Plantenga, 2011).

In sum, four studies found that individuals with a preference for immediate food reward, as assessed by behavioral paradigms, show elevated weight gain (Evans et al., 2012; Francis & Susman, 2009; Schlam et al., 2013; Seeyave et al., 2009), with similar results emerging from studies that used self-report measures of inhibitory control (Anzman & Birch, 2009; Duckworth, Tsukayama, & Geier, 2010; Sutin et al., 2011). Data also indicate that individuals with inhibitory control deficits show a poorer response to weight loss treatment (Nederkoorn et al., 2007; Weygandt et al., 2013) and poorer maintenance of weight loss after treatment (Weygandt et al., 2015). One imaging study found that individuals who show less recruitment of inhibitory control regions in tasks that require inhibition showed elevated future weight gain (Kishinevsky et al., 2012), but this effect did not replicate in a second study that used a different inhibitory control paradigm (Batterink et al., 2010). There was also a marginal trend showing that individuals with reduced volume in prefrontal inhibitory control regions show elevated future weight gain (Yokum et al., 2012). One study found that individuals that showed less recruitment of inhibitory control regions during a delay discounting task showed significantly less weight loss in response to a short-term diet (Weygandt et al., 2013) and less weight loss maintenance over longer-term follow-up (Weygandt et al., 2015). Collectively, these data provide prospective support for the inhibitory control deficit theory of obesity (Nederkoorn et al., 2006a), though many of the predictive effects were small and the findings were somewhat mixed. Interestingly, there is also emerging evidence that obese individuals show less recruitment of inhibitory control regions in response to food stimuli. However, only two prospective brain imaging studies examined the relation of reduced responsivity of inhibitory control regions to future weight gain, making it difficult to draw firm inferences regarding the relation of inhibitory control deficits to future weight gain at present.

Dynamic Vulnerability Model of Obesity

Researchers attempted to synthesize the above theories into a unifying etiologic model regarding neural vulnerability factors that increase risk for overeating, and changes in neural responsivity that result from overeating that may contribute to future escalations in caloric intake. According to the dynamic vulnerability model (Burger & Stice, 2011; Stice et al., 2011; Fig. 1 ), individuals who show greater responsivity of reward regions that is coupled with greater responsivity of gustatory and oral somatosensory regions to palatable food receipt are at increased risk for overeating and resulting weight gain, consistent with the reward surfeit model of obesity. These predictions were based on the finding that adolescents at high- versus low-risk for future weight gain because of parental obesity showed greater responsivity of striatal, gustatory, and oral somatosensory regions to palatable food tastes and monetary reward (Stice et al., 2011). This model also hypothesized that the TaqIA polymorphism may moderate the relation of reward region response to palatable food receipt and future weight gain, based on the evidence that adolescents who showed stronger caudate response to palatable food receipt and had a genetic propensity for greater dopamine signaling showed elevated future weight gain, as did those who showed weaker caudate response to palatable food receipt and had a genetic propensity for less dopamine signaling (Stice et al., 2008a). The emergence of habitual overeating was then thought to lead to a reduction in D2 receptors and striatal responsivity to palatable food intake. These predictions were primarily based on animal overfeeding experiments (Bello et al., 2002; Davis et al., 2008; Geiger et al., 2009; Kelley et al., 2003; Johnson & Kenny et al., 2010; Thanos et al., 2008) and an imaging study that found that weight gain was associated with a decrease in striatal response to palatable food (Stice et al., 2010a). The emergence of habitual overeating was also thought to increase incentive valuation region responsivity to food cues repeatedly associated with palatable food intake, in line with the incentive sensitization model. Conditioning experiments with primates and rodents have documented an increase in dopamine signaling in reward regions, such as in the midbrain dopamine neurons in response to stimuli repeatedly paired with palatable food receipt (Mackintosh, 1974; Mirenowicz & Schultz, 1994; Stuber et al., 2008; Zellner & Ranaldi, 2010), though this process has only been documented in vivo using neuroimaging with humans in one study (Burger & Stice, 2014). The reduced responsivity of striatal regions to palatable food intake was hypothesized to increase risk for a further overeating, as people may increase caloric intake to achieve the reward they once experienced, based on the reward deficit model, though there was no prospective or experimental support for this aspect of the dynamic vulnerability model. Further, the increased responsivity of reward valuation regions to food cues was also thought to drive an escalation in overeating and weight gain, in line with the incentive sensitization theory. Experiments with rodents and people have documented that food cues often spur greater consumption of the food versus when the cues are not present (e.g., Cornell, Rodin, & Weingarten, 1989; Petrovich, 2011; Holland & Petrovich, 2005; Weingarten, 1983).

