Annual Research Review: Neural mechanisms of eating disorders in youth - from current theory and findings to future directions.

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Learn more: PMC Disclaimer | PMC Copyright Notice J Child Psychol Psychiatry . 2025 Aug 12;67(4):444–466. doi: 10.1111/jcpp.70029 Search in PMC Search in PubMed View in NLM Catalog Add to search Annual Research Review: Neural mechanisms of eating disorders in youth – from current theory and findings to future directions Kelsey Hagan Kelsey Hagan 1 Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA 2 Institute for Women's Health, Virginia Commonwealth University, Richmond, VA, USA Find articles by Kelsey Hagan 1, 2, ✉ , E Caitlin Lloyd E Caitlin Lloyd 3 Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA 4 New York State Psychiatric Institute, New York, NY, USA Find articles by E Caitlin Lloyd 3, 4 , Sasha Gorrell Sasha Gorrell 5 Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA Find articles by Sasha Gorrell 5 Author information Article notes Copyright and License information 1 Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA 2 Institute for Women's Health, Virginia Commonwealth University, Richmond, VA, USA 3 Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA 4 New York State Psychiatric Institute, New York, NY, USA 5 Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA * Correspondence , Kelsey Hagan, 501 N. 2nd Street, P.O. Box 980253, Richmond, VA 23284, USA; Email: [email protected] ✉ Corresponding author. Accepted 2025 Jul 3; Issue date 2026 Apr. © 2025 The Author(s). Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMC Copyright notice PMCID: PMC13036399  NIHMSID: NIHMS2151955 PMID: 40791012 Abstract Eating disorders are prevalent and profoundly debilitating psychiatric conditions with multifactorial etiology that frequently manifest during adolescence. This developmental stage is characterized by significant neurostructural and neurofunctional change, which may create a context conducive to the emergence of eating pathology. In this Annual Research Review, we examine notable changes in brain structure and function that occur during adolescence and elucidate theoretical models that connect neural modifications to eating disorders. Subsequently, we present a narrative review and critical analysis of the extant research on the neural correlates of eating disorders in adolescents and young adults (up to age 24). We conclude by pinpointing gaps in the literature and highlighting avenues for future inquiries into the neural correlates of eating disorders in youth. Overall, this Annual Research Review emphasizes the scarcity of research focused on the neural correlates of eating disorders in young persons and its predominant emphasis on anorexia nervosa in comparison to other eating disorders thus far. Future neurobiological investigations in adolescent eating disorders hold the promise of advancing our knowledge of these complex conditions and improving therapeutic outcomes through the development of mechanistic interventions. Keywords: Eating disorders, anorexia nervosa, bulimia nervosa, binge‐eating disorder, avoidant/restrictive food intake disorder, neurobiology, adolescent eating disorders Introduction Eating disorders (EDs) are complex psychiatric illnesses hallmarked by persistent disturbances in eating behavior and are associated with psychosocial impairment and distress (American Psychiatric Association, 2022 ). The etiology and maintenance of EDs are multifactorial, with research suggesting a dynamic interplay of sociocultural, familial, psychological, and neurobiological processes (Culbert, Racine, & Klump, 2015 ). EDs commonly emerge in adolescence (Solmi et al., 2022 ), coinciding with pubertal onset and major neurofunctional and neurostructural changes. However, knowledge of the neural mechanisms underpinning EDs has lagged behind the understanding of other factors that are thought to drive risk for, and maintenance of, ED pathology. The overarching aims of this Annual Research Review are: (1) to provide a comprehensive background of the development of major neural systems in youth and their theorized links to EDs and (2) to summarize the literature to date on neural mechanisms of EDs in youth. We conclude by critically synthesizing this literature and identifying gaps and priority areas for future