Laura A. Berner

Individuals often consume tasty, calorically dense foods in response to negative emotions, a phenomenon exemplified by notions of ‘stress eating’ and ‘comfort food’. While this link between food and mood can become pathological in binge eating, the decision-making processes underlying this link are poorly understood. Here, we investigated the impact of acute increases in negative affect on when and how strongly the perceived tastiness and healthiness of foods influence food choices in healthy adults and individuals with bulimia nervosa (BN), an eating disorder characterized by cycles of over- and under-consumption of food. In a randomized crossover design, 25 women with BN and 21 healthy controls completed two sessions where they received either a neutral or negative affect induction and then completed a food choice task. Using a time-varying diffusion decision model, we assessed how negative affect influences food choice dynamics for high- and low-fat foods. In the neutral affect condition, individuals with BN considered tastiness relative to healthiness of high-fat foods sooner than healthy controls but maintained a restrictive food choice policy by reducing the weight on tastiness. After a negative affect induction, both groups showed a stronger bias toward considering tastiness before healthiness, but this bias was exaggerated in individuals with BN. This affect-induced bias for high-fat foods predicted more frequent subjective binge episodes over 3 months. These results provide insights into how negative emotion influences food choices and may explain why binge eating in BN is more likely during high negative affect, while dietary restriction is more likely during low negative affect.

Both obsessive thoughts and an urge to act have been linked to compulsions cross-sectionally. However, prominent theories of compulsivity disagree quite fundamentally about whether compulsions are goal-oriented attempts to reduce or eliminate obsessive thoughts or are urge-driven and habitual. To shed light on this debate, we used multilevel vector autoregression modelling to analyse contemporaneous and temporal associations based on self-report symptoms and cognition from N = 179 individuals with and without diagnoses who performed compulsions. Six weeks of three-times daily timeseries data with a mean lag of five hours between assessments were gathered from a transdiagnostic sample who checked their phone compulsively, binge ate, or engaged in compulsions commonly seen in OCD (e.g. checking, cleaning). Across compulsions, the strongest within-person correlate of compulsions were urges (β = 0.61, p _FDR < 0.001) and this was 3 times larger than obsessive thoughts and 4 times larger than anxiety. We found that urges predicted compulsions at the next assessment time point (β = 0.09, p _FDR < 0.001), while compulsions themselves had a markedly low tendency to predict themselves (β = 0.04) compared to the other mental health symptoms sampled (0.12 < β < 0.23). Finally, like habits, compulsions occurred more often when executive functioning was low (β = 0.09, p _FDR < 0.001). Based on real-time dynamics of compulsions, we find that urges, but not obsessive thoughts, predict compulsivity five hours later. Intriguingly, the associations were remarkably similar across compulsions, supporting the notion that transdiagnostically, a habit-like mechanism may underlie compulsive actions.

Background: In a recent coordinated meta-analysis of neuroimaging data, we reported gray matter (GM) alterations in acutely underweight patients with anorexia nervosa (AN). Here, we extend these findings by examining individual variation in brain structure within AN, individual-level differentiation between AN and healthy controls (HC), and differences between AN subtypes, with potential relevance for understanding clinical heterogeneity. Methods and findings: We analyzed individual-level data from 11 international sites in the ENIGMA Eating Disorders Working Group, including 570 female participants with AN and 739 HC. We examined cortical thickness, cortical surface area and subcortical volumes in AN versus HC using three complementary approaches: (i) group-level differences in a mega-analysis correcting for age effects, (ii) frequencies of extreme deviations (infra-/supranormal; z < −1.96/z > 1.96) based on normative reference models by the CentileBrain Initiative, and (iii) individual-level classification performance using machine learning. The same analytic framework was applied to compare AN restricting versus binge-eating/purging subtype, additionally correcting for BMI effects. Mega-analyses reinforced previous meta-analytic findings of pronounced and widespread GM deficits in AN compared to HC. Normative modelling revealed that the frequency of infranormal z-scores (23/68 cortical thickness, 13/14 subcortical volume metrics) and supranormal z-scores (35/68 cortical thickness, 17/68 cortical surface area metrics) was significantly higher in AN than expected based on reference data. Individuals with AN could be reliably differentiated from HC using machine-learning classifiers (ROC–AUC = 0.75–0.81). In contrast, neither group-level differences nor frequency of extreme z-scores differed between AN subtypes, and individuals with different subtypes could not be reliably differentiated from each other. Importantly, the observational design cannot distinguish neurobiological differences related to AN from the effects of starvation or low BMI in the AN versus HC analyses. The lack of differences between subtypes does not exclude brain structural differences between AN subtypes that might be detectable with other modalities or analytic approaches. Conclusion: Using a mega-analytic approach, we confirm widespread GM deficits in AN, show that these alterations are (in some patients) extreme, and demonstrate that they enable robust classification with superior performance compared to most MRI-based psychiatric classification studies. The absence of differences between AN subtypes may reflect shared neurobiology, though other imaging modalities may reveal distinctions beyond brain structure.