Presentation of the Dynamic Vulnerability Model of Obesity

Results from studies reviewed previously provide support for some of the hypothesized relation in the dynamic vulnerability model, but not others. The relations that have been supported by prospective or experimental findings are depicted with solid arrows Figure 1 , and those that have not been supported are depicted with dotted arrows. First, one study found that elevated reward region responsivity to palatable food receipt predicted future weight gain (Geha et al., 2013), and adolescents at high- versus low-risk for parental obesity showed elevated responsivity of reward regions to palatable food receipt (Stice et al., 2011). However, other studies did not provide evidence that elevated reward region response to palatable food receipt predicted future weight gain (Stice et al., 2008a; Stice et al., 2015; Sun et al., 2015). The fact that only one out of four studies provided support for this prediction implies it may not be a particularly robust relation, though it would be useful for additional studies tested this relation, particularly studies that administer a broader range of palatable foods and are adequately powered.

Second, there is mounting support for the interaction between reward region response to palatable food receipt and the TaqIA polymorphism. As noted, three prospective studies have now found that individuals who showed stronger reward region response to palatable food receipt that was coupled with a genetic propensity for greater dopamine signaling showed elevated future weight gain, as did individuals who showed weaker reward region response to palatable food that was coupled with a genetic propensity for lower dopamine signaling (Stice et al., 2008a; Stice et al., 2015; Sun et al., 2015). These results imply that having too little or too much DA signaling and reward region responsivity may both increase risk for overeating, suggesting that homeostatic mechanisms that regulate feeding may operate optimally when there is moderate dopamine signaling and reward region responsivity. The fact that elevated reward region response to palatable food receipt only predicted future weight gain in one of four studies, whereas the interaction between reward region response to palatable food receipt interacted with the TaqIA polymorphism in three out of three prospective studies that tested this relation implies that omitting the TaqIA polymorphism from the former analyses may have led to miss-specified models that explains the inconsistent results. It will be critical for future studies from independent labs to evaluate this hypothesis using large enough samples to provide sufficient sensitivity for detecting interactions, which are often difficult to replicate.

Third, none of the prospective fMRI studies with humans (which used whole-brain analyses) provided support for the hypothesis that elevated responsivity of gustatory and oral somatosensory regions to palatable food receipt would predict future weight gain (Geha et al., 2013; Stice et al., 2015; Sun et al., 2015). This prediction too was based on the evidence that adolescents at high- versus low-risk for parental obesity showed elevated responsivity of gustatory and oral somatosensory regions to palatable food receipt (Stice et al., 2011). Given that there has been no support for these hypotheses from prospective studies, it seems prudent to remove them from the working multivariate etiologic model of neural vulnerability factors that predict weight gain. It is noteworthy that results from the study that evaluated neural response to food stimuli among youth at high- versus low-risk for future obesity showed limited convergence with results from the prospective studies, some of which had larger samples and more sensitivity. This pattern of findings implies that high-risk designs may identify differences between those at high- versus low-risk for weight gain, but that these differences may not predict future weight gain, suggesting that the former design may not be as useful as the prospective design in elucidating neural vulnerability factors that predict future weight gain.

Fourth, one repeated-measure fMRI study provided evidence that overeating, which resulted in weight gain, was associated with a reduction in reward region responsivity to palatable food receipt compared to changes observed in weight stable participants (Stice et al., 2010a), converging with results from overeating experiments with animals (Bello et al., 2002; Davis et al., 2008; Geiger et al., 2009; Kelley et al., 2003; Johnson & Kenny et al., 2010; Thanos et al., 2008; Val-Laillet et al., 2011). These results dovetail with evidence that weight loss increases D2 receptor availability in humans (Steele et al., 2010) and rats (Thanos et al., 2008), and responsivity of reward circuitry to food cues (Cornier et al., 2012; Deckersbach et al., 2014; Rosenbaum et al., 2008). An important direction for future research would be to conduct larger repeated-measures fMRI studies to assess changes in neural responsivity to food stimuli that result from weight gain versus weight stability.