research on the neural mechanisms of EDs in youth. ED definitions The specified eating and feeding disorders recognized by major nosological systems include anorexia nervosa (AN), bulimia nervosa (BN), binge‐eating disorder (BED), avoidant/restrictive food intake disorder (ARFID), pica, and rumination disorder. Here, pica and rumination disorder are not reviewed, given the paucity of research on the neural mechanisms of these conditions. AN is hallmarked by persistent restrictive eating, resulting in objectively low body weight (e.g., <5 th BMI‐for‐age percentile) or failure to gain weight as expected. This self‐starvation is driven by overvaluation of weight and/or shape on self‐worth and/or fear of weight gain; though some with AN may not endorse this motivation (Vanzhula et al., 2024 ). There are two subtypes: one characterized by persistent restrictive eating (AN‐R) and one characterized by engagement in binge eating and/or purging (e.g., self‐induced vomiting, laxative misuse; AN‐BP). Recently, the ED field has acknowledged atypical AN, a presentation in which all criteria for AN are fulfilled except for underweight. Consensus on where to place atypical AN within the diagnostic nomenclature has not been reached, but research often lumps atypical AN and AN participants in analyses. ARFID is delineated by a persistent pattern of avoidant or selective eating that fails to meet nutritional needs and results in low body weight (or failure to gain weight), nutritional deficiency, dependence on enteral feeding or supplements, and/or psychosocial impairment. Eating disturbance in ARFID may be driven by one or a combination of three presentations: (1) fear of aversive consequences following a traumatic experience with eating (e.g., choking and vomiting), (2) sensory sensitivity, and (3) apparent lack of interest in eating or low appetite. Body‐image concerns do not drive ARFID. BN is characterized by body‐image concerns alongside recurrent episodes of inappropriate compensatory behavior (e.g., fasting, self‐induced vomiting, laxative misuse, and maladaptive exercise) and binge eating (consuming a large amount of food in a discrete period and experiencing a sense of loss‐of‐control over eating) in persons who are not underweight. BED is marked by recurrent binge‐eating episodes and associated cognitive features (e.g., eating rapidly and eating in the absence of hunger) in persons who are not underweight. Persons with BED do not engage in inappropriate compensatory behaviors. Historically, full‐threshold BED was thought to be rare in youth; however, recent meta‐analytic findings suggest that BED is as common as AN and BN among youth (Kjeldbjerg & Clausen, 2023 ). ED prevalence and correlates Disordered eating affects ~22% of youth (López‐Gil et al., 2023 ), and the prevalence of EDs is on the rise globally (Galmiche, Déchelotte, Lambert, & Tavolacci, 2019 ). Although girls experience EDs at higher rates than other genders, EDs also affect boys (Brown & Keel, 2023 ) and disproportionately affect transgender youth (Diemer, Grant, Munn‐Chernoff, Patterson, & Duncan, 2015 ). EDs have high mortality rates due to their physical effects and death by suicide (Krug et al., 2025 ). Moreover, EDs are associated with impairing psychosocial correlates (e.g., internalizing disorders, substance use disorders) and medical consequences (e.g., cardiovascular and gastrointestinal problems). EDs have tremendous economic impacts, costing the United States $64.7 billion in 2019 (Streatfeild et al., 2021 ), and are associated with a considerable decrease in well‐being among those affected (Santomauro et al., 2021 ). The median age of onset for a majority of EDs is in mid‐adolescence (Solmi et al., 2022 ), and pubertal status and timing appear to activate ED risk, particularly in girls (Klump, 2013 ). However, extreme dieting and loss‐of‐control eating may emerge before puberty (Tanofsky‐Kraff, Faden, Yanovski, Wilfley, & Yanovski, 2005 ). Adolescent brain development This section provides an overview of the neurostructural and neurofunctional changes that occur during childhood and adolescence, which may provide context for the development of EDs. Neurostructural and neurofunctional changes Gray matter (GM) Brain GM contains neuronal cell bodies, unmyelinated and lightly myelinated axons, dendrites, and synapses. GM has been a considerable focus of