Adaptive decision-making requires dynamic arbitration between internal beliefs and incoming evidence, yet the role that environment structure plays in shaping these dynamics has received little systematic attention. We used three inductive reasoning and three bandit tasks to investigate these trial-by-trial dynamics in N=120 healthy adults. Using a novel computational model, we found that, in a two-option bandit task with positive valence, as well as in a three-option bandit task with negative valence, a significant number of participants were characterized by suboptimal arbitrations, reflecting increased reliance on priors under high uncertainty and increased reliance on evidence under high confidence. These findings demonstrate that design elements in widely used tasks can bias belief updating dynamics in healthy individuals leading to population-level suboptimal behaviors. These biases may have been overlooked in the past due to simplifications in the characterization of belief updating dynamics, potentially affecting analysis and comparisons with clinical cohorts.

Craving and maladaptive choices are intertwined across addictive disorders, yet the specific computational mechanisms mediating their interactions remain elusive. Here we tested a hypothesis that momentary craving and reinforcement learning influence each other during substance-related decision-making. Two substance-using groups with moderate to high addiction risk levels (alcohol drinkers and cannabis users; total n = 132) performed a decision-making task in which they received a group-specific addictive cue or monetary outcomes and reported moment-to-moment subjective craving. Computational modeling revealed that momentary craving biased substance-specific learning rate in both groups, but in opposite directions. In addition, expected values and outcomes jointly influenced elicited craving across groups and decision contexts. Finally, regressions incorporating model-derived parameters best predicted alcohol, but not cannabis, addiction risk scores, supporting the selective utility of using these model-based parameters in making clinical predictions. Together, these findings provide a computational framework that accounts for the interaction between craving and maladaptive choices across addictive domains.

The increased prevalence of binge eating during puberty is predominantly in girls, coinciding with a surge in pubertal hormones. This suggests that hormone-activated alterations in widespread brain networks, such as attention network, can contribute to the pathophysiology of the disorder in girls, while distinct mechanisms may exist in boys. This study proposed to examine the topological properties and their temporal dynamics of the sustained attention network in preadolescent children with binge eating symptoms (BE) and matched controls and to test the relation of these properties to circulating levels of pubertal hormones. Data from 77 children with BE and 104 group-matched controls were analyzed. In a static network comprising the entire task duration, the nodal topological properties, i.e., nodal efficiency, betweenness-centrality and degree, of the caudate nucleus, hippocampus and inferior parietal gyrus (IPG) significantly differentiated children with and without BE; and that of left caudate were significantly associated with pubertal hormone levels in girls with and without BE, but not in boys. During different substages of sustained attention processing, Variability of the topological properties in key network nodes, such as bilateral IPG, bilateral precentral gyrus and left hippocampus, demonstrated significant between-group differences and/or unique group-by-sex interactions. These results suggest that the association between pubertal hormones and network topological organization may contribute towards the specific rise of BE in girls, while neural mechanisms of BE in boys may alternatively link to suboptimal functional dynamics associated with precentral gyrus, during their interactions with other cortical and subcortical regions when sustained attention is performed.

Lee and Chi analyze publication trends and research themes on eating disorders over the last five decades. Among the factors driving increased annual citations were publications in journals focused on neuroscience. This observation underscores a growing focus on the neurobiological basis of eating disorders. This commentary highlights a few features of this development and outlines several potentially fruitful areas for future neuroscience‐based research.

The increased prevalence of binge eating during puberty is predominantly in girls, coinciding with a surge in pubertal hormones. This suggest that hormone-activated alterations in widespread brain networks, such as attention network, can contribute to the pathophysiology of the disorder in girls, while distinct mechanisms may exist in boys. This study proposed to examine the topological properties and their temporal dynamics of the sustained attention network in preadolescent children with binge eating symptoms (BE) and matched controls and to test the relation of these properties to circulating levels of pubertal hormones. Data from 77 children with BE and 104 group-matched controls were analyzed. The nodal topological properties, i.e., nodal efficiency, betweenness-centrality and degree, of the caudate nucleus, hippocampus and inferior parietal gyrus (IFG) significantly differentiated children with and without BE; and that of left caudate were significantly associated with pubertal hormone levels in girls with and without BE, but not in boys. Variability of the topological properties in key network nodes, such as bilateral IFG, bilateral precentral gyrus and left hippocampus, demonstrated significant between-group differences and/or unique group-by-sex interactions during different substages of sustained attention processing. These results suggest that the specific rise of BE in girls may be partially related to hormone-triggered alterations associated with caudate nucleus, while neural mechanisms of BE in boys may link to suboptimal functional dynamics associated with precentral gyrus, during their interactions with other cortical and subcortical neural clusters when sustained attention is performed.

Bulimia nervosa (BN) is a disabling eating disorder that is associated with costly medical morbidity and often follows a chronic course. Novel treatments are needed, particularly those that directly target symptom-maintaining mechanisms. One such mechanism may be reduced activation of the prefrontal cortex (PFC) during attempts to control behavioral responses. In this proof-of-principle project, we propose to develop, establish feasibility, and preliminarily test a novel neurofeedback procedure that is intended to increase PFC activation and enhance the ability to control the consumption of common binge foods. We will compare the effects of one session of real and sham neurofeedback during eating on neural activation, inhibitory control, and clinical symptoms in women with BN. To our knowledge, this will be the first test of neurofeedback in BN to date. Results will establish this new technique’s potential to clarify causal mechanisms of BN symptoms and inform future clinical trials. Trial Registration: NCT05614024.

Objective: Bulimia nervosa (BN) is associated with loss-of-control (LOC) eating episodes that frequently occur in response to negative emotions. According to recent neurocomputational models, this link could be explained by a failure to accurately update beliefs about the body in states of high arousal. Specifically, these interoceptive inference models suggest that under-relying on signals from one’s body about sensory experience (“low sensory precision”) and/or over-relying on previously held beliefs (“excessively precise priors”) lead to inaccurate perception and maladaptive behaviors. We conducted an initial test of these core predictions of the interoceptive inference model in BN using self-report measures. Methods: We compared women with BN (n = 30) and age-, BMI-, and full-scale IQ-matched controls (n = 31) on trust in sensory information from the body and two types of beliefs about what can be done to regulate high negative affect. Within the BN group, we tested interrelations among these measures and explored their associations with LOC eating frequency. Results: Compared with healthy controls, the BN group reported lower levels of trust in sensory information and stronger beliefs that once upset, there is little one can do, apart from eating, to self-regulate. These beliefs were associated with each other and with lower body trust. Beliefs about the uncontrollability of emotion were associated with more frequent subjective binge-eating episodes. Conclusions: Findings provide initial support for the core predictions of an interoceptive inference account of BN: low trust in sensory information (“sensory precision”) may promote an overreliance on maladaptive “prior beliefs” about the effects of eating on negative emotions, ultimately interfering with accurate updating of beliefs about other strategies that could regulate emotions and maintain LOC eating. Low body trust, strong expectations about emotions, and their neurocomputational underpinnings could be promising combined treatment targets for BN.