Fifth, although numerous experiments with animals have found that cues that are repeatedly paired with palatable food intake come to activate incentive valuation regions and prompt increased dopamine signaling (Mackintosh, 1974; Mirenowicz & Schultz, 1994; Stuber et al., 2008; Zellner & Ranaldi, 2010), no repeated-measures brain imaging studies with humans have linked overeating that produces consequent weight gain to increased responsivity of reward valuation regions to food cues. As such, this represents one a key translational gap in the literature. Yet, one fMRI study did capture the increase in striatal (ventral pallidum) response to novel cues that predicted impending tastes of palatable milkshake after repeated pairings (Burger & Stice, 2014). The fact that the increase in striatal response to food reward cues emerged after only 16 exposures to the cues that predicted impending milkshake receipt implies that the incentive sensitization process can emerge quickly, consistent with results from animal conditioning experiments (Datla et al., 2002; Stuber et al., 2008).

Sixth, prospective and experimental studies have provided little support for the hypothesis that reduced striatal responsivity to palatable foods would result in escalated caloric intake. Specifically, there was no evidence that adolescents at high- versus low-risk for future weight gain showed lower reward region response to receipt or anticipated receipt of palatable food (Stice et al., 2011) and none of the nine prospective studies found a main effect wherein lower reward region response to food stimuli predicted future weight gain (Chouinard-Decorte et al., 2010; Demos et al., 2012; Geha et al. 2013; Stice et al., 2008a, 2010b, 2015; Sun et al., 2015; Yokum et al., 2011, 2014). Indeed, research with animals and humans converged in providing evidence that experimental reductions in reward region responsivity to palatable food was associated with decreased caloric intake and a reduction in willingness to work for the palatable food (Hardman et al., 2012; Tellez et al., 2013). Thus this aspect of the dynamic vulnerability model appears to be incompatible with existing findings and in need of revision.

Seventh, elevated responsivity of reward regions to food cues/images has emerged as the most frequent predictor of future weight gain in the prospective brain imaging studies (Chouinard-Decorte et al., 2010; Demos et al., 2012; Stice et al., 2015; Yokum et al., 2011; Yokum et al., 2014). Somewhat unexpectedly based on the associative learning process specified in the incentive sensitization theory, elevated reward region responsivity to cues that signal impending palatable food receipt even predicted future weight gain in a sample that contained only healthy weight adolescents at baseline (Stice et al., 2015), which implies that the incentive sensitization process can emerge before excessive weight gain. The fact that reward region response to food cues showed the most consistent relation to future weight gain suggests that this pathway should take center stage in a revised working etiologic model.

Refined Dynamic Vulnerability Model of Obesity

Based on findings from the studies conducted to date, we propose a refined version of the dynamic vulnerability model of obesity. The revised model of obesity is shown in Figure 2 . The thick black arrows represent well-established relations and thinner black arrows represent relations with a more provisional degree of empirical support.

Presentation of a refined version of the Dynamic Vulnerability Model of Obesity

We hypothesize that individuals who show elevated reward region responsivity to palatable food receipt are more likely to overeat and show consequent weight gain, based primarily on the finding from Geha and associates (2013). Data suggest that this reward region hyper-responsivity is general, rather than specific to palatable food reward (Stice et al., 2011). It will be vital for future prospective neuroimaging studies to examine whether elevated reward region response to food reward and monetary reward predicts future weight gain.