neuroimaging research in EDs due to its role in information processing and because different GM metrics can be easily imaged with T1‐weighted MRI scans, which are common in neuroimaging protocols. Across childhood and adolescence, the brain undergoes significant, region‐specific, nonlinear microstructural maturation (e.g., increased myelination) in cortical and subcortical GM (Alex et al., 2024 ; Bethlehem, Hansson, Ossenkoppele, & Alexander‐Bloch, 2022 ; Corrigan et al., 2021 ). Cortical GM volumes peak in childhood and undergo a protracted development that extends into early adulthood (Bethlehem et al., 2022 ). Further, increased myelination near the gray‐white matter boundary in adolescence may result in apparent cortical GM thinning during this time (Natu et al., 2019 ). Subcortical GM volumes peak in adolescence and stabilize faster than cortical GM volumes, and subcortical GM volumetric growth may be underpinned by microstructural growth (e.g., myelination) (Palmer et al., 2022 ). White matter (WM) Brain WM contains the myelinated axons (nerves) that connect GM regions into functional circuits, and white matter volume (WMV) linearly increases until approximately age 30 (Bethlehem et al., 2022 ). The microstructural changes observed within WM tracts throughout child development are consistent with the mechanism of WM growth comprising myelination (i.e., more extensive insulation of nerve fibers) (Bottenhorn, Cardenas‐Iniguez, Mills, Laird, & Herting, 2023 ; Lebel et al., 2012 ). WM changes also vary by tract and region, with faster maturation of commissural fibers that connect hemispheres and association fibers that connect cortical areas within a hemisphere. The maturation of projection fibers connecting cortical and subcortical regions is typically slower and continues into early adulthood (Slater et al., 2019 ). WM tract maturation supports the emergence of structural brain networks associated with cognitive functioning (Bagautdinova et al., 2023 ) and changes in the brain's global organization. In late adolescence, there is a particular strengthening of longer‐range connections between cortical (especially frontal) and subcortical regions and some loss of subcortical–subcortical connections (Baker et al., 2015 ). These changes lead to a more efficient brain network that supports information integration across a distributed set of brain regions (Riedel, van den Heuvel, & Markett, 2022 ) and multiple functional configurations (Senden, Deco, de Reus, Goebel, & van den Heuvel, 2014 ). Connectivity and brain networks Structural connectivity refers to physical connections (typically white matter tracts) that link different brain regions (Sporns, Tononi, & Kötter, 2005 ). Functional connectivity reflects the temporal correlation between neural activation in distinct brain regions (Friston, 2011 ). Functional connectivity thus differs from structural connectivity in that it reflects statistical dependencies between brain activation and not anatomical connections. Structural and functional connectivity coupling increases throughout adolescence (Baum et al., 2020 ), and a more efficient functional brain connectivity pattern emerges (Gozdas, Holland, Altaye, & CMIND Authorship Consortium, 2019 ). Functional brain networks are sets of distinct brain regions that demonstrate coordinated activity during rest (e.g., resting‐state fMRI) or specific cognitive tasks (e.g., task‐based fMRI) and are defined based on functional connectivity (Smith et al., 2009 ). There are several well‐characterized functional brain networks, including the default‐mode network [DMN] – which corresponds to the absence of goal‐directed cognition – and the cingulo‐opercular network – which supports sustained focused attention (Cole, Bassett, Power, Braver, & Petersen, 2014 ; Fransson, 2006 ). Functional brain networks emerge early in development and change in a manner consistent with decreasing interference and increasing coordination throughout adolescence (Anderson, Ferguson, Lopez‐Larson, & Yurgelun‐Todd, 2011 ; Chai, Ofen, Gabrieli, & Whitfield‐Gabrieli, 2014 ; Marek, Hwang, Foran, Hallquist, & Luna, 2015 ). Specific connections strengthen during adolescence, particularly between the prefrontal cortex (PFC) and subcortical regions, including the amygdala and striatum (van Duijvenvoorde, Achterberg, Braams, Peters, & Crone, 2016 ). Resting‐state