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Aberrant functional connectivity between reward and inhibitory control networks in pre-adolescent binge eating disorder

Published online by Cambridge University Press:  18 March 2022

Stuart B. Murray Open the ORCID record for Stuart B. Murray [Opens in a new window] ,
Celina Alba ,
Christina J. Duval ,
Jason M. Nagata ,
Ryan P. Cabeen ,
Darrin J. Lee ,
Arthur W. Toga ,
Steven J. Siegel  and
Kay Jann
Stuart B. Murray*
Affiliation:
Department of Psychiatry & Behavioral Sciences, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
Celina Alba
Affiliation:
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
Christina J. Duval
Affiliation:
Department of Psychiatry & Behavioral Sciences, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
Jason M. Nagata
Affiliation:
Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
Ryan P. Cabeen
Affiliation:
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
Darrin J. Lee
Affiliation:
Department of Psychiatry & Behavioral Sciences, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, USA USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA
Arthur W. Toga
Affiliation:
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
Steven J. Siegel
Affiliation:
Department of Psychiatry & Behavioral Sciences, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
Kay Jann
Affiliation:
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
*
Author for correspondence: Stuart B. Murray, E-mail: [email protected], [email protected]
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Abstract

Background

Behavioral features of binge eating disorder (BED) suggest abnormalities in reward and inhibitory control. Studies of adult populations suggest functional abnormalities in reward and inhibitory control networks. Despite behavioral markers often developing in children, the neurobiology of pediatric BED remains unstudied.

Methods

58 pre-adolescent children (aged 9–10-years) with BED (mBMI = 25.05; s.d. = 5.40) and 66 age, BMI and developmentally matched control children (mBMI = 25.78; s.d. = 0.33) were extracted from the 3.0 baseline (Year 0) release of the Adolescent Brain Cognitive Development (ABCD) Study. We investigated group differences in resting-state functional MRI functional connectivity (FC) within and between reward and inhibitory control networks. A seed-based approach was employed to assess nodes in the reward [orbitofrontal cortex (OFC), nucleus accumbens, amygdala] and inhibitory control [dorsolateral prefrontal cortex, anterior cingulate cortex (ACC)] networks via hypothesis-driven seed-to-seed analyses, and secondary seed-to-voxel analyses.

Results

Findings revealed reduced FC between the dlPFC and amygdala, and between the ACC and OFC in pre-adolescent children with BED, relative to controls. These findings indicating aberrant connectivity between nodes of inhibitory control and reward networks were corroborated by the whole-brain FC analyses.

Conclusions

Early-onset BED may be characterized by diffuse abnormalities in the functional synergy between reward and cognitive control networks, without perturbations within reward and inhibitory control networks, respectively. The decreased capacity to regulate a reward-driven pursuit of hedonic foods, which is characteristic of BED, may in part, rest on this dysconnectivity between reward and inhibitory control networks.

Keywords

Binge eating disordereating disordersfunctional connectivityinhibitory controlpre-adolescent eating disordersreward sensitivity
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 9 , July 2023 , pp. 3869 - 3878
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Eating disorders (ED) affect more than 9% of the US population in their lifetime, which equates to more than 28 million Americans (Deloitte Access Economics, 2020). This constellation of pernicious and burdensome psychiatric disorders typically run a chronic and relapsing illness course (Fichter, Quadflieg, Crosby, & Koch, Reference Fichter, Quadflieg, Crosby and Koch2017; Steinhausen, Reference Steinhausen2002; Watson & Bulik, Reference Watson and Bulik2013; Zipfel, Giel, Bulik, Hay, & Schmidt, Reference Zipfel, Giel, Bulik, Hay and Schmidt2015), and result in more than 10 000 deaths per year in the United States (Hoek, Reference Hoek2006). The most prevalent of all ED phenotypes is binge eating disorder (BED), which affects up to 3–5% of the US population (Deloitte Access Economics, 2020). BED is characterized by (i) frequent consumption of an objectively large amount of food in a discrete-time period, (ii) a subjective and self-reported loss of control, and (iii) an absence of compensatory behaviors to offset caloric intake (American Psychiatric Association, 2013).

Importantly, BED portends an array of deleterious medical and psychiatric sequelae, including weight gain (American Psychiatric Association, 2013), metabolic syndrome (Hudson et al., Reference Hudson, Lalonde, Coit, Tsuang, EcElroy, Crow and Pope2010; Tanofsky-Kraff et al., Reference Tanofsky-Kraff, Shomaker, Stern, Miller, Sebring, Dellavalle and Yanovski2012), diabetes (American Psychiatric Association, 2013), hypertension, dyslipidemia, abnormal cardiac function (American Psychiatric Association, 2013), and elevated suicidality (Olguin et al., Reference Olguin, Fuentes, Gabler, Guerdjikova, Keck and McElroy2017; Udo, Bitley, & Grilo, Reference Udo, Bitley and Grilo2019). With specific regards to weight, more than 75% of individuals with BED have a BMI classified as overweight or obese (Udo & Grilo, Reference Udo and Grilo2019), which in itself ranks among the leading causes of preventable death on a global scale (Flegal, Carroll, Kit, & Ogden, Reference Flegal, Carroll, Kit and Ogden2012; Hales, Carroll, Fryar, & Ogden, Reference Hales, Carroll, Fryar and Ogden2017; Ng et al., Reference Ng, Fleming, Robinson, Thomson, Graetz, Margono and Gakidou2013). With treatment options for BED remaining limited (Brownley, Berkman, Sedway, Lohr, & Bulik, Reference Brownley, Berkman, Sedway, Lohr and Bulik2007; Brownley et al., Reference Brownley, Berkman, Peat, Lohr, Cullen, Bann and Bulik2016; Grilo, Reference Grilo2017; Linardon, Reference Linardon2018), and with fewer treatment trials having been undertaken in children, the need to identify and target the mechanisms underpinning pediatric BED psychopathology is critical. This is further underscored by data suggesting that BED may be the most prevalent ED phenotype among pre-adolescent children in the United States (Murray et al., Reference Murray, Ganson, Chu, Jann and Nagatain press), and that early-onset BED is reliably associated with greater psychiatric and medical morbidity (Allen, Byrne, Oddy, & Crosby, Reference Allen, Byrne, Oddy and Crosby2013; Brewerton, Rance, Dansky, O'Neil, & Kilpatrick, Reference Brewerton, Rance, Dansky, O'Neil and Kilpatrick2014).

Interestingly, whereas dietary restraint typically precedes binge eating behaviors in other ED presentations (Malkoff, Marcus, Grant, Moulton, & Vayonis, Reference Malkoff, Marcus, Grant, Moulton and Vayonis1993), this is typically not the case in individuals with BED, where a developmental emergence of binge episodes precedes attempts at dieting (Grilo & Masheb, Reference Grilo and Masheb2000; Wilson, Nonas, & Rosenblum, Reference Wilson, Nonas and Rosenblum1993). Indeed, low pre-meal ghrelin level further suggests an absence of homeostatic antecedents to binge episodes in BED (Geliebter, Gluck, & Hashim, Reference Geliebter, Gluck and Hashim2005; Geliebter, Hashim, & Gluck, Reference Geliebter, Hashim and Gluck2008). Instead, the heightened drive toward high-fat and high-sugar foods during binge episodes (DeZwan, Reference DeZwan2001; DeZwan, Nutzinger, & Shoenbeck, Reference DeZwan, Nutzinger and Shoenbeck1992; Yanovski et al., Reference Yanovski, Leet, Yanovski, Flood, Gold, Kissileff and Walsh1992) when not calorically restricted suggests altered reward sensitivity and impulsivity in BED. Hedonic eating – the intense pleasure derived from eating highly palatable food, even when not hungry or calorically deprived, is elevated among individuals with BED (Davis et al., Reference Davis, Levitan, Reid, Carter, Kaplan, Patte and Kennedy2009), and prospectively predicts the frequency and intensity of food cravings and binge episodes (Lowe, Arigo, & Butryn, Reference Lowe, Arigo and Butryn2016; Witt & Lowe, Reference Witt and Lowe2014). In concert, elevated food-related impulsivity (Giel, Teufel, Junne, Zipfel, & Schag, Reference Giel, Teufel, Junne, Zipfel and Schag2017) and difficulty diverting attention away from food-related cues (Schag et al., Reference Schag, Teufel, Junne, Preissl, Hautzinger, Zipfel and Giel2013b) is evident among those with BED (Schag, Schönleber, Teufel, Zipfel, & Giel, Reference Schag, Schönleber, Teufel, Zipfel and Giel2013a), and is intricately linked to the inability to abstain from food consumption (i.e. loss of control eating) which characterizes BED. Broad deficits in behavioral inhibition have been noted, although this is particularly evident with respect to food stimuli (Svaldi, Naumann, Trentowska, & Schmitz, Reference Svaldi, Naumann, Trentowska and Schmitz2014). Interestingly, elevated hedonic eating appears to drive greater palatable (as opposed to bland) food consumption when low inhibitory control is present (Appelhans et al., Reference Appelhans, Woolf, Pagoto, Schneider, Whited and Liebman2011), suggesting critical intersectionality of altered reward and inhibitory control-related processes in BED.

