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Overlapping neurobehavioral circuits in ADHD, obesity, and binge eating: evidence from neuroimaging research

Published online by Cambridge University Press:  22 June 2015

Karen E. Seymour ,
Shauna P. Reinblatt ,
Leora Benson  and
Susan Carnell
Karen E. Seymour*
Affiliation:
Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
Shauna P. Reinblatt
Affiliation:
Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
Leora Benson
Affiliation:
Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
Susan Carnell
Affiliation:
Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
*
*Address for correspondence, Karen E. Seymour, PhD, Johns Hopkins University School of Medicine, Division of Child & Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, 550 N. Broadway, Baltimore, MD 21205, USA.(Email: kseymou2@jhmi.edu)
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Abstract

Attention-deficit/hyperactivity disorder (ADHD) and conditions involving excessive eating (eg, obesity, binge/loss of control eating) are increasingly prevalent within pediatric populations, and correlational and some longitudinal studies have suggested inter-relationships between these disorders. In addition, a number of common neural correlates are emerging across conditions, eg, functional abnormalities within circuits subserving reward processing and executive functioning. To explore this potential cross-condition overlap in neurobehavioral underpinnings, we selectively review relevant functional neuroimaging literature, specifically focusing on studies probing (i) reward processing, (ii) response inhibition, and (iii) emotional processing and regulation, and we outline 3 specific shared neurobehavioral circuits. Based on our review, we also identify gaps within the literature that would benefit from further research.

Keywords

Attention-deficit/hyperactivity disordercomorbidityfMRIobesityoverweight
Type
Review Articles
Information
CNS Spectrums , Volume 20 , Special Issue 4: Pediatric Neuroimaging , August 2015 , pp. 401 - 411
Copyright
© Cambridge University Press 2015 

Introduction

Attention-deficit hyperactivity disorder (ADHD) and obesity—a condition involving eating beyond metabolic needs—are significant problems in pediatric populations.Reference Getahun, Jacobsen, Fassett, Chen, Demissie and Rhoads 1 – 5 ADHD is one of the most common psychiatric diagnoses of childhood, affecting approximately 8–10% of U.S. school-age children, while worldwide estimates are slightly lower at approximately 5%.Reference Getahun, Jacobsen, Fassett, Chen, Demissie and Rhoads 1 , 2 , Reference Polanczyk, de Lima, Horta, Biederman and Rohde 6 Additionally, more than 40 million children under the age of 5 worldwide 5 and one-third of children and adolescents in the U.S. are currently considered overweight or obese.Reference Ogden, Carroll, Kit and Flegal 3 , Reference Barlow 4 There is also evidence for substantial prevalence of loss of control (LOC) eating in children; this phenotype is a counterpart to the diagnosis of binge eating disorder in adults, in that it features episodes of perceived loss of control over eating; 2 due to the complexity of calculating metabolic needs in children, the amount consumed in a binge episode is not required to be “objectively” large.Reference Tanofsky-Kraff, Shomaker and Olsen 7 Prevalence of LOC in children aged 6–14 years old ranges from 2–10% in studies conducted in various countries including the U.S., Germany, and Israel, with studies of overweight children reporting a 15–37% prevalence.Reference Tanofsky-Kraff, Marcus, Yanovski and Yanovski 8

ADHD and obesity

Within the ADHD literature, studies of both clinical and community samples have suggested an association between ADHD diagnosis and/or symptoms and overweight/obesity throughout development. For example, Holtkamp et al Reference Holtkamp, Konrad and Muller 9 found that 19.6% of ADHD boys in sample of inpatients and outpatients referred to psychiatry met criteria for overweight and 7.2% for obesity. A recent study comparing rates of obesity and overweight in boys diagnosed with ADHD to those in typically developing (TD) boys found that the ADHD group had higher rates of overweight.Reference Hanć, Słopień and Wolańczyk 10 Further, in a large population-based study of adolescent health, Fuemmeler et al Reference Fuemmeler, Ostbye, Yang, McClernon and Kollins 11 found that, compared with those reporting no symptoms of inattention or hyperactivity/impulsivity, participants with greater than 3 symptoms had higher body mass index (BMI) in adolescence. Moreover, those with over 3 symptoms of inattention or hyperactivity/impulsivity in adolescence had the highest risk for obesity in adulthood.Reference Fuemmeler, Ostbye, Yang, McClernon and Kollins 11 Using a longitudinal design, a prospective follow-up study of 6–12-year-old boys with and without ADHD showed that at 33-year follow-up (average age=42 years old), men diagnosed with ADHD in childhood had significantly higher BMIs and obesity rates than those without childhood ADHD.Reference Cortese, Ramos Olazagasti, Klein, Castellanos, Proal and Mannuzza 12

Perhaps more research has taken the reverse approach of examining the presence of ADHD diagnosis and traits in children who are overweight or obese. In a study of obese children, almost 58% of participants met criteria for comorbid ADHD.Reference Agranat-Meged, Deitcher, Goldzweig, Leibenson, Stein and Galili-Weisstub 13 Increased rates of diagnosed ADHD have also been found in obese compared with TD adolescents (ie, 13% vs. 3.3%).Reference Erermis, Cetin, Tamar, Bukusoglu, Akdeniz and Goksen 14 Additionally, studies have demonstrated greater ADHD symptomatology (ie, inattention, hyperactivity/ impulsivity) in overweight/obese children, with one large prospective study of 8,106 children (7–8 years old at baseline) finding that childhood ADHD symptoms significantly predicted adolescent obesity at age 16.Reference Khalife, Kantomaa and Glover 15 In contrast, childhood obesity did not predict ADHD symptoms in adolescence, suggesting that ADHD symptomatology may predispose to overweight/obesity rather than the other way round.Reference Khalife, Kantomaa and Glover 15

ADHD and binge/loss of control eating

Although ADHD has been associated with binge eating disorder in adults,Reference Cortese, Angriman and Maffeis 16 , Reference Mattos, Saboya, Ayrao, Segenreich, Duchesne and Coutinho 17 relatively little is known about the relationship between ADHD and binge/LOC eating in children. However, one study of adolescents, aged 12–20 years old, found that those with either ADHD or ADD were more likely to binge compared to controls.Reference Neumark-Sztainer, Story, Resnick, Garwick and Blum 18 Additionally, in a retrospective chart review of patients from 2 community child and adolescent psychiatry clinics (mean age 10.8 [3.7 SD]) there was a significant association between ADHD and binge eating as assessed by the Children’s Binge Eating Disorder Scale.Reference Reinblatt, Leoutsakos, Mahone, Forrester, Wilcox and Riddle 19 ADHD symptoms have been suggested to contribute to eating pathology,Reference Cortese, Angriman and Maffeis 16 , Reference Reinblatt, Leoutsakos, Mahone, Forrester, Wilcox and Riddle 19 and particularly impulsivity symptoms, in adolescents,Reference Mikami, Hinshaw, Patterson and Lee 20 and impulsivity is a characteristic feature of both ADHDReference Alderson, Rapport and Kofler 21 and LOCReference Hartmann, Rief and Hilbert 22 eating in children. This marked phenotypic overlap suggests that further research to explore potential comorbidity is warranted.

Why do we observe these associations between ADHD, obesity, and binge/LOC eating phenotypes? Since all conditions are either behaviorally defined (ADHD, binge/LOC eating) or are the result of long-term engagement in a particular pattern of behavior (obesity), the brain is likely to play an important role,Reference Cortese and Morcillo Penalver 23 and given the type of deficits displayed in each condition, abnormalities within circuits subserving reward processing,Reference Liu, Li, Yang and Wang 24 , Reference Campbell and Eisenberg 25 and executive functioning, eg, response inhibition,Reference Graziano, Bagner, Waxmonsky, Reid, McNamara and Geffken 26 – Reference Dempsey, Dyehouse and Schafer 30 seem likely candidates. Here, we draw on functional neuroimaging literatures for pediatric ADHD, obesity, and binge/LOC eating to review evidence for overlapping neurobiological correlates. In line with previous theories regarding the relationship between ADHD and obesity,Reference Cortese, Angriman and Maffeis 16 , Reference Cortese and Vincenzi 31 we include functional magnetic resonance imaging (fMRI) studies that probe reward processing and executive function, specifically response inhibition. However, since affective symptoms are a core part of the binge/LOC eating phenotype, as well as an associated feature of ADHD, we also include relevant studies that probe emotional processing and regulation.

Since this is a brief, conceptually driven review, we focus on presenting the most informative key studies rather than comprehensively describing all potentially relevant studies and findings. However, where pediatric fMRI studies are limited (eg, for binge/LOC eating), we refer to related adult studies, neuropsychological studies, and studies using alternative neuroimaging modalities, on the basis that there are likely to be significant similarities in the identified neurobehavioral circuits. In order to make our search strategy as transparent as possible, we report here the terms used to source studies for our core topic areas:

  • ∙ [obesity or binge or loss of control] and [children or adolescents] and [fMRI] and [food or reward or inhibition or disinhibition or impulsivity or [eating and emotion]]

  • ∙ [attention-deficit/hyperactivity disorder or ADHD] and [children or adolescents] and [fMRI] and [emotion or emotion regulation or response inhibition or reward processing]

Reward Processing

In humans, reward processing is subserved by a cortico-basal ganglia network, which includes (but is not confined to) the midbrain, the ventral striatum (VS) (particularly nucleus accumbens [NAcc]), orbitofrontal cortex (OFC), and other areas of the prefrontal cortex (PFC)Reference Haber and Knutson 32 – Reference Paloyelis, Mehta, Faraone, Asherson and Kuntsi 34 (see Figure 1a). fMRI paradigms probing reward circuitry often examine anticipation of reward, receipt of reward, and impact of reward delay (ie, immediate vs. delayed reward).

Figure 1 Neural circuitry involved in (a) reward processing, (b) response inhibition, and (c) emotional processing and regulation. Note. ACC=anterior cingulate cortex, Amg=amygdala, dlPFC=dorsolateral prefrontal cortex, GP=globus pallidus, OFC=orbitofrontal cortex, pSMA=pre-supplementary motor area, vlPFC=ventrolateral prefrontal cortex, vmPFC=ventromedial prefrontal cortex, VS=ventral striatum.

ADHD

The behavioral and neuropsychological heterogeneity seen in children with ADHD has led to theoretical models positing multiple developmental pathways to the disorder, involving impairments in cognition, motivation, and self-regulation.Reference Nigg and Casey 35 , Reference Sonuga-Barke 36 The dual-pathway model proposed by Sonuga-Barke,Reference Sonuga-Barke 36 for example, suggests 2 distinct “pathways” or subtypes of the disorder. Via one pathway, ADHD symptoms arise from abnormalities within cortico-striatal circuits, resulting in poor inhibitory control; via the other, symptoms are correlated with abnormalities within reward circuitry, particularly the NAcc, manifesting in altered reward processing and anticipation.

Consistent with the model outlined above, a number of functional neuroimaging studies suggest abnormalities in motivational and reward processing circuitry in children with ADHD compared to TD children. For example, studies of reward processing have consistently revealed differential VS activation in ADHD compared to TD youth. Specifically, using a monetary incentive delay (MID) task, in which a participant responds to stimuli after being cued about whether the trial is a “gain” trial (ie, opportunity to win money) or a “loss-avoidance” trial (ie, opportunity to avoid losing money), Scheres et al Reference Scheres, Milham, Knutson and Castellanos 37 found a relative hypo-activation in the VS in anticipation of gain (but not loss) trials among ADHD (vs. TD) adolescents that was associated with higher levels of hyperactivity/impulsivity, but not inattention. Group differences in VS activity were not shown when comparing trials of increasing monetary rewards, indicating that VS hypo-activation in ADHD may be specific to reward anticipation.Reference Scheres, Milham, Knutson and Castellanos 37 VS hypo-activation during reward anticipation has also been shown in adults with ADHD, for both immediate and delayed rewards.Reference Strohle, Stoy and Wrase 38 , Reference Plichta and Scheres 39 In contrast, using a different task, Paloyelis et al Reference Paloyelis, Mehta, Faraone, Asherson and Kuntsi 34 found that adolescent boys with ADHD-combined type (vs. TD adolescent boys) showed increased VS activity to reward receipt (ie, a successful outcome), but no group differences during reward anticipation. These contrasting findings could reflect study differences in sample characteristics (eg, gender) or tasks used (ie, MID vs novel paradigm).

Reward tasks have also revealed other regional functional differences. Specifically, using a reward-based continuous performance task, in which subjects are presented with a stream of letters and are required to respond to a target letter to obtain monetary rewards, Rubia et al Reference Rubia, Halari, Cubillo, Mohammad, Brammer and Taylor 40 found that compared to TD children, those with ADHD showed hyper-responsivity in the left ventrolateral OFC and bilateral superior temporal lobe during reward receipt, which was attenuated with stimulant medication, supporting theories of increased reward sensitivity in ADHD as well as a “normalizing effect” of medication.Reference Luman, Oosterlaan and Sergeant 41 A recent resting-state functional connectivity (rs-fcRI) study additionally found that compared to TD children, children with ADHD had atypical functional connections between NAcc and cortical regions (ie, increased connectivity between NAcc, left anterior PFC, and ventromedial PFC [vmPFC]), which were related to greater impulsivity during a delayed discounting task.Reference Costa Dias, Wilson and Bathula 42 Together these results suggest that abnormal responses to reward anticipation and receipt among children with ADHD may be associated with atypical functional activation and connectivity within reward processing circuitry, particularly the VS/NAcc and prefrontal regions.

Obesity and binge/loss of control eating

Food, particularly hyperpalatable high-fat and high-sugar food, constitutes a primary reward, and phenotypes involving excessive eating, such as binge/LOC eating, are known to involve motivation and reward processing regions within dopaminergic pathways.Reference Rada, Avena and Hoebel 43 – Reference Corwin and Hajnal 48

A common method of studying food reward processing in the context of adult obesity has been to assess whole-brain activation patterns associated with the presentation of food pictures, ie, salient and familiar cues previously associated with food reward,Reference Carnell, Gibson, Benson, Ochner and Geliebter 49 – Reference Stice, Figlewicz, Gosnell, Levine and Pratt 51 and there have now been several similar studies in children. One study of 10–17-year-olds found that obese vs. lean youth showed greater pre-meal (post 4 h fast) activation in the PFC; greater post-meal activation in the OFC; and relatively smaller post-meal (vs. pre-meal) decreases in NAcc, limbic, and PFC activation in response to food (vs. non-food) pictures.Reference Bruce, Holsen and Chambers 52 A different study of adolescent girls observed that BMI was positively associated with OFC, frontal operculum, and putamen responses to appetizing food pictures following a 4–6 h fast.Reference Stice, Yokum, Bohon, Marti and Smolen 53

However, there is also evidence for impoverished neural responses, rather than greater or more persistent activation, in relation to reward among obese youth. Following a 4 h fast, obese vs. lean children showed lesser activation in the middle frontal gyrus and middle temporal gyrus in response to food logos vs. blurred baseline images, and lesser activation in a range of frontal, temporal, and limbic regions, as well as the insula, in response to food vs. non-food logos.Reference Bruce, Lepping and Bruce 54 In a study comparing responses to food vs. non-food commercials following a 5 h fast, obese vs. lean 14–17-year-olds showed lesser activation in the cuneus, cerebellum, vmPFC, anterior cingulate cortex (ACC), and precuneus, but greater activation in the medial temporal gyrus.Reference Gearhardt, Yokum, Stice, Harris and Brownell 55 Finally, compared to lean children, overweight/obese children showed increased dorsolateral PFC (dlPFC) responses, but lesser caudate and hippocampus responses, to food pictures; however, the prior nutritional state of the participants was not controlled.Reference Davids, Lauffer and Thoms 56 As yet, no neuroimaging studies have examined neural food or reward responses in the context of LOC eating. However, a recent study of 8–17-year-olds with and without LOC used a visual probe task consisting of pictures of high and low palatable foods and neutral objects to measure sustained attention. The study found a 2-way interaction between BMI-z score and LOC eating such that among those with LOC (but not those without LOC), attentional bias toward high palatable foods (vs. neutral) was positively associated with BMI-z.Reference Shank, Tanofsky-Kraff and Nelson 57 On balance, the studies described suggest hyperactivation of reward regions and hypoactivation of cognitive control regions among obese children, with contradictory findings (eg, Davids et al Reference Davids, Lauffer and Thoms 56 ) potentially explicable by differences in pre-scan intake or sample characteristics (eg, conscious dietary restraint).

Another approach has been to examine neural responses to receipt of liquid food rewards, as well as to conditioned cues signaling the delivery of that reward. An initial study using this design revealed that obese vs. lean adolescent girls showed greater activation in the anterior and middle insula and somatosensory cortex in response to the conditioned cues, but lesser caudate responsivity to receipt itself.Reference Stice, Spoor, Bohon, Veldhuizen and Small 58 Using region-of-interest (ROI) analyses of the caudate, putamen, and ventral pallidum, the same research group observed that greater increases in striato-pallidal responses to conditioned cues throughout the task (suggesting cue-reward learning) and greater decreases in striato-pallidal responses to food receipt (suggesting habituation) predicted significantly larger 2-year increases in BMI.Reference Burger and Stice 59 These results suggest that with exposure to food and associated cues, obese people may develop a phenotype of both hyper-responsivity to food cues and hypo-responsivity to food receipt, which could promote greater initiation of eating followed by overeating to compensate for a blunted consummatory response.

Another stimulating question is to what degree might obese or binge/LOC individuals also differ in general reward processing? A study of delay discounting (ie, the decline in the present value of a reward with delay to its receipt) among obese women found that lesser activation of inferior, middle, and superior frontal gyri on difficult vs. easy trials (ie, trials where the “now” and “later” choices had similar subjective values) predicted greater 1–3 year weight gain, suggesting a failure to recruit cognitive control circuitry in relation to delayed reward among those at highest obesity risk.Reference Kishinevsky, Cox, Murdaugh, Stoeckel, Cook and Weller 60 In contrast, using an adapted MID paradigm, one study of adolescents found that those with heavier parents showed greater caudate, putamen, insula, thalamus, and OFC responses to receipt, but not anticipation, of monetary reward.Reference Stice, Yokum, Burger, Epstein and Small 61 Further, although we are not aware of any fMRI studies that have probed reward processing in children with binge/LOC eating, a MID study comparing obese binge eating adults with obese non-binge eaters observed diminished ventral striatum activation during anticipatory reward and loss at baseline.Reference Balodis, Kober and Worhunsky 62 Among those who continued to binge eat post-treatment, results showed diminished recruitment of the ventral striatum and the inferior frontal gyrus during anticipatory reward and reduced activity in the medial prefrontal cortex recruitment during reward outcome.Reference Balodis, Grilo and Kober 63

Together, these results support the possibility of a general, as well as food-specific, dysregulation of reward circuitry in obese and binge/LOC individuals. However, the pattern of food-specific and general results obtained using MID do not completely agree, which suggests a distinction between neural responsivity to monetary reward (which could reflect innate differences in reward processing) and neural responsivity to food reward (which could reflect learned relationships with food or an innate, specific vulnerability to food reward).

Response Inhibition

Response inhibition refers to the ability to inhibit a “prepotent” response to a stimulus, or the deliberate suppression of actions in order to achieve a goal, and is a key component of executive function. It is typically assessed using behavioral paradigms, including go/no-go (GNG) tasks (ie, responding to one or more stimuli while withholding response to another stimuli) and/or stop-signal tasks (ie, responding to a stimulus until cued by an unpredictable separate signal not to respond). Neural correlates of response inhibition in TD individuals highlight the importance of frontal (premotor, prefrontal) to subcortical (striatal, cerebellar) circuits, in addition to fronto-temporal networks (see Figure 1b).Reference Mostofsky, Schafer and Abrams 64 – Reference Blasi, Goldberg and Weickert 66

ADHD

Impaired response inhibition is one of the most common deficits associated with ADHD, with 40–50% of children with ADHD exhibiting response inhibition deficits.Reference Willcutt, Doyle, Nigg, Faraone and Pennington 67 – Reference Willcutt, Sonuga-Barke, Nigg and Sergeant 69 In particular, children with ADHD demonstrate increased intrasubject variability (ISV) of reaction time (RT) compared to TD children during response inhibition tasks.

A robust literature of event-related fMRI studies examines the neural correlates of response inhibition in children with ADHD. Here we focus on studies utilizing GNG and stop-signal tasks, which tap into the same cognitive construct of motor response inhibition.Reference Rubia, Russell and Overmeyer 70 – Reference Eagle, Bari and Robbins 72 For example, when required to inhibit a response during GNG tasks (ie, no-go trials), children with ADHD have been shown to display less activation within prefrontal regions (eg, inferior, frontal, superior and medial frontal gyri, ventral PFC)Reference Booth, Burman and Meyer 73 – Reference Tamm, Menon, Ringel and Reiss 75 and striatal-thalamic regions (caudate, globus pallidus, and thalamus).Reference Booth, Burman and Meyer 73 , Reference Durston, Tottenham and Thomas 76 Less consistently, reduced activation in the anterior cingulate gyrusReference Durston, Tottenham and Thomas 76 and increased activation in the temporal lobes have been observed.Reference Tamm, Menon, Ringel and Reiss 75 In children with ADHD, increased ISV during no-go trials has been associated with increased activation in pre-motor regions including the rostral supplementary motor area (pre-SMA, important for response preparation and selection), while in TD children increased pre-SMA activation was associated with less ISV.Reference Suskauer, Simmonds, Caffo, Denckla, Pekar and Mostofsky 77

Similar results are found in studies using stop-signal paradigms. During successful inhibition trials, children with ADHD compared to TD children demonstrate reduced prefrontal activity (eg, inferior PFC, VLPFC, DLPFC)Reference Rubia, Smith, Brammer, Toone and Taylor 78 , Reference Rubia, Overmeyer and Taylor 79 and, during unsuccessful inhibition trials, reduced activity in the posterior cingulate, precuneus, ACC, and VLPFC.Reference Rubia, Smith, Brammer, Toone and Taylor 78 – Reference Pliszka, Glahn and Semrud-Clikeman 80 Further, compared to other forms of psychopathology (eg, obsessive compulsive disorder and pediatric bipolar disorder), children with ADHD demonstrate less activation in prefrontal regions (DLPFC, VLPFC, inferior prefrontal gyrus).Reference Passarotti, Sweeney and Pavuluri 81 , Reference Rubia, Cubillo, Smith, Woolley, Heyman and Brammer 82 These deficits appear to persist into adulthood, with Cubillo et al Reference Cubillo, Halari, Ecker, Giampietro, Taylor and Rubia 83 finding that, despite equitable behavioral performance on the stop-signal task, adults who were diagnosed with ADHD in childhood demonstrated reduced bilateral fronto-striatal activation compared to TD adults. Moreover, functional connectivity analyses revealed reduced functional inter-regional connectivity during the stop-signal task in fronto-fronto, frontostriatal, fronto-cingulate, and fronto-parietal networks, suggesting a diffuse pattern of atypical functional connectivity in relation to response control in adults with ADHD.

Hart et al Reference Hart, Radua, Nakao, Mataix-Cols and Rubia 84 conducted a recent meta-analysis of the fMRI response control literature in ADHD and concluded that during motor response inhibition tasks, individuals with ADHD (vs. TD) show decreased activation in right inferior frontal cortex (IFC) and insula, right SMA and ACC, right thalamus, left caudate, and right occipital lobe. These results complement a prior meta-analysis that showed that children with ADHD demonstrated hypo-activation during inhibition paradigms in several frontal regions bilaterally and the right superior temporal gyrus, left inferior occipital gyrus, thalamus, and midbrain.Reference Cortese, Kelly and Chabernaud 85 Additionally, use of stimulant medication in children with ADHD has been shown to enhance right IFC/insula activation during response inhibition tasks to normative levels.Reference Hart, Radua, Nakao, Mataix-Cols and Rubia 84 In sum, the literature supports aberrant neural activity in fronto-striatal regions in relation to deficits in response control in individuals with ADHD.

Obesity and binge/loss of control eating

Both obesity and binge/LOC eating phenotypes may be thought of as resulting in part, or entirely, from a cumulative failure to appropriately inhibit responses, specifically those around food. Some studies investigating the neural correlates of response inhibition in relation to these phenotypes have used food-adapted versions of existing response inhibition tasks, while others have used existing tasks in the groups of interest.

For example, one study required adolescent girls to press a button in response to vegetable pictures (go trials), but to inhibit in response to dessert pictures (no-go trials), and found that higher BMI was associated with reduced no-go activation in a variety of inhibition-associated regions, including the superior frontal gyrus, middle frontal gyrus, ventrolateral PFC (vlPFC), medial PFC, and OFC.Reference Batterink, Yokum and Stice 86 In contrast, in an adult study using a stop-signal task, obese vs. lean women showed reduced activation in the insula, inferior parietal cortex, cuneus, and supplementary motor area (although no behavioral differences in accuracy and response latency) during “stop” as compared to “go” trials.Reference Hendrick, Luo, Zhang and Li 87 Ineffectual inhibition of food intake may also be associated with certain structural characteristics: one large study of 14–21-year-olds found that obese vs. lean participants had lower OFC volume, as well as higher scores on a questionnaire measure of dietary inhibition, i.e. how likely one is to be triggered to eat by environmental food cues after a period of restraint.Reference Maayan, Hoogendoorn, Sweat and Convit 88

As yet, no neuroimaging studies have examined response inhibition deficits in non-purging youth with binge or LOC eating. However, one study of stop-signal behavioral performance, pre- and post-negative mood induction, compared youth with either ADHD symptoms or LOC eating to TD youth. Results showed that LOC youth presented with greater increases in negative mood and greater increases in impulsivity (ie, “go” reaction time variability) than ADHD and TD youth, suggesting a behavioral interaction between negative affect and response inhibition in LOC youth, which is likely to have a neural basis.Reference Hartmann, Rief and Hilbert 22

Taken together, the results generally suggest that heavier individuals may recruit traditional inhibition-associated frontal neural circuits during both food-related and general inhibition tasks to a lesser degree than lean individuals, and that deficits in response inhibition may increase in relation to negative affect for LOC individuals. However, the lack of significant performance differences in some studies suggests that alternative circuits may also be indicated, and inhibition-related neural circuits in youth with LOC eating have yet to be explored.

Emotional Processing and Regulation

Emotion regulation has been defined as the individual’s ability to modify an emotional state to promote adaptive and goal-oriented behaviors and is essential to interpersonal, academic, and adaptive functioning.Reference Thompson 89 Affective neuroscience research suggests that effective emotion regulation requires 2 distinct but interrelated neural systems: (1) circuits involved in the appraisal of emotional stimuli and generation of affective responses (eg, amygdala, insula, VS, OFC, mPFC) and (2) cognitive control systems involved in regulating these responses (eg, ACC, dlPFC, medial PFC, vlPFC) (see Figure 1c).Reference Phillips, Drevets, Rauch and Lane 90 , Reference Ochsner and Gross 91

ADHD

Emerging evidence suggests that emotional processing and regulation deficits are key impairments in children and adults with ADHD.Reference Shaw, Stringaris, Nigg and Leibenluft 92 Since the human face is a particularly salient emotional stimulus, the majority of work examining emotional processing has focused on responses to emotional faces.Reference Leibenluft, Gobbini, Harrison and Haxby 93 – Reference Gobbini and Haxby 95 However, while behavioral studies consistently show that children with ADHD (vs. TD) have deficits in identifying emotional faces, in particular those depicting negative emotions, the neural systems that underlie this deficit are less clearly elucidated.Reference Dickstein and Castellanos 96 One large study found that children with ADHD showed amygdala hyperactivity when completing subjective fear ratings of neutral faces in comparison to TD children, and those with severe mood dysregulation or bipolar disorder.Reference Brotman, Rich and Guyer 97 Similarly, during a subliminal face processing task involving fearful faces, adolescents with ADHD (vs. TD) had greater right amygdala activation as well as greater connectivity detected between the lateral PFC and amygdala—a finding which could represent an amplification of the negative affect associated with fearful faces.Reference Posner, Nagel and Maia 98 In contrast, Marsh et al Reference Marsh, Finger and Mitchell 99 found no amygdala activation differences between ADHD and TD participants while processing fearful face expression, which is an argument for additional research to clarify the discrepant findings.

A related body of literature has examined the impact of emotional stimuli on attentional control in children with ADHD. Deficits in attentional control, particularly vigilance and attentional shifting, are hallmarks of ADHD, but less is known about the interaction between attentional control and emotional processing in children with ADHD.Reference Willcutt, Doyle, Nigg, Faraone and Pennington 67 , Reference Willcutt, Sonuga-Barke, Nigg and Sergeant 69 However, using both a standard and an Emotional Stroop task, Posner et al Reference Posner, Maia, Fair, Peterson, Sonuga-Barke and Nagel 100 found that on the Emotional Stroop, adolescents with ADHD (vs. TD) showed increased reactivity in the medial PFC, even after controlling for cognitive control differences, which suggests a disturbance in emotional processing over and above cognitive control deficits in children with ADHD. Interestingly, this increased activation in the medial PFC was attenuated with stimulant medication to levels approximating those in the TD sample. Compared to bipolar and TD participants, children with ADHD have also shown disorder-specific decreased vlPFC activation for negative compared to neutral words on an Emotional Stroop task, and greater activation in the dlPFC and parietal cortex relative to TD participants.Reference Passarotti, Sweeney and Pavuluri 101 Together, these studies suggest that deficits in emotional processing and regulation in children with ADHD are likely to derive from functional abnormalities within fronto-striatal regions.

Obesity and binge/loss of control eating

To our knowledge, there are no reports of behavioral or neuroimaging studies using emotional processing paradigms such as those described above in the context of obesity in either children or adults. However, there have been a few imaging studies on stress, which has an emotional component and is associated with increased food intakeReference Dallman, Pecoraro and Akana 102 and weight gain,Reference Block, He, Zaslavsky, Ding and Ayanian 103 as well as altered neural responses in several key brain areas.Reference Dedovic, D'Aguiar and Pruessner 104 For example, in a study eliciting imagination of a personalized stressor, overweight/obese vs. normal-weight adults reported both greater subjective anxiety and greater VS activation—although it should be noted that similar results were also obtained when imagining a relaxing, neutral situation.Reference Jastreboff, Potenza, Lacadie, Hong, Sherwin and Sinha 105 In addition, a recent study of overweight/obese women found increased right amygdala activation in response to milkshake receipt during imagery of stressful, but not neutral scenarios, and that this response was associated with basal cortisol levels. Further, there was a positive relationship between BMI and stress-related OFC response to visual presentation of the milkshake.Reference Rudenga, Sinha and Small 106

There is a similar dearth of neuroimaging evidence regarding emotional processing in binge/LOC eating. However, both binge eating in adults and loss of control eating in children have been associated with symptoms including dysfunctional emotional regulation; maladaptive coping strategiesReference Czaja, Rief and Hilbert 107 ; alexithymia (ie, difficulty identifying or coping with emotions), as manifested by self-reports of numbing and dissociative symptoms during LOC eating episodesReference Tanofsky-Kraff, Goossens and Eddy 108 ; and low mood and depressive disorders.Reference Tanofsky-Kraff, Faden, Yanovski, Wilfley and Yanovski 109 Additionally there is neurobehavioral evidence to suggest that response inhibition is poorer when children with LOC eating are faced with a negative affect-inducing task, as noted previously.Reference Hartmann, Rief and Hilbert 22 Further, a small number of studies has investigated the neural correlates of emotional eating, ie, the tendency to eat in response to negative emotion—a behavior similar to binge eating. For example, a positron emission tomography (PET) study of lean adults found that higher emotional eating scores were associated with greater dopamine responses in the dorsal striatum to combined gustatory and olfactory stimulation with a favorite food following an overnight fast.Reference Volkow, Wang and Maynard 110 More pertinently, a study of adolescent girls assessed fMRI responses to milkshake receipt and anticipated receipt while in a negative or neutral music-induced mood, following a 4–6 h fast. During the negative vs. neutral state, those in the top quartile of emotional eating showed greater parahippocampal gyrus and ACC activation for anticipated receipt, and greater ventral pallidum, thalamus, and ACC activation for actual receipt, while those in the lowest quartile showed the reverse pattern.Reference Bohon, Stice and Spoor 111

To conclude, the research base is currently modest, but together the findings support the possibility that obese individuals and those who report high levels of emotion-related eating may demonstrate differential patterns of neural responsivity to emotion/stress, and also to food consumed during emotional/stressful situations.