brain changes are mirrored in task‐based fMRI. Task‐relevant networks (e.g., frontoparietal network that coordinates goal‐directed behavior and attention) increase their influence over the DMN during adolescence (Stevens, Kiehl, Pearlson, & Calhoun, 2007 ). Although functional brain networks have advanced our understanding of the brain, they have important limitations, including low anatomical precision (in part due to group averaging) and high inter‐individual variability, which may contribute to discrepancies in findings involving these networks across different samples. Precision functional mapping is an emerging approach that seeks to address these issues with functional brain networks by collecting large amounts of fMRI data from one individual to reliably map an individual's brain networks (Gordon et al., 2017 ). As an alternative to examining network‐based connectivity, several studies have probed the connectivity between specific regions of these networks. It has been of particular interest to understand how reward response and modulation develop during adolescence. Adolescence is characterized by heightened striatal activation in response to reward (Van Leijenhorst et al., 2010 ), which appears to be increasingly modulated by frontal systems throughout development (van den Bos, Cohen, Kahnt, & Crone, 2012 ). This heightened reward‐system activation, followed by modulation during adolescence and early adulthood, may contribute to elevated risk‐taking behavior observed in adolescents relative to children and adults (Peper, Braams, Blankenstein, Bos, & Crone, 2018 ). Functional interactions between the PFC and amygdala similarly increase throughout adolescence and are associated with improved emotion regulation (Silvers et al., 2017 ). Dopamine Dopaminergic neurochemistry reflects the structural and functional maturation of the striatum and frontal cortex during adolescence. Dopamine concentrations in the striatum increase during adolescence (Larsen et al., 2020 ), supporting reward learning. Human and animal studies suggest that dopaminergic innervation of the PFC increases throughout adolescence (Lambe, Krimer, & Goldman‐Rakic, 2000 ; Reynolds et al., 2018 ). This may contribute to refining the excitatory and inhibitory brain activation balance into early adulthood (Parr et al., 2024 ), which is suggested to underlie improved emotional and behavioral regulation. Gonadal hormones and brain development Adolescent brain development is likely associated with hormonal changes. Adolescence is associated with changes in hypothalamic–pituitary–gonadal axis function and increased secretion of gonadal hormones, which support the development of secondary sex characteristics. While hormonal changes and pubertal development are associated with neurostructural changes, the precise mechanisms underpinning this correspondence are unclear (Herting & Sowell, 2017 ). Associations between brain function and pubertal stage have been inconsistent (Dai & Scherf, 2019 ), likely due to variations in experimental methods and analytic approaches. Though hormonal changes have explicitly been hypothesized to underlie changes in dopaminergic functioning during adolescence (Sinclair, Purves‐Tyson, Allen, & Weickert, 2014 ), this hypothesis has been empirically – yet indirectly – tested via only a handful of studies. This emerging literature has found that estrogen and testosterone modulate striatal activation and connectivity during reward‐related neurocognitive tasks (Braams, van Duijvenvoorde, Peper, & Crone, 2015 ). Greater levels of these hormones have corresponded with elevated reward responsiveness and lower modulation of reward response. Brain systems and ED symptoms Here, we review the major neural models of EDs, which have primarily proposed that alterations in specific neurofunctional systems underlie ED symptoms. These systems generally align with the United States' National Institute of Mental Health's Research Domain Criteria (RDoC), a transdiagnostic and dimensional framework of neurobiologically grounded constructs proposed to underpin normative and atypical psychological function. Although the RDoC framework provides a useful organizational lens, it has been critiqued for its rigid nature and reductionism (Morris et al., 2022 ). With these caveats in mind, we use the RDoC domains to organize current evidence, outlining their