Mechanistically, the neural circuitry underpinning reward and inhibitory control-related processes are well defined. The anticipation and receipt of reward and the initiation of goal-directed behavior involve a well-defined neural circuit comprising the anterior cingulate cortex (ACC), amygdala, ventral tegmental area (VTA), bed nucleus of the stria terminalis (BNST), orbitofrontal cortex (OFC), ventral striatum (VS)/nucleus accumbens (NAcc), and the prefrontal cortex (PFC) (Antons, Brand, & Potenza, Reference Antons, Brand and Potenza2020; Ivanov, Schulz, London, & Newcorn, Reference Ivanov, Schulz, London and Newcorn2008). Upon receipt of hedonic cues, the experience of pleasure appears to converge in the VS and OFC, where μ-opioid and endocannabinoid receptors mediate the hedonic perception of reward. Inhibitory control processes that suppress the dominant or habitual response to cue and goal-directed behavior are associated with activity in the PFC: the dorsolateral prefrontal cortex (dlPFC), inferior frontal cortex (IFC) and ACC. The PFC receives input from the VTA, thalamus and amygdala and in turn modulates reactivity of those areas through top-down control over the serotonergic and dopaminergic neurotransmitter systems (Robbins, Reference Robbins2005).

To date, relatively few neuroimaging studies have assessed the neural circuits involved in the psychopathology of BED (Steward, Menchon, Jiménez-Murcia, Soriano-Mas, & Fernandez-Aranda, Reference Steward, Menchon, Jiménez-Murcia, Soriano-Mas and Fernandez-Aranda2018), and those that exist have predominantly assessed adults. Existing studies of those engaging in binge eating episodes have revealed no consistent alterations in gray matter morphometry (Abdo, Boyd, Baboumian, Pantazatos, & Geliebter, Reference Abdo, Boyd, Baboumian, Pantazatos and Geliebter2020; Hagan & Bohon, Reference Hagan and Bohon2021), although arterial spin labeling studies suggest that women with binge type eating disorder presentations (BED, BN) demonstrate increased regional cerebral blood flow in both reward and inhibitory control regions (Martins et al., Reference Martins, Leslie, Rodan, Zelaya, Treasure and Paloyelis2020). Functional MRI (fMRI) studies have illustrated elevated OFC and VS activity during food cue presentation tasks (Lee, Namkoong, & Jung, Reference Lee, Namkoong and Jung2017; Weygandt, Schaefer, Schienle, & Haynes, Reference Weygandt, Schaefer, Schienle and Haynes2012), which predicts clinical BED symptom severity (Wang et al., Reference Wang, Geliebter, Volkow, Telang, Logan, Jayne and Fowler2011). In contrast, however, non-food-related reward processing tasks illustrate attenuated striatal activity during reward anticipation (Balodis et al., Reference Balodis, Kober, Worhunsky, White, Stevens, Pearlson and Potenza2013a, Reference Balodis, Molina, Kober, Worhunsky, White, Sinha and Potenza2013b), which is consistent with the blunted anticipatory processing observed in other disorders characterized by impulsivity (Balodis et al., Reference Balodis, Kober, Worhunsky, Stevens, Pearlson and Potenza2012; Beck et al., Reference Beck, Schlagenhauf, Wustenberg, Hein, Kienast, Kahnt and Wrase2009). During inhibitory control tasks, BED is characterized by reduced activity in nodes involved in inhibitory control, such as the PFC and inferior frontal gyrus (IFG) (Balodis et al., Reference Balodis, Kober, Worhunsky, White, Stevens, Pearlson and Potenza2013a, Reference Balodis, Molina, Kober, Worhunsky, White, Sinha and Potenza2013b). Moreover, PFC and IFG hypoactivity is directly related to impaired dietary restraint in individuals with BED (Balodis et al., Reference Balodis, Kober, Worhunsky, White, Stevens, Pearlson and Potenza2013a, Reference Balodis, Molina, Kober, Worhunsky, White, Sinha and Potenza2013b), suggesting that the diminished recruitment of impulse-control circuitry is related to an impaired ability to limit food intake.

Few studies to date have assessed functional connectivity (FC) in individuals with BED. One recent study of adults with BED noted hypoconnectivity between striatal regions which regulate reward processing, and prefrontal regions involved in cognitive and executive control (Haynos et al., Reference Haynos, Camchong, Pearson, Lavender, Mueller, Peterson and Lim2021). Moreover, this hypoconnectivity was associated with impaired performance on a compulsive reward-seeking task, and with increased binge frequency (Haynos et al., Reference Haynos, Camchong, Pearson, Lavender, Mueller, Peterson and Lim2021), suggesting that a disruption in the functional architecture of reward networks may drive BED psychopathology. In another study of adults with obesity, some of whom had BED, frontostriatal hypoconnectivity was also evident, relative to adults without obesity (Baek, Morris, Kundu, & Voon, Reference Baek, Morris, Kundu and Voon2017). However, while providing critical preliminary insights into the functional architecture of BED, these studies have been limited by relatively small sample sizes, and an exclusive focus on adult presentations of BED.

As such, the need for further assessment of BED in pediatric populations has been well stated (Bohon, Reference Bohon2019), although this may be especially pertinent for neuroimaging studies, since the putative circuit alterations underlying BED are in rapid development during childhood and adolescence (Constantinidis & Luna, Reference Constantinidis and Luna2019; Galvan, Reference Galvan2010). Indeed, despite clinical markers of BED being evident in pre-adolescent childhood (i.e. 6–12 years of age) (Birch & Fisher, Reference Birch and Fisher2000; Faith et al., Reference Faith, Berkowitz, Stallings, Kerns, Storey and Stunkard2006; Fogel et al., Reference Fogel, Mccrickerd, Fries, Goh, Quah, Chan and Forde2018; Miller et al., Reference Miller, Gearhardt, Retzloff, Sturza, Kaciroti and Lumeng2018; Tanofsky-Kraff, Marcus, Yanovski, & Yanovski, Reference Tanofsky-Kraff, Marcus, Yanovski and Yanovski2008), few studies have assessed BED in adolescence, and fewer still have assessed BED in pre-adolescence. This, in part, stems from ambiguity around BED diagnoses in children, where for instance, it can be challenging to define a ‘large amount of food’ for boys and girls at different developmental stages (Tanofsky-Kraff, Schvey, & Grilo, Reference Tanofsky-Kraff, Schvey and Grilo2020). As a result, more studies among pediatric populations have focused on the loss of control eating (irrespective of quantity consumed), rather than BED per se. This body of evidence suggests that children who report loss of control eating in middle childhood are more likely to develop subsequent subthreshold and full threshold BED (Hilbert, Hartmann, Czaja, & Schoebi, Reference Hilbert, Hartmann, Czaja and Schoebi2013; Tanofsky-Kraff et al., Reference Tanofsky-Kraff, Shomaker, Olsen, Roza, Wolkoff, Columbo and Yanovski2011), with earlier onset generally being associated with more severe binge eating in later life (Goldschmidt, Wall, Zhang, Loth, & Neumark-Sztainer, Reference Goldschmidt, Wall, Zhang, Loth and Neumark-Sztainer2016). However, natural remission of loss of control eating occurs in more than half of pre-adolescent children afflicted (Hilbert et al., Reference Hilbert, Hartmann, Czaja and Schoebi2013). As such, loss of control eating cannot necessarily be taken as a proxy or be conflated with clinical presentations BED amongst children, and the standalone assessment of pediatric BED would represent a significant addition to the literature.

The present study therefore aimed to leverage the Adolescent Brain Cognitive Development (ABCD) Study (Casey et al., Reference Casey, Cannonier, Conley, Cohen, Barch and Heitzeg2018) to undertake a comprehensive assessment of resting-state FC in pre-adolescent children with BED. With respect to the abundance of literature illustrating altered neurobiology and FC based on BMI classification (Moreno-Lopez, Contreras-Rodriguez, Soriano-Mas, Stamatakis, & Verdejo-Garcia, Reference Moreno-Lopez, Contreras-Rodriguez, Soriano-Mas, Stamatakis and Verdejo-Garcia2016), we compared those with BED to weight-matched controls, to avoid a potential confound of a differential between group weight status. In particular, we aimed to assess FC within and between nodes of the reward and inhibitory control networks in pre-adolescent children with BED, relative to age- and developmentally matched control pre-adolescent children. Owing to the centrality of altered reward and cognitive control circuit activity in presentations of BED, alongside recently demonstrated fronto-striatal dysconnectivity, it was hypothesized that reduced resting-state FC would be evident in children with BED, within and between both reward and inhibitory control-related networks, respectively.

Methods

Study sample

The ABCD Study is a large, diverse, and prospective cohort study of brain development and health throughout adolescence. We analyzed data from the ABCD 3.0 release from baseline (Year 0), which consisted of 11 875 pre-adolescent children aged 9–10 years collected in 2016–2018, recruited from 21 sites around the U.S. Further details of the study sample, recruitment process, exclusion criteria, procedures, and measures have been previously reported (Barch et al., Reference Barch, Albaugh, Avenevoli, Chang, Clark, Glantz and Sher2018). Centralized institutional review board (IRB) approval was obtained from the University of California, San Diego. Study sites obtained approval from their local IRBs. Caregivers provided written informed consent and each child provided written assent.

