Key PointsQuestionÌý
Is brain reward response associated with specific behaviors across the eating disorder diagnostic spectrum?
FindingsÌý
In this cross-sectional functional brain imaging study of 197 women with anorexia nervosa, other specified feeding and eating disorders, bulimia nervosa, and binge eating disorder and a matched cohort of 120 healthy controls, brain salience response was significantly inversely correlated with body mass index and binge eating severity and positively correlated with ventral striatal-hypothalamic circuitry.
MeaningÌý
Results of this study suggest that eating disorder behaviors change brain reward processing, which may alter food intake control circuitry and reinforce the individual’s eating disorder behavior.
ImportanceÌý
Eating disorders are severe psychiatric disorders; however, disease models that cross subtypes and integrate behavior and neurobiologic factors are lacking.
ObjectiveÌý
To assess brain response during unexpected receipt or omission of a salient sweet stimulus across a large sample of individuals with eating disorders and healthy controls and test for evidence of whether this brain response is associated with the ventral striatal-hypothalamic circuitry, which has been associated with food intake control, and whether salient stimulus response and eating disorder related behaviors are associated.
Design, Setting, and ParticipantsÌý
In this cross-sectional functional brain imaging study, young adults across the eating disorder spectrum were matched with healthy controls at a university brain imaging facility and eating disorder treatment program. During a sucrose taste classic conditioning paradigm, violations of learned associations between conditioned visual and unconditioned taste stimuli evoked the dopamine-related prediction error. Dynamic effective connectivity during expected sweet taste receipt was studied to investigate hierarchical brain activation between food intake relevant brain regions. The study was conducted from June 2014 to November 2019. Data were analyzed from December 2019 to February 2020.
Main Outcomes and MeasuresÌý
Prediction error brain reward response across insula and striatum; dynamic effective connectivity between hypothalamus and ventral striatum; and demographic and behavior variables and their correlations with prediction error brain response and connectivity edge coefficients.
ResultsÌý
Of 317 female participants (197 with eating disorders and 120 healthy controls), the mean (SD) age was 23.8 (5.6) years and mean (SD) body mass index was 20.8 (5.4). Prediction error response was elevated in participants with anorexia nervosa (Wilks λ, 0.843; P = .001) and in participants with eating disorders inversely correlated with body mass index (left nucleus accumbens: r = −0.291; 95% CI, −0.413 to −0.167; P < .001; right dorsal anterior insula: r = −0.228; 95% CI, −0.366 to −0.089; P = .001), eating disorder inventory–3 binge eating tendency (left nucleus accumbens: r = −0.207; 95% CI, −0.333 to −0.073; P = .004; right dorsal anterior insula: r = −0.220; 95% CI, −0.354 to −0.073; P = .002), and trait anxiety (left nucleus accumbens: r = −0.148; 95% CI, −0.288 to −0.003; P = .04; right dorsal anterior insula: r = −0.221; 95% CI, −0.357 to −0.076; P = .002). Ventral striatal to hypothalamus directed connectivity was positively correlated with ventral striatal prediction error in eating disorders (r = 0.189; 95% CI, 0.045-0.324; P = .01) and negatively correlated with feeling out of control after eating (right side: r = –0.328; 95% CI, –0.480 to –0.164; P < .001; left side: r = –0.297; 95% CI, –0.439 to –0.142; P = .001).
Conclusions and RelevanceÌý
The results of this cross-sectional imaging study support that body mass index modulates prediction error and food intake control circuitry in the brain. Once altered, this circuitry may reinforce eating disorder behaviors when paired with behavioral traits associated with overeating or undereating.
Eating disorders are severe psychiatric disorders with high mortality.1 Anorexia nervosa (AN) is characterized by severe underweight with intermittent binge eating or purging episodes; individuals with bulimia nervosa (BN) are at normal to high weight and regularly binge and purge. Binge-eating disorder (BED) is associated with binge-eating episodes and frequently elevated body weight.2 Eating disorders that do not meet full criteria for those diagnoses have been recognized as specific subgroups within the other specified feeding and eating disorders (OSFED) category of the DSM-5. Food restriction, episodic binge eating, or purging vary across the diagnostic groups, whereas body dissatisfaction and drive for thinness are typically elevated across all eating disorders, as are anxious traits and sensitivity to salient stimuli. Identifying how those behaviors are associated with particular biologic mechanisms could help create a better understanding of the underlying eating disorder pathophysiologic factors and development of specific treatments.3 To adopt a dimensional conceptualization of eating disorder specific behaviors and neurobiologic factors, we recruited individuals across the eating disorder spectrum and applied the prediction error construct from the National Institute of Mental Health Research Domain Criteria (RDoC) project.4
Brain reward circuits have been repeatedly implicated in eating disorders, and altered reward learning may play a particularly important role.5 In reward learning, the difference between an expectation and outcome yields a prediction error, a dopamine-associated signal that reinforces new associations.6,7 The direction of the prediction error is indicated by its sign, which indicates a better (positive) or worse (negative) outcome than expected. The absolute value reflects the degree of deviation of the outcome from the expectation and is related to surprise or conceptualized as a motivational salience signal.8,9 The dopamine system adapts in opposite directions to extremes of food intake.10-13 Food restriction enhances dopamine circuit activity14,15 and excessive food intake downregulates dopamine circuit activity,12 which could be relevant for eating disorder pathophysiologic factors.16-19 Studies in AN found elevated prediction error response to taste and monetary stimuli compared with healthy controls but a lower response in small studies in individuals with BN and individuals with overweight.20-23 Those studies suggested that the prediction error signal is inversely correlated with eating disorder behaviors from restrictive to loss of control food intake (binge eating).24 Furthermore, prediction error response was positively correlated in adolescent AN with ventral striatum-hypothalamus directed effective connectivity, a circuitry that has been associated with food intake control.23
For consistency with the RDoC approach, we studied a large group of individuals with eating disorders, varying on a spectrum of restrictive undereating to loss-of-control overeating. To validate previous results, we also recruited a healthy control group. First, we hypothesized that we would find inverse correlations between prediction error response and eating disorder behavior from undereating to overeating, as reflected in body mass index (BMI) and binge eating severity. This hypothesis would support basic and translational science research by externally validating a core behavioral dimension via its associations with reward-responsiveness. Second, we hypothesized that effective connectivity would be directed from the ventral striatum to the hypothalamus in the eating disorder sample. This hypothesis would support a potential trait mechanism across eating disorders to attempt to control eating drive.23 Third, we hypothesized that associations between biological and behavioral data may help develop a model to explain how traits, eating disorder behaviors, and neurobiologic factors interact and reinforce the often chronic nature of eating disorders.25
The Colorado Multiple institutional review board approved the study. All participants provided written informed consent. We recruited 197 women with an eating disorder: 69 AN restricting subtype, 22 AN binge-eating/purging subtype, 17 OSFED atypical AN subtype, 17 OSFED purging disorder subtype, 56 BN, 3 OSFED binge-eating subtype, and 13 binge eating disorder (BED). Participants with eating disorders were recruited from eating disorder partial hospitalization specialty care (EDCare Denver or Children’s Hospital Colorado) within the first 2 weeks of treatment, to mitigate effects of acute starvation or dehydration.26 Following RDoC instructions, we recruited any interested patient with eating disorders who was admitted to treatment. In addition, we recruited 120 women as healthy controls (HCs) through local advertisements. The study was conducted from June 2014 to November 2019. Data were analyzed from December 2019 to February 2020. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology () reporting guideline for cross-sectional studies.