relevant neural circuits and correlates alongside corresponding models of eating pathology (Figure 1 ). We do not include the sensorimotor systems domain due to its limited application to EDs. Figure 1. Open in a new tab RDoC systems, corresponding brain regions, and links to eating disorder models Negative valence systems The negative valence system domain considers responses to aversive stimuli or situations. This domain comprises reactions to noxious and threatening stimuli and conditions, including acute and sustained threats, nonreward, loss, and potential threat. The brain circuits responsible for detecting and responding to potential threats vary based on the perceived distance of the threat. The detection of imminent threats is associated with peri‐aqueductal gray and central amygdala activation (Abend et al., 2022 ; Mobbs et al., 2010 ). In contrast, distant threats tend to activate the basolateral amygdala, posterior regions of the cingulate cortex, and hippocampus (Meyer, Padmala, & Pessoa, 2019 ; Qi et al., 2018 ). Worry – or anxiety about potential adverse future events – engages a brain network that overlaps with the DMN and amygdala (Meeten et al., 2016 ; Shuhama et al., 2016 ). Although the ventromedial PFC activates in response to possible threats, this activity modulates threat response in other regions, particularly the amygdala (Motzkin, Philippi, Wolf, Baskaya, & Koenigs, 2015 ). Fear learning – learning the stimuli and situations that lead to or predict an aversive outcome – is encoded in a distributed brain network (Fullana et al., 2016 ) but depends on the amygdala (Büchel, Morris, Dolan, & Friston, 1998 ). Likewise, fear‐learning extinction relies on the formation of memory traces in the amygdala (Graner, Stjepanović, & LaBar, 2020 ). This extinction process is further supported by the ventromedial PFC (Phelps, Delgado, Nearing, & LeDoux, 2004 ) and hippocampus (Milad et al., 2007 ), which also appear to prevent fear expression following extinction. Safety learning – or learning that one stimulus is safe when another is present – is associated with anterior cingulate cortex (ACC), hippocampus, and ventromedial PFC function (Tashjian, Cussen, Deng, Zhang, & Mobbs, 2025 ). Application to EDs Negative valence systems are implicated in multiple models of ED development. Anxiety and EDs are highly comorbid (e.g., Kaye, Bulik, Thornton, Barbarich, & Masters, 2004 ), and the management of anxiety symptoms by ED behaviors has been proposed as a mechanism of illness in AN, BN, and BED (Lloyd, Frampton, Verplanken, & Haase, 2017 ; Pallister & Waller, 2008 ). These proposals assert that disordered eating functions to alleviate anxiety (rather than influence body weight/shape). Others have speculated that anxiety about eating and its consequences (e.g., weight gain) facilitates the emergence and entrenchment of eating pathology. For example, one model hypothesizes that negative expectations about and experiences with eating lead to anticipatory anxiety about eating (driven by the amygdala) and, in turn, food avoidance (Kaye, Fudge, & Paulus, 2009 ). In this model, individuals with AN are particularly vulnerable to developing anxiety about eating, perhaps due to altered neurotransmitter system function that leads to general anxiety. Moreover, this model postulates that reductions in serotonergic activity resulting from food restriction alleviate anxiety to reinforce dietary restriction. A different model proposes that a conflict between biological drives to eat and cognitive desires not to eat escalates anxiety in AN and ultimately reinforces restrictive eating (to assuage anxiety), thereby worsening eating behavior and perturbing the homeostatic hunger system (Frank, DeGuzman, & Shott, 2019 ). In addition to anxiety, fear of the consequences of eating is a transdiagnostic feature of eating pathology (e.g., weight gain in AN, vomiting in ARFID). As such, several models have postulated that fear learning plays a direct role in EDs. For example, amygdala‐mediated fear learning is proposed to give rise to conditioned fear and avoidance responses to eating (Strober, 2004 ; Thomas et al., 2017 ). Positive valence systems The positive valence systems domain comprises distinct yet interrelated reward‐based constructs, including reward response, learning, and valuation. Reward responsiveness The neural substrates of