Measures

Diagnostic interview

Parents/caregivers completed the eating disorder module of the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS-5) (Kaufman, Birmaher, & Brent, Reference Kaufman, Birmaher and Brent1997a), assessing frequency, duration, and associated distress of their child's eating behavior. With the challenges presented for young children in rating complex cognitive constructs such as loss of control, and with evidence noting that parents are particularly important reporters for these behaviors in this age range (Braet et al., Reference Braet, Soetens, Moens, Mels, Goossens and Vlierberghe2007; Kaufman et al., Reference Kaufman, Birmaher, Brent, Rao, Flynn, Moreci and Ryan1997b), we used parent/caregiver reports of child behavior. Diagnoses were made according to DSM-5 criteria for BED (American Psychiatric Association, 2013).

Pubertal development scale (PDS)

Owing to the narrow age range of this sample (9–10 years), and the differential rates of neurodevelopmental maturation among pre-adolescent children of the same chronological age, a self-reported measure of pubertal status (Herting et al., Reference Herting, Uban, Gonzalez, Baker, Kan, Thompson and Sowell2021; Petersen, Crockett, Richards, & Boxer, Reference Petersen, Crockett, Richards and Boxer1988), frequently used as a measure of developmental maturation when studying brain function and structure (Blakemore, Burnett, & Dahl, Reference Blakemore, Burnett and Dahl2010; Goddings, Beltz, Peper, Crone, & Braams, Reference Goddings, Beltz, Peper, Crone and Braams2019), was used in the present study to control for differential rates of maturation.

Body mass index

BMI was based on the average of two-to-three measured heights and weights by trained research staff. Children's height (to the nearest inch) and weight (to the nearest 0.1 pound) in light clothing and stocking feet were measured according to a modified PhenX Anthropometric assessment (Barch et al., Reference Barch, Albaugh, Avenevoli, Chang, Clark, Glantz and Sher2018). With recent empirical data suggesting that BMIz scores can be less informative in the context of children with severe obesity (Kelly & Daniels, Reference Kelly and Daniels2017), and with the mean BMI percentile being above the 99th percentile in our cohort of children with BED, raw BMI scores were used as opposed to BMIz scores.

Control group participant matching

Control group participants were matched according to BMI and pubertal development. The mean and standard deviation for these variables were calculated for the BED group. Subsequently, non-BED participants were extracted from the parent data set if they had a (i) BMI, (ii) self-reported PDS score and (iii) parent-reported PDS score within half of one standard deviation of the mean of the BED group.

MRI data acquisition and preprocessing

Structural and resting-state fMRI data used in this study were from the minimally preprocessed imaging data in the ABCD study as provided on NIH Data Archive (Release 3.0). Since head motion is a significant factor in MR data specifically in pediatric imaging, children in the ABCD study underwent pre-scan simulations in dedicated mock scanners to help acclimate to the scanner environment and minimize head motion during MRI data acquisition (Casey et al., Reference Casey, Cannonier, Conley, Cohen, Barch and Heitzeg2018). Structural T1 weighted images used for normalization and atlas parcellation were collected on Siemens Prisma (TR/TE = 2500/2.88 ms, 176 slices, 256 × 256 matrix size, voxel size 1 × 1 × 1 mm3, FA = 8°), Philips Achieva (TR/TE = 6.31/2.9 ms, 225 slices, 256 × 240 matrix size, voxel size 1 × 1 × 1 mm3, FA = 8°) and GE MR750 (TR/TE = 2500/2 ms, 208 slices, 256 × 256 matrix size, voxel size 1 × 1 × 1 mm3, FA = 8°) scanner platforms. Resting-state functional MRI (rs-fMRI) data were acquired with the same imaging parameters across the three scanner platforms: TR/TE = 800/30 ms, 60 slices, 216 × 216 matrix size, voxel size 2.4 × 2.4 × 2.4 mm3, FA = 52°, MB = 6, Volumes = 383. Acquisition time was 5 min for rs-fMRI runs. During rs-fMRI, children were instructed to keep their eyes open and fixate on a cross or to watch a movie that was played.

MRI data were analyzed in ConnToolbox (Whitfield-Gabrieli & Nieto-Castanon, Reference Whitfield-Gabrieli and Nieto-Castanon2012). Preprocessing included motion alignment and bandpass filtering between 0.01 Hz and 0.1 Hz. To account for physiological noise, we corrected for a signal from the white matter and cerebrospinal fluid from the functional data using aCompCor (Behzadi, Restom, Liau, & Liu, Reference Behzadi, Restom, Liau and Liu2007). Furthermore, the six motion parameters from realignment and their first derivatives were regressed from the data and finally BOLD timeseries were motion scrubbed, which is particularly salient in pediatric populations (Power, Barnes, Snyder, Schlaggar, & Petersen, Reference Power, Barnes, Snyder, Schlaggar and Petersen2012; Power et al., Reference Power, Mitra, Laumann, Snyder, Schlaggar and Petersen2014). Subjects with excessive motion, i.e. mean framewise displacement above 0.5, were excluded from further analysis. Noise corrected fMRI images were then coregistered to anatomical T1 weighted images, normalized into MNI standard space and split into ROIs based on the Harvard-Oxford atlas.

MRI data analysis

Seed-based FC analysis for selected ROIs representing key nodes of the inhibitory control and reward networks and which have been consistently reported in the BED literature (i.e. orbito-frontal cortex, ACC, nucleus accumbens, dlPFC, and amygdala, Fig. 1a) provided subject-specific seed-to-seed connectivity matrices. FC was calculated as Fisher-transformed bivariate correlation coefficients. Second-level analysis to test for group differences between the BED and control (CONT) groups included the subject-specific connectivity matrices as a dependent variable, group as independent variable and covariates for pubertal status (PDS), gender and BMI. Significance was set at p < 0.05 with correction for 36 (all possible connections between 9 ROIs) multiple comparisons using Benjamini–Hochberg FDR correction at q < 0.05 (Noble, Reference Noble2009). Additional seed-to-voxel FC analyses were performed to reveal whole-brain patterns of connectivity between ROIs within the inhibitory control and reward networks with brain areas in the rest of the brain. Second-level analysis of the seed-to-voxel models mirrored the seed-to-seed model as it evaluated the group differences between the BED and CONT groups, controlling for pubertal status (PDS), gender and BMI as covariates. Significance was set at p < 0.05 with correction for multiple comparisons using false discovery rate (FDR q < 0.05).

Fig. 1. Seed-to-seed rs-fMRI analyses comparing pre-adolescent children with binge eating disorder and control pre-adolescent children, while adjusting for BMI and pubertal development. Seed regions included the bilateral amygdala, nucleus accumbens, orbitofrontal cortex (FOrb), dorsolateral prefrontal cortex (MidFG), and the anterior cingulate (AC). Darker red coloring indicates a more positive z-score, and darker blue indicates a more negative z-score (of which there were no significant effects).

Results

Sample demographics

We identified 72 pre-adolescent children with BED diagnoses based on DMS-5 criteria in the baseline visit of the ABCD study. From this sample, 13 were excluded due to incomplete MR data (N = 13) and excessive motion (N = 1), leaving a sample of 58 children in the BED group (28 females; 30 males). We also selected 68 healthy control pre-adolescent children (36 females; 32 males) that matched those with BED in age (p = 0.1197), gender (p = 0.6016), BMI (p = 0.1617) and PDS (p = 0.4854). Table 1 illustrates a detailed characterization of the two cohorts. Among the overall sample, approximately 58% identified as White, 16% as Black, 14% as Mixed Race, 1% as Asian, and 11% as ‘other’. Additionally, 24% of the sample identified as Hispanic. Motion during rs-fMRI was assessed with framewise displacement (FD), revealing no significant difference in the amount of motion between the groups (t = 0.604; p = 0.547).

Table 1. An overview of sample characteristics from the Adolescent Brain Cognitive Development Study, delineated by group

Seed-to-seed connectivity

Statistical analysis between the BED cohort (BED) and control group cohort (CONT) for specific seed-to-seed connectivity revealed significantly lower connectivity between areas of the inhibitory control and reward network. Specifically, left OFC to ACC, bilateral dlPFC to the bilateral amygdala (Amyg) and right OFC to left dlPFC (Fig. 1b). However, only two of these findings survived correction for multiple comparison: the AC to L-OFC and the L-dlPFC to R-OFC (indicated by the asterisk in Fig. 1b). Notably, all findings indicate differences in between network connectivity, as we found no significant differences for connections between ROIs within the reward or cognitive control network, respectively.

Seed-to-voxel connectivity

Statistical analysis of the connectivity from each bilateral pair of ROIs to all other voxels in the brain resulted in connectivity patterns indicating widespread hypoconnectivity in the BED group as compared to the CONT group (Fig. 2). For the amygdala seeds, we saw hypo connectivity in the BED group to dlPFC, PCC, superior frontal gyrus (SFG) and temporal lobe. The OFC showed reduced connectivity to PCC, ACC, dlPFC and ventromedial prefrontal cortex (VMPFC). The ACC displayed hypoconnectivity to OFC, SFG, parahippocampal gyrus and inferior temporal gyrus. Finally, the dlPFC exhibited reduced connectivity to OFC, middle temporal gyrus, SFG and IFG. We did not find any significant group differences for the NAcc seed. Hyperconnectivity was observed from one seed region in those with BED, where a small cluster in the temporal pole demonstrated hyperconnectivity with the amygdala.

Fig. 2. Seed-to-voxel analyses comparing pre-adolescent children with binge eating disorder and control pre-adolescent children, while adjusting for BMI and pubertal development. Seed regions included (a) the amygdala, (b) the orbitofrontal cortex (FOrb), (c) the anterior cingulate (AC), and (d) the dorsolateral prefrontal cortex (MidFG). Hot colors in red, yellow, and white indicate a more positive z-score, and cooler colors in blue, purple, and pink indicate a more negative z-score. Gray brain areas showed no significant difference between groups.

Discussion

This study examined the functional organization and interaction within and between reward and cognitive control networks in pre-adolescent children with BED, using hypothesis-driven seed-based rsFC analyses. With theoretical and task-evoked neuroimaging evidence suggesting that BED may be characterized by abnormal neural activity in these networks (Gazzillo et al., Reference Gazzillo, Lingiardi, Peloso, Giordani, Vesco, Zanna and Vicari2013; Holliday, Tchanturia, Landau, Collier, & Treasure, Reference Holliday, Tchanturia, Landau, Collier and Treasure2005; Peñas-Lledó et al., Reference Peñas-Lledó, Jiménez-Murcia, Granero, Penelo, Agüera, Alvarez-Moya and Fernández-Aranda2010; Thompson-Brenner et al., Reference Thompson-Brenner, Eddy, Franko, Dorer, Vashchenko, Kass and Herzog2008; Wierenga et al., Reference Wierenga, Ely, Bischoff-Grethe, Bailer, Simmons and Kaye2014), and with one previous study of adult BED noting hypoconnectivity between reward and cognitive control networks (Haynos et al., Reference Haynos, Camchong, Pearson, Lavender, Mueller, Peterson and Lim2021), we examined rsFC within and between reward and cognitive control networks in pre-adolescent children with BED. Contrary to hypotheses, our findings suggest no alterations in FC within reward and cognitive control networks, respectively. However, findings illustrated a diffuse dysconnectivity between nodes of the reward and cognitive control networks in pre-adolescent children with BED, indicating that abnormalities in the functional synergy between reward and cognitive control networks may be evident in BED among children as young as 9 years of age.

Specifically, we noted reduced connectivity between the dlPFC and amygdala, and between the ACC and OFC in pre-adolescent children with BED. These findings illustrating hypoconnectivity between reward and cognitive control networks accord with theoretical models of the psychopathology of BED, which posit a critical interplay between reward and impulse control-related processes (Davis et al., Reference Davis, Levitan, Carter, Kaplan, Reid, Curtis and Kennedy2008). Essentially, it is thought that the increased food intake characteristic of BED typically develops when an increased reward value is placed on food, therefore facilitating a higher drive to eat, and persists over time if this heightened drive cannot be inhibited (Schag et al., Reference Schag, Teufel, Junne, Preissl, Hautzinger, Zipfel and Giel2013b). Our findings replicate recent findings in (i) adults with BED, noting hypoconnectivity between seed regions involved in reward processing, such as the NAcc and dorsal caudate, and prefrontal regions involved in cognitive control, such as the SFG (Haynos et al., Reference Haynos, Camchong, Pearson, Lavender, Mueller, Peterson and Lim2021) and (ii) pre-adolescent children who overeat, noting that eating in the absence of hunger is associated with the FC between the caudate and dlPFC (Shapiro et al., Reference Shapiro, Johnson, Sutton, Legget, Dabelea and Tregellas2019). Crucially, our hypothesis-driven findings extend these earlier findings by implicating additional regions of dysconnectivity between reward and cognitive control regions – namely the (i) dlPFC and amygdala, and (ii) dACC and OFC.

These hypotheses-driven seed-to-seed findings are further supported by exploratory seed-to-voxel whole-brain analyses. These statistically significant and corrected findings suggest broad dysconnectivity between reward and cognitive control circuits, illustrating that the dysconnectivity from (i) the amygdala to regions in the dlPFC and SFG, (ii) the OFC to regions in the dlPFC, ACC, vmPFC and PCC (iii) the ACC to regions in the vmPFC, SFG, and OFC, and (iv) from the dlPFC to regions in the OFC and vmPFC, rank among the most dysconnected of any voxels in the whole brain from these respective seed regions. This broad dysconnectivity between reward and cognitive control networks may have profound implications for the adaptive responding to rewarding cues, which rests on an intricate calibration of approach behaviors and cognitive/behavioral inhibition. The broadly reduced synchronicity of these networks in pre-adolescent children with early-onset BED, and the notion that this dysconnectivity is associated with binge frequency and volume in adults with BED (Haynos et al., Reference Haynos, Camchong, Pearson, Lavender, Mueller, Peterson and Lim2021; Shapiro et al., Reference Shapiro, Johnson, Sutton, Legget, Dabelea and Tregellas2019), suggests that these processes and networks are particularly important targets for treatment efforts among children with BED.

With respect to our assessment of the FC of nodes within the reward network, we found no evidence of dysconnectivity in our sample of pre-adolescent children with BED, relative to weight-matched controls. This accords with previous whole-brain seed-to-voxel analyses in adults with BED, which revealed no altered connectivity patterns within nodes of the reward network, relative to weight-matched controls (Haynos et al., Reference Haynos, Camchong, Pearson, Lavender, Mueller, Peterson and Lim2021). Interestingly, previous studies of FC in individuals reporting binge type behaviors based on BMI classification reveal mixed findings with regard to within reward network FC. For instance, in a study comparing individuals with obesity (some of whom had BED) and individuals without obesity, reduced connectivity was noted in those with obesity between nodes within the basal ganglia reward circuit, including the pallidum, putamen and amygdala, which was negatively correlated with patient weight and binge eating symptoms (Baek et al., Reference Baek, Morris, Kundu and Voon2017). In contrast, food addiction among individuals with obesity has been associated with increased connectivity between nodes within the reward network, relative to individuals without obesity or food addiction (Contreras-Rodriguez et al., Reference Contreras-Rodriguez, Burrow, Pursey, Stanwell, Parkes, Soriano-Mas and Verdejo-Garcia2019; Ravichandran et al., Reference Ravichandran, Bhatt, Pandit, Osadchiy, Alaverdyan, Vora and Gupta2021), and was associated with both the frequency and intensity of general food cravings in individuals with obesity (Ravichandran et al., Reference Ravichandran, Bhatt, Pandit, Osadchiy, Alaverdyan, Vora and Gupta2021). In contrast, FC analyses in adults without obesity and children who report binge eating (Oliva, Morys, Horstmann, Castiello, & Begliomini, Reference Oliva, Morys, Horstmann, Castiello and Begliomini2020) and overeating (Shapiro et al., Reference Shapiro, Johnson, Sutton, Legget, Dabelea and Tregellas2019) episodes, respectively, have revealed non-disturbed FC within the reward network (Oliva et al., Reference Oliva, Morys, Horstmann, Castiello and Begliomini2020), raising the interesting possibility that reward network functional abnormalities may be gated by bodyweight among those who engage in binge type behaviors. Standalone alterations in FC of the reward network have been well demonstrated among individuals with obesity, irrespective of the presence of binge eating (Syan et al., Reference Syan, McIntyre-Wood, Minuzzi, Hall, McCabe and MacKillop2021), and recent machine learning efforts have outlined a robust obesity FC phenotype involving hyperconnectivity within the reward network, which correlates with body mass, waist circumference, and waist-to-hip ratio (Park et al., Reference Park, Byeon, Lee, Chung, Kim, Morys and Park2020). Since controls in the present study were BMI-matched, it is noteworthy that reward network FC in overweight pre-adolescent children with BED was not discrepant from that of overweight pre-adolescent children without BED.

With respect to the hyperconnectivity between the amygdala and a small cluster in the temporal pole among individuals with BED, this finding warrants further investigation. The temporal pole has been described as an enigmatic brain region, which is incompletely understood both in terms of function and cytoarchitectonic structure (Olsen, Plotzker, & Ezzyat, Reference Olsen, Plotzker and Ezzyat2007; Pascual et al., Reference Pascual, Masdeu, Hollenbeck, Makris, Insausti, Ding and Dickerson2015), and its role in the psychopathology of BED remains unclear. However, with evidence suggesting functional and structural projections to the amygdala and OFC (Olsen et al., Reference Olsen, Plotzker and Ezzyat2007), further research ought to investigate how neural activity within the temporal pole relates to BED symptomatology.