Participants were right-handed without history of head trauma, neurological disease, major medical illness, bipolar disorder, psychosis, or current (past 3 months) substance use disorder. The healthy controls were studied during the first 10 days of the menstrual cycle to reduce potential hormonal variations. For eating disorders, treatment stage was the primary variable we controlled for, but we recorded days from last menstrual cycle as a proxy to test for hormonal variation.
Psychiatric diagnoses were assessed using the Structured Clinical Interview for DSM-5 by a doctoral-level interviewer.27 Participants completed the Eating Disorder Inventory–3 (EDI-3) for drive for thinness (intense fear of weight gain), bulimia (tendency to engage in binge eating), body dissatisfaction (discontentment with size of body regions),28 Revised Sensitivity to Punishment and Reward Questionnaire,29 State-Trait Anxiety Inventory,30 Temperament and Character Inventory for Novelty Seeking and Harm Avoidance,31 and Beck Depression Inventory-II32; participants blindly rated sugar solutions for sweetness and pleasantness using a 9-point Likert scale. A subset of participants (eating disorder, n = 128, HC, n = 84) completed the Eating Expectancy Inventory for eating leads to feeling out of control.33
Functional Magnetic Resonance Imaging
Between 7:00 am and 9:00 am on the study day, participants with eating disorders ate their meal-plan breakfast and HC ate a quality-matched and calorie-matched breakfast (Table 1). Brain imaging was performed between 8:00 am and 9:00 am using the 3-T Signa (General Electric Company) or Skyra 3-T scanner (Siemens) (eMethods 1 in the Supplement). A scanner covariate was included in the multivariate analysis of covariance model for imaging group contrasts.
The design of this study was adapted from O’Doherty et al34 (eMethods 2 and eFigure 2 in the Supplement). Participants learned to associate 3 unconditioned taste stimuli (1 molar sucrose solution, no solution, or artificial saliva) with paired conditioned visual stimuli. Each conditioned visual stimulus was probabilistically associated with its unconditioned taste stimulus such that 20% of sucrose and no solution conditioned visual stimuli trials were unexpectedly followed by no solution or sucrose unconditioned taste stimuli, respectively.
Functional Magnetic Resonance Imaging Analysis
Image preprocessing and analysis were performed using Statistical Parametric Mapping, version 1235 (Wellcome Trust Centre for Neuroimaging). Images were realigned to the first volume, normalized to the Montreal Neurological Institute template, smoothed at 6-mm full width at half maximum gaussian kernel. Data were preprocessed with slice time correction and modeled with a hemodynamic response convolved function using the general linear model, including temporal and dispersion derivatives. A 128-second high-pass filter (removing low-frequency blood oxygen level dependent signal fluctuations), motion parameters (as first-level analysis regressors), and the SPM FAST (prewhitening attenuation of autocorrelation effects) were applied.36
Prediction Error Analysis
Each participant’s prediction error signal was modeled based on trial sequence (absolute of positive and negative prediction error) and regressed with brain activation across all trials20,21,34 (eMethods 3 in the Supplement). We extracted mean parameter estimates across all voxels from 5 predefined anatomical regions of interest (ROIs) bilaterally, based on ROIs that differentiated groups previously23: bilateral dorsal anterior insula (automated anatomical labeling Atlas37), ventral anterior insula,37 caudate head,37 ventral striatum,38 and nucleus accumbens.39
Effective Connectivity Analysis
We extracted ROI functional activation for trials of expected receipt of 1 molar sucrose solution (n = 80).40 The Tetrad-V41 was used to infer effective connectivity with independent multisample greedy equivalence search and linear non-gaussian orientation, fixed structure search algorithms. We extracted edge coefficients for ventral striatum-hypothalamus (hypothalamus ROI, SPM12 WFU_PickAtlas extension42) connectivity to test for correlations with behavior or PE values based on our previous studies23 (eMethods 4 in the Supplement).