Discussion

We have described above the results of a number of studies that have examined the neural correlates of reward processing, response inhibition, and emotional processing/regulation in both pediatric ADHD and conditions characterized by excessive eating (eg, obesity, binge/LOC eating). Although there are gaps in the evidence base (eg, neuroimaging studies of binge/LOC eating in children, studies using the same methodologies across different subject groups), there appears to be significant neural overlap in the circuits involved, with all disorders demonstrating functional abnormalities in circuits subserving reward (cortico-basal ganglia network including midbrain, ventral and dorsal striatum, amygdala, OFC, and other areas of PFC), response inhibition [frontal (premotor, prefrontal) to subcortical (striatal, cerebellar) circuits, in addition to fronto-temporal networks], and emotional processing (amygdala, insula, VS, OFC) and regulation (eg, ACC, dlPFC, medial PFC, vlPFC).

Notably though, with regard to reward processing, while aberrant neural activity (vs. TD children) is demonstrated by both ADHD children, obese children, and binge/LOC eating, there appears to be a dissociation within the literature. Specifically, children with ADHD appear to show less neural responsivity to reward anticipation, but greater responsivity to reward receipt, in line with their behavioral preferences for immediate vs. delayed rewards. In contrast, obese/overweight children display the opposite pattern of increased responses to reward anticipation (ie, food cues) and decreased responses to reward receipt (ie, food receipt). This phenomenon has important implications for both the neurobiological underpinnings and the appropriate treatment approaches for each condition.

In terms of response inhibition, evidence suggests that both ADHD and obese/overweight individuals have decreased PFC activity during response inhibition tasks; however, for ADHD individuals, fronto-striatal and basal ganglia regions are also implicated, whereas in obese/overweight individuals the modest literature suggests involvement of other regions (e.g. parietal and occipital cortices) - possibly relating to the differing clinical presentations of these 2 groups. In LOC adolescents, there also appears to be an impact of negative mood on response inhibition that may contribute to impulsive responding in relation to food choices.

Finally, with regard to emotional processing and regulation, the literature suggests that for children with ADHD, abnormalities in fronto-striatal responsivity and possibly amygdala activity underlie observed deficits. There is a paucity of research examining emotional processing and regulation in obese/overweight children and those with binge/LOC eating, but existing work points toward a potential interaction between negative emotional states and response to reward as well as increased impulsivity (ie, deficits in response inhibition).

Although the studies of neural correlates that we have described are informative, it is important to acknowledge that they do not constitute studies of neural mechanisms, per se. Such studies can, however, be informative about mechanisms, both by (a) confirming and extending mechanistic work in animals or model systems to human disorders and (b) highlighting areas of differential functioning in which more mechanistic hypotheses can be tested. An example of the former is that physiological work, particularly with primates, has implicated the importance of the amygdala in mediating many aspects of emotional responding (eg, encoding motivational significance or value of a stimuli, etc),Reference Phelps and LeDoux 112 , Reference Paton, Belova, Morrison and Salzman 113 resulting in the exploration of the role of the limbic system in human psychiatric disorders, including ADHD, which are often characterized by deficits in emotional processing or regulation. An example of the latter might be using data on neural correlates to highlight an inhibitory region or circuit that shows hypofunction in the condition of interest, then testing whether a targeted intervention strategy is able to boost activation in that circuit (eg, Yokum and SticeReference Yokum and Stice 114 ), and ultimately whether the degree to which activity is boosted is predictive of success.

A number of broad questions, then, remains, each with associated research directions. First, to what extent does the circuitry that overlaps between conditions reflect overlapping symptoms? To address this, comorbidity studies that examine each disorder both in isolation and in combination with the other disorder of interest will be required. Second, how clinically meaningful are these observed neural similarities? Several studies demonstrate a disconnect between neural and behavioral abnormalities, raising the question of whether neural plasticity may allow nonconventional brain areas to perform the experimental tasks administered just as well as those used by typically developing children. To explore this further, longitudinal studies beginning early in life will be required. Third, how might the neurobehavioral domains we have specified interact with each other? There is already some stimulating evidence for deficits in response inhibition being potentiated by difficulties with emotional regulation, and the complexity of the brain makes it extremely likely that the circuits we have described are in communication, possibly leading to additive and interactive effects on symptomatology. Functional connectivity approaches may help to shed light on this question, particularly those examining co-activation between circuits as well as individual brain regions. Finally, and perhaps most importantly, how does atypical neural activity in each disorder relate to prognosis and treatment outcomes, in particular efficacy? In the case of ADHD, it appears that in some domains (ie, response inhibition), stimulant medication use can “normalize” neural activation patterns, but studies of how treatment affects neural activation in children with conditions characterized by excessive eating are needed, and studies of neurobehaviorally informed interventions that specifically target dysfunctional circuits will be a long-term goal for all of the conditions discussed. Pursuing all of the avenues described above should help not only to reveal intervention-relevant information regarding ADHD, obesity, and binge eating, but to shed light on more fundamental behavior–brain relationships.

Disclosures

All authors are employees of Johns Hopkins University School of Medicine. Dr Reinblatt is supported by the National Institute of Mental Health (NIMH) (Grant Number K23MH083000). Dr Carnell is supported by the National Institute of Diabetes and Ingestive and Kidney Diseases (NIDDK) (Grant Number R00DK088360), and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the Office of the Director, National Institutes of Health (OD) under Grant Number U54HD070725 to the Global Obesity Prevention Center (GOPC) at Johns Hopkins.

References

1. Getahun, D, Jacobsen, SJ, Fassett, MJ, Chen, W, Demissie, K, Rhoads, GG. Recent trends in childhood attention-deficit/hyperactivity disorder. JAMA Pediatr. 2013; 167(3): 282–288.Google Scholar
2. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Washington, DC: American Psychiatric Publishing; 2013.Google Scholar
3. Ogden, CL, Carroll, MD, Kit, BK, Flegal, KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 2014; 311(8): 806–814.Google Scholar
4. Barlow, SE. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007; 120(Suppl 4): S164–S192.Google Scholar
5. World Health Organization. Obesity and Overweight: Fact Sheet no . 311. 2011. http://www.who.int/mediacentre/factsheets/fs311/en/Google Scholar
6. Polanczyk, G, de Lima, MS, Horta, BL, Biederman, J, Rohde, LA. The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry. 2007; 164(6): 942–948.Google Scholar
7. Tanofsky-Kraff, M, Shomaker, LB, Olsen, C, et al. A prospective study of pediatric loss of control eating and psychological outcomes. J Abnorm Psychol. 2011; 120(1): 108–118.CrossRefGoogle ScholarPubMed
8. Tanofsky-Kraff, M, Marcus, MD, Yanovski, SZ, Yanovski, JA. Loss of control eating disorder in children age 12 years and younger: proposed research criteria. Eat Behav. 2008; 9(3): 360–365.CrossRefGoogle ScholarPubMed
9. Holtkamp, K, Konrad, K, Muller, B, et al. Overweight and obesity in children with attention-deficit/hyperactivity disorder. Int J Obes Relat Metab Disord. 2004; 28(5): 685–689.Google Scholar
10. Hanć, T, Słopień, A, Wolańczyk, T, et al. ADHD and overweight in boys: cross-sectional study with birth weight as a controlled factor. Eur Child Adolesc Psychiatry. 2015; 24(1): 41–53.Google Scholar
11. Fuemmeler, BF, Ostbye, T, Yang, C, McClernon, FJ, Kollins, SH. Association between attention-deficit/hyperactivity disorder symptoms and obesity and hypertension in early adulthood: a population-based study. Int J Obes (Lond). 2011; 35(6): 852–862.Google Scholar
12. Cortese, S, Ramos Olazagasti, MA, Klein, RG, Castellanos, FX, Proal, E, Mannuzza, S. Obesity in men with childhood ADHD: a 33-year controlled, prospective, follow-up study. Pediatrics. 2013; 131(6): e1731–e1738.Google Scholar
13. Agranat-Meged, AN, Deitcher, C, Goldzweig, G, Leibenson, L, Stein, M, Galili-Weisstub, E. Childhood obesity and attention deficit/hyperactivity disorder: a newly described comorbidity in obese hospitalized children. Int J Eat Disord. 2005; 37(4): 357–359.CrossRefGoogle Scholar
14. Erermis, S, Cetin, N, Tamar, M, Bukusoglu, N, Akdeniz, F, Goksen, D. Is obesity a risk factor for psychopathology among adolescents? Pediatr Int. 2004; 46(3): 296–301.Google Scholar
15. Khalife, N, Kantomaa, M, Glover, V, et al. Childhood attention-deficit/hyperactivity disorder symptoms are risk factors for obesity and physical inactivity in adolescence. J Am Acad Child Adolesc Psychiatry. 2014; 53(4): 425–436.Google Scholar
16. Cortese, S, Angriman, M, Maffeis, C, et al. Attention-deficit/hyperactivity disorder (ADHD) and obesity: a systematic review of the literature. Crit Rev Food Sci Nutr. 2008; 48(6): 524–537.Google Scholar
17. Mattos, P, Saboya, E, Ayrao, V, Segenreich, D, Duchesne, M, Coutinho, G. [Comorbid eating disorders in a Brazilian attention-deficit/hyperactivity disorder adult clinical sample]. Rev Bras Psiquiatr. 2004; 26(4): 248–250.Google Scholar
18. Neumark-Sztainer, D, Story, M, Resnick, MD, Garwick, A, Blum, RW. Body dissatisfaction and unhealthy weight-control practices among adolescents with and without chronic illness: a population-based study. Arch Pediatr Adolesc Med. 1995; 149(12): 1330–1335.CrossRefGoogle ScholarPubMed
19. Reinblatt, SP, Leoutsakos, JM, Mahone, EM, Forrester, S, Wilcox, HC, Riddle, MA. Association between binge eating and attention-deficit/hyperactivity disorder in two pediatric community mental health clinics. Int J Eat Disord. In press. DOI: 10.1002/eat.22342.Google Scholar
20. Mikami, AY, Hinshaw, SP, Patterson, KA, Lee, JC. Eating pathology among adolescent girls with attention-deficit/hyperactivity disorder. J Abnorm Psychol. 2008; 117(1): 225–235.CrossRefGoogle ScholarPubMed
21. Alderson, RM, Rapport, MD, Kofler, MJ. Attention-deficit/hyperactivity disorder and behavioral inhibition: a meta-analytic review of the stop-signal paradigm. J Abnorm Child Psychol. 2007; 35(5): 745–758.Google Scholar
22. Hartmann, AS, Rief, W, Hilbert, A. Impulsivity and negative mood in adolescents with loss of control eating and ADHD symptoms: an experimental study. Eat Weight Disord. 2013; 18(1): 53–60.Google Scholar
23. Cortese, S, Morcillo Penalver, C. Comorbidity between ADHD and obesity: exploring shared mechanisms and clinical implications. Postgrad Med. 2010; 122(5): 88–96.Google Scholar
24. Liu, LL, Li, BM, Yang, J, Wang, YW. Does dopaminergic reward system contribute to explaining comorbidity obesity and ADHD? Med Hypotheses. 2008; 70(6): 1118–1120.Google Scholar
25. Campbell, BC, Eisenberg, D. Obesity, attention deficit-hyperactivity disorder and the dopaminergic reward system. Coll Antropol. 2007; 31(1): 33–38.Google Scholar
26. Graziano, PA, Bagner, DM, Waxmonsky, JG, Reid, A, McNamara, JP, Geffken, GR. Co-occurring weight problems among children with attention deficit/hyperactivity disorder: the role of executive functioning. Int J Obes (Lond). 2012; 36(4): 567–572.CrossRefGoogle ScholarPubMed
27. Pauli-Pott, U, Albayrak, O, Hebebrand, J, Pott, W. Association between inhibitory control capacity and body weight in overweight and obese children and adolescents: dependence on age and inhibitory control component. Child Neuropsychol. 2010; 16(6): 592–603.Google Scholar
28. Cortese, S, Comencini, E, Vincenzi, B, Speranza, M, Angriman, M. Attention-deficit/hyperactivity disorder and impairment in executive functions: a barrier to weight loss in individuals with obesity? BMC Psychiatry. 2013; 13: 286.Google Scholar
29. Choudhry, Z, Sengupta, SM, Grizenko, N, et al. Body weight and ADHD: examining the role of self-regulation. PLoS one. 2013; 8(1): e55351.CrossRefGoogle ScholarPubMed
30. Dempsey, A, Dyehouse, J, Schafer, J. The relationship between executive function, AD/HD, overeating, and obesity. West J Nurs Res. 2011; 33(5): 609–629.Google Scholar
31. Cortese, S, Vincenzi, B. Obesity and ADHD: clinical and neurobiological implications. Curr Top Behav Neurosci. 2012; 9: 199–218.Google Scholar
32. Haber, SN, Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology. 2010; 35(1): 4–26.Google Scholar
33. Knutson, B, Cooper, JC. Functional magnetic resonance imaging of reward prediction. Curr Opin Neurol. 2005; 18(4): 411–417.Google Scholar
34. Paloyelis, Y, Mehta, MA, Faraone, SV, Asherson, P, Kuntsi, J. Striatal sensitivity during reward processing in attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2012; 51(7): 722–732.Google Scholar
35. Nigg, JT, Casey, BJ. An integrative theory of attention-deficit/ hyperactivity disorder based on the cognitive and affective neurosciences. Dev Psychopathol. 2005; 17(3): 785–806.Google Scholar
36. Sonuga-Barke, EJ. Causal models of attention-deficit/hyperactivity disorder: from common simple deficits to multiple developmental pathways. Biol Psychiatry. 2005; 57(11): 1231–1238.CrossRefGoogle ScholarPubMed
37. Scheres, A, Milham, MP, Knutson, B, Castellanos, FX. Ventral striatal hyporesponsiveness during reward anticipation in attention-deficit/hyperactivity disorder. Biol Psychiatry. 2007; 61(5): 720–724.Google Scholar
38. Strohle, A, Stoy, M, Wrase, J, et al. Reward anticipation and outcomes in adult males with attention-deficit/hyperactivity disorder. Neuroimage. 2008; 39(3): 966–972.Google Scholar
39. Plichta, MM, Scheres, A. Ventral-striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: a meta-analytic review of the fMRI literature. Neurosci Biobehav Rev. 2014; 38: 125–134.Google Scholar
40. Rubia, K, Halari, R, Cubillo, A, Mohammad, AM, Brammer, M, Taylor, E. Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in medication-naive children with ADHD during a rewarded continuous performance task. Neuropharmacology. 2009; 57(7–8): 640–652.Google Scholar
41. Luman, M, Oosterlaan, J, Sergeant, JA. The impact of reinforcement contingencies on AD/HD: a review and theoretical appraisal. Clin Psychol Rev. 2005; 25(2): 183–213.Google Scholar
42. Costa Dias, TG, Wilson, VB, Bathula, DR, et al. Reward circuit connectivity relates to delay discounting in children with attention-deficit/hyperactivity disorder. Eur Neuropsychopharmacol. 2013; 23(1): 33–45.Google Scholar
43. Rada, P, Avena, NM, Hoebel, BG. Daily bingeing on sugar repeatedly releases dopamine in the accumbens shell. Neuroscience. 2005; 134(3): 737–744.Google Scholar
44. Avena, NM, Carrillo, CA, Needham, L, Leibowitz, SF, Hoebel, BG. Sugar-dependent rats show enhanced intake of unsweetened ethanol. Alcohol. 2004; 34(2–3): 203–209.CrossRefGoogle ScholarPubMed
45. Davis, C, Levitan, RD, Muglia, P, Bewell, C, Kennedy, JL. Decision-making deficits and overeating: a risk model for obesity. Obes Res. 2004; 12(6): 929–935.Google Scholar
46. Davis, C, Strachan, S, Berkson, M. Sensitivity to reward: implications for overeating and overweight. Appetite. 2004; 42(2): 131–138.Google Scholar
47. Avena, NM, Hoebel, BG. A diet promoting sugar dependency causes behavioral cross-sensitization to a low dose of amphetamine. Neuroscience. 2003; 122(1): 17–20.Google Scholar
48. Corwin, RL, Hajnal, A. Too much of a good thing: neurobiology of non-homeostatic eating and drug abuse. Physiol Behav. 2005; 86(1–2): 5–8.Google Scholar
49. Carnell, S, Gibson, C, Benson, L, Ochner, CN, Geliebter, A. Neuroimaging and obesity: current knowledge and future directions. Obes Rev. 2012; 13(1): 43–56.Google Scholar
50. Kennedy, J, Dimitropoulos, A. Influence of feeding state on neurofunctional differences between individuals who are obese and normal weight: a meta-analysis of neuroimaging studies. Appetite. 2014; 75: 103–109.Google Scholar
51. Stice, E, Figlewicz, DP, Gosnell, BA, Levine, AS, Pratt, WE. The contribution of brain reward circuits to the obesity epidemic. Neurosci Biobehav Rev. 2013; 37(9 Pt A): 2047–2058.Google Scholar
52. Bruce, AS, Holsen, LM, Chambers, RJ, et al. Obese children show hyperactivation to food pictures in brain networks linked to motivation, reward and cognitive control. Int J Obes (Lond). 2010; 34(10): 1494–1500.Google Scholar
53. Stice, E, Yokum, S, Bohon, C, Marti, N, Smolen, A. Reward circuitry responsivity to food predicts future increases in body mass: moderating effects of DRD2 and DRD4. Neuroimage. 2010; 50(4): 1618–1625.Google Scholar
54. Bruce, AS, Lepping, RJ, Bruce, JM, et al. Brain responses to food logos in obese and healthy weight children. J Pediatr. 2013; 162(4): 759–764.Google Scholar
55. Gearhardt, AN, Yokum, S, Stice, E, Harris, JL, Brownell, KD. Relation of obesity to neural activation in response to food commercials. Soc Cogn Affect Neurosci. 2014; 9(7): 932–938.Google Scholar
56. Davids, S, Lauffer, H, Thoms, K, et al. Increased dorsolateral prefrontal cortex activation in obese children during observation of food stimuli. Int J Obes (Lond). 2010; 34(1): 94–104.Google Scholar
57. Shank, LM, Tanofsky-Kraff, M, Nelson, EE, et al. Attentional bias to food cues in youth with loss of control eating. Appetite. 2015; 87: 68–75.Google Scholar
58. Stice, E, Spoor, S, Bohon, C, Veldhuizen, MG, Small, DM. Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. J Abnorm Psychol. 2008; 117(4): 924–935.CrossRefGoogle ScholarPubMed
59. Burger, KS, Stice, E. Greater striatopallidal adaptive coding during cue-reward learning and food reward habituation predict future weight gain. Neuroimage. 2014; 99: 122–128.Google Scholar
60. Kishinevsky, FI, Cox, JE, Murdaugh, DL, Stoeckel, LE, Cook, EW 3rd, Weller, RE. fMRI reactivity on a delay discounting task predicts weight gain in obese women. Appetite. 2012; 58(2): 582–592.Google Scholar
61. Stice, E, Yokum, S, Burger, KS, Epstein, LH, Small, DM. Youth at risk for obesity show greater activation of striatal and somatosensory regions to food. J Neurosci. 2011; 31(12): 4360–4366.Google Scholar
62. Balodis, IM, Kober, H, Worhunsky, PD, et al. Monetary reward processing in obese individuals with and without binge eating disorder. Biol Psychiatry. 2013; 73(9): 877–886.Google Scholar
63. Balodis, IM, Grilo, CM, Kober, H, et al. A pilot study linking reduced fronto-striatal recruitment during reward processing to persistent bingeing following treatment for binge-eating disorder. Int J Eat Disord. 2014; 47(4): 376–384.CrossRefGoogle ScholarPubMed
64. Mostofsky, SH, Schafer, JG, Abrams, MT, et al. fMRI evidence that the neural basis of response inhibition is task-dependent. Brain Res Cogn Brain Res. 2003; 17(2): 419–430.Google Scholar
65. Rubia, K, Smith, AB, Woolley, J, et al. Progressive increase of frontostriatal brain activation from childhood to adulthood during event-related tasks of cognitive control. Hum Brain Mapp. 2006; 27(12): 973–993.Google Scholar
66. Blasi, G, Goldberg, TE, Weickert, T, et al. Brain regions underlying response inhibition and interference monitoring and suppression. Eur J Neurosci. 2006; 23(6): 1658–1664.Google Scholar
67. Willcutt, E, Doyle, AE, Nigg, JT, Faraone, SV, Pennington, BF. Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biol Psychiatry. 2005; 57(11): 1336–1346.Google Scholar
68. Nigg, JT. Neuropsychologic theory and findings in attention-deficit/hyperactivity disorder: the state of the field and salient challenges for the coming decade. Biol Psychiatry. 2005; 57(11): 1424–1435.CrossRefGoogle ScholarPubMed
69. Willcutt, E, Sonuga-Barke, EJ, Nigg, JT, Sergeant, JA. Recent developments in neuropsychological models of childhood psychiatric disorders. In: Banaschewski T, Rohde LA, eds. Biological Child Psychiatry: Recent Trends and Developments. Basel, Switzerland: Karger; 2008: 195–226.Google Scholar
70. Rubia, K, Russell, T, Overmeyer, S, et al. Mapping motor inhibition: conjunctive brain activations across different versions of go/no-go and stop tasks. Neuroimage. 2001; 13: 250–261.CrossRefGoogle ScholarPubMed
71. Nigg, JT. On inhibition/disinhibition in developmental psychopathology: views from cognitive and personality psychology and a working inhibition taxonomy. Psychol Bull. 2000; 126(2): 220–246.Google Scholar
72. Eagle, DM, Bari, A, Robbins, TW. The neuropsychopharmacology of action inhibition: cross-species translation of the stop-signal and go/no-go tasks. Psychopharmacology (Berl). 2008; 199(3): 439–456.Google Scholar
73. Booth, JR, Burman, DD, Meyer, JR, et al. Larger deficits in brain networks for response inhibition than for visual selective attention in attention deficit hyperactivity disorder (ADHD). J Child Psychol Psychiatry. 2005; 46(1): 94–111.Google Scholar
74. Smith, AB, Taylor, E, Brammer, M, Toone, B, Rubia, K. Task-specific hypoactivation in prefrontal and temporoparietal brain regions during motor inhibition and task switching in medication-naive children and adolescents with attention deficit hyperactivity disorder. Am J Psychiatry. 2006; 163(6): 1044–1051.Google Scholar
75. Tamm, L, Menon, V, Ringel, J, Reiss, AL. Event-related fMRI evidence of frontotemporal involvement in aberrant response inhibition and task switching in attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2004; 43(11): 1430–1440.Google Scholar
76. Durston, S, Tottenham, NT, Thomas, KM, et al. Differential patterns of striatal activation in young children with and without ADHD. Biol Psychiatry. 2003; 15(10): 871–878.Google Scholar
77. Suskauer, SJ, Simmonds, DJ, Caffo, BS, Denckla, MB, Pekar, JJ, Mostofsky, SH. fMRI of intrasubject variability in ADHD: anomalous premotor activity with prefrontal compensation. J Am Acad Child Adolesc Psychiatry. 2008; 47(10): 1141–1150.Google Scholar
78. Rubia, K, Smith, AB, Brammer, MJ, Toone, B, Taylor, E. Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD. Am J Psychiatry. 2005; 162(6): 1067–1075.Google Scholar
79. Rubia, K, Overmeyer, S, Taylor, E, et al. Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: a study with functional MRI. Am J Psychiatry. 1999; 156(6): 891–896.Google Scholar
80. Pliszka, SR, Glahn, DC, Semrud-Clikeman, M, et al. Neuroimaging of inhibitory control areas in children with attention deficit hyperactivity disorder who were treatment naive or in long-term treatment. Am J Psychiatry. 2006; 163(6): 1052–1060.Google Scholar
81. Passarotti, AM, Sweeney, JA, Pavuluri, MN. Neural correlates of response inhibition in pediatric bipolar disorder and attention deficit hyperactivity disorder. Psychiatry Res. 2010; 181(1): 36–43.CrossRefGoogle ScholarPubMed
82. Rubia, K, Cubillo, A, Smith, AB, Woolley, J, Heyman, I, Brammer, MJ. Disorder-specific dysfunction in right inferior prefrontal cortex during two inhibition tasks in boys with attention-deficit hyperactivity disorder compared to boys with obsessive-compulsive disorder. Hum Brain Mapp. 2010; 31(2): 287–299.CrossRefGoogle ScholarPubMed
83. Cubillo, A, Halari, R, Ecker, C, Giampietro, V, Taylor, E, Rubia, K. Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood attention-deficit hyperactivity disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching. J Psychiatr Res. 2010; 44(10): 629–639.CrossRefGoogle ScholarPubMed
84. Hart, H, Radua, J, Nakao, T, Mataix-Cols, D, Rubia, K. Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder: exploring task-specific, stimulant medication, and age effects. JAMA Psychiatry. 2013; 70(2): 185–198.Google Scholar
85. Cortese, S, Kelly, C, Chabernaud, C, et al. Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. Am J Psychiatry. 2012; 169(10): 1038–1055.Google Scholar
86. Batterink, L, Yokum, S, Stice, E. Body mass correlates inversely with inhibitory control in response to food among adolescent girls: an fMRI study. Neuroimage. 2010; 52(4): 1696–1703.Google Scholar
87. Hendrick, OM, Luo, X, Zhang, S, Li, CS. Saliency Processing and Obesity: A Preliminary Imaging Study of the Stop Signal Task. Obesity (Silver Spring). 2012; 20(9): 1796–1802.Google Scholar
88. Maayan, L, Hoogendoorn, C, Sweat, V, Convit, A. Disinhibited eating in obese adolescents is associated with orbitofrontal volume reductions and executive dysfunction. Obesity (Silver Spring). 2011; 19(7): 1382–1387.Google Scholar
89. Thompson, RA. Emotion regulation: a theme in search of definition. Monogr Soc Res Child Dev. 1994; 59(2–3): 25–52.Google Scholar
90. Phillips, ML, Drevets, WC, Rauch, SL, Lane, R. Neurobiology of emotion perception I: the neural basis of normal emotion perception. Biol Psychiatry. 2003; 54(5): 504–514.Google Scholar
91. Ochsner, KN, Gross, JJ. The cognitive control of emotion. Trends Cogn Sci. 2005; 9(5): 242–249.Google Scholar
92. Shaw, P, Stringaris, A, Nigg, J, Leibenluft, E. Emotion dysregulation in attention deficit hyperactivity disorder. Am J Psychiatry. 2014; 171(3): 276–293.Google Scholar
93. Leibenluft, E, Gobbini, MI, Harrison, T, Haxby, JV. Mothers’ neural activation in response to pictures of their children and other children. Biol Psychiatry. 2004; 56(4): 225–232.Google Scholar
94. Gobbini, MI, Leibenluft, E, Santiago, N, Haxby, JV. Social and emotional attachment in the neural representation of faces. Neuroimage. 2004; 22(4): 1628–1635.Google Scholar
95. Gobbini, MI, Haxby, JV. Neural systems for recognition of familiar faces. Neuropsychologia. 2007; 45(1): 32–41.Google Scholar
96. Dickstein, DP, Castellanos, FX. Face processing in attention deficit/hyperactivity disorder. Curr Top Behav Neurosci. 2012; 9: 219–237.Google Scholar
97. Brotman, MA, Rich, BA, Guyer, AE, et al. Amygdala activation during emotion processing of neutral faces in children with severe mood dysregulation versus ADHD or bipolar disorder. Am J Psychiatry. 2010; 167(1): 61–69.Google Scholar
98. Posner, J, Nagel, BJ, Maia, TV, et al. Abnormal amygdalar activation and connectivity in adolescents with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2011; 50(8): 828–837.Google Scholar
99. Marsh, AA, Finger, EC, Mitchell, DG, et al. Reduced amygdala response to fearful expressions in children and adolescents with callous-unemotional traits and disruptive behavior disorders. Am J Psychiatry. 2008; 165(6): 712–720.Google Scholar
100. Posner, J, Maia, TV, Fair, D, Peterson, BS, Sonuga-Barke, EJ, Nagel, BJ. The attenuation of dysfunctional emotional processing with stimulant medication: an fMRI study of adolescents with ADHD. Psychiatry Res. 2011; 193(3): 151–160.Google Scholar
101. Passarotti, AM, Sweeney, JA, Pavuluri, MN. Differential engagement of cognitive and affective neural systems in pediatric bipolar disorder and attention deficit hyperactivity disorder. J Int Neuropsychol Soc. 2010; 16(1): 106–117.Google Scholar
102. Dallman, MF, Pecoraro, N, Akana, SF, et al. Chronic stress and obesity: a new view of “comfort food”. Proc Natl Acad Sci U S A. 2003; 100(20): 11696–11701.Google Scholar
103. Block, JP, He, Y, Zaslavsky, AM, Ding, L, Ayanian, JZ. Psychosocial stress and change in weight among US adults. Am J Epidemiol. 2009; 170(2): 181–192.Google Scholar
104. Dedovic, K, D'Aguiar, C, Pruessner, JC. What stress does to your brain: a review of neuroimaging studies. Can J Psychiatry. 2009; 54(1): 6–15.Google Scholar
105. Jastreboff, AM, Potenza, MN, Lacadie, C, Hong, KA, Sherwin, RS, Sinha, R. Body mass index, metabolic factors, and striatal activation during stressful and neutral-relaxing states: an FMRI study. Neuropsychopharmacology. 2011; 36(3): 627–637.Google Scholar
106. Rudenga, KJ, Sinha, R, Small, DM. Acute stress potentiates brain response to milkshake as a function of body weight and chronic stress. Int J Obes (Lond). 2013; 37(2): 309–316.Google Scholar
107. Czaja, J, Rief, W, Hilbert, A. Emotion regulation and binge eating in children. Int J Eat Disord. 2009; 42(4): 356–362.Google Scholar
108. Tanofsky-Kraff, M, Goossens, L, Eddy, KT, et al. A multisite investigation of binge eating behaviors in children and adolescents. J Consult Clin Psychol. 2007; 75(6): 901–913.Google Scholar
109. Tanofsky-Kraff, M, Faden, D, Yanovski, SZ, Wilfley, DE, Yanovski, JA. The perceived onset of dieting and loss of control eating behaviors in overweight children. Int J Eat Disord. 2005; 38(2): 112–122.Google Scholar
110. Volkow, ND, Wang, GJ, Maynard, L, et al. Brain dopamine is associated with eating behaviors in humans. Int J Eat Disord. 2003; 33(2): 136–142.Google Scholar
111. Bohon, C, Stice, E, Spoor, S. Female emotional eaters show abnormalities in consummatory and anticipatory food reward: a functional magnetic resonance imaging study. Int J Eat Disord. 2009; 42(3): 210–221.Google Scholar
112. Phelps, EA, LeDoux, JE. Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron. 2005; 48(2): 175–187.Google Scholar
113. Paton, JJ, Belova, MA, Morrison, SE, Salzman, CD. The primate amygdala represents the positive and negative value of visual stimuli during learning. Nature. 2006; 439(7078): 865–870.Google Scholar
114. Yokum, S, Stice, E. Cognitive regulation of food craving: effects of three cognitive reappraisal strategies on neural response to palatable foods. Int J Obes (Lond). 2013; 37(12): 1565–1570.Google Scholar
Figure 0

Figure 1 Neural circuitry involved in (a) reward processing, (b) response inhibition, and (c) emotional processing and regulation. Note. ACC=anterior cingulate cortex, Amg=amygdala, dlPFC=dorsolateral prefrontal cortex, GP=globus pallidus, OFC=orbitofrontal cortex, pSMA=pre-supplementary motor area, vlPFC=ventrolateral prefrontal cortex, vmPFC=ventromedial prefrontal cortex, VS=ventral striatum.