With respect to connectivity within cognitive control networks, we found no evidence of dysconnectivity in BED. This echoes findings from previous studies of BED (Baek et al., Reference Baek, Morris, Kundu and Voon2017; Haynos et al., Reference Haynos, Camchong, Pearson, Lavender, Mueller, Peterson and Lim2021), and several studies assessing FC and overeating in populations both with and without obesity. Notably, one previous study of restrained eaters noted diminished inter-hemispheric dlPFC FC, relative to non-restrained eaters, which was associated with greater binge-type eating symptoms (Chen, Dong, Jackson, Su, & Chen, Reference Chen, Dong, Jackson, Su and Chen2016). While the present study did not assess inter-hemispheric FC, our findings suggesting that inter-node connectivity within cognitive control networks is not fundamentally disturbed in individuals with BED, relative to weight-matched controls. This accords with studies demonstrating comparable performance in neuropsychological tests of inhibitory control among women with obesity, with and without BED (Bub, Robinson, & Curtis, Reference Bub, Robinson and Curtis2016; Kollei et al., Reference Kollei, Rustemeier, Schroeder, Jongen, Herpertz and Loeber2018; Manasse et al., Reference Manasse, Forman, Ruocco, Butryn, Juarascio and Fitzpatrick2015). However, even in the context of comparable task performance on measures of inhibitory control, some studies have noted distinct neural activity in prefrontal regions among individuals with obesity who also have BED, relative to individuals with obesity who do not have BED, and control individuals without obesity (Balodis et al., Reference Balodis, Kober, Worhunsky, White, Stevens, Pearlson and Potenza2013a, Reference Balodis, Molina, Kober, Worhunsky, White, Sinha and Potenza2013b), suggesting divergent neural substrates underpinning inhibitory control. Our findings suggest that any functional atypicality in neural activity among those with BED during tasks of inhibitory control do not stem from alterations in FC within the cognitive control network. However, with studies illustrating reliable differences in cognitive control between individuals with and without obesity (Liang, Matheson, Kaye, & Boutelle, Reference Liang, Matheson, Kaye and Boutelle2014; Skoranski et al., Reference Skoranski, Most, Lutz-Stehl, Hoffman, Hassink and Simons2013), it is noteworthy that both cohorts in the present study had obesity. A crucial next step lies in parsing functional differences among individuals with BED across the weight spectrum.

Strengths of the present study include the relatively large sample size, which is among the largest neuroimaging studies of pediatric BED to date, and the use of a control sample that was tightly matched on gender, age, pubertal development and BMI. Additionally, the focus on pre-adolescent BED offers important insights on the mechanistic underpinnings of early-onset BED, which has been seldom studied. With much evidence noting the benefits of early intervention for ED (Allen et al., Reference Allen, Mountford, Brown, Richards, Grant, Austin and Schmidt2020; Brown et al., Reference Brown, McClelland, Boysen, Mountford, Glennon and Schmidt2016), an explication of the precise mechanisms underpinning pediatric BED may help mitigate the suite of deleterious outcomes associated with its longer-term course (Mitchell, Reference Mitchell2015; Wassenaar, Friedman, & Mehler, Reference Wassenaar, Friedman and Mehler2019). Relatedly, and owing to the centrality of impulse control in theoretical models of BED (Davis et al., Reference Davis, Levitan, Carter, Kaplan, Reid, Curtis and Kennedy2008; Gazzillo et al., Reference Gazzillo, Lingiardi, Peloso, Giordani, Vesco, Zanna and Vicari2013; Holliday et al., Reference Holliday, Tchanturia, Landau, Collier and Treasure2005; Peñas-Lledó et al., Reference Peñas-Lledó, Jiménez-Murcia, Granero, Penelo, Agüera, Alvarez-Moya and Fernández-Aranda2010; Thompson-Brenner et al., Reference Thompson-Brenner, Eddy, Franko, Dorer, Vashchenko, Kass and Herzog2008; Wierenga et al., Reference Wierenga, Ely, Bischoff-Grethe, Bailer, Simmons and Kaye2014), our assessment of pre-adolescent children accounted for developmental maturation, which is critical given the potentially discrepant development of the late-maturing PFC.

Limitations of the present study are also noteworthy, and findings should be interpreted with appropriate caution. First, and owing to the nature of the parent ABCD dataset, no dimensional measure of ED symptomatology was included. It is critical that future studies investigate how these patterns of functional dysconnectivity relate to ED symptoms among those with BED. Second, our findings relating to pre-adolescent children with BED cannot necessarily be seamlessly extrapolated to adults with BED. Owing to our cross-sectional design, it is unclear whether the dysconnectivity we observed represents a pathway through which BED develops and extends into adulthood, or whether this represents an age-specific abnormality which resolves upon further maturation of the regions implicated in reward and inhibitory control. Indeed, the natural remission of loss of control eating has been documented in more than half of all pre-adolescent children demonstrating this behavior (Hilbert et al., Reference Hilbert, Hartmann, Czaja and Schoebi2013), and so further investigation may seek to model this trajectory of dysconnectivity in BED throughout later adolescence. Third, food consumption prior to scanning was unknown. With evidence suggesting that neural activity in those with ED, and particularly in reward-related regions, may differ during sated and hungry states (Ely et al., Reference Ely, Wierenga, Bischoff-Grethe, Bailer, Berner, Fudge and Kaye2017; Kaye et al., Reference Kaye, Wierenga, Bischoff-Grethe, Berner, Ely, Bailer and Fudge2020), the inability to control for pre-scan food consumption and hunger levels may impact the generalizability of the findings. In addition, the variability in BMI among those with BED in our sample was wide, whereas the variability among those in our control sample was narrow. This means that a potentially disparate sample (in terms of BMI) of individuals with BED were compared to a highly circumscribed sample of control individuals. Further research may seek to parse the effect of patient weight upon FC patterns among those demonstrating BED and binge eating behaviors, given the variable findings among individuals with and without obesity (Baek et al., Reference Baek, Morris, Kundu and Voon2017; Oliva et al., Reference Oliva, Morys, Horstmann, Castiello and Begliomini2020; Ravichandran et al., Reference Ravichandran, Bhatt, Pandit, Osadchiy, Alaverdyan, Vora and Gupta2021; Shapiro et al., Reference Shapiro, Johnson, Sutton, Legget, Dabelea and Tregellas2019). Lastly, children were not administered the ED-focused module of the diagnostic interviews, and diagnoses were therefore based on parental reports. With evidence suggesting discordance between child and parental reports of eating behaviors (Steinberg et al., Reference Steinberg, Tanofsky-Kraff, Cohen, Elberg, Freedman, Semega-Janneh and Yanovski2004; Tanofsky-Kraff, Yanovski, & Yanovski, Reference Tanofsky-Kraff, Yanovski and Yanovski2005), this represents a limitation.

Notwithstanding, these findings offer novel insights into the neural architecture of early-onset BED, and suggest that rather than inherent FC abnormalities within reward and cognitive control networks, early-onset BED may be characterized by abnormalities in the functional synchrony between reward and cognitive control networks. Future research may extend these findings by assessing FC patterns during disorder-relevant tasks, and by carefully explicating the relationship between FC patterns and clinical variables in those with BED.

Financial support

S. B. M. is supported by the Della Martin Endowed Professorship, and the National Institute of Mental Health (K23MH115184). J. M. N. was funded by the National Heart, Lung, and Blood Institute (K08HL159350) and the American Heart Association (CDA34760281). R. P. C. is supported by the Chan Zuckerberg Initiative (2020-225670). S. J. S. is supported by Astellas. Additional Information: The ABCD Study was supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy.org/nihcollaborators. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report.

Financial disclosures

S. B. M. receives royalties from Oxford University Press, Routledge, and Springer. J. M. N. receives royalties from Springer. S. J. S. is a Consultant to Zynerba, and an Advisor to Skyland Trail.

Conflict of interest

The authors all declare that they have no competing interests.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