Statistical analysis was performed with SPSS 27 software (IBM). Data were tested for normality with Shapiro-Wilk test and ranked and normalized using Rankit procedure if nonnormally distributed.43 Demographic and behavior data were analyzed using analysis of variance, and post hoc analyses were Bonferroni corrected. Multivariate analysis of covariance and correlation analyses were used to test effect sizes of potential confounding categorical or continuous variables such as comorbidity, medication use, or age. Variables associated with the primary outcome variable brain response were included in a group-comparison multivariate analysis of covariance and estimated marginal means post hoc comparisons Bonferroni corrected. Partial η2 was calculated for effect size in addition to power calculations. Pearson correlation analysis was used to test associations between behavior and brain activation, CIs were calculated using bootstrap (1000 samples) and results were multiple comparisons controlled using false discovery rate.44 All P values were 2-tailed, and a P value less than .05 was considered statistically significant.
Demographic and Behavioral Variables
Of 317 female participants (197 with eating disorders and 120 healthy controls), the mean (SD) age was 23.8 (5.6) years and mean (SD) BMI (calculated as weight in kilograms divided by height in meters squared) was 20.8 (5.4). eTable 1 in the Supplement provides demographic and behavioral data for all groups. To increase power for comparison with HC, we combined restrictive and binge-eating/purging AN subgroups (AN, severe food restriction), OSFED atypical AN and purging disorder subgroups (OSFEDr, intermediate restrictive eating, normal BMI), and OSFED binge-eating and BED groups (BED, loss of control eating, elevated BMI). Combined subgroups were similar in BMI and psychological measures (Table 1; eFigure 1 in the Supplement). The overall age range was narrow across groups, but significantly lower in AN and OSFEDr and higher in BED compared with HC. BMI was lower in AN compared with the remaining groups and higher in BED compared with AN, OSFEDr and BN. High and low lifetime BMI showed similar patterns. Regular menses occurred in 16 participants with AN (18%, mean [SD] 15 [7] days from last cycle), 17 with OSFEDr (50%, mean [SD] 16 [8] days), all with HC (mean [SD] 6 [3] days), 33 with BN (59%, mean [SD] 12 [8] days), and 6 with BED (38%, mean [SD] 10 [6] days). Novelty seeking was lower in AN vs HC, BN and BED; harm avoidance, depression, drive for thinness, body dissatisfaction, bulimia, eating leads to feeling out of control, intolerance of uncertainty, reward and punishment sensitivity, and state and trait anxiety were higher in eating disorder groups vs HC. Sucrose pleasantness was lower in OSFEDr vs HC and AN. Frequency of weekly binge eating and purging episodes was higher in BN vs remaining groups. Breakfast calories were similar across groups.
Correlations between behavior data were consistent with previous research (eTable 2 in the Supplement). EDI-3 body dissatisfaction and EDI-3 drive for thinness were significantly positively correlated with scores for harm avoidance (body dissatisfaction r = 0.345; 95% CI, 0.222-0.456; P < .001; drive for thinness r = 0.357; 95% CI, 0.210-0.482; P < .001), depression (body dissatisfaction r = 0.436; 95% CI, 0.294-0.562; P < .001; drive for thinness r = 0.378; 95% CI, 0.237-0.508; P < .001), intolerance of uncertainty (body dissatisfaction r = 0.274; 95% CI, 0.138-0.396; P < .001; drive for thinness r = 0.400; 95% CI, 0.274-0.507; P < .001), sensitivity to punishment (body dissatisfaction r = 0.335; 95% CI, 0.201-0.459; P < .001; drive for thinness r = 0.354; 95% CI, 0.201-.492; P < .001), and trait anxiety (body dissatisfaction r = 0.448; 95% CI, 0.322-.566; P < .001; drive for thinness r = 0.480; 95% CI, 0.333-0.598; P &±ô³Ù; .001). EDI-3 bulimia was significantly positively correlated with BMI (r = 0.516; P &±ô³Ù; .001).
Forty-five HC and 40 participants with eating disorders took oral contraceptives (χ2 = 16.329; P &±ô³Ù; .001). Use of antidepressant or antipsychotic medication, or comorbidity with major depression, obsessive-compulsive disorder, or posttraumatic stress disorder were not differentially distributed between eating disorder groups, but comorbid anxiety disorder was (χ2 = 7.935; P = .047).
Brain Response–Behavior Correlations
In the HC group, correlations between age, BMI, or behavior and brain imaging values were not significant or were not found after multiple comparison correction.
The eating disorder group showed significant correlations between BMI (left nucleus accumbens: r = −0.291; 95% CI, −0.413 to −0.167; P < .001; left ventral anterior insula: r = −0.208; 95% CI, −0.339 to −0.070; P = .004), binge-eating frequency (left nucleus accumbens: r = −0.183; 95% CI, −0.312 to −0.055; P = .01; left ventral anterior insula: r = −0.084; 95% CI, −0.212 to −0.047; P = .26), EDI-3 bulimia (left nucleus accumbens: r = −0.207; 95% CI, −0.333 to −0.073; P = .004; left ventral anterior insula: r = −0.143; 95% CI, −0.282 to −0.010; P = .047), trait anxiety (left nucleus accumbens: r = −0.148; 95% CI, −0.288 to −0.003; P = .04; left ventral anterior insula: r = −0.166; 95% CI, −0.315 to −0.001; P = .02), and prediction error response (Table 2; eFigure 3 in the Supplement). In a partial correlation analysis, significant correlations between regional prediction error response and BMI were found when controlling for binge-eating frequency (left nucleus accumbens r = -0.192; 95% CI, −0.315 to −0.059; P = .01; right nucleus accumbens r = -0.178; 95% CI, −0.324 to −0.025; P = .02; left caudate head r = −0.237; 95% CI, −0.368 to −0.095; P = .001; right caudate head r = −0.205; 95% CI, −0.348 to −0.056 P = .01; left ventral striatum r = −0.196, 95% CI, −0.329 to −0.059; P = .01; right ventral striatum r = −0.198; 95% CI, −0.334 to −0.056; P = .01; left dorsal anterior insula r = −0.154; 95% CI, −0.277 to −0.036; P = .04; right dorsal anterior insula r = −0.153; 95% CI, −0.286 to −0.017; P = .04; left ventral anterior insula r = −0.193; 95% CI, −0.319 to −0.061; P = .01), or EDI-3 bulimia (left nucleus accumbens r = −0.213; 95% CI, −0.330 to −0.088; P = .003; right nucleus accumbens r = −0.190; 95% CI, −0.322 to −0.051; P = .01; left caudate head r = −0.200; 95% CI, −0.321 to −0.074; P = .005; right caudate head r = −0.157; 95% CI, −0.287 to −0.017; P = .03; left ventral striatum r = −0.178; 95% CI, −0.299 to −0.047; P = .01; right ventral striatum r = −0.151; 95% CI, −0.275 to −0.014; P = .04; left dorsal anterior insula r = −0.151; 95% CI, −0.264 to −0.034; P = .04; right dorsal anterior insula r = −0.144; 95% CI, −0.257 to −0.023; P = .045; left ventral anterior insula r = −0.164; 95% CI, −0.290 to −0.035; P = .02), although significant correlations with bulimia or binge frequency were not found after controlling for BMI. Number of days from last menstrual cycle was not significantly correlated with prediction error response in any group. Exploratory analysis of the combined sample did not improve results (eMethods 5 in the Supplement).