reward “wanting” or motivation are widely distributed. Limbic system (e.g., ventral striatum, ventral tegmental area) activation is associated with reward anticipation and response to reward‐predicting cues (Oldham et al., 2018 ). The neural processing of reward receipt is located in the ventral striatum, medial PFC, and cingulate cortices (Oldham et al., 2018 ). Activation of these regions corresponds to the experience of hedonic pleasure, and inactivation prevents a reward response. Valuation of positive outcomes – a process in which an individual judges how rewarding a particular choice option is to determine the optimal course of action – involves the ventral striatum and ventromedial PFC (Bartra, McGuire, & Kable, 2013 ). The value of rewarding outcomes may diminish with the delay or effort required to receive them; weighing a given outcome's relative benefits and costs involves valuation regions and networks engaged in future thinking and cognitive control (Botvinick, Huffstetler, & McGuire, 2009 ; Koban et al., 2023 ). Reward learning This construct describes how individuals learn to maximize reward and minimize punishment. Research has focused on two dissociable forms of reinforcement learning: model‐based and model‐free (Daw, Niv, & Dayan, 2005 ). Model‐based learning involves the development of a cognitive representation that maps associations between actions and outcomes to support goal‐directed behavior (Gläscher, Daw, Dayan, & O'Doherty, 2010 ) and depends on interactions between medial prefrontal and hippocampal regions (Bornstein & Daw, 2013 ; Daw et al., 2005 ; Doll, Duncan, Simon, Shohamy, & Daw, 2015 ). In contrast, model‐free learning involves the development of rules about how to behave based on prior experience. When a behavioral response leads to a desired outcome, this striatum‐dependent system links the stimuli present during this outcome to the behavioral response (Daw et al., 2005 ; Gläscher et al., 2010 ; Schultz, Dayan, & Montague, 1997 ). In model‐free learning, this formation of stimulus–response associations, or habits, allows the rewarded behavior to be automatically instigated in the presence of linked stimuli. While efficient, habitual behaviors are performed without consideration of their contingent outcomes, potentially leading to unwanted consequences. Application to EDs Reward hyper‐ and hypo‐sensitivity have been proposed as ED mechanisms. The habit hypothesis of AN proposes that restrictive eating in AN is initially a goal‐directed effort reinforced for various reasons (e.g., positive feedback from others, reduced negative emotions) and is contingent upon reward receipt (Walsh, 2013 ). Those predisposed to AN may be particularly sensitive to these reinforcers (perhaps due to alterations in reward circuitry) (O'Hara, Campbell, & Schmidt, 2015 ), leading them to continue restrictive eating. With time and repetition, restrictive eating is proposed to shift from a dependence on goal‐directed neurocircuitry to habit‐based systems (in particular, the dorsal striatum). Similarly, BN and binge‐eating models have postulated that dorsostriatal (or habit‐supporting) circuits underlie habitual binge‐eating and purging behavior following initial reinforcement (Berner & Marsh, 2014 ). Other models have proposed that excessive reward responses to ED behaviors without a shift to habit‐based circuits may underpin EDs. In such models of AN, restrictive eating and maladaptive exercise are driven by reward‐system activation (specifically ventral striatum, PFC, ACC) in response to anticipation and experiencing these behaviors (Keating, Tilbrook, Rossell, Enticott, & Fitzgerald, 2012 ). Whilst restrictive eating and maladaptive exercise are arguably effortful, one model proposes that individuals vulnerable to EDs may come to find the effortful aspect of these behaviors rewarding, thereby reinforcing their continuation (Haynos, Koithan, & Hagan, 2023 ). In parallel, generally appetitive stimuli (e.g., food, social) are proposed to become less rewarding in AN, making engaging in healthier eating behaviors and rewards unrelated to the ED (Kaye, Wierenga, Bailer, Simmons, & Bischoff‐Grethe, 2013 ) more difficult. Binge eating has been proposed to initially arise from excessive reward responsivity (via the ventral striatum) to highly palatable food (Bodell & Racine, 2023 ). Further, others have suggested that hypo‐responsiveness to the anticipation and receipt of food may lead to excessive consumption (e.g., binge eating) in an attempt to experience food‐related reward (Wierenga et al., 2014 ). Cognitive systems The cognitive systems domain encompasses circuits contributing to executive function, covering attention, memory, and cognitive control. To a varying extent, these functions depend on cortical regions (prefrontal, parietal, ACC) and the hippocampus and thalamus. Attention Higher‐order sensory cortices (parietal and temporal lobes) orient attention towards salient stimuli (Downar, Crawley, Mikulis, & Davis, 2002 ), and a frontoparietal network is necessary for the conscious processing of stimuli (Haynes, Driver, & Rees, 2005 ). The PFC (especially the dorsolateral and inferior areas) is essential for inhibiting irrelevant information and sustaining and coordinating (Fassbender et al., 2004 ; Ortuño et al., 2002 ; Zanto, Rubens, Thangavel, & Gazzaley, 2011 ). Memory Working memory largely depends on a prefrontal‐parietal network (Murphy, Bertolero, Papadopoulos, Lydon‐Staley, & Bassett, 2020 ), with evidence that the thalamus modulates this network (Chen, Wang, et al., 2023 ; Chen, Sorenson, & Hwang, 2023 ). In contrast, declarative memory – long‐term memory for facts or events – requires coordinated activation of a network that closely corresponds with the DMN (Benoit & Schacter, 2015 ). Hippocampal regions may uniquely contribute to event recall (i.e., episodic memory) rather than semantic information recollection (Beaty et al., 2020 ). Cognitive control This system encompasses the modulation of thoughts and actions to pursue future goals. A traditional view, primarily based on brain lesion studies, was that the PFC subserved all facets of cognitive control. However, recent models propose that subregions of the PFC underlie distinct aspects of cognitive control. For instance, inhibitory control of motor responses has been mapped to the posterior parietal cortex (Osada et al., 2019 ). Cognitive flexibility involves the dorsal and ventrolateral PFC, including switching from one task to another or resisting the influence of prior task rules (Aron, Robbins, & Poldrack, 2004 ; Hyafil, Summerfield, & Koechlin, 2009 ). The dorsolateral PFC also maintains cognitive stability or inhibits distraction from irrelevant stimuli (Armbruster‐Genç, Ueltzhöffer, & Fiebach, 2016 ). The association of dorsolateral PFC activation with maintaining stability and supporting flexibility is explained by attractor states or distinct firing patterns that a system is inclined to adopt and remain in (cognitive stability). However, it may also move between (cognitive flexibility) (Armbruster, Ueltzhöffer, Basten, & Fiebach, 2012 ). The representation of alternative behavioral strategies, which allows for changing responses to be aligned with current goals, has been associated with the dorsal ACC (Kerns, 2006 ). Connectivity between the ACC and dorsolateral PFC regions may allow the implementation of the alternative behavioral strategies signaled (Kerns, 2006 ). Application to EDs ED models have generally proposed greater cognitive control among those with AN, which is thought to underlie persistent dietary restriction. Specifically, these models propose dorsolateral PFC hyperfunction as the neural mechanism of excessive behavioral control (e.g., extreme dietary restriction, maladaptive exercise) in AN (Brooks, Rask‐Andersen, Benedict, & Schiöth, 2012 ; Ehrlich et al., 2015 ; Kaye et al., 2009 ). Conversely, binge‐eating pathology is thought to arise from poor cognitive control (due to underactivity in the PFC) in the setting of heightened reward responses to food (Brooks et al., 2012 ; Wierenga et al., 2014 ). Exaggerated cognitive control in AN has also been proposed to contribute to inflexible behavior and, ultimately, difficulties adapting to changing rules (e.g., food rules) and refraining from repetitive behaviors (e.g., body checking). One model implies this rigidity may arise from the combination of overactive PFC circuitry and hypoactive ACC signaling of alternative behavioral responses (Friederich & Herzog, 2011 ). Attempts to over‐control responses to affective stimuli in AN are proposed to contribute to rumination about eating and weight, which causes negative affect and reinforces restrictive eating to dampen rumination (Pauligk et al., