A Pilot Study of Neural Correlates of Loss of Control Eating in Children With Overweight/Obesity: Probing Intermittent Access to Food as a Means of Eliciting Disinhibited Eating

A Pilot Study of Neural Correlates of Loss of Control Eating in Children With Overweight/Obesity:... Abstract Objective Neural substrates of loss of control (LOC) eating are undercharacterized. We aimed to model intermittent access to food to elicit disinhibited eating in youth undergoing neuroimaging, given evidence that restricted food access may increase subsequent food intake via enhancing reward value of food and diminishing eating-related self-control. Methods Participants were 18 preadolescents (aged 9–12 years) who were overweight/obese with recent LOC eating (OW-LOC; n = 6); overweight/obese with no history of LOC eating (OW-CON; n = 5); or non-overweight with no history of LOC eating (NW-CON; n = 7). Participants underwent functional magnetic resonance imaging during a simulated food restriction paradigm in which they were alternately given restricted or unrestricted access to milkshake solutions. Results There were no significant main effects of restricted versus unrestricted access to milkshake flavors. Group main effects revealed increased activation for OW-LOC relative to OW-CON in areas related to attentional processes (right middle frontal gyrus), inhibitory control/attentional shifts (right and left cuneus), and emotion regulation (left cingulate gyrus); and for OW-LOC relative to NW-CON in areas related to response inhibition (right inferior frontal gyrus). Significant block type × group interaction effects were found for the right middle frontal gyrus, left cingulate gyrus, and left cuneus, but these appeared to be accounted for primarily by group. Discussion There were clear group differences in neural activity in brain regions related to self-regulation during a food restriction paradigm. Elevations in these regions among OW-LOC relative to OW-CON and NW-CON, respectively, may suggest that youth with LOC eating expended more cognitive effort to regulate ingestive behavior. dietary restriction, disinhibited eating, executive functioning, loss of control eating, neural activation Loss of control (LOC) eating is a form of disinhibited eating (i.e., eating characterized by a lack of healthy restraint, or eating in response to nonphysiological cues) involving a sense that one cannot control what or how much one is eating (Goldschmidt, 2017). LOC eating predominantly affects youth who are overweight/obese (i.e., up to 30% of children and adolescents with overweight/obesity, vs. <5% of youth who are nonoverweight; He, Cai, & Fan, 2017; Tanofsky-Kraff et al., 2004), is associated with decreased quality of life and psychosocial functioning, and predicts excess weight gain and eating disorder onset (Goldschmidt, 2017). Neural substrates of LOC eating in youth are unclear, thereby impeding a comprehensive understanding of its development and course. One significant challenge to studying neural correlates of LOC while eating relates to the difficulties associated with assessing ingestive behavior (e.g., head movement while chewing/swallowing) while participants lie prone in a magnetic resonance imaging (MRI) scanner. Previous neuroimaging studies have tried to overcome these limitations by administering milkshake solutions to model reward response to palatable foods in individuals with eating- and weight-related problems (Bohon & Stice, 2011, n = 26; Stice, Spoor, Bohon, Veldhuizen, & Small, 2008, n = 33), but these procedures have not yet been used to model disinhibited eating and associated cognitive processes. As restricting access to food leads to increased intake of restricted foods in children (Fisher & Birch, 1999) and animals (Avena, 2010), purportedly via enhancing their reward value (Parkes, Furlong, Black, & Balleine, 2017) and/or attenuating self-control related to eating (Furlong, Jayaweera, Balleine, & Corbit, 2014), this may be a viable method for assessing disinhibited eating and its neural substrates in children. Research on the neurocircuitry of LOC eating in pediatric samples (including those who do and do not engage in purging behaviors) suggests that reward-related regions may be hypersensitive to both food and nonfood cues (Berner & Marsh, 2014), perhaps reflecting a tendency to seek out large amounts of palatable food during LOC episodes. Data on cognitive control regions have been mixed, with some studies showing increased engagement in prefrontal circuits during tasks requiring self-regulation (Lock, Garrett, Beenhakker, & Reiss, 2011, n = 40), and others showing decreased engagement (Jarcho et al., 2015, n = 22; Marsh et al., 2011, n = 36; Marsh et al., 2009, n = 40). The former findings may reflect a form of neural compensation for deficient self-regulatory control in youth with LOC eating (Berner & Marsh, 2014), which is consistent with the adult literature (Karhunen et al., 2000, n = 23; Tammela et al., 2010, n = 25). Individuals without eating disorders demonstrate increased activity in prefrontal regions during tasks requiring behavioral inhibition/delay of gratification (Hare, Camerer, & Rangel, 2009, n = 37; Weygandt et al., 2013, n = 16), and interestingly, this prefrontal activity appears to attenuate during episodes involving failed self-control (e.g., making a more immediately rewarding but less healthy food choice; Hare et al., 2009; Weygandt et al., 2013). Furthermore, stronger connectivity between brain regions related to self-regulation and reward sensitivity has been observed in successful dieters with obesity (Weygandt et al., 2013), suggesting that neural responses to the expectation or receipt of reward may interact with self-regulatory functions to promote control over eating. Taken together, appropriate regulation of energy intake may be associated with enhanced cognitive control, which helps suppress appetitive or hedonic responses to food stimuli, whereas disinhibition may be related to a breakdown in this function. The aim of this study is to describe a newly developed behavioral paradigm designed to model restricted access to food and disinhibited eating in children with and without overweight/obesity and LOC eating within a neuroimaging framework, and report preliminary findings (including task- and group-related effects, and associations between neural activation patterns and severity of disinhibited eating constructs such as LOC eating frequency and tendencies to eat in response to external cues). We hypothesize that, relative to their peers with and without overweight/obesity and no reported LOC eating, children with both LOC eating and overweight/obesity will demonstrate enhanced prefrontal activation during exposure to a food reward after a period of restricted access, reflecting increased exertion required to regulate ingestive behavior, particularly following a period of restricted access to food which may diminish eating-related self-control (Furlong et al., 2014). The ultimate goal is to stimulate further research on neural substrates related to the momentary occurrence of maladaptive eating behavior by elucidating regions purported to be involved self-regulation and disinhibition over food intake. Methods Participants and Procedures Participants were 18 right-handed children recruited from the community [44.4% female; M body mass index (BMI; kg/m2) z-score = 1.04 ± 1.44], aged 9–12 years (M age = 10.5 ± 1.1 year), of whom 6 were overweight/obese (BMI ≥ 85th percentile for age and sex) and reported LOC eating in the past 3 months (OW-LOC), 5 were controls who were overweight/obese and did not report any history of LOC eating (OW-CON), and 7 were controls who were nonoverweight and did not report any history of LOC eating (NW-CON). Participants were recruited from a larger study of executive functioning and eating behavior in youth (Goldschmidt et al., 2017). Most participants were African-American (n = 13; 72.2%) with the remainder self-identifying as Hispanic/Latino (n = 3; 16.7%) or Caucasian (n = 2; 11.1%), consistent with the demographics of the study location and the composition of the larger study sample. Participants were excluded if they had medical conditions or were taking medications known to influence weight or appetite; met criteria for an eating disorder other than binge eating disorder; reported moderately to greatly disliking vanilla and/or chocolate milkshakes; or had a diagnosis of attention deficit–hyperactivity disorder. Each participant and his/her caregiver provided written informed assent/consent, respectively. Study procedures were approved by the University of Chicago Institutional Review Board. An intermittent access paradigm was designed to model restricted access to food/disinhibition over ingestive behavior. The paradigm was adapted from procedures developed by Fisher and Birch (1999), which involved manipulating children’s access to two different snack food items to which they were exposed in vivo. In the MRI-compatible adaptation, participants were provided with a response box, wherein each button elicited different milkshake flavors (vanilla or chocolate). Participants were randomly assigned to conditions involving restricted access to one flavor, such that half of participants had restricted access to vanilla-flavored milkshake and unrestricted access to chocolate-flavored milkshake during alternating blocks, and half had restricted access to chocolate-flavored milkshake and unrestricted access to vanilla-flavored milkshake. Participants underwent 30 consecutive jittered (15–25 s) blocks of alternating unrestricted or restricted access to the milkshakes, each separated by 3–4 s of rest (fixation cross; see Figure 1). During the unrestricted blocks, children could choose either milkshake flavor and thus had ad libitum access to both vanilla- and chocolate-flavored milkshake flavors for 15–25 s. During the restricted blocks, children were permitted to activate receipt of the unrestricted milkshake flavor only; thus, during the restricted trials, participants had 15–25 s of ad libitum access to the unrestricted milkshake flavor, during which time button presses corresponding to the restricted milkshake were preprogrammed not to activate milkshake receipt. Images of palatable milkshakes were projected in the scanner to indicate the beginning and end of each block. Participants were instructed (both directly and through the participating caregiver) to avoid ice cream products for 1 week before scanning to enhance the effects of the restriction paradigm, and to avoid eating for 3 hr before scanning. On the day of scanning, participants and caregivers were queried about their compliance with these instructions. A total of four participants (22.2%) reported noncompliance with prescanning instructions, three of whom (16.7%) reported noncompliance with the first set of instructions, and two of whom reported noncompliance with the latter (11.1%); of these, one participant (5.6%) reported noncompliance with both sets of instructions. Figure 1. View largeDownload slide Visual depiction of trial structure. Figure 1. View largeDownload slide Visual depiction of trial structure. Measures Anthropometric and Sociodemographic Variables Height and weight were measured in light indoor clothing by a trained research assistant via stadiometer and calibrated digital scale, respectively, at a baseline eligibility evaluation. Child BMI z-scores were calculated using Centers for Disease Control and Prevention (CDC) growth charts and accompanying procedures (Kuczmarski et al., 2000). Overweight was defined as BMI between the 85th and 95th percentile for age and sex, while obesity was defined as BMI at or >95th percentile. Demographic data were reported by children and parents, and included children’s age, gender, race/ethnicity (White, Black/African-American, Hispanic/Latino, Asian, Native Hawaiian or Other Pacific Islander, American Indian or Alaska Native, or multiracial/other), current medications, and medical problems. Eating Behavior Diagnostic items from the Child Eating Disorder Examination 12.0 (Child EDE; Bryant-Waugh, Cooper, Taylor, & Lask, 1996) were administered by trained bachelor’s level research assistants at the baseline evaluation to assess current and lifetime LOC eating. The Child EDE is a semistructured, interviewer-based instrument based on the well-validated adult EDE, with modifications including the use of simpler language appropriate for a younger audience. The Child EDE has adequate reliability and validity in children as young as 8 years old (Bryant-Waugh et al., 1996). LOC eating was coded as present if participants reported experiencing at least one episode of LOC eating in the past 3 months, given previous data demonstrating that LOC eating, regardless of frequency, is associated with adverse health-related correlates (Tanofsky-Kraff et al., 2004). Assessors underwent several hours of Child EDE training with advanced assessors, and were required to complete at least two interviews to perfect reliability with a Child EDE expert before conducting interviews independently. Monthly meetings were held to resolve coding ambiguities and to prevent rater drift. The Dutch Eating Behavior Questionnaire for Children (DEBQ-C; van Strien & Oosterveld, 2008) was completed at baseline to assess trait-level dietary restraint (α = .77), emotional disinhibition (eating in response to emotional cues; α = .56), and external disinhibition (eating in response to the presence of food; α = .75). It has adequate reliability and validity in youth as young as 7 years old (van Strien & Oosterveld, 2008). Participants were asked to self-report their baseline food preferences separately for vanilla and chocolate milkshake via a five-point Likert scale (1 = Do not like to 5 = Like very much). A similar scale was also used to assess hunger levels immediately before scanning (1 = Not hungry at all to 5 = Extremely hungry). Functional MRI (fMRI) Data Acquisition and Analysis Imaging data were collected on a 3T Philips Achieva Quasar scanner. During the task, 361 functional image volumes were acquired with an echo-planar sequence (30 oblique slices with 4 mm thickness, 0.5 mm gap; repetition time/echo time = 2,200/25 ms, flip = 77; field of view = 192 mm, matrix = 64 × 64). Analyses were conducted using SPM. DARTEL procedures (Ashburner, 2007) were used to normalize data to the child brain. Data were realigned for motion correction (threshold = 3 mm), coregistered to the mean, spatially normalized, and smoothed. First-level analyses involved creating whole-brain contrasts (given the pilot nature of the study) for each block type. These contrast images were fed into a second-level analysis using factorial analysis of variance to compare block types, groups, and block type × group interactions with respect to activation in specific brain regions. Owing to the pilot nature of the study, which was designed to generate effect sizes for future research, alpha was set at .001 uncorrected for all main and interaction effects, and the minimum cluster size was set at 10. For illustrative purposes, percentages of signal change in selected brain areas were extracted using MarsBaR procedures. Signal changes were then partially correlated (adjusting for group) with LOC frequency in the past 3 months as reported on the Child EDE (natural log-transformed to adjust for positive skew), and DEBQ-C subscale scores using SPSS 19.0. Results At baseline, participants reported high average levels of liking for both chocolate (M = 4.8 ± 0.6) and vanilla milkshake (M = 4.1 ± 1.1). Before scanning, participants reported moderate levels of hunger (M = 3.3 ± 1.4). Groups did not differ on age, gender, race/ethnicity, or DEBQ-C subscales (all ps > .15). The primary contrasts of interest were for restricted versus unrestricted blocks. For the restricted > unrestricted contrast, there were no suprathreshold clusters for main effects of block type that met the specified alpha level. Significant main effects for group were found for areas involved in attentional processes, inhibitory control, and emotion regulation. Specifically, OW-LOC demonstrated increased activation in the right middle frontal gyrus (Cohen’s d = 0.70), right (Cohen’s d = 0.03) and left cuneus (Cohen’s d = 0.28), and left cingulate gyrus (Cohen’s d = 0.13) relative to OW-CON, who showed decreased activation in these areas; and in the right inferior frontal gyrus (Cohen’s d = 0.17) relative to NW-CON, who showed decreased activation in these areas. When analyses were rerun excluding participants who did not adhere to prescanning instructions, differences between OW-LOC and OW-CON remained significant for the right middle frontal gyrus and left cuneus. Significant block type × group interaction effects were found for areas including the right middle frontal gyrus, left cingulate gyrus, and left cuneus, but these interactions were primarily accounted for by the main effects of group (see Table I and Figure 2). Table I. Effects of Group, Block Type, and Their Interaction on Brain Activation During a Disinhibited Eating Paradigm Effect Region kE T Z-score Duncorr Coordinates Main effect for block type Restricted versus unrestricted — — — — — — Main effect for group OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 61 5.94 4.84 .022 27, 12, 36 L, cuneus, BA 17 90 5.93 4.84 .007 −9, −84, 12 L, cingulate gyrus, BA 31 46 4.91 4.21 .043 −24, −33, 36 R, claustrum 48 4.70 4.07 .039 27, 24, 9 L, declive 46 4.60 4.00 .043 −9, −78, −18 R, superior temporal gyrus, BA 41 44 4.57 3.98 .047 48, −36, 9 R, cuneus, BA 17 47 4.54 3.96 .041 15, −78, 12 R, caudate 42 4.52 3.94 .052 21, −15, 21 OW-LOC versus NW-CON R, precentral gyrus, BA 6 29 6.08 4.92 .099 36, −3, 39 R, lingual gyrus, BA 19 78 5.97 4.86 .011 30, −69, 6 L, cingulate gyrus, BA 31 46 4.63 4.02 .043 −12, −33, 33 L, supramarginal gyrus, BA 40 33 4.42 3.88 .080 −36, −42, 30 R, postcentral gyrus, BA 3 30 4.37 3.84 .094 24, −33, 48 R, inferior frontal gyrus, BA 9 26 4.09 3.64 .116 42, 0, 21 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 26 4.33 3.82 .116 57, −60, −9 Block × group interaction OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 38 5.13 4.35 .062 27, 12, 36 L, cuneus, BA 17 93 5.44 4.54 .006 −9, −84, 12 L, cingulate gyrus, BA 31 35 4.63 4.02 .072 −24, −33, 36 L, declive 87 4.85 4.17 .008 −9, −78, −18 R, superior temporal gyrus, BA 41 45 4.62 4.01 .045 48, -36, 9 OW-LOC versus NW-CON R, lingual gyrus, BA 19 43 5.81 4.77 .049 30, −69, 6 R, postcentral gyrus, BA 3 34 4.46 3.90 .076 24, −33, 48 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 34 4.57 3.98 .076 54, −60, −9 L, superior frontal gyrus, BA 10 26 4.54 3.96 .116 −21, 69, −3 Effect Region kE T Z-score Duncorr Coordinates Main effect for block type Restricted versus unrestricted — — — — — — Main effect for group OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 61 5.94 4.84 .022 27, 12, 36 L, cuneus, BA 17 90 5.93 4.84 .007 −9, −84, 12 L, cingulate gyrus, BA 31 46 4.91 4.21 .043 −24, −33, 36 R, claustrum 48 4.70 4.07 .039 27, 24, 9 L, declive 46 4.60 4.00 .043 −9, −78, −18 R, superior temporal gyrus, BA 41 44 4.57 3.98 .047 48, −36, 9 R, cuneus, BA 17 47 4.54 3.96 .041 15, −78, 12 R, caudate 42 4.52 3.94 .052 21, −15, 21 OW-LOC versus NW-CON R, precentral gyrus, BA 6 29 6.08 4.92 .099 36, −3, 39 R, lingual gyrus, BA 19 78 5.97 4.86 .011 30, −69, 6 L, cingulate gyrus, BA 31 46 4.63 4.02 .043 −12, −33, 33 L, supramarginal gyrus, BA 40 33 4.42 3.88 .080 −36, −42, 30 R, postcentral gyrus, BA 3 30 4.37 3.84 .094 24, −33, 48 R, inferior frontal gyrus, BA 9 26 4.09 3.64 .116 42, 0, 21 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 26 4.33 3.82 .116 57, −60, −9 Block × group interaction OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 38 5.13 4.35 .062 27, 12, 36 L, cuneus, BA 17 93 5.44 4.54 .006 −9, −84, 12 L, cingulate gyrus, BA 31 35 4.63 4.02 .072 −24, −33, 36 L, declive 87 4.85 4.17 .008 −9, −78, −18 R, superior temporal gyrus, BA 41 45 4.62 4.01 .045 48, -36, 9 OW-LOC versus NW-CON R, lingual gyrus, BA 19 43 5.81 4.77 .049 30, −69, 6 R, postcentral gyrus, BA 3 34 4.46 3.90 .076 24, −33, 48 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 34 4.57 3.98 .076 54, −60, −9 L, superior frontal gyrus, BA 10 26 4.54 3.96 .116 −21, 69, −3 Note. OW-CON = overweight/obese without loss of control eating; OW-LOC = overweight/obese with loss of control eating; NW-CON = nonoverweight without loss of control eating. In the interest of space, only significant peak-level effects are presented. Table I. Effects of Group, Block Type, and Their Interaction on Brain Activation During a Disinhibited Eating Paradigm Effect Region kE T Z-score Duncorr Coordinates Main effect for block type Restricted versus unrestricted — — — — — — Main effect for group OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 61 5.94 4.84 .022 27, 12, 36 L, cuneus, BA 17 90 5.93 4.84 .007 −9, −84, 12 L, cingulate gyrus, BA 31 46 4.91 4.21 .043 −24, −33, 36 R, claustrum 48 4.70 4.07 .039 27, 24, 9 L, declive 46 4.60 4.00 .043 −9, −78, −18 R, superior temporal gyrus, BA 41 44 4.57 3.98 .047 48, −36, 9 R, cuneus, BA 17 47 4.54 3.96 .041 15, −78, 12 R, caudate 42 4.52 3.94 .052 21, −15, 21 OW-LOC versus NW-CON R, precentral gyrus, BA 6 29 6.08 4.92 .099 36, −3, 39 R, lingual gyrus, BA 19 78 5.97 4.86 .011 30, −69, 6 L, cingulate gyrus, BA 31 46 4.63 4.02 .043 −12, −33, 33 L, supramarginal gyrus, BA 40 33 4.42 3.88 .080 −36, −42, 30 R, postcentral gyrus, BA 3 30 4.37 3.84 .094 24, −33, 48 R, inferior frontal gyrus, BA 9 26 4.09 3.64 .116 42, 0, 21 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 26 4.33 3.82 .116 57, −60, −9 Block × group interaction OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 38 5.13 4.35 .062 27, 12, 36 L, cuneus, BA 17 93 5.44 4.54 .006 −9, −84, 12 L, cingulate gyrus, BA 31 35 4.63 4.02 .072 −24, −33, 36 L, declive 87 4.85 4.17 .008 −9, −78, −18 R, superior temporal gyrus, BA 41 45 4.62 4.01 .045 48, -36, 9 OW-LOC versus NW-CON R, lingual gyrus, BA 19 43 5.81 4.77 .049 30, −69, 6 R, postcentral gyrus, BA 3 34 4.46 3.90 .076 24, −33, 48 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 34 4.57 3.98 .076 54, −60, −9 L, superior frontal gyrus, BA 10 26 4.54 3.96 .116 −21, 69, −3 Effect Region kE T Z-score Duncorr Coordinates Main effect for block type Restricted versus unrestricted — — — — — — Main effect for group OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 61 5.94 4.84 .022 27, 12, 36 L, cuneus, BA 17 90 5.93 4.84 .007 −9, −84, 12 L, cingulate gyrus, BA 31 46 4.91 4.21 .043 −24, −33, 36 R, claustrum 48 4.70 4.07 .039 27, 24, 9 L, declive 46 4.60 4.00 .043 −9, −78, −18 R, superior temporal gyrus, BA 41 44 4.57 3.98 .047 48, −36, 9 R, cuneus, BA 17 47 4.54 3.96 .041 15, −78, 12 R, caudate 42 4.52 3.94 .052 21, −15, 21 OW-LOC versus NW-CON R, precentral gyrus, BA 6 29 6.08 4.92 .099 36, −3, 39 R, lingual gyrus, BA 19 78 5.97 4.86 .011 30, −69, 6 L, cingulate gyrus, BA 31 46 4.63 4.02 .043 −12, −33, 33 L, supramarginal gyrus, BA 40 33 4.42 3.88 .080 −36, −42, 30 R, postcentral gyrus, BA 3 30 4.37 3.84 .094 24, −33, 48 R, inferior frontal gyrus, BA 9 26 4.09 3.64 .116 42, 0, 21 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 26 4.33 3.82 .116 57, −60, −9 Block × group interaction OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 38 5.13 4.35 .062 27, 12, 36 L, cuneus, BA 17 93 5.44 4.54 .006 −9, −84, 12 L, cingulate gyrus, BA 31 35 4.63 4.02 .072 −24, −33, 36 L, declive 87 4.85 4.17 .008 −9, −78, −18 R, superior temporal gyrus, BA 41 45 4.62 4.01 .045 48, -36, 9 OW-LOC versus NW-CON R, lingual gyrus, BA 19 43 5.81 4.77 .049 30, −69, 6 R, postcentral gyrus, BA 3 34 4.46 3.90 .076 24, −33, 48 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 34 4.57 3.98 .076 54, −60, −9 L, superior frontal gyrus, BA 10 26 4.54 3.96 .116 −21, 69, −3 Note. OW-CON = overweight/obese without loss of control eating; OW-LOC = overweight/obese with loss of control eating; NW-CON = nonoverweight without loss of control eating. In the interest of space, only significant peak-level effects are presented. Figure 2. View largeDownload slide View largeDownload slide Significant clusters for the main and interaction effects. Note. OW-CON = overweight/obese without loss of control eating; OW-LOC = overweight/obese with loss of control eating; NW-CON = nonoverweight without loss of control eating. Figure 2. View largeDownload slide View largeDownload slide Significant clusters for the main and interaction effects. Note. OW-CON = overweight/obese without loss of control eating; OW-LOC = overweight/obese with loss of control eating; NW-CON = nonoverweight without loss of control eating. DEBQ-C external eating was negatively correlated with right middle frontal gyrus activation during restricted blocks (r = −.52; p = .03). Although several correlations between percentage of signal change in selected brain regions and eating behavior data were in the medium range, none were significant (see Table II). Table II. Partial Correlations Between Percentage Signal Change in Selected Brain Regions, and Eating Behavior Data Brain region Condition 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6 7 8 9 1. R, middle frontal gyrus a. Restricted — .53* .50* −.04 0.31 −.12 .65** 0.43 .71** 0.27 −.21 −.17 −.52* 0.16 b. Unrestricted — — .56* .56* 0.3 0.37 0.43 .52* 0.4 0.44 0.25 0.11 −.13 −.25 2. L, cuneus a. Restricted — — — .66** .77*** .49* .74** 0.42 .71** 0.41 0.15 −.03 −.33 0.02 b. Unrestricted — — — — .52* .84*** 0.42 .50* 0.29 0.39 0.42 −.19 −.01 −.11 3. L, cingulate gyrus a. Restricted — — — — — .52* .80*** .49* .58* 0.08 0.41 0.07 −.15 0.09 b. Unrestricted — — — — — — 0.41 .61* 0.19 .49* 0.47 −.09 0.26 −.15 4. R, cuneus a. Restricted — — — — — — — .59* .87*** 0.28 0.02 −.22 −.38 0.22 b. Unrestricted — — — — — — — — 0.4 0.47 0.25 −.17 −.04 −.15 5. R, inferior temporal gyrus a. Restricted — — — — — — — — — 0.43 −.24 −.29 −.38 0.33 b. Unrestricted — — — — — — — — — — −.15 0 0.07 −.31 6. LOC eating frequencya — — — — — — — — — — — .53* 0.38 −.16 7. DEBQ-C Emotional Eating — — — — — — — — — — — — 0.35 −.43 8. DEBQ-C External Eating — — — — — — — — — — — — — −.31 9. DEBQ-C Restraint — — — — — — — — — — — — — — Brain region Condition 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6 7 8 9 1. R, middle frontal gyrus a. Restricted — .53* .50* −.04 0.31 −.12 .65** 0.43 .71** 0.27 −.21 −.17 −.52* 0.16 b. Unrestricted — — .56* .56* 0.3 0.37 0.43 .52* 0.4 0.44 0.25 0.11 −.13 −.25 2. L, cuneus a. Restricted — — — .66** .77*** .49* .74** 0.42 .71** 0.41 0.15 −.03 −.33 0.02 b. Unrestricted — — — — .52* .84*** 0.42 .50* 0.29 0.39 0.42 −.19 −.01 −.11 3. L, cingulate gyrus a. Restricted — — — — — .52* .80*** .49* .58* 0.08 0.41 0.07 −.15 0.09 b. Unrestricted — — — — — — 0.41 .61* 0.19 .49* 0.47 −.09 0.26 −.15 4. R, cuneus a. Restricted — — — — — — — .59* .87*** 0.28 0.02 −.22 −.38 0.22 b. Unrestricted — — — — — — — — 0.4 0.47 0.25 −.17 −.04 −.15 5. R, inferior temporal gyrus a. Restricted — — — — — — — — — 0.43 −.24 −.29 −.38 0.33 b. Unrestricted — — — — — — — — — — −.15 0 0.07 −.31 6. LOC eating frequencya — — — — — — — — — — — .53* 0.38 −.16 7. DEBQ-C Emotional Eating — — — — — — — — — — — — 0.35 −.43 8. DEBQ-C External Eating — — — — — — — — — — — — — −.31 9. DEBQ-C Restraint — — — — — — — — — — — — — — a Analyses involve square-root transformed values. * p<.05; **p<.01; ***p<.001. Note. All partial correlations adjust for group status. DEBQ-C = Dutch Eating Behavior Questionnaire for Children; LOC = loss of control. Table II. Partial Correlations Between Percentage Signal Change in Selected Brain Regions, and Eating Behavior Data Brain region Condition 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6 7 8 9 1. R, middle frontal gyrus a. Restricted — .53* .50* −.04 0.31 −.12 .65** 0.43 .71** 0.27 −.21 −.17 −.52* 0.16 b. Unrestricted — — .56* .56* 0.3 0.37 0.43 .52* 0.4 0.44 0.25 0.11 −.13 −.25 2. L, cuneus a. Restricted — — — .66** .77*** .49* .74** 0.42 .71** 0.41 0.15 −.03 −.33 0.02 b. Unrestricted — — — — .52* .84*** 0.42 .50* 0.29 0.39 0.42 −.19 −.01 −.11 3. L, cingulate gyrus a. Restricted — — — — — .52* .80*** .49* .58* 0.08 0.41 0.07 −.15 0.09 b. Unrestricted — — — — — — 0.41 .61* 0.19 .49* 0.47 −.09 0.26 −.15 4. R, cuneus a. Restricted — — — — — — — .59* .87*** 0.28 0.02 −.22 −.38 0.22 b. Unrestricted — — — — — — — — 0.4 0.47 0.25 −.17 −.04 −.15 5. R, inferior temporal gyrus a. Restricted — — — — — — — — — 0.43 −.24 −.29 −.38 0.33 b. Unrestricted — — — — — — — — — — −.15 0 0.07 −.31 6. LOC eating frequencya — — — — — — — — — — — .53* 0.38 −.16 7. DEBQ-C Emotional Eating — — — — — — — — — — — — 0.35 −.43 8. DEBQ-C External Eating — — — — — — — — — — — — — −.31 9. DEBQ-C Restraint — — — — — — — — — — — — — — Brain region Condition 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6 7 8 9 1. R, middle frontal gyrus a. Restricted — .53* .50* −.04 0.31 −.12 .65** 0.43 .71** 0.27 −.21 −.17 −.52* 0.16 b. Unrestricted — — .56* .56* 0.3 0.37 0.43 .52* 0.4 0.44 0.25 0.11 −.13 −.25 2. L, cuneus a. Restricted — — — .66** .77*** .49* .74** 0.42 .71** 0.41 0.15 −.03 −.33 0.02 b. Unrestricted — — — — .52* .84*** 0.42 .50* 0.29 0.39 0.42 −.19 −.01 −.11 3. L, cingulate gyrus a. Restricted — — — — — .52* .80*** .49* .58* 0.08 0.41 0.07 −.15 0.09 b. Unrestricted — — — — — — 0.41 .61* 0.19 .49* 0.47 −.09 0.26 −.15 4. R, cuneus a. Restricted — — — — — — — .59* .87*** 0.28 0.02 −.22 −.38 0.22 b. Unrestricted — — — — — — — — 0.4 0.47 0.25 −.17 −.04 −.15 5. R, inferior temporal gyrus a. Restricted — — — — — — — — — 0.43 −.24 −.29 −.38 0.33 b. Unrestricted — — — — — — — — — — −.15 0 0.07 −.31 6. LOC eating frequencya — — — — — — — — — — — .53* 0.38 −.16 7. DEBQ-C Emotional Eating — — — — — — — — — — — — 0.35 −.43 8. DEBQ-C External Eating — — — — — — — — — — — — — −.31 9. DEBQ-C Restraint — — — — — — — — — — — — — — a Analyses involve square-root transformed values. * p<.05; **p<.01; ***p<.001. Note. All partial correlations adjust for group status. DEBQ-C = Dutch Eating Behavior Questionnaire for Children; LOC = loss of control. Discussion The current study describes the development and pilot testing of a novel neuroimaging paradigm designed to model disinhibited eating in children with LOC eating and overweight/obesity. While most neuroimaging paradigms used in the eating disorders field have been undertaken in adults, and were designed to maximize group differences in neural response (i.e., in blood-oxygenated-level dependent or BOLD response) between participants who engage in maladaptive eating relative to those who do not, a particularly unique aspect of this paradigm was that it was designed to model (for the first time) the momentary experience of disinhibited eating via intermittent access to a palatable food. In a small sample of preadolescents, we found that the paradigm resulted in greater BOLD response in several brain areas related to executive functioning in children with overweight/obesity and LOC eating relative to their peers who were overweight and nonoverweight. These included the middle front gyrus, which is implicated in attentional processes (Japee, Holiday, Satyshur, Mukai, & Ungerleider, 2015); the cingulate gyrus, which is involved in emotion regulation (Etkin, Egner, & Kalisch, 2011); the inferior frontal gyrus, which has been found to play a role in response inhibition (Hampshire, Chamberlain, Monti, Duncan, & Owen, 2010); and the cuneus, which has been related to inhibitory control and attentional shifts in pediatric populations with impulse control disorders (Solanto, Schulz, Fan, Tang, & Newcorn, 2009). Elevations in these regions among children with LOC relative to controls who were overweight and nonoverweight, respectively, may suggest that youth with LOC eating expended more cognitive effort to regulate their eating during both restricted and unrestricted milkshake blocks. Of note, significant interaction effects appeared to be primarily related to LOC eating status rather than block type, suggesting that further refinement of the paradigm is needed to model the experience of counter-regulation/disinhibition of ingestive behavior. We propose further modifying this paradigm to facilitate ongoing research on LOC eating behavior in children. Indeed, one potential explanation for the lack of significant task-related effects may be that children were compelled to restrict their milkshake consumption by virtue of the design (i.e., they would not receive one flavor of milkshake during restricted blocks, even if they tried to access that flavor through a corresponding button press). This design element may approximate the type of structured dietary restriction that has been shown to decrease LOC eating symptoms in experimental studies of youth (Presnell & Stice, 2003). Developing an imaging paradigm in which restricted access to food is modeled as voluntary (e.g., whereby participants choose between food options that they tend to avoid eating when trying to control their weight) may better approximate the types of naturalistic dietary restraint-related behaviors that have been shown to predict LOC eating in youth. There were several other limitations worth noting. The lack of counterbalancing for restricted and unrestricted blocks was a methodological nuance that was not included in this pilot study. Thus, although blocks were jittered, participants were not exposed to an element of surprise in the occurrence of restricted versus unrestricted milkshake access. The sample comprised children who predominantly identified as African-American and did not include children who were nonoverweight with LOC eating (although research suggests that LOC eating is approximately three times as common in youth who are overweight/obese relative to those who are nonoverweight; Tanofsky-Kraff et al., 2004), which may limit generalizability. Indeed, this latter subgroup of youth may have distinct neural activation patterns in response to restricted food access, given previous research in adults suggesting that those who are nonoverweight and report binge eating endorse higher rates of weight control behaviors than their peers who are overweight/obese (Goldschmidt et al., 2011). Most importantly, given that the aim of the study was to test the feasibility and preliminary outcome of the neuroimaging paradigm, the sample size was small (and included a number of participants who did not comply with prescanning instructions), and alpha was set using a threshold that was liberal (.001 uncorrected) in light of the large number of analyses. Thus, future research should use larger samples based on a priori power calculations (Poldrack et al., 2017). Indeed, the small sample size may have precluded the detection of some significant effects, particularly with respect to correlations between percentage signal change of selected brain regions and self-report/interview measures of eating behavior. Only one such correlation was significant, indicating a negative association between external eating tendencies and right middle frontal gyrus activation during restricted trials; this may reflect that attentional processes are involved in children’s responsiveness to internal and external cues related to eating. However, several other correlations were in the medium range but failed to reach significance. Despite these limitations, results add to the small literature on neural substrates of LOC eating in youth (Jarcho et al., 2015), and used an ecologically valid stimulus in the form of actual food cues to model eating behavior. In summary, there are limited data investigating the momentary experience of eating-related constructs such as LOC and their neural correlates in children. This was the first neuroimaging study to probe intermittent access to food as a means of assessing cognitive processes related to LOC, based on research suggesting the relevance of these constructs to the understanding of LOC eating. The current findings suggest that LOC eating may be related to unique patterns of neural activation in response to ingestion of palatable foods, particularly in regions associated with self-regulation. This may imply that interventions aiming to improve self-regulation (e.g., cognitive training) could improve LOC eating behaviors and reduce BOLD response in related brain regions. Future studies should continue to explore neural correlates of eating behavior in this population via improved modeling of food restriction and other factors shown to predict the subjective experience of LOC while eating (e.g., negative affect), which could enhance research aiming to assess effects of interventions targeting these processes. Funding This work was supported by National Center for Research Resources (grant number KL2-RR025000); the University of Chicago Clinical and Translational Science Awards (grant number UL1-TR000430); the National Institute of Diabetes and Digestive and Kidney Diseases (grant number K23-DK105234); and the National Institute of Mental Health (grant number K23-MH105553). Conflicts of interest: None declared. References Ashburner J. ( 2007 ). A fast diffeomorphic image registration algorithm . Neuroimage , 38 , 95 – 113 . doi: 10.1016/j.neuroimage.2007.07.007 Google Scholar CrossRef Search ADS PubMed Avena N. M. ( 2010 ). The study of food addiction using animal models of binge eating . Appetite , 55 , 734 – 737 . doi: 10.1016/j.appet.2010.09.010 Google Scholar CrossRef Search ADS PubMed Berner L. A. , Marsh R. ( 2014 ). Frontostriatal circuits and the development of bulimia nervosa . 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For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Pediatric Psychology Oxford University Press