Effective connectivity was directed in HC from hypothalamus to ventral striatum. In the eating disorder sample, effective connectivity was directed from ventral striatum to hypothalamus (Figure 1). eFigure 4 in the Supplement shows individual graphs for AN and BN groups.
Extracted effective connectivity edge coefficients from right ventral striatum to hypothalamus in eating disorder correlated significantly with right-sided ventral striatum prediction error response (r = 0.189; 95% CI, 0.045-0.324; P = .01); left-sided correlation was also positive but nonsignificant (r = 0.104; 95% CI –0.030 to 95% CI 0.231; P = .15). Edge coefficients correlated in eating disorders significantly in 3 ways: first, bilaterally negatively with eating leads to feeling out of control (right sided: r = –0.328; 95% CI, –0.480 to –0.164; P < .001; left sided: r = –0.297; 95% CI –0.439 to –0.142; P = .001), intolerance of uncertainty (right sided: r = –0.213; 95% CI –0.355 to –0.047; P = .004; left sided: r = –0.221; 95% CI, –0.354 to –0.080; P = .003), and sensitivity to punishment (right sided: r = –0.163; 95% CI, –0.295 to –0.011; P = .03; left sided: r = –0.166; 95% CI, –0.307 to –0.007; P = .03); second, on the right side negatively with bulimia (r = –0.162; 95% CI, –0.291 to –0.005; P = .03), and body dissatisfaction (r = –0.147; 95% CI, –0.279 to –0.001; P = .047); and third, on the left side with drive for thinness (r = –0.182; 95% CI, –0.317 to –0.044; P = .01), harm avoidance (r = –0.183; 95% CI, –0.334 to –0.029; P = .01), and trait anxiety (r = –0.151; 95% CI, –0.278 to –0.007; P = .04).
Confounding Variables Assessment on Prediction Error Response
Multivariate analysis of covariance in the combined eating disorder group indicated no significant effect sizes for antidepressants (Wilks λ, 0.930; P = .21), antipsychotics (Wilks λ, 0.923; P = .145), major depressive disorder (Wilks λ, 0.945; P = .41), anxiety disorder (Wilks λ, 0.941; P = .09), or posttraumatic stress disorder (Wilks λ, 0.943; P = .38). However, there were significant effect sizes for scanner (Wilks λ, 0.843; P = .001), age (Wilks λ, 0.897; P = .03), and obsessive-compulsive disorder (Wilks λ, 0.900; P = .04), which were included in the prediction error group-contrast model.
Prediction Error Group Contrasts
Prediction error response significantly differentiated groups (Wilks λ, 0.843; P = .001). After Bonferroni correction, prediction error remained elevated in AN compared with HC, OSFEDr and BN in the left caudate head, compared with HC and BN and BED in left nucleus accumbens, compared with HC and BN in the right nucleus accumbens, compared with HC and BED in the left ventral striatum, and compared with BN in the left dorsal insula (Table 3; eFigure 5 in the Supplement).
This cross-sectional study in a large sample of women across the eating disorder diagnostic spectrum indicates elevated prediction error response in AN compared with HC, BN, and BED, which is consistent with previous studies. In eating disorders, prediction error response was inversely correlated with BMI and binge eating behaviors. Furthermore, ventral striatal prediction error response correlated with effective connectivity from the ventral striatum to the hypothalamus in eating disorders, indicating an association between prediction error responsiveness and strength of a circuitry that has been associated with food intake control.45
The results support basic science studies showing that prediction error response adapts to patterns of food intake.10-13 Regional prediction error response was higher the more restrictive a person’s food intake was, reflected by BMI. Binge eating frequency and the EDI-3 bulimia score were also inversely correlated but not after controlling for BMI.
Previously, prediction error was associated with harm avoidance in adolescent AN, which we did not find in adults across the eating disorder spectrum.23 It is possible that at an earlier age prediction error response affects anxiety and triggers eating disorder behaviors.46 However, during longer illness, such associations may be attenuated, after eating disorder behaviors have transformed to become a way of maintaining a sense of control.47
Ventral striatal-hypothalamic effective connectivity during sugar tasting in opposite directions between HC and eating disorder groups, together with positive correlation with ventral striatum prediction error response, extends previous results in smaller eating disorder samples.23,40 Dopamine and prediction error signaling have been associated with energy homeostasis regulation,48 and a fear mediated dopamine circuit from the ventral striatum to the hypothalamus has been identified that inhibits food intake.45,49,50 A fear-driven and dopamine-mediated circuitry to suppress eating drive thus could be a trait common to all eating disorders, which is most effective in the context of sensitized dopamine circuits, reflected in high prediction error response.