We hypothesize that the relation of reward region responsivity to future weight gain is moderated by genotypes that impact dopamine signaling, wherein individuals who show stronger reward region responsivity to food intake will exhibit greater weight gain if they have a genetic propensity for elevated dopamine signaling, but individuals who show a weaker reward region responsivity to food intake will exhibit greater weight gain if they have a genetic propensity for weaker dopamine signaling. This prediction is based on the interactions observed in three studies (Stice et al., 2008a; Stice et al., 2015; Sun et al., 2015) which imply that there may be two qualitatively distinct pathways to obesity that conform to the reward surfeit and reward deficit models. It will be important for future studies to test whether genotypes that affect dopamine signaling capacity moderate the relation of reward region responsivity to future weight gain. Future studies should also investigate whether these genotypes, alone or in combination in a multilocus score, predict future weight gain, based on the findings from Yokum and associates (2015). In addition, it might be useful for future studies to investigate whether a genetic propensity for greater opioid signaling predicts future weight gain and amplifies the relation between reward region responsivity and subsequent weight gain.

Elevated reward region responsivity to palatable food receipt is also hypothesized to contribute to more potent food cue-reward learning, based on findings from Burger and Stice (2014). And more potent food cue reward learning is thought to increase risk for future weight gain, also based on the findings from Burger and Stice (2014). Theoretically, greater food cue-reward learning results in elevated incentive valuation region responsivity to food cues, which drives overeating when the ubiquitous food cues are encountered in the present obesogenic environment, consistent with the incentive sensitization model. This hyper-responsivity of reward valuation regions to food cues appears to be a more potent driver of overeating than the initial hyper-responsivity of reward regions to palatable food intake, based on results from the prospective studies reviewed previously (Chouinard-Decorte et al., 2010; Demos et al., 2012; Geha et al., 2013; Stice et al., 2015; Yokum et al., 2011; Yokum et al., 2014), implying that obesity can be conceptualized as resulting from aberrant learning processes. It would be useful for large prospective studies to test whether individuals who show more pronounced food reward-cue learning, captured during an fMRI scan are at elevated risk for future weight gain, as well as whether elevated reward region response to palatable food predicts greater reward-cue learning. It would also be useful to conduct repeated-measures imaging studies to document that overeating that results in weight gain leads to greater reward region response to food cues, as suggested by the incentive sensitization model and animal experiments, because there is limited evidence regarding the processes that give rise to elevated reward region response to food cues.

In addition, data suggest that elevated reward region responsivity appears to contribute to greater food reinforcer satiation, which has also been found to predict future weight gain (Burger & Stice, 2014). However, the mechanism by which greater food reinforcement satiation drives overeating is unclear, and may simply be an artifact of an initial elevated reward region response to palatable food intake. It would therefore be useful for large prospective studies to test whether individuals who show more pronounced food reinforce satiation, captured during an fMRI scan are at elevated risk for future weight gain, as well as whether elevated reward region response to palatable food predicts greater food reinforce satiation.

Overeating is hypothesized to result in a reduction in reward region responsivity to palatable food, based primarily on results from overfeeding experiments with animals (Bello et al., 2002; Davis et al., 2008; Geiger et al., 2009; Kelley et al., 2003; Johnson & Kenny et al., 2010; Thanos et al., 2008), but also based on evidence that overeating that contributes to weight gain seems to reduce reward region response to palatable food receipt (Stice et al., 2010a). However, reduced reward region response to palatable food appears to decrease overeating on an acute basis, rather than contribute to increased overeating, at least based on extant data. It would be useful for prospective repeated-measures fMRI studies to test whether individuals who show obesity onset exhibit a reduction in striatal response to high-calorie food intake and other neural plasticity after weight gain relative to comparison to weight stable participants, and whether those who show the most pronounced neural plasticity show differential weight gain relative to those who show less pronounced changes in neural responsivity. It would also be useful for human experiments to test whether assignment to habitual intake of high-calorie foods results in reduced reward region responsivity to such foods, and whether this leads to reduced caloric intake and willingness to work for high-calorie foods, as suggested by the animal studies.

There was also evidence that genotypes that impact dopamine signaling moderated the relation of reward region responsivity to food cues and future weight gain (Stice et al., 2010b). However, because this hypothesis has only been evaluated in one study, this represents another important direction for future research.

Further, available data suggest that a bias for immediate reward also constitutes an important risk factor for overeating and subsequent weight gain (Evans et al., 2012; Francis & Susman, 2009; Schlam et al., 2013; Seeyave et al., 2009). Given the evidence that a bias for immediate food reward in childhood predicts future weight gain over very long-term follow-up, this may constitute another key initial vulnerability factor for obesity. This vulnerability factor presumably has a neural basis, but only a few neuroimaging studies with humans have tested whether reduced recruitment of inhibitory regions in response to tasks involving inhibition to food stimuli predicts future weight gain. This immediate reward bias may contribute to the initial emergence of overeating that contributes to the incentive sensitization process. Although it is tempting to suggest that elevated reward region responsivity to palatable food and a bias for immediate reward may interact in the prediction of overeating, we were unable to locate any prospective data that support an interactive model over one in which these two factors simply each exhibit main effects. Thus, another important direction for future research will be to test whether inhibitory control deficits and elevated reward region responsivity to food and food cues each show unique additive relations to future weight gain or whether they show a synergistic interaction in the prediction of weight gain.

Clinical Implications

The results from this review have a several implications for the prevention and treatment of obesity. With regard to the former, results imply that interventions that reduce habitual intake of energy dense foods during childhood and adolescence might prove useful in reducing elevated incentive valuation region responsivity to food cues that appears to drive overeating. A related policy implication is that reducing the presence of cues for energy dense foods, such as advertisements for fast foods, should also reduce overeating in those with this vulnerability factor. Further, prevention programs that promote executive function and inhibitory control, which includes resisting temptation (e.g., Diamond et al., 2007), might reduce the immediate reward bias that increases risk for overeating.

In terms of treatment implications, response training might prove useful in reducing valuation of food cues and promoting inhibitory responses to food cues (Veling, Holland, & Knippenberg, 2008). Consistent with this, response training experiments show that repeatedly presenting high-calorie food images with signals indicating that participants should withhold a prepotent behavioral response in stop-signal or go/no-go tasks decreases later consumption of that food versus high-calorie foods not repeatedly paired with inhibitory signals (Houben, 2011; Houben & Jansen, 2011; Lawrence et al., 2014). There is even emerging evidence that response training produces weight loss (Lawrence et al., 2014; Veling, Koningsbruggen, Aarts, & Stroebe, 2014). Evaluative conditioning (Martijn, Vanderlinden, Roefs, Huijding, & Jansen, 2010), wherein images of energy dense food would be associated with negative social stimuli (e.g., images of frowning faces), might also prove useful in reducing valuation of these types of foods and treating obesity.

Another important treatment implication is that it might be possible to use brain imaging to predict likely response to obesity treatment, which should allow clinicians to focus on those individuals most likely to show a positive response to weight loss treatment. Individuals who show less reward and attention region response to high-calorie food images (Murdaugh et al., 2012) and individuals who show greater recruitment of inhibitory control regions (dorsolateral prefrontal cortex) during a delay discounting task (Weygandt et al., 2013, 2015) exhibit a more positive response to behavioral weight loss treatment. Likewise, there is evidence that individuals with a genetic propensity for lower dopamine signaling capacity in reward circuitry show a more positive response to behavioral weight loss treatment (Yokum et al., 2015).

It might even be possible to use brain imaging to guide personalized treatment of obesity. For individuals who show elevated reward region response to energy dense food receipt, it might be optimal to prescribe Naltrexone, which attenuates reward region responsivity (Murrey et al., 2014). For individuals who show elevated reward region responsivity to food cues, it might be useful to use response training or evaluative conditioning to reduce valuation of food cues. For individuals who show deficits in inhibitory control, it might be useful to have them complete an intervention that promotes inhibitory control.