A Pilot Study of Neural Correlates of Loss of Control Eating in Children With Overweight/Obesity: Probing Intermittent Access to Food as a Means of Eliciting Disinhibited Eating

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Abstract

Abstract Objective Neural substrates of loss of control (LOC) eating are undercharacterized. We aimed to model intermittent access to food to elicit disinhibited eating in youth undergoing neuroimaging, given evidence that restricted food access may increase subsequent food intake via enhancing reward value of food and diminishing eating-related self-control. Methods Participants were 18 preadolescents (aged 9–12 years) who were overweight/obese with recent LOC eating (OW-LOC; n = 6); overweight/obese with no history of LOC eating (OW-CON; n = 5); or non-overweight with no history of LOC eating (NW-CON; n = 7). Participants underwent functional magnetic resonance imaging during a simulated food restriction paradigm in which they were alternately given restricted or unrestricted access to milkshake solutions. Results There were no significant main effects of restricted versus unrestricted access to milkshake flavors. Group main effects revealed increased activation for OW-LOC relative to OW-CON in areas related to attentional processes (right middle frontal gyrus), inhibitory control/attentional shifts (right and left cuneus), and emotion regulation (left cingulate gyrus); and for OW-LOC relative to NW-CON in areas related to response inhibition (right inferior frontal gyrus). Significant block type × group interaction effects were found for the right middle frontal gyrus, left cingulate gyrus, and left cuneus, but these appeared to be accounted for primarily by group. Discussion There were clear group differences in neural activity in brain regions related to self-regulation during a food restriction paradigm. Elevations in these regions among OW-LOC relative to OW-CON and NW-CON, respectively, may suggest that youth with LOC eating expended more cognitive effort to regulate ingestive behavior. dietary restriction, disinhibited eating, executive functioning, loss of control eating, neural activation Loss of control (LOC) eating is a form of disinhibited eating (i.e., eating characterized by a lack of healthy restraint, or eating in response to nonphysiological cues) involving a sense that one cannot control what or how much one is eating (Goldschmidt, 2017). LOC eating predominantly affects youth who are overweight/obese (i.e., up to 30% of children and adolescents with overweight/obesity, vs. <5% of youth who are nonoverweight; He, Cai, & Fan, 2017; Tanofsky-Kraff et al., 2004), is associated with decreased quality of life and psychosocial functioning, and predicts excess weight gain and eating disorder onset (Goldschmidt, 2017). Neural substrates of LOC eating in youth are unclear, thereby impeding a comprehensive understanding of its development and course. One significant challenge to studying neural correlates of LOC while eating relates to the difficulties associated with assessing ingestive behavior (e.g., head movement while chewing/swallowing) while participants lie prone in a magnetic resonance imaging (MRI) scanner. Previous neuroimaging studies have tried to overcome these limitations by administering milkshake solutions to model reward response to palatable foods in individuals with eating- and weight-related problems (Bohon & Stice, 2011, n = 26; Stice, Spoor, Bohon, Veldhuizen, & Small, 2008, n = 33), but these procedures have not yet been used to model disinhibited eating and associated cognitive processes. As restricting access to food leads to increased intake of restricted foods in children (Fisher & Birch, 1999) and animals (Avena, 2010), purportedly via enhancing their reward value (Parkes, Furlong, Black, & Balleine, 2017) and/or attenuating self-control related to eating (Furlong, Jayaweera, Balleine, & Corbit, 2014), this may be a viable method for assessing disinhibited eating and its neural substrates in children. Research on the neurocircuitry of LOC eating in pediatric samples (including those who do and do not engage in purging behaviors) suggests that reward-related regions may be hypersensitive to both food and nonfood cues (Berner & Marsh, 2014), perhaps reflecting a tendency to seek out large amounts of palatable food during LOC episodes. Data on cognitive control regions have been mixed, with some studies showing increased engagement in prefrontal circuits during tasks requiring self-regulation (Lock, Garrett, Beenhakker, & Reiss, 2011, n = 40), and others showing decreased engagement (Jarcho et al., 2015, n = 22; Marsh et al., 2011, n = 36; Marsh et al., 2009, n = 40). The former findings may reflect a form of neural compensation for deficient self-regulatory control in youth with LOC eating (Berner & Marsh, 2014), which is consistent with the adult literature (Karhunen et al., 2000, n = 23; Tammela et al., 2010, n = 25). Individuals without eating disorders demonstrate increased activity in prefrontal regions during tasks requiring behavioral inhibition/delay of gratification (Hare, Camerer, & Rangel, 2009, n = 37; Weygandt et al., 2013, n = 16), and interestingly, this prefrontal activity appears to attenuate during episodes involving failed self-control (e.g., making a more immediately rewarding but less healthy food choice; Hare et al., 2009; Weygandt et al., 2013). Furthermore, stronger connectivity between brain regions related to self-regulation and reward sensitivity has been observed in successful dieters with obesity (Weygandt et al., 2013), suggesting that neural responses to the expectation or receipt of reward may interact with self-regulatory functions to promote control over eating. Taken together, appropriate regulation of energy intake may be associated with enhanced cognitive control, which helps suppress appetitive or hedonic responses to food stimuli, whereas disinhibition may be related to a breakdown in this function. The aim of this study is to describe a newly developed behavioral paradigm designed to model restricted access to food and disinhibited eating in children with and without overweight/obesity and LOC eating within a neuroimaging framework, and report preliminary findings (including task- and group-related effects, and associations between neural activation patterns and severity of disinhibited eating constructs such as LOC eating frequency and tendencies to eat in response to external cues). We hypothesize that, relative to their peers with and without overweight/obesity and no reported LOC eating, children with both LOC eating and overweight/obesity will demonstrate enhanced prefrontal activation during exposure to a food reward after a period of restricted access, reflecting increased exertion required to regulate ingestive behavior, particularly following a period of restricted access to food which may diminish eating-related self-control (Furlong et al., 2014). The ultimate goal is to stimulate further research on neural substrates related to the momentary occurrence of maladaptive eating behavior by elucidating regions purported to be involved self-regulation and disinhibition over food intake. Methods Participants and Procedures Participants were 18 right-handed children recruited from the community [44.4% female; M body mass index (BMI; kg/m2) z-score = 1.04 ± 1.44], aged 9–12 years (M age = 10.5 ± 1.1 year), of whom 6 were overweight/obese (BMI ≥ 85th percentile for age and sex) and reported LOC eating in the past 3 months (OW-LOC), 5 were controls who were overweight/obese and did not report any history of LOC eating (OW-CON), and 7 were controls who were nonoverweight and did not report any history of LOC eating (NW-CON). Participants were recruited from a larger study of executive functioning and eating behavior in youth (Goldschmidt et al., 2017). Most participants were African-American (n = 13; 72.2%) with the remainder self-identifying as Hispanic/Latino (n = 3; 16.7%) or Caucasian (n = 2; 11.1%), consistent with the demographics of the study location and the composition of the larger study sample. Participants were excluded if they had medical conditions or were taking medications known to influence weight or appetite; met criteria for an eating disorder other than binge eating disorder; reported moderately to greatly disliking vanilla and/or chocolate milkshakes; or had a diagnosis of attention deficit–hyperactivity disorder. Each participant and his/her caregiver provided written informed assent/consent, respectively. Study procedures were approved by the University of Chicago Institutional Review Board. An intermittent access paradigm was designed to model restricted access to food/disinhibition over ingestive behavior. The paradigm was adapted from procedures developed by Fisher and Birch (1999), which involved manipulating children’s access to two different snack food items to which they were exposed in vivo. In the MRI-compatible adaptation, participants were provided with a response box, wherein each button elicited different milkshake flavors (vanilla or chocolate). Participants were randomly assigned to conditions involving restricted access to one flavor, such that half of participants had restricted access to vanilla-flavored milkshake and unrestricted access to chocolate-flavored milkshake during alternating blocks, and half had restricted access to chocolate-flavored milkshake and unrestricted access to vanilla-flavored milkshake. Participants underwent 30 consecutive jittered (15–25 s) blocks of alternating unrestricted or restricted access to the milkshakes, each separated by 3–4 s of rest (fixation cross; see Figure 1). During the unrestricted blocks, children could choose either milkshake flavor and thus had ad libitum access to both vanilla- and chocolate-flavored milkshake flavors for 15–25 s. During the restricted blocks, children were permitted to activate receipt of the unrestricted milkshake flavor only; thus, during the restricted trials, participants had 15–25 s of ad libitum access to the unrestricted milkshake flavor, during which time button presses corresponding to the restricted milkshake were preprogrammed not to activate milkshake receipt. Images of palatable milkshakes were projected in the scanner to indicate the beginning and end of each block. Participants were instructed (both directly and through the participating caregiver) to avoid ice cream products for 1 week before scanning to enhance the effects of the restriction paradigm, and to avoid eating for 3 hr before scanning. On the day of scanning, participants and caregivers were queried about their compliance with these instructions. A total of four participants (22.2%) reported noncompliance with prescanning instructions, three of whom (16.7%) reported noncompliance with the first set of instructions, and two of whom reported noncompliance with the latter (11.1%); of these, one participant (5.6%) reported noncompliance with both sets of instructions. Figure 1. View largeDownload slide Visual depiction of trial structure. Figure 1. View largeDownload slide Visual depiction of trial structure. Measures Anthropometric and Sociodemographic Variables Height and weight were measured in light indoor clothing by a trained research assistant via stadiometer and calibrated digital scale, respectively, at a baseline eligibility evaluation. Child BMI z-scores were calculated using Centers for Disease Control and Prevention (CDC) growth charts and accompanying procedures (Kuczmarski et al., 2000). Overweight was defined as BMI between the 85th and 95th percentile for age and sex, while obesity was defined as BMI at or >95th percentile. Demographic data were reported by children and parents, and included children’s age, gender, race/ethnicity (White, Black/African-American, Hispanic/Latino, Asian, Native Hawaiian or Other Pacific Islander, American Indian or Alaska Native, or multiracial/other), current medications, and medical problems. Eating Behavior Diagnostic items from the Child Eating Disorder Examination 12.0 (Child EDE; Bryant-Waugh, Cooper, Taylor, & Lask, 1996) were administered by trained bachelor’s level research assistants at the baseline evaluation to assess current and lifetime LOC eating. The Child EDE is a semistructured, interviewer-based instrument based on the well-validated adult EDE, with modifications including the use of simpler language appropriate for a younger audience. The Child EDE has adequate reliability and validity in children as young as 8 years old (Bryant-Waugh et al., 1996). LOC eating was coded as present if participants reported experiencing at least one episode of LOC eating in the past 3 months, given previous data demonstrating that LOC eating, regardless of frequency, is associated with adverse health-related correlates (Tanofsky-Kraff et al., 2004). Assessors underwent several hours of Child EDE training with advanced assessors, and were required to complete at least two interviews to perfect reliability with a Child EDE expert before conducting interviews independently. Monthly meetings were held to resolve coding ambiguities and to prevent rater drift. The Dutch Eating Behavior Questionnaire for Children (DEBQ-C; van Strien & Oosterveld, 2008) was completed at baseline to assess trait-level dietary restraint (α = .77), emotional disinhibition (eating in response to emotional cues; α = .56), and external disinhibition (eating in response to the presence of food; α = .75). It has adequate reliability and validity in youth as young as 7 years old (van Strien & Oosterveld, 2008). Participants were asked to self-report their baseline food preferences separately for vanilla and chocolate milkshake via a five-point Likert scale (1 = Do not like to 5 = Like very much). A similar scale was also used to assess hunger levels immediately before scanning (1 = Not hungry at all to 5 = Extremely hungry). Functional MRI (fMRI) Data Acquisition and Analysis Imaging data were collected on a 3T Philips Achieva Quasar scanner. During the task, 361 functional image volumes were acquired with an echo-planar sequence (30 oblique slices with 4 mm thickness, 0.5 mm gap; repetition time/echo time = 2,200/25 ms, flip = 77; field of view = 192 mm, matrix = 64 × 64). Analyses were conducted using SPM. DARTEL procedures (Ashburner, 2007) were used to normalize data to the child brain. Data were realigned for motion correction (threshold = 3 mm), coregistered to the mean, spatially normalized, and smoothed. First-level analyses involved creating whole-brain contrasts (given the pilot nature of the study) for each block type. These contrast images were fed into a second-level analysis using factorial analysis of variance to compare block types, groups, and block type × group interactions with respect to activation in specific brain regions. Owing to the pilot nature of the study, which was designed to generate effect sizes for future research, alpha was set at .001 uncorrected for all main and interaction effects, and the minimum cluster size was set at 10. For illustrative purposes, percentages of signal change in selected brain areas were extracted using MarsBaR procedures. Signal changes were then partially correlated (adjusting for group) with LOC frequency in the past 3 months as reported on the Child EDE (natural log-transformed to adjust for positive skew), and DEBQ-C subscale scores using SPSS 19.0. Results At baseline, participants reported high average levels of liking for both chocolate (M = 4.8 ± 0.6) and vanilla milkshake (M = 4.1 ± 1.1). Before scanning, participants reported moderate levels of hunger (M = 3.3 ± 1.4). Groups did not differ on age, gender, race/ethnicity, or DEBQ-C subscales (all ps > .15). The primary contrasts of interest were for restricted versus unrestricted blocks. For the restricted > unrestricted contrast, there were no suprathreshold clusters for main effects of block type that met the specified alpha level. Significant main effects for group were found for areas involved in attentional processes, inhibitory control, and emotion regulation. Specifically, OW-LOC demonstrated increased activation in the right middle frontal gyrus (Cohen’s d = 0.70), right (Cohen’s d = 0.03) and left cuneus (Cohen’s d = 0.28), and left cingulate gyrus (Cohen’s d = 0.13) relative to OW-CON, who showed decreased activation in these areas; and in the right inferior frontal gyrus (Cohen’s d = 0.17) relative to NW-CON, who showed decreased activation in these areas. When analyses were rerun excluding participants who did not adhere to prescanning instructions, differences between OW-LOC and OW-CON remained significant for the right middle frontal gyrus and left cuneus. Significant block type × group interaction effects were found for areas including the right middle frontal gyrus, left cingulate gyrus, and left cuneus, but these interactions were primarily accounted for by the main effects of group (see Table I and Figure 2). Table I. Effects of Group, Block Type, and Their Interaction on Brain Activation During a Disinhibited Eating Paradigm Effect Region kE T Z-score Duncorr Coordinates Main effect for block type Restricted versus unrestricted — — — — — — Main effect for group OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 61 5.94 4.84 .022 27, 12, 36 L, cuneus, BA 17 90 5.93 4.84 .007 −9, −84, 12 L, cingulate gyrus, BA 31 46 4.91 4.21 .043 −24, −33, 36 R, claustrum 48 4.70 4.07 .039 27, 24, 9 L, declive 46 4.60 4.00 .043 −9, −78, −18 R, superior temporal gyrus, BA 41 44 4.57 3.98 .047 48, −36, 9 R, cuneus, BA 17 47 4.54 3.96 .041 15, −78, 12 R, caudate 42 4.52 3.94 .052 21, −15, 21 OW-LOC versus NW-CON R, precentral gyrus, BA 6 29 6.08 4.92 .099 36, −3, 39 R, lingual gyrus, BA 19 78 5.97 4.86 .011 30, −69, 6 L, cingulate gyrus, BA 31 46 4.63 4.02 .043 −12, −33, 33 L, supramarginal gyrus, BA 40 33 4.42 3.88 .080 −36, −42, 30 R, postcentral gyrus, BA 3 30 4.37 3.84 .094 24, −33, 48 R, inferior frontal gyrus, BA 9 26 4.09 3.64 .116 42, 0, 21 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 26 4.33 3.82 .116 57, −60, −9 Block × group interaction OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 38 5.13 4.35 .062 27, 12, 36 L, cuneus, BA 17 93 5.44 4.54 .006 −9, −84, 12 L, cingulate gyrus, BA 31 35 4.63 4.02 .072 −24, −33, 36 L, declive 87 4.85 4.17 .008 −9, −78, −18 R, superior temporal gyrus, BA 41 45 4.62 4.01 .045 48, -36, 9 OW-LOC versus NW-CON R, lingual gyrus, BA 19 43 5.81 4.77 .049 30, −69, 6 R, postcentral gyrus, BA 3 34 4.46 3.90 .076 24, −33, 48 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 34 4.57 3.98 .076 54, −60, −9 L, superior frontal gyrus, BA 10 26 4.54 3.96 .116 −21, 69, −3 Effect Region kE T Z-score Duncorr Coordinates Main effect for block type Restricted versus unrestricted — — — — — — Main effect for group OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 61 5.94 4.84 .022 27, 12, 36 L, cuneus, BA 17 90 5.93 4.84 .007 −9, −84, 12 L, cingulate gyrus, BA 31 46 4.91 4.21 .043 −24, −33, 36 R, claustrum 48 4.70 4.07 .039 27, 24, 9 L, declive 46 4.60 4.00 .043 −9, −78, −18 R, superior temporal gyrus, BA 41 44 4.57 3.98 .047 48, −36, 9 R, cuneus, BA 17 47 4.54 3.96 .041 15, −78, 12 R, caudate 42 4.52 3.94 .052 21, −15, 21 OW-LOC versus NW-CON R, precentral gyrus, BA 6 29 6.08 4.92 .099 36, −3, 39 R, lingual gyrus, BA 19 78 5.97 4.86 .011 30, −69, 6 L, cingulate gyrus, BA 31 46 4.63 4.02 .043 −12, −33, 33 L, supramarginal gyrus, BA 40 33 4.42 3.88 .080 −36, −42, 30 R, postcentral gyrus, BA 3 30 4.37 3.84 .094 24, −33, 48 R, inferior frontal gyrus, BA 9 26 4.09 3.64 .116 42, 0, 21 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 26 4.33 3.82 .116 57, −60, −9 Block × group interaction OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 38 5.13 4.35 .062 27, 12, 36 L, cuneus, BA 17 93 5.44 4.54 .006 −9, −84, 12 L, cingulate gyrus, BA 31 35 4.63 4.02 .072 −24, −33, 36 L, declive 87 4.85 4.17 .008 −9, −78, −18 R, superior temporal gyrus, BA 41 45 4.62 4.01 .045 48, -36, 9 OW-LOC versus NW-CON R, lingual gyrus, BA 19 43 5.81 4.77 .049 30, −69, 6 R, postcentral gyrus, BA 3 34 4.46 3.90 .076 24, −33, 48 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 34 4.57 3.98 .076 54, −60, −9 L, superior frontal gyrus, BA 10 26 4.54 3.96 .116 −21, 69, −3 Note. OW-CON = overweight/obese without loss of control eating; OW-LOC = overweight/obese with loss of control eating; NW-CON = nonoverweight without loss of control eating. In the interest of space, only significant peak-level effects are presented. Table I. Effects of Group, Block Type, and Their Interaction on Brain Activation During a Disinhibited Eating Paradigm Effect Region kE T Z-score Duncorr Coordinates Main effect for block type Restricted versus unrestricted — — — — — — Main effect for group OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 61 5.94 4.84 .022 27, 12, 36 L, cuneus, BA 17 90 5.93 4.84 .007 −9, −84, 12 L, cingulate gyrus, BA 31 46 4.91 4.21 .043 −24, −33, 36 R, claustrum 48 4.70 4.07 .039 27, 24, 9 L, declive 46 4.60 4.00 .043 −9, −78, −18 R, superior temporal gyrus, BA 41 44 4.57 3.98 .047 48, −36, 9 R, cuneus, BA 17 47 4.54 3.96 .041 15, −78, 12 R, caudate 42 4.52 3.94 .052 21, −15, 21 OW-LOC versus NW-CON R, precentral gyrus, BA 6 29 6.08 4.92 .099 36, −3, 39 R, lingual gyrus, BA 19 78 5.97 4.86 .011 30, −69, 6 L, cingulate gyrus, BA 31 46 4.63 4.02 .043 −12, −33, 33 L, supramarginal gyrus, BA 40 33 4.42 3.88 .080 −36, −42, 30 R, postcentral gyrus, BA 3 30 4.37 3.84 .094 24, −33, 48 R, inferior frontal gyrus, BA 9 26 4.09 3.64 .116 42, 0, 21 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 26 4.33 3.82 .116 57, −60, −9 Block × group interaction OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 38 5.13 4.35 .062 27, 12, 36 L, cuneus, BA 17 93 5.44 4.54 .006 −9, −84, 12 L, cingulate gyrus, BA 31 35 4.63 4.02 .072 −24, −33, 36 L, declive 87 4.85 4.17 .008 −9, −78, −18 R, superior temporal gyrus, BA 41 45 4.62 4.01 .045 48, -36, 9 OW-LOC versus NW-CON R, lingual gyrus, BA 19 43 5.81 4.77 .049 30, −69, 6 R, postcentral gyrus, BA 3 34 4.46 3.90 .076 24, −33, 48 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 34 4.57 3.98 .076 54, −60, −9 L, superior frontal gyrus, BA 10 26 4.54 3.96 .116 −21, 69, −3 Effect Region kE T Z-score Duncorr Coordinates Main effect for block type Restricted versus unrestricted — — — — — — Main effect for group OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 61 5.94 4.84 .022 27, 12, 36 L, cuneus, BA 17 90 5.93 4.84 .007 −9, −84, 12 L, cingulate gyrus, BA 31 46 4.91 4.21 .043 −24, −33, 36 R, claustrum 48 4.70 4.07 .039 27, 24, 9 L, declive 46 4.60 4.00 .043 −9, −78, −18 R, superior temporal gyrus, BA 41 44 4.57 3.98 .047 48, −36, 9 R, cuneus, BA 17 47 4.54 3.96 .041 15, −78, 12 R, caudate 42 4.52 3.94 .052 21, −15, 21 OW-LOC versus NW-CON R, precentral gyrus, BA 6 29 6.08 4.92 .099 36, −3, 39 R, lingual gyrus, BA 19 78 5.97 4.86 .011 30, −69, 6 L, cingulate gyrus, BA 31 46 4.63 4.02 .043 −12, −33, 33 L, supramarginal gyrus, BA 40 33 4.42 3.88 .080 −36, −42, 30 R, postcentral gyrus, BA 3 30 4.37 3.84 .094 24, −33, 48 R, inferior frontal gyrus, BA 9 26 4.09 3.64 .116 42, 0, 21 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 26 4.33 3.82 .116 57, −60, −9 Block × group interaction OW-LOC versus OW-CON R, middle frontal gyrus, BA 8 38 5.13 4.35 .062 27, 12, 36 L, cuneus, BA 17 93 5.44 4.54 .006 −9, −84, 12 L, cingulate gyrus, BA 31 35 4.63 4.02 .072 −24, −33, 36 L, declive 87 4.85 4.17 .008 −9, −78, −18 R, superior temporal gyrus, BA 41 45 4.62 4.01 .045 48, -36, 9 OW-LOC versus NW-CON R, lingual gyrus, BA 19 43 5.81 4.77 .049 30, −69, 6 R, postcentral gyrus, BA 3 34 4.46 3.90 .076 24, −33, 48 OW-CON versus NW-CON R, inferior temporal gyrus, BA 37 34 4.57 3.98 .076 54, −60, −9 L, superior frontal gyrus, BA 10 26 4.54 3.96 .116 −21, 69, −3 Note. OW-CON = overweight/obese without loss of control eating; OW-LOC = overweight/obese with loss of control eating; NW-CON = nonoverweight without loss of control eating. In the interest of space, only significant peak-level effects are presented. Figure 2. View largeDownload slide View largeDownload slide Significant clusters for the main and interaction effects. Note. OW-CON = overweight/obese without loss of control eating; OW-LOC = overweight/obese with loss of control eating; NW-CON = nonoverweight without loss of control eating. Figure 2. View largeDownload slide View largeDownload slide Significant clusters for the main and interaction effects. Note. OW-CON = overweight/obese without loss of control eating; OW-LOC = overweight/obese with loss of control eating; NW-CON = nonoverweight without loss of control eating. DEBQ-C external eating was negatively correlated with right middle frontal gyrus activation during restricted blocks (r = −.52; p = .03). Although several correlations between percentage of signal change in selected brain regions and eating behavior data were in the medium range, none were significant (see Table II). Table II. Partial Correlations Between Percentage Signal Change in Selected Brain Regions, and Eating Behavior Data Brain region Condition 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6 7 8 9 1. R, middle frontal gyrus a. Restricted — .53* .50* −.04 0.31 −.12 .65** 0.43 .71** 0.27 −.21 −.17 −.52* 0.16 b. Unrestricted — — .56* .56* 0.3 0.37 0.43 .52* 0.4 0.44 0.25 0.11 −.13 −.25 2. L, cuneus a. Restricted — — — .66** .77*** .49* .74** 0.42 .71** 0.41 0.15 −.03 −.33 0.02 b. Unrestricted — — — — .52* .84*** 0.42 .50* 0.29 0.39 0.42 −.19 −.01 −.11 3. L, cingulate gyrus a. Restricted — — — — — .52* .80*** .49* .58* 0.08 0.41 0.07 −.15 0.09 b. Unrestricted — — — — — — 0.41 .61* 0.19 .49* 0.47 −.09 0.26 −.15 4. R, cuneus a. Restricted — — — — — — — .59* .87*** 0.28 0.02 −.22 −.38 0.22 b. Unrestricted — — — — — — — — 0.4 0.47 0.25 −.17 −.04 −.15 5. R, inferior temporal gyrus a. Restricted — — — — — — — — — 0.43 −.24 −.29 −.38 0.33 b. Unrestricted — — — — — — — — — — −.15 0 0.07 −.31 6. LOC eating frequencya — — — — — — — — — — — .53* 0.38 −.16 7. DEBQ-C Emotional Eating — — — — — — — — — — — — 0.35 −.43 8. DEBQ-C External Eating — — — — — — — — — — — — — −.31 9. DEBQ-C Restraint — — — — — — — — — — — — — — Brain region Condition 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6 7 8 9 1. R, middle frontal gyrus a. Restricted — .53* .50* −.04 0.31 −.12 .65** 0.43 .71** 0.27 −.21 −.17 −.52* 0.16 b. Unrestricted — — .56* .56* 0.3 0.37 0.43 .52* 0.4 0.44 0.25 0.11 −.13 −.25 2. L, cuneus a. Restricted — — — .66** .77*** .49* .74** 0.42 .71** 0.41 0.15 −.03 −.33 0.02 b. Unrestricted — — — — .52* .84*** 0.42 .50* 0.29 0.39 0.42 −.19 −.01 −.11 3. L, cingulate gyrus a. Restricted — — — — — .52* .80*** .49* .58* 0.08 0.41 0.07 −.15 0.09 b. Unrestricted — — — — — — 0.41 .61* 0.19 .49* 0.47 −.09 0.26 −.15 4. R, cuneus a. Restricted — — — — — — — .59* .87*** 0.28 0.02 −.22 −.38 0.22 b. Unrestricted — — — — — — — — 0.4 0.47 0.25 −.17 −.04 −.15 5. R, inferior temporal gyrus a. Restricted — — — — — — — — — 0.43 −.24 −.29 −.38 0.33 b. Unrestricted — — — — — — — — — — −.15 0 0.07 −.31 6. LOC eating frequencya — — — — — — — — — — — .53* 0.38 −.16 7. DEBQ-C Emotional Eating — — — — — — — — — — — — 0.35 −.43 8. DEBQ-C External Eating — — — — — — — — — — — — — −.31 9. DEBQ-C Restraint — — — — — — — — — — — — — — a Analyses involve square-root transformed values. * p<.05; **p<.01; ***p<.001. Note. All partial correlations adjust for group status. DEBQ-C = Dutch Eating Behavior Questionnaire for Children; LOC = loss of control. Table II. Partial Correlations Between Percentage Signal Change in Selected Brain Regions, and Eating Behavior Data Brain region Condition 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6 7 8 9 1. R, middle frontal gyrus a. Restricted — .53* .50* −.04 0.31 −.12 .65** 0.43 .71** 0.27 −.21 −.17 −.52* 0.16 b. Unrestricted — — .56* .56* 0.3 0.37 0.43 .52* 0.4 0.44 0.25 0.11 −.13 −.25 2. L, cuneus a. Restricted — — — .66** .77*** .49* .74** 0.42 .71** 0.41 0.15 −.03 −.33 0.02 b. Unrestricted — — — — .52* .84*** 0.42 .50* 0.29 0.39 0.42 −.19 −.01 −.11 3. L, cingulate gyrus a. Restricted — — — — — .52* .80*** .49* .58* 0.08 0.41 0.07 −.15 0.09 b. Unrestricted — — — — — — 0.41 .61* 0.19 .49* 0.47 −.09 0.26 −.15 4. R, cuneus a. Restricted — — — — — — — .59* .87*** 0.28 0.02 −.22 −.38 0.22 b. Unrestricted — — — — — — — — 0.4 0.47 0.25 −.17 −.04 −.15 5. R, inferior temporal gyrus a. Restricted — — — — — — — — — 0.43 −.24 −.29 −.38 0.33 b. Unrestricted — — — — — — — — — — −.15 0 0.07 −.31 6. LOC eating frequencya — — — — — — — — — — — .53* 0.38 −.16 7. DEBQ-C Emotional Eating — — — — — — — — — — — — 0.35 −.43 8. DEBQ-C External Eating — — — — — — — — — — — — — −.31 9. DEBQ-C Restraint — — — — — — — — — — — — — — Brain region Condition 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6 7 8 9 1. R, middle frontal gyrus a. Restricted — .53* .50* −.04 0.31 −.12 .65** 0.43 .71** 0.27 −.21 −.17 −.52* 0.16 b. Unrestricted — — .56* .56* 0.3 0.37 0.43 .52* 0.4 0.44 0.25 0.11 −.13 −.25 2. L, cuneus a. Restricted — — — .66** .77*** .49* .74** 0.42 .71** 0.41 0.15 −.03 −.33 0.02 b. Unrestricted — — — — .52* .84*** 0.42 .50* 0.29 0.39 0.42 −.19 −.01 −.11 3. L, cingulate gyrus a. Restricted — — — — — .52* .80*** .49* .58* 0.08 0.41 0.07 −.15 0.09 b. Unrestricted — — — — — — 0.41 .61* 0.19 .49* 0.47 −.09 0.26 −.15 4. R, cuneus a. Restricted — — — — — — — .59* .87*** 0.28 0.02 −.22 −.38 0.22 b. Unrestricted — — — — — — — — 0.4 0.47 0.25 −.17 −.04 −.15 5. R, inferior temporal gyrus a. Restricted — — — — — — — — — 0.43 −.24 −.29 −.38 0.33 b. Unrestricted — — — — — — — — — — −.15 0 0.07 −.31 6. LOC eating frequencya — — — — — — — — — — — .53* 0.38 −.16 7. DEBQ-C Emotional Eating — — — — — — — — — — — — 0.35 −.43 8. DEBQ-C External Eating — — — — — — — — — — — — — −.31 9. DEBQ-C Restraint — — — — — — — — — — — — — — a Analyses involve square-root transformed values. * p<.05; **p<.01; ***p<.001. Note. All partial correlations adjust for group status. DEBQ-C = Dutch Eating Behavior Questionnaire for Children; LOC = loss of control. Discussion The current study describes the development and pilot testing of a novel neuroimaging paradigm designed to model disinhibited eating in children with LOC eating and overweight/obesity. While most neuroimaging paradigms used in the eating disorders field have been undertaken in adults, and were designed to maximize group differences in neural response (i.e., in blood-oxygenated-level dependent or BOLD response) between participants who engage in maladaptive eating relative to those who do not, a particularly unique aspect of this paradigm was that it was designed to model (for the first time) the momentary experience of disinhibited eating via intermittent access to a palatable food. In a small sample of preadolescents, we found that the paradigm resulted in greater BOLD response in several brain areas related to executive functioning in children with overweight/obesity and LOC eating relative to their peers who were overweight and nonoverweight. These included the middle front gyrus, which is implicated in attentional processes (Japee, Holiday, Satyshur, Mukai, & Ungerleider, 2015); the cingulate gyrus, which is involved in emotion regulation (Etkin, Egner, & Kalisch, 2011); the inferior frontal gyrus, which has been found to play a role in response inhibition (Hampshire, Chamberlain, Monti, Duncan, & Owen, 2010); and the cuneus, which has been related to inhibitory control and attentional shifts in pediatric populations with impulse control disorders (Solanto, Schulz, Fan, Tang, & Newcorn, 2009). Elevations in these regions among children with LOC relative to controls who were overweight and nonoverweight, respectively, may suggest that youth with LOC eating expended more cognitive effort to regulate their eating during both restricted and unrestricted milkshake blocks. Of note, significant interaction effects appeared to be primarily related to LOC eating status rather than block type, suggesting that further refinement of the paradigm is needed to model the experience of counter-regulation/disinhibition of ingestive behavior. We propose further modifying this paradigm to facilitate ongoing research on LOC eating behavior in children. Indeed, one potential explanation for the lack of significant task-related effects may be that children were compelled to restrict their milkshake consumption by virtue of the design (i.e., they would not receive one flavor of milkshake during restricted blocks, even if they tried to access that flavor through a corresponding button press). This design element may approximate the type of structured dietary restriction that has been shown to decrease LOC eating symptoms in experimental studies of youth (Presnell & Stice, 2003). Developing an imaging paradigm in which restricted access to food is modeled as voluntary (e.g., whereby participants choose between food options that they tend to avoid eating when trying to control their weight) may better approximate the types of naturalistic dietary restraint-related behaviors that have been shown to predict LOC eating in youth. There were several other limitations worth noting. The lack of counterbalancing for restricted and unrestricted blocks was a methodological nuance that was not included in this pilot study. Thus, although blocks were jittered, participants were not exposed to an element of surprise in the occurrence of restricted versus unrestricted milkshake access. The sample comprised children who predominantly identified as African-American and did not include children who were nonoverweight with LOC eating (although research suggests that LOC eating is approximately three times as common in youth who are overweight/obese relative to those who are nonoverweight; Tanofsky-Kraff et al., 2004), which may limit generalizability. Indeed, this latter subgroup of youth may have distinct neural activation patterns in response to restricted food access, given previous research in adults suggesting that those who are nonoverweight and report binge eating endorse higher rates of weight control behaviors than their peers who are overweight/obese (Goldschmidt et al., 2011). Most importantly, given that the aim of the study was to test the feasibility and preliminary outcome of the neuroimaging paradigm, the sample size was small (and included a number of participants who did not comply with prescanning instructions), and alpha was set using a threshold that was liberal (.001 uncorrected) in light of the large number of analyses. Thus, future research should use larger samples based on a priori power calculations (Poldrack et al., 2017). Indeed, the small sample size may have precluded the detection of some significant effects, particularly with respect to correlations between percentage signal change of selected brain regions and self-report/interview measures of eating behavior. Only one such correlation was significant, indicating a negative association between external eating tendencies and right middle frontal gyrus activation during restricted trials; this may reflect that attentional processes are involved in children’s responsiveness to internal and external cues related to eating. However, several other correlations were in the medium range but failed to reach significance. Despite these limitations, results add to the small literature on neural substrates of LOC eating in youth (Jarcho et al., 2015), and used an ecologically valid stimulus in the form of actual food cues to model eating behavior. In summary, there are limited data investigating the momentary experience of eating-related constructs such as LOC and their neural correlates in children. This was the first neuroimaging study to probe intermittent access to food as a means of assessing cognitive processes related to LOC, based on research suggesting the relevance of these constructs to the understanding of LOC eating. The current findings suggest that LOC eating may be related to unique patterns of neural activation in response to ingestion of palatable foods, particularly in regions associated with self-regulation. This may imply that interventions aiming to improve self-regulation (e.g., cognitive training) could improve LOC eating behaviors and reduce BOLD response in related brain regions. Future studies should continue to explore neural correlates of eating behavior in this population via improved modeling of food restriction and other factors shown to predict the subjective experience of LOC while eating (e.g., negative affect), which could enhance research aiming to assess effects of interventions targeting these processes. 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Journal of Pediatric PsychologyOxford University Press

Published: Sep 1, 2018

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