The results suggest that a data-driven model of biological and behavioral interactions that promote restrictive or excessive eating behaviors is warranted (Figure 2). Consistent with previous research, trait anxiety correlated positively with the EDI-3 bulimia score, supporting that negative emotions trigger binge eating.51 Novelty seeking and bulimia are known risk traits for elevated BMI.52,53 The negative correlation between BMI and prediction error response supports basic science, indicating a modulatory effect of amount of food intake on dopaminergic circuitry.11,12 The positive correlation between prediction error and effective connectivity further implicates dopamine circuitry in modulating brain connectivity54 and suggests that higher dopaminergic activity strengthens the ventral striatal-hypothalamic food-control circuitry enabling individuals with AN to override normal hunger cues. BN or BED, however, have lower dopaminergic activity than AN, cannot maintain consistent food intake control, which facilitates intermittent binge-eating episodes.55,56 The negative correlation between effective connectivity and eating leads to feeling out of control could indicate that stronger food intake control circuitry leads to less of an out-of-control sensation, but this is speculative and requires further study. Behaviorally, higher BMI was associated with higher body dissatisfaction, which correlated positively with intolerance of uncertainty, harm avoidance, drive for thinness, depression and sensitivity to punishment, which also correlated positively with Trait Anxiety, consistent with previous research.57-59 Body dissatisfaction triggers drive for thinness, which reinforces and is reinforced by anxiety, depression, and punishment sensitivity, increasing poor self-esteem and further promoting eating disorder behaviors.
This study has limitations. The study was well powered for group comparisons, but effect sizes were small to moderate. Correlation analyses in the eating disorder sample showed moderate to large or very large effect sizes, but correlation analyses cannot prove mechanism. The prediction error model is based on dopamine function, but other neurotransmitter systems, such as serotonin, noradrenaline, or adenosine, are likely factors in reward processing and behavior control in eating disorder behaviors.60-62 Furthermore, dopamine neuronal function was not directly measured in this study and functional magnetic resonance imaging prediction error response is only an indirect approximation.63 Whether altered prediction error response affects food intake acutely will require further study, and inverse relationships between this brain response and BMI may also exist in other conditions. The hypothalamus ROI did not separate subnuclei. While HC were studied during the first 10 days of the menstrual cycle to keep hormonal variation low, the eating disorder population was either amenorrheic or had more days from the last menstrual cycle. Not having hormonal measures is a limitation, but days from last menstrual cycle did not correlate with brain response in either group. Because eating disorder results were either higher or lower compared with HC, we do not believe that there was a systemic confound. For the prediction error analysis, we used the unsigned (absolute) prediction error. Pleasantness ratings for the 1 molar sucrose solution varied from very high to very low. Unexpectedly receiving sucrose solution could therefore be associated with positive (better than expected) or negative (worse than expected) prediction error. Studying the absolute prediction error accounts for interindividual variation and measures degree of deviation from expectation, reducing effects of subjective pleasantness.64,65 Our theoretical framework was primarily based on sensitivity to salient stimuli and adaptation of the related circuitry to food intake. We believe that using the unsigned prediction error yields more reliable results, independent from individual value computation.
Results of this study suggest that behavioral traits are factors in eating disorder initiation and extremes of eating and then alter prediction error–related reward response. This process reinforces in opposite ways the ventral striatal-hypothalamic food control circuitry, which is activated in response to sugar taste as a trait in eating disorders. Clinically it therefore may be important to implement weight gain in eating disorders in people with underweight and weight loss in eating disorders associated with overweight to normalize brain function and behavior. This topic is controversial, though, and the critical question remains what the best BMI for a person is in this context. Furthermore, temperamental traits are biologically oriented behaviors that affect eating disorder behaviors. Treatment modules that specifically target those behaviors may be a key element to promote behavior change and lasting recovery.
Accepted for Publication: May 3, 2021.
Published Online: June 30, 2021. doi:10.1001/jamapsychiatry.2021.1580
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Frank GKW et al. JAMA Psychiatry.
Corresponding Author: Guido K. W. Frank, MD, University of California San Diego, Department of Psychiatry, 4510 Executive Dr, Ste 315, San Diego, CA 92121 (gfrank@health.ucsd.edu).
Author Contributions: Dr Frank had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Frank, Shott, Pryor.
Acquisition, analysis, or interpretation of data: Frank, Shott, Stoddard, Swindle.
Drafting of the manuscript: Frank, Shott, Pryor.
Critical revision of the manuscript for important intellectual content: Frank, Shott, Stoddard, Swindle.
Statistical analysis: Frank, Shott, Stoddard.
Obtained funding: Frank.
Administrative, technical, or material support: Frank, Shott, Stoddard, Pryor.
Supervision: Frank.
Conflict of Interest Disclosures: Dr Frank reported grants from National Institute of Mental Health Grant for the study, no conflict of interest during the conduct of the study. Dr Stoddard reported grants from NIMH RO1MH103436 and grants from NIMH K23MH113731 during the conduct of the study. No other disclosures were reported.
Funding/Support: The study was supported by NIMH grants MH096777 and MH103436.
Role of the Funder/Sponsor: The funding organization did not directly have a role in design or conduct of the study, collection, analysis or interpretation of the data, or publication decision. Data and/or research tools used in the preparation of this manuscript were obtained from the National Institute of Mental Health (NIMH) Data Archive (NDA). NDA is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in mental health. Dataset identifier(s): Collection ID C2138, DOI: 10.15154/1520900.
Disclaimer: This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDA.
Additional Contributions: We thank all the individuals who participated in this study.
1.Crow
ÌýSJ, Peterson
ÌýCB, Swanson
ÌýSA,
Ìýet al. ÌýIncreased mortality in bulimia nervosa and other eating disorders.Ìý ÌýAm J Psychiatry. 2009;166(12):1342-1346. doi:
2.American Psychiatric Association. ÌýDesk Reference to the Diagnostic Criteria From DSM-5. Washington, DC: American Psychiatric Publishing; 2013.
3.Kaye
ÌýWH, Wierenga
ÌýCE, Bailer
ÌýUF, Simmons
ÌýAN, Bischoff-Grethe
ÌýA. ÌýNothing tastes as good as skinny feels: the neurobiology of anorexia nervosa.Ìý ÌýTrends Neurosci. 2013;36(2):110-120. doi:
4.National Institute of Mental Health. Research domain criteria (RDoC). Accessed May 26, 2021.
5.Schaefer
ÌýLM, Steinglass
ÌýJE. ÌýReward learning through the lens of RDoC: a review of theory, assessment, and empirical findings in the eating disorders.Ìý ÌýCurr Psychiatry Rep. 2021;23(1):2. doi:
6.Schultz
ÌýW. ÌýDopamine reward prediction error coding.Ìý ÌýDialogues Clin Neurosci. 2016;18(1):23-32. doi:
7.O’Doherty
ÌýJP, Cockburn
ÌýJ, Pauli
ÌýWM. ÌýLearning, reward, and decision making.Ìý ÌýAnnu Rev Psychol. 2017;68:73-100. doi:
8.Fouragnan
ÌýE, Queirazza
ÌýF, Retzler
ÌýC, Mullinger
ÌýKJ, Philiastides
ÌýMG. ÌýSpatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans.Ìý ÌýSci Rep. 2017;7(1):4762. doi:
9.D’Ardenne
ÌýK, Lohrenz
ÌýT, Bartley
ÌýKA, Montague
ÌýPR. ÌýComputational heterogeneity in the human mesencephalic dopamine system.Ìý ÌýCogn Affect Behav Neurosci. 2013;13(4):747-756. doi:
10.Avena
ÌýNM, Rada
ÌýP, Hoebel
ÌýBG. ÌýUnderweight rats have enhanced dopamine release and blunted acetylcholine response in the nucleus accumbens while bingeing on sucrose.Ìý Ìý±·±ð³Ü°ù´Ç²õ³¦¾±±ð²Ô³¦±ð. 2008;156(4):865-871. doi:
11.Carr
ÌýKD. ÌýChronic food restriction: enhancing effects on drug reward and striatal cell signaling.Ìý ÌýPhysiol Behav. 2007;91(5):459-472. doi:
12.Johnson
ÌýPM, Kenny
ÌýPJ. ÌýDopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats.Ìý ÌýNat Neurosci. 2010;13(5):635-641. doi:
13.Volkow
ÌýND. ÌýAddiction reviews: introduction.Ìý ÌýAnn N Y Acad Sci. 2008;1141:xi-xii. doi:
14.Oinio
ÌýV, Bäckström
ÌýP, Uhari-Väänänen
ÌýJ, Raasmaja
ÌýA, Piepponen
ÌýP, Kiianmaa
ÌýK. ÌýDopaminergic modulation of reward-guided decision making in alcohol-preferring AA rats.Ìý ÌýBehav Brain Res. 2017;326:87-95. doi:
15.Kelley
ÌýAE. ÌýVentral striatal control of appetitive motivation: role in ingestive behavior and reward-related learning.Ìý ÌýNeurosci Biobehav Rev. 2004;27(8):765-776. doi:
16.Carr
ÌýKD, Tsimberg
ÌýY, Berman
ÌýY, Yamamoto
ÌýN. ÌýEvidence of increased dopamine receptor signaling in food-restricted rats.Ìý Ìý±·±ð³Ü°ù´Ç²õ³¦¾±±ð²Ô³¦±ð. 2003;119(4):1157-1167. doi:
17.Carr
ÌýKD, Cabeza de Vaca
ÌýS, Sun
ÌýY, Chau
ÌýLS. ÌýReward-potentiating effects of D-1 dopamine receptor agonist and AMPAR GluR1 antagonist in nucleus accumbens shell and their modulation by food restriction.Ìý ÌýPsychopharmacology (Berl). 2009;202(4):731-743. doi:
18.Monteleone
ÌýAM, Castellini
ÌýG, Volpe
ÌýU,
Ìýet al. ÌýNeuroendocrinology and brain imaging of reward in eating disorders: a possible key to the treatment of anorexia nervosa and bulimia nervosa.Ìý ÌýProg Neuropsychopharmacol Biol Psychiatry. 2018;80(Pt B):132-142. doi:
19.Frank
ÌýGKW, DeGuzman
ÌýMC, Shott
ÌýME. ÌýMotivation to eat and not to eat: the psycho-biological conflict in anorexia nervosa.Ìý ÌýPhysiol Behav. 2019;206:185-190. doi:
20.DeGuzman
ÌýM, Shott
ÌýME, Yang
ÌýTT, Riederer
ÌýJ, Frank
ÌýGKW. ÌýAssociation of elevated reward prediction error response with weight gain in adolescent anorexia nervosa.Ìý ÌýAm J Psychiatry. 2017;174(6):557-565. doi:
21.Frank
ÌýGK, Reynolds
ÌýJR, Shott
ÌýME,
Ìýet al. ÌýAnorexia nervosa and obesity are associated with opposite brain reward response.Ìý Ìý±·±ð³Ü°ù´Ç±è²õ²â³¦³ó´Ç±è³ó²¹°ù³¾²¹³¦´Ç±ô´Ç²µ²â. 2012;37(9):2031-2046. doi:
22.Frank
ÌýGKW, Reynolds
ÌýJR, Shott
ÌýME, O’Reilly
ÌýRC. ÌýAltered temporal difference learning in bulimia nervosa.Ìý ÌýBiol Psychiatry. 2011;70(8):728-735. doi:
23.Frank
ÌýGKW, DeGuzman
ÌýMC, Shott
ÌýME, Laudenslager
ÌýML, Rossi
ÌýB, Pryor
ÌýT. ÌýAssociation of brain reward learning response with harm avoidance, weight gain, and hypothalamic effective connectivity in adolescent anorexia nervosa.Ìý ÌýJAMA Psychiatry. 2018;75(10):1071-1080. doi:
24.Frank
ÌýGKW, Shott
ÌýME, DeGuzman
ÌýMC. ÌýThe neurobiology of eating disorders.Ìý ÌýChild Adolesc Psychiatr Clin N Am. 2019;28(4):629-640. doi:
25.Wonderlich
ÌýS, Mitchell
ÌýJE, Crosby
ÌýRD,
Ìýet al. ÌýMinimizing and treating chronicity in the eating disorders: a clinical overview.Ìý ÌýInt J Eat Disord. 2012;45(4):467-475. doi:
26.Frank
ÌýGKW, Favaro
ÌýA, Marsh
ÌýR, Ehrlich
ÌýS, Lawson
ÌýEA. ÌýToward valid and reliable brain imaging results in eating disorders.Ìý ÌýInt J Eat Disord. 2018;51(3):250-261. doi:
27.First
ÌýMB, Williams
ÌýJBW, Karg
ÌýRS, Spitzer
ÌýRL. ÌýUser’s Guide for the Structured Clinical Interview for DSM-5 Disorders, Research Version (SCID-5-RV). Arlington, VA.: American Psychiatric Association.; 2015.