References

Abdo, N., Boyd, E., Baboumian, S., Pantazatos, S. P., & Geliebter, A. (2020). Relationship between binge eating and associated eating behaviors with subcortical brain volumes and cortical thickness. Journal of Affective Disorders, 274, 1201–1205.CrossRefGoogle ScholarPubMed
Allen, K. L., Byrne, S. M., Oddy, W. H., & Crosby, R. D. (2013). Early-onset binge eating and purging eating disorders: Course and outcome in a population-based study of adolescents. Journal of Abnormal Child Psychology, 41(7), 1083–1096.CrossRefGoogle Scholar
Allen, K. L., Mountford, V., Brown, A., Richards, K., Grant, N., Austin, A., … Schmidt, U. (2020). First episode rapid early intervention for eating disorders (FREED): From research to routine clinical practice. Early Intervention in Psychiatry, 14(5), 625–630.CrossRefGoogle ScholarPubMed
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.Google Scholar
Antons, S., Brand, M., & Potenza, M. N. (2020). Neurobiology of cue-reactivity, craving, and inhibitory control in non-substance addictive behaviors. Journal of the Neurological Sciences, 415, 116952.CrossRefGoogle ScholarPubMed
Appelhans, B. M., Woolf, K., Pagoto, S. L., Schneider, K. L., Whited, M. C., & Liebman, R. (2011). Inhibiting food reward: Delay discounting, food reward sensitivity, and palatable food intake in overweight and obese women. Obesity (Silver Spring), 19(11), 2175–2182.CrossRefGoogle ScholarPubMed
Baek, K., Morris, L. S., Kundu, P., & Voon, V. (2017). Disrupted resting-state brain network properties in obesity: Decreased global and putaminal cortico-striatal network efficiency. Psychological Medicine, 47(4), 585–596.CrossRefGoogle ScholarPubMed
Balodis, I. M., Kober, H., Worhunsky, P. D., Stevens, M. C., Pearlson, G. D., & Potenza, M. N. (2012). Diminished frontostriatal activity during processing of monetary rewards and losses in pathological gambling. Biological Psychiatry, 71(8), 749–757.CrossRefGoogle ScholarPubMed
Balodis, I. M., Kober, H., Worhunsky, P. D., White, M. A., Stevens, M. C., Pearlson, G. D., … Potenza, M. N. (2013a). Monetary reward processing in obese individuals with and without binge eating disorder. Biological Psychiatry, 73(9), 877–886.CrossRefGoogle ScholarPubMed
Balodis, I. M., Molina, N. D., Kober, H., Worhunsky, P. D., White, M. A., Sinha, R., … Potenza, M. N. (2013b). Divergent neural substrates of inhibitory control in binge eating disorder relative to other manifestations of obesity. Obesity, 21(2), 367–377.CrossRefGoogle ScholarPubMed
Barch, D. M., Albaugh, M. D., Avenevoli, S., Chang, L., Clark, D. B., Glantz, M. D., … Sher, K. J. (2018). Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description. Development Cognitive Neuroscience, 32, 55–66.CrossRefGoogle ScholarPubMed
Beck, A., Schlagenhauf, F., Wustenberg, T., Hein, J., Kienast, T., Kahnt, T., … Wrase, A. (2009). Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics. Biological Psychiatry, 66(8), 734–742.CrossRefGoogle ScholarPubMed
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component-based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage, 37(1), 90–101.CrossRefGoogle ScholarPubMed
Birch, L. L., & Fisher, J. O. (2000). Mothers' child-feeding practices influence daughters' eating and weight. American Journal of Clinical Nutrition, 71(5), 1054–1061.CrossRefGoogle ScholarPubMed
Blakemore, S. J., Burnett, S., & Dahl, R. E. (2010). The role of puberty in the developing adolescent brain. Human Brain Mapping, 31(6), 926–933.CrossRefGoogle ScholarPubMed
Bohon, C. (2019). Binge eating disorder in children and adolescents. Child and Adolescent Psychiatric Clinics of North America, 28(4), 549–555.CrossRefGoogle ScholarPubMed
Braet, C., Soetens, B., Moens, E., Mels, S., Goossens, L., & Vlierberghe, L. V. (2007). Are two informants better than one? Parent-child agreement on the eating styles of children who are overweight. European Eating Disorders Review, 15(6), 410–417.CrossRefGoogle ScholarPubMed
Brewerton, T. D., Rance, S. J., Dansky, B. S., O'Neil, P. M., & Kilpatrick, D. G. (2014). A comparison of women with child-adolescent versus adult-onset binge eating: Results from the national women's study. International Journal of Eating Disorders, 47(7), 836–843.CrossRefGoogle ScholarPubMed
Brown, A., McClelland, J., Boysen, E., Mountford, V., Glennon, D., & Schmidt, U. (2016). The FREED Project (first episode and rapid early intervention in eating disorders): Service model, feasibility and acceptability. Early Intervention in Psychiatry, 12(2), 250–257.CrossRefGoogle ScholarPubMed
Brownley, K. A., Berkman, N. D., Peat, C. M., Lohr, K. N., Cullen, K. E., Bann, C. M., & Bulik, C. M. (2016). Binge-eating disorder in adults: A systematic review and meta-analysis. Annals of Internal Medicine, 165(6), 409–420.CrossRefGoogle ScholarPubMed
Brownley, K. A., Berkman, N. D., Sedway, J. A., Lohr, K. N., & Bulik, C. M. (2007). Binge eating disorder treatment: A systematic review of randomized controlled trials. International Journal of Eating Disorders, 40(4), 337–348.CrossRefGoogle ScholarPubMed
Bub, K. L., Robinson, L. E., & Curtis, D. S. (2016). Longitudinal associations between self-regulation and health across childhood and adolescence. Health Psychology, 35(11), 1235–1245.CrossRefGoogle ScholarPubMed
Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., & Heitzeg, M. M., … ABCD Imaging Acquisition Workgroup. (2018). The adolescent brain cognitive development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43–54.CrossRefGoogle ScholarPubMed
Chen, S., Dong, D., Jackson, T., Su, Y., & Chen, H. (2016). Altered frontal inter-hemispheric resting-state functional connectivity is associated with bulimic symptoms among restrained eaters. Neuropsychologia, 81, 22–30.CrossRefGoogle ScholarPubMed
Constantinidis, C., & Luna, B. (2019). Neural substrates of inhibitory control maturation in adolescence. Trends in Neurosciences, 42(9), 604–616.CrossRefGoogle ScholarPubMed
Contreras-Rodriguez, O., Burrow, T., Pursey, K. M., Stanwell, P., Parkes, L., Soriano-Mas, C., & Verdejo-Garcia, A. (2019). Food addiction linked to changes in ventral striatum functional connectivity between fasting and satiety. Appetite, 133, 18–23.CrossRefGoogle ScholarPubMed
Davis, C., Levitan, R. D., Carter, J., Kaplan, A. S., Reid, C., Curtis, C., … Kennedy, J. L. (2008). Personality and eating behaviors: A case–control study of binge eating disorder. International Journal of Eating Disorders, 41(3), 243–250.CrossRefGoogle ScholarPubMed
Davis, C. A., Levitan, R. D., Reid, C., Carter, J. C., Kaplan, A. S., Patte, K. A., … Kennedy, J. L. (2009). Dopamine for “wanting” and opioids for “liking”: A comparison of obese adults with and without binge eating. Obesity, 17(6), 1220–1225.CrossRefGoogle ScholarPubMed
Deloitte Access Economics. (2020). The Social and Economic Cost of Eating Disorders in the United States of America: A Report for the Strategic Training Initiative for the Prevention of Eating Disorders and the Academy for Eating. Retrieved September 4, 2021, from https://www.hsph.harvard.edu/striped/report-economic-costs-of-eating-disorders/.Google Scholar
DeZwan, M. (2001). Binge eating and obesity. International Journal of Obesity and Related Metabolic Disorders, 25, 51–55.CrossRefGoogle Scholar
DeZwan, M., Nutzinger, D. O., & Shoenbeck, G. (1992). Binge eating in overweight women. Comprehensive Psychiatry, 33(4), 256–261.CrossRefGoogle Scholar
Ely, A. V., Wierenga, C. E., Bischoff-Grethe, A., Bailer, U. F., Berner, L. A., Fudge, J. L., … Kaye, W. H. (2017). Response in taste circuitry is not modulated by hunger and satiety in women remitted from bulimia nervosa. Journal of Abnormal Psychology, 126(5), 519–530.CrossRefGoogle Scholar
Faith, M. S., Berkowitz, R. I., Stallings, V. A., Kerns, J., Storey, M., & Stunkard, A. J. (2006). Eating in the absence of hunger: A genetic marker for childhood obesity in prepubertal boys? Obesity (Silver Spring, Md), 14(1), 131–138.CrossRefGoogle Scholar
Fichter, M. M., Quadflieg, N., Crosby, R. D., & Koch, S. (2017). Long-term outcome of anorexia nervosa: Results from a large clinical longitudinal study. International Journal of Eating Disorders, 50(9), 1018–1030.CrossRefGoogle ScholarPubMed
Flegal, K. M., Carroll, M. D., Kit, B. K., & Ogden, C. L. (2012). Prevalence of obesity and trends in the distribution of body mass index among US adults. JAMA, 307(5), 491–497.CrossRefGoogle ScholarPubMed
Fogel, A., Mccrickerd, K., Fries, L. R., Goh, A. T., Quah, P. L., Chan, M. J., … Forde, C. G. (2018). Eating in the absence of hunger: Stability over time and associations with eating behaviours and body composition in children. Physiology & Behavior, 192, 82–89.CrossRefGoogle ScholarPubMed
Galvan, A. (2010). Adolescent development of the reward system. Frontiers in Human Neuroscience, 4, 6.Google ScholarPubMed
Gazzillo, F., Lingiardi, V., Peloso, A., Giordani, S., Vesco, S., Zanna, V., … Vicari, S. (2013). Personality subtypes in adolescents with anorexia nervosa. Comprehensive Psychiatry, 54(6), 702–712.CrossRefGoogle ScholarPubMed
Geliebter, A., Gluck, M. E., & Hashim, S. A. (2005). Plasma ghrelin concentrations are lower in binge-eating disorder. Journal of Nutrition, 135(5), 1326–1330.CrossRefGoogle ScholarPubMed
Geliebter, S., Hashim, S. A., & Gluck, M. E. (2008). Appetite-related gut peptides, ghrelin, PYY, and GLP-1 in obese women with and without binge eating disorder (BED). Physiology & Behavior, 94(5), 696–699.CrossRefGoogle ScholarPubMed
Giel, K., Teufel, M., Junne, F., Zipfel, S., & Schag, K. (2017). Food-related impulsivity in obesity and binge eating disorder– a systematic update of the evidence. Nutrients, 9(11), 1170.CrossRefGoogle ScholarPubMed
Goddings, A. L., Beltz, A., Peper, J. S., Crone, E. A., & Braams, B. R. (2019). Understanding the role of puberty in structural and functional development of the adolescent brain. Journal of Research on Adolescence, 29(1), 32–53.CrossRefGoogle ScholarPubMed
Goldschmidt, A. B., Wall, M. M., Zhang, J., Loth, K. A., & Neumark-Sztainer, D. (2016). Overeating and binge eating in emerging adulthood: 10-year stability and risk factors. Developmental Psychology, 52(3), 475–483.CrossRefGoogle ScholarPubMed
Grilo, C. M. (2017). Psychological and behavioral treatments for binge-eating disorder. The Journal of Clinical Psychiatry, 78(1), 20–24.CrossRefGoogle ScholarPubMed
Grilo, C. M., & Masheb, R. (2000). Onset of dieting vs binge eating in outpatients with binge eating disorder. International Journal of Obesity and Related Metabolic Disorders, 24(4), 404–409.CrossRefGoogle ScholarPubMed
Hagan, K. E., & Bohon, C. (2021). Subcortical brain volume and cortical thickness in adolescent girls and women with binge eating. The International Journal of Eating Disorders, 54(8), 1527–1536.CrossRefGoogle ScholarPubMed
Hales, C. M., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2017). Prevalence of obesity among adults and youth: United States. NCHS Data Brief, 288, 1–8. Retrieved from https://www.cdc.gov/nchs/data.Google Scholar
Haynos, A. F., Camchong, J., Pearson, C. M., Lavender, J. M., Mueller, B. A., Peterson, C. B., … Lim, K. O. (2021). Resting-state hypoconnectivity of reward networks in binge eating disorders. Cerebral Cortex, 31(5), 2494–2504.CrossRefGoogle Scholar
Herting, M. M., Uban, K. A., Gonzalez, M. R., Baker, F. C., Kan, E. C., Thompson, W. K., … Sowell, E. R. (2021). Correspondence between perceived pubertal development and hormone levels in 9–10 year-olds from the adolescent brain cognitive development study. Frontiers in Endocrinology (Lausanne), 11, 549928.CrossRefGoogle ScholarPubMed
Hilbert, A., Hartmann, A. S., Czaja, J., & Schoebi, D. (2013). Natural course of preadolescent loss of control eating. Journal of Abnormal Psychology, 122, 684–693.CrossRefGoogle ScholarPubMed
Hoek, H. W. (2006). Incidence, prevalence and mortality of eating disorders. Current Opinion in Psychiatry, 19(4), 398–394.CrossRefGoogle ScholarPubMed
Holliday, J., Tchanturia, K., Landau, S., Collier, D., & Treasure, J. (2005). Is impaired set-shifting an endophenotype of anorexia nervosa? American Journal of Psychiatry, 162(12), 2269–2275.CrossRefGoogle ScholarPubMed
Hudson, J. I., Lalonde, J. K., Coit, C. E., Tsuang, M. T., EcElroy, S. L., Crow, S. J., … Pope, H. G. Jr. (2010). Longitudinal study of the diagnosis of components of the metabolic syndrome in individuals with binge-eating disorder. The American Journal of Clinical Nutrition, 91(6), 1568–1573.CrossRefGoogle ScholarPubMed
Ivanov, I., Schulz, K. P., London, E. D., & Newcorn, J. H. (2008). Inhibitory control deficits in childhood and risk for substance use disorders: A review. The American Journal of Drug and Alcohol Abuse, 34(3), 239–258.CrossRefGoogle ScholarPubMed
Kaufman, J., Birmaher, B., & Brent, D. (1997a). Kiddie schedule for affective disorders and schizophrenia. Journal of the American Academy of Child and Adolescent Psychiatry, 36(4), 545–553.Google Scholar
Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., … Ryan, N. (1997b). Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child and Adolescent Psychiatry, 36(7), 980–988.CrossRefGoogle ScholarPubMed
Kaye, W. H., Wierenga, C. E., Bischoff-Grethe, A., Berner, L. A., Ely, A. V., Bailer, U. F., … Fudge, J. L. (2020). Neural insensitivity to the effects of hunger in women remitted from anorexia nervosa. American Journal of Psychiatry, 177(7), 601–610.CrossRefGoogle Scholar
Kelly, A. S., & Daniels, S. R. (2017). Rethinking the use of body mass index z-score in children and adolescents with severe obesity: Time to kick it to the curb? Journal of Pediatrics, 188, 7–8.CrossRefGoogle Scholar
Kollei, I., Rustemeier, M., Schroeder, S., Jongen, S., Herpertz, S., & Loeber, S. (2018). Cognitive control functions in individuals with obesity with and without binge-eating disorder. International Journal of Eating Disorders, 51(3), 233–240.CrossRefGoogle ScholarPubMed
Lee, J. E., Namkoong, K., & Jung, Y. C. (2017). Impaired prefrontal cognitive control over interference by food images in binge-eating disorder and bulimia nervosa. Neuroscience Letters, 651, 95–101.CrossRefGoogle ScholarPubMed
Liang, J., Matheson, B. E., Kaye, W. H., & Boutelle, K. N. (2014). Neurocognitive correlates of obesity and obesity-related behaviors in children and adolescents. International Journal of Obesity, 38(4), 494–506.CrossRefGoogle ScholarPubMed
Linardon, J. (2018). Rates of abstinence following psychological or behavioral treatments for binge-eating disorder: Meta-analysis. International Journal of Eating Disorders, 51(8), 785–797.CrossRefGoogle ScholarPubMed
Lowe, M. R., Arigo, D., & Butryn, M. L. (2016). Hedonic hunger prospectively predicts onset and maintenance of loss of control eating among college women. Health Psychology, 35(3), 238–244.CrossRefGoogle ScholarPubMed
Malkoff, S. B., Marcus, M. D., Grant, A., Moulton, M. M., & Vayonis, C. (1993). The relationship between dieting and binge eating among obese individuals. Annals of Behavioral Medicine, 15, 40.Google Scholar
Manasse, S. M., Forman, E. M., Ruocco, A. C., Butryn, M. L., Juarascio, A. S., & Fitzpatrick, K. K. (2015). Do executive functioning deficits underpin binge eating disorder? A comparison of overweight women with and without binge eating pathology. International Journal of Eating Disorders, 48(6), 677–683.CrossRefGoogle ScholarPubMed
Martins, D., Leslie, M., Rodan, S., Zelaya, F., Treasure, J., & Paloyelis, Y. (2020). Investigating resting brain perfusion abnormalities and disease target-engagement by intranasal oxytocin in women with bulimia nervosa and binge-eating disorder and healthy controls. Nature: Translational Psychiatry, 10(1), 180.Google ScholarPubMed
Miller, A. L., Gearhardt, A. N., Retzloff, L., Sturza, J., Kaciroti, N., & Lumeng, J. C. (2018). Early childhood stress and child age predict longitudinal increases in obesogenic eating among low-income children. Academic Pediatrics, 18(6), 685–691.CrossRefGoogle ScholarPubMed
Mitchell, J. E. (2015). Medical comorbidity and medical complications associated with binge-eating disorder. International Journal of Eating Disorders, 49(3), 319–323.CrossRefGoogle ScholarPubMed
Moreno-Lopez, L., Contreras-Rodriguez, O., Soriano-Mas, C., Stamatakis, E. A., & Verdejo-Garcia, A. (2016). Disrupted functional connectivity in adolescent obesity. NeuroImage: Clinical, 12, 262–268.CrossRefGoogle ScholarPubMed
Murray, S. B., Ganson, K. T., Chu, J., Jann, K., & Nagata, J. M. (In Press). The prevalence of preadolescent eating disorders in the United States. Journal of Adolescent Health.Google Scholar
Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., … Gakidou, E. (2013). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: A systematic analysis for the global burden of disease study. The Lancet, 384(9945), 766–781.CrossRefGoogle Scholar
Noble, W. S. (2009). How does multiple testing correction work? Nature Biotechnology, 27(12), 1135–1137.CrossRefGoogle ScholarPubMed
Olguin, P., Fuentes, M., Gabler, G., Guerdjikova, A. I., Keck, P. E. Jr., & McElroy, S. L. (2017). Medical comorbidity of binge eating disorder. Eating and Weight Disorders, 22(1), 13–26.CrossRefGoogle ScholarPubMed
Oliva, R., Morys, F., Horstmann, A., Castiello, U., & Begliomini, C. (2020). Characterizing impulsivity and resting-state functional connectivity in normal-weight binge eaters. International Journal of Eating Disorders, 53(3), 478–488.CrossRefGoogle ScholarPubMed
Olsen, I. R., Plotzker, A., & Ezzyat, Y. (2007). The enigmatic temporal pole: A review of findings on social and emotional processing. Brain, 130, 1718–1731.CrossRefGoogle Scholar
Park, B. Y., Byeon, K., Lee, M. J., Chung, C. S., Kim, S. H., Morys, F., … Park, H. (2020). Whole-brain functional connectivity correlates of obesity phenotypes. Human Brain Mapping, 41(17), 4912–4924.CrossRefGoogle ScholarPubMed
Pascual, B., Masdeu, J. C., Hollenbeck, M., Makris, N., Insausti, R., Ding, S. L., & Dickerson, B. C. (2015). Large-scale brain networks of the human left temporal pole: A functional connectivity MRI study. Cerebral Cortex, 25, 680–702.CrossRefGoogle Scholar
Peñas-Lledó, E., Jiménez-Murcia, S., Granero, R., Penelo, E., Agüera, Z., Alvarez-Moya, E., & Fernández-Aranda, F. (2010). Specific eating disorder clusters based on social anxiety and novelty seeking. Journal of Anxiety Disorders, 24(7), 767–773.CrossRefGoogle ScholarPubMed
Petersen, A. C., Crockett, L., Richards, M., & Boxer, A. (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth & Adolescence, 17(2), 117–133.CrossRefGoogle ScholarPubMed
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142–2154.CrossRefGoogle ScholarPubMed
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting-state fMRI. NeuroImage, 84, 320–341.CrossRefGoogle ScholarPubMed
Ravichandran, S., Bhatt, R. R., Pandit, B., Osadchiy, V., Alaverdyan, A., Vora, P., … Gupta, A. (2021). Alterations in reward network functional connectivity are associated with increased food addiction in obese individuals. Nature Scientific Reports, 11(1), 3386.CrossRefGoogle ScholarPubMed
Robbins, T. W. (2005). Chemistry of the mind: Neurochemical modulation of prefrontal cortical function. The Journal of Comparative Neurology, 493(1), 140–146.CrossRefGoogle ScholarPubMed
Schag, K., Schönleber, J., Teufel, M., Zipfel, S., & Giel, K. E. (2013a). Food-related impulsivity in obesity and binge eating disorder – a systematic review. Obesity Reviews, 14(6), 477–495.CrossRefGoogle ScholarPubMed
Schag, K., Teufel, M., Junne, F., Preissl, H., Hautzinger, M., Zipfel, S., & Giel, K. E. (2013b). Impulsivity in binge eating disorder: Food cues elicit increased reward responses and disinhibition. PLoS One, 8(10), e76542.CrossRefGoogle ScholarPubMed
Shapiro, A., Johnson, S. L., Sutton, B., Legget, K. T., Dabelea, D., & Tregellas, J. R. (2019). Eating in the absence of hunger in young children is related to brain reward network hyperactivity and reduced functional connectivity in executive control networks. Pediatric Obesity, 14(6), e12502.CrossRefGoogle ScholarPubMed
Skoranski, A. M., Most, S. B., Lutz-Stehl, M., Hoffman, J. E., Hassink, S. G., & Simons, R. F. (2013). Response monitoring and cognitive control in childhood obesity. Biological Psychiatry, 92(2), 199–204.CrossRefGoogle ScholarPubMed
Steinberg, E., Tanofsky-Kraff, M., Cohen, M. L., Elberg, J., Freedman, R. J., Semega-Janneh, M., … Yanovski, J. A. (2004). Comparison of the child and parent forms of the questionnaire on eating and weight patterns in the assessment of children's eating disordered behaviors. International Journal of Eating Disorders, 36(2), 183–194.CrossRefGoogle ScholarPubMed
Steinhausen, H. C. (2002). The outcome of anorexia nervosa in the 20th century. American Journal of Psychiatry, 159(89), 1284–1293.CrossRefGoogle ScholarPubMed
Steward, T., Menchon, J. M., Jiménez-Murcia, S., Soriano-Mas, C., & Fernandez-Aranda, F. (2018). Neural network alterations across eating disorders: A narrative review of fMRI studies. Current Neuropharmacology, 16(8), 1150–1163.CrossRefGoogle ScholarPubMed
Svaldi, J., Naumann, E., Trentowska, M., & Schmitz, F. (2014). General and food-specific inhibitory deficits in binge eating disorder. International Journal of Eating Disorders, 47(5), 534–542.CrossRefGoogle ScholarPubMed
Syan, S. K., McIntyre-Wood, C., Minuzzi, L., Hall, G., McCabe, R. E., & MacKillop, J. (2021). Dysregulated resting-state functional connectivity and obesity: A systematic review. Neuroscience & Biobehavioral Reviews, 131, 270–292.CrossRefGoogle ScholarPubMed
Tanofsky-Kraff, M., Marcus, M. D., Yanovski, S. Z., & Yanovski, J. A. (2008). Loss of control eating disorder in children age 12 years and younger: Proposed research criteria. Eating Behaviors, 9, 360–365.CrossRefGoogle ScholarPubMed
Tanofsky-Kraff, M., Schvey, N. A., & Grilo, C. M. (2020). A developmental framework of binge eating disorder based on pediatric loss of control eating. American Psychologist, 75, 189–203.CrossRefGoogle ScholarPubMed
Tanofsky-Kraff, M., Shomaker, L. B., Olsen, C., Roza, C. A., Wolkoff, L. E., Columbo, K. M., … Yanovski, J. A. (2011). A prospective study of pediatric loss of control eating and psychological outcomes. Journal of Abnormal Psychology, 120, 108–118.CrossRefGoogle ScholarPubMed
Tanofsky-Kraff, M., Shomaker, L. B., Stern, E. A., Miller, R., Sebring, N., Dellavalle, D., … Yanovski, J. A. (2012). Children's binge eating and development of metabolic syndrome. International Journal of Obesity: Journal of the International Association for the Study of Obesity, 36(7), 956–962.CrossRefGoogle ScholarPubMed
Tanofsky-Kraff, M., Yanovski, S. Z., & Yanovski, J. A. (2005). Comparison of child interview and parent reports of children's eating disordered behaviors. Eating Behaviors, 6, 95–99.CrossRefGoogle ScholarPubMed
Thompson-Brenner, H., Eddy, K. T., Franko, D. L., Dorer, D. J., Vashchenko, M., Kass, A. E., & Herzog, D. B. (2008). A personality classification system for eating disorders: A longitudinal study. Comprehensive Psychiatry, 49(6), 551–560.CrossRefGoogle ScholarPubMed
Udo, T., Bitley, S., & Grilo, C. M. (2019). Suicide attempts in US adults with lifetime DSM-5 eating disorders. BMC Medicine, 17(1), 120.CrossRefGoogle ScholarPubMed
Udo, T., & Grilo, C. M. (2019). Psychiatric and medical correlates of DSM-5 eating disorders in a nationally representative sample of adults in the United States. International Journal of Eating Disorders, 52(1), 42–50.CrossRefGoogle Scholar
Wang, G. J., Geliebter, A., Volkow, N. D., Telang, F. W., Logan, J., Jayne, M. C., … Fowler, J. S. (2011). Enhanced striatal dopamine release during food stimulation in binge eating disorder. Obesity, 19(8), 1601–1608.CrossRefGoogle ScholarPubMed
Wassenaar, E., Friedman, J., & Mehler, P. S. (2019). Medical complications of binge eating disorder. Psychiatric Clinics, 42(2), 275–286.Google ScholarPubMed
Watson, H. J., & Bulik, C. M. (2013). Update on the treatment of anorexia nervosa: Review of clinical trials, practice guidelines and emerging interventions. Psychological Medicine, 43(12), 2477–5000.CrossRefGoogle ScholarPubMed
Weygandt, M., Schaefer, A., Schienle, A., & Haynes, J. D. (2012). Diagnosing different binge-eating disorders based on reward-related brain activation patterns. Human Brain Mapping, 33(9), 2135–2146.CrossRefGoogle ScholarPubMed
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity, 2(3), 125–141.CrossRefGoogle Scholar
Wierenga, C. E., Ely, A., Bischoff-Grethe, A., Bailer, U. F., Simmons, A. N., & Kaye, W. H. (2014). Are extremes of consumption in eating disorders related to an altered balance between reward and inhibition? Frontiers in Behavioral Neuroscience, 8, 410.CrossRefGoogle Scholar
Wilson, G. T., Nonas, C. A., & Rosenblum, G. D. (1993). Assessment of binge eating in obese patients. International Journal of Eating Disorders, 13(1), 25–33.3.0.CO;2-T>CrossRefGoogle ScholarPubMed
Witt, A. A., & Lowe, M. R. (2014). Hedonic hunger and binge eating among women with eating disorders. International Journal of Eating Disorders, 47(3), 273–280.CrossRefGoogle ScholarPubMed
Yanovski, S. Z., Leet, M., Yanovski, J. A., Flood, M., Gold, P. W., Kissileff, H. R., & Walsh, B. T. (1992). Food selection and intake of obese women with binge-eating disorder. The American Journal of Clinical Nutrition, 56(6), 975–980.CrossRefGoogle ScholarPubMed
Zipfel, S., Giel, K. E., Bulik, C. M., Hay, P., & Schmidt, U. (2015). Anorexia nervosa: Aetiology, assessment, and treatment. The Lancet Psychiatry, 2(12), 1099–1111.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1. Seed-to-seed rs-fMRI analyses comparing pre-adolescent children with binge eating disorder and control pre-adolescent children, while adjusting for BMI and pubertal development. Seed regions included the bilateral amygdala, nucleus accumbens, orbitofrontal cortex (FOrb), dorsolateral prefrontal cortex (MidFG), and the anterior cingulate (AC). Darker red coloring indicates a more positive z-score, and darker blue indicates a more negative z-score (of which there were no significant effects).

Figure 1

Table 1. An overview of sample characteristics from the Adolescent Brain Cognitive Development Study, delineated by group

Figure 2

Fig. 2. Seed-to-voxel analyses comparing pre-adolescent children with binge eating disorder and control pre-adolescent children, while adjusting for BMI and pubertal development. Seed regions included (a) the amygdala, (b) the orbitofrontal cortex (FOrb), (c) the anterior cingulate (AC), and (d) the dorsolateral prefrontal cortex (MidFG). Hot colors in red, yellow, and white indicate a more positive z-score, and cooler colors in blue, purple, and pink indicate a more negative z-score. Gray brain areas showed no significant difference between groups.