Conclusion

Early cross-sectional brain imaging studies could not differentiate precursors from consequences of overeating and did not examine responsivity to food intake. Yet, recent prospective studies have identified neural vulnerabilities that predict future weight gain and have begun to document neural plasticity associated with overeating. Elevated activation of brain regions implicated in reward (midbrain, nucleus accumbens) and incentive valuation (orbitofrontal cortex) in response to food cues, anticipated receipt of high-calorie food, and receipt of high calorie food has been found to predict future weight gain and poorer response to behavioral weight loss treatment. These findings dovetail with evidence that healthy weight adolescents at high- versus low-risk for future weight gain show greater reward region responsivity to receipt of high-calorie food and monetary reward, adolescents with a genetic propensity for greater dopamine signaling in reward circuitry showed greater weight gain, and adolescents who show more pronounced food reward-cue learning showed greater future weight gain. Although animal experiments and repeated-measures human imaging studies indicate that overeating reduces reward region response to high-calorie foods, reduced reward region responsivity is associated with lower caloric intake, converging with a general lack of evidence that weaker reward region responsivity in humans predicts future weight gain. Thus, prospective and experimental data provide strong support for the incentive sensitization theory of obesity, and moderate support for the reward surfeit theory, inhibitory control deficit theory, and dynamic vulnerability model of obesity, but only minimal support for the reward deficit theory. The predictive effects for reward region responsivity are larger than those for other established obesity risk factors, suggesting it will be important to continue to conduct research on the neural vulnerability factors that increase risk for obesity. Based on the current findings a working multivariate etiologic model is proposed that contains falsifiable hypotheses.

Acknowledgments

Support for this work was provided by National Institutes of Health grants DK-080760 and DK-092468. The National Institutes of Health had no role in the study design, collection, interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

Effect of thymoquinone on the structure of the cerebral cortex of adult male albino rats treated with tramadol

Background Pain-associated depression is a symptom of many diseases such as cancer, and postoperative and myocardial infarction. Tramadol (TRM) is a centrally acting synthetic opioid, similar to an analgesic, used worldwide to treat severe pain with an anti-depressant-like effect. TRM is more popular abused among adults in most countries to relive pain and increase sexual activities. Thymoquinone (TQ), a volatile oil, is one of the main constituents of Nigella sativa seeds. It has anti-inflammatory, antioxidant, anticonvulsant, antitussive, and anti-tumor effects. The aim of work The present study was designed to evaluate the effects of TRM on the structure of cerebral cortex of the adult male albino rats and the possible impact of using TQ to improve these changes and to test the analgesic, anti-depressant, and antioxidant effects of TRM and/or TQ. Materials and methods Forty-eight male albino rats weighting 180-200 g were used in the present study. The rats were divided into four groups: control group (GI): 12 rats received food and water. TQ group (GII): 12 rats received an oral dose of TQ (20 mg/kg) for 4 weeks. TRM group (GIII): 12 rats received an oral dose of TRM HCl (50 mg/kg) for 4 weeks. Combined group (GIV): 12 rats received both TRM (50 mg/kg) and TQ (20 mg/kg) for 4 weeks. Results TQ supplementation significantly increased the analgesic effect of TRM after acute and chronic treatment by the thermal and chemical methods and attenuated the development of tolerance. TQ also significantly improved the anti-depressant effect of TRM. Furthermore, TQ significantly increased the suppressed levels of glutathione content and activities of superoxide dismutase, catalase, and glutathione peroxidase induced by TRM. It also significantly reduced the elevated levels of malondialdehyde and nitric oxide caused by TRM. Histological examination of TRM-treated cerebral cortex showed distortion of its layers, increased vascularity, and cellularity, with a significantly increased number of apoptotic cells. TRM also induced a significant increase in the mean area percentage of both apoptotic index and the optical density of BAX immune-stain compared with the control group. These changes were improved in TQ-treated rats. Conclusion TQ supplementation improved the analgesic, anti-depressant effects of TRM, with an improvement in the cerebral cortex structure and antioxidant markers and amelioration of oxidative stress markers. Furthermore, it attenuated TRM tolerance and neurotoxic effects.

Tramadol (TRM) is a synthetic centrally acting opioid-like analgesic that was firstly registered in 1998. It is available in oral and parenteral dosage forms for the treatment of pain syndromes [1]. TRM was originally considered to have a much better safety profile than other opioid analgesics [2].

The analgesic and anti-depressant efficacy of TRM can be attributed to its partial affinity for the [micro]-opioid receptors and its inhibition of norepinephrine and serotonin reuptake [3]. It was reported that increasing serotonin and norepinephrine levels reduced the inflammatory cytokines that are released by the brain in response to stress, which explains the anti-inflammatory effect of TRM [4],[5].