28.Garner
ÌýD. ÌýEating Disorder Inventoryâ„¢-3 (EDIâ„¢-3). Lutz, FL: Psychological Assessment Resources, Inc.; 2004.
29.Torrubia
ÌýR, Avila
ÌýC, Molto
ÌýJ, Caseras
ÌýX. ÌýThe Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) as a measure of Gray's anxiety and impulsivity dimensions.Ìý ÌýPers Individ Dif. 2001;31:837-862. doi:
30.Spielberger
ÌýCD. ÌýManual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press, Inc.; 1983.
31.Cloninger
ÌýCR, Przybeck
ÌýTR, Svrakic
ÌýDM, Wetzel
ÌýRD. ÌýThe Temperament and Character Inventory (TCI): A Guide to its Development and Use. St. Louis, MO: Center for Psychobiology of Personality, Washington University; 1994.
32.Beck
ÌýAT, Steer
ÌýRA, Ball
ÌýR, Ranieri
ÌýW. ÌýComparison of Beck Depression Inventories -IA and -II in psychiatric outpatients.Ìý ÌýJ Pers Assess. 1996;67(3):588-597. doi:
33.Hohlstein
ÌýL, Smith
ÌýG, Atlas
ÌýJ. ÌýAn application of expectancy theory on eating disorders: development and validation of measures of eating and dieting expectancies.Ìý ÌýPsychol Assess. 1998;10:49-58. doi:
34.O’Doherty
ÌýJP, Dayan
ÌýP, Friston
ÌýK, Critchley
ÌýH, Dolan
ÌýRJ. ÌýTemporal difference models and reward-related learning in the human brain.Ìý Ìý±·±ð³Ü°ù´Ç²Ô. 2003;38(2):329-337. doi:
35.Mapping
ÌýStatistical Parametric. SPM12. Accessed May 26, 2021.
36.Olszowy
ÌýW, Aston
ÌýJ, Rua
ÌýC, Williams
ÌýGB. ÌýAccurate autocorrelation modeling substantially improves fMRI reliability.Ìý ÌýNat Commun. 2019;10(1):1220. doi:
37.Tzourio-Mazoyer
ÌýN, Landeau
ÌýB, Papathanassiou
ÌýD,
Ìýet al. ÌýAutomated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.Ìý Ìý±·±ð³Ü°ù´Ç¾±³¾²¹²µ±ð. 2002;15(1):273-289. doi:
38.O’Doherty
ÌýJ, Dayan
ÌýP, Schultz
ÌýJ, Deichmann
ÌýR, Friston
ÌýK, Dolan
ÌýRJ. ÌýDissociable roles of ventral and dorsal striatum in instrumental conditioning.Ìý Ìý³§³¦¾±±ð²Ô³¦±ð. 2004;304(5669):452-454. doi:
39.Breiter
ÌýHC, Gollub
ÌýRL, Weisskoff
ÌýRM,
Ìýet al. ÌýAcute effects of cocaine on human brain activity and emotion.Ìý Ìý±·±ð³Ü°ù´Ç²Ô. 1997;19(3):591-611. doi:
40.Frank
ÌýGK, Shott
ÌýME, Riederer
ÌýJ, Pryor
ÌýTL. ÌýAltered structural and effective connectivity in anorexia and bulimia nervosa in circuits that regulate energy and reward homeostasis.Ìý ÌýTransl Psychiatry. 2016;6(11):e932. doi:
41.Ramsey
ÌýJD, Hanson
ÌýSJ, Hanson
ÌýC, Halchenko
ÌýYO, Poldrack
ÌýRA, Glymour
ÌýC. ÌýSix problems for causal inference from fMRI.Ìý Ìý±·±ð³Ü°ù´Ç¾±³¾²¹²µ±ð. 2010;49(2):1545-1558. doi:
42.NITRC. WFU_PickAtlas. Accessed May 26, 2021.
43.Solomon
ÌýS, Sawilowsky
ÌýS. ÌýImpact of rank-based normalizing transformations on the accuracy of test scores.Ìý ÌýJournal of Modern Applied Statistical Methods. 2009;8(2):448-462. doi:
44.Benjamini
ÌýY, Hochberg
ÌýY. ÌýControlling the false discovery rate: a practical and powerful approach to multiple testing.Ìý ÌýJ R Statist Soc B. 1995;57(289-300). doi:
45.Stratford
ÌýTR, Kelley
ÌýAE. ÌýEvidence of a functional relationship between the nucleus accumbens shell and lateral hypothalamus subserving the control of feeding behavior.Ìý ÌýJ Neurosci. 1999;19(24):11040-11048. doi:
46.Kraus
ÌýN, Niedeggen
ÌýM, Hesselmann
ÌýG. ÌýTrait anxiety is linked to increased usage of priors in a perceptual decision making task.Ìý Ìý°ä´Ç²µ²Ô¾±³Ù¾±´Ç²Ô. 2021;206:104474. doi:
47.Dignon
ÌýA, Beardsmore
ÌýA, Spain
ÌýS, Kuan
ÌýA. Ìý‘Why I won’t eat’: patient testimony from 15 anorexics concerning the causes of their disorder.Ìý ÌýJ Health Psychol. 2006;11(6):942-956. doi:
48.Chakravarthy
ÌýS, Balasubramani
ÌýPP, Mandali
ÌýA, Jahanshahi
ÌýM, Moustafa
ÌýAA. ÌýThe many facets of dopamine: toward an integrative theory of the role of dopamine in managing the body’s energy resources.Ìý ÌýPhysiol Behav. 2018;195:128-141. doi:
49.O’Connor
ÌýEC, Kremer
ÌýY, Lefort
ÌýS,
Ìýet al. ÌýAccumbal D1R neurons projecting to lateral hypothalamus authorize feeding.Ìý Ìý±·±ð³Ü°ù´Ç²Ô. 2015;88(3):553-564. doi:
50.Castro
ÌýDC, Cole
ÌýSL, Berridge
ÌýKC. ÌýLateral hypothalamus, nucleus accumbens, and ventral pallidum roles in eating and hunger: interactions between homeostatic and reward circuitry.Ìý ÌýFront Syst Neurosci. 2015;9:90. doi:
51.Forrest
ÌýLN, Sarfan
ÌýLD, Ortiz
ÌýSN, Brown
ÌýTA, Smith
ÌýAR. ÌýBridging eating disorder symptoms and trait anxiety in patients with eating disorders: a network approach.Ìý ÌýInt J Eat Disord. 2019;52(6):701-711. doi:
52.Hintsanen
ÌýM, Jokela
ÌýM, Cloninger
ÌýCR,
Ìýet al. ÌýTemperament and character predict body-mass index: a population-based prospective cohort study.Ìý ÌýJ Psychosom Res. 2012;73(5):391-397. doi:
53.Vainik
ÌýU, GarcÃa-GarcÃa
ÌýI, Dagher
ÌýA. ÌýUncontrolled eating: a unifying heritable trait linked with obesity, overeating, personality and the brain.Ìý ÌýEur J Neurosci. 2019;50(3):2430-2445. doi:
54.Nagano-Saito
ÌýA, Leyton
ÌýM, Monchi
ÌýO, Goldberg
ÌýYK, He
ÌýY, Dagher
ÌýA. ÌýDopamine depletion impairs frontostriatal functional connectivity during a set-shifting task.Ìý ÌýJ Neurosci. 2008;28(14):3697-3706. doi:
55.Volkow
ÌýND, Fowler
ÌýJS, Wang
ÌýGJ. ÌýRole of dopamine in drug reinforcement and addiction in humans: results from imaging studies.Ìý ÌýBehav Pharmacol. 2002;13(5-6):355-366. doi:
56.Kelley
ÌýAE, Berridge
ÌýKC. ÌýThe neuroscience of natural rewards: relevance to addictive drugs.Ìý ÌýJ Neurosci. 2002;22(9):3306-3311. doi:
57.Cruz-Sáez
ÌýS, Pascual
ÌýA, Wlodarczyk
ÌýA, Echeburúa
ÌýE. ÌýThe effect of body dissatisfaction on disordered eating: the mediating role of self-esteem and negative affect in male and female adolescents.Ìý ÌýJ Health Psychol. 2020;25(8):1098-1108. doi:
58.Jappe
ÌýLM, Frank
ÌýGK, Shott
ÌýME,
Ìýet al. ÌýHeightened sensitivity to reward and punishment in anorexia nervosa.Ìý ÌýInt J Eat Disord. 2011;44(4):317-324. doi:
59.Frank
ÌýGK, Roblek
ÌýT, Shott
ÌýME,
Ìýet al. ÌýHeightened fear of uncertainty in anorexia and bulimia nervosa.Ìý ÌýInt J Eat Disord. 2012;45(2):227-232. doi:
60.Fischer
ÌýAG, Ullsperger
ÌýM. ÌýAn update on the role of serotonin and its interplay with dopamine for reward.Ìý ÌýFront Hum Neurosci. 2017;11:484. doi:
61.Morita
ÌýK, Kawaguchi
ÌýY. ÌýA dual role hypothesis of the cortico-basal-ganglia pathways: opponency and temporal difference through dopamine and adenosine.Ìý ÌýFront Neural Circuits. 2019;12:111. doi:
62.Verhagen
ÌýLA, Luijendijk
ÌýMC, Korte-Bouws
ÌýGA, Korte
ÌýSM, Adan
ÌýRA. ÌýDopamine and serotonin release in the nucleus accumbens during starvation-induced hyperactivity.Ìý ÌýEur Neuropsychopharmacol. 2009;19(5):309-316. doi:
63.Diederen
ÌýKM, Ziauddeen
ÌýH, Vestergaard
ÌýMD, Spencer
ÌýT, Schultz
ÌýW, Fletcher
ÌýPC. ÌýDopamine modulates adaptive prediction error coding in the human midbrain and striatum.Ìý ÌýJ Neurosci. 2017;37(7):1708-1720. doi:
64.Diederen
ÌýKMJ, Fletcher
ÌýPC. ÌýDopamine, prediction error and beyond.Ìý Ìý±·±ð³Ü°ù´Ç²õ³¦¾±±ð²Ô³Ù¾±²õ³Ù. 2021;27(1):30-46. doi:
65.Schultz
ÌýW. ÌýRecent advances in understanding the role of phasic dopamine activity.Ìý Ìý¹ó1000¸é±ð²õ. 2019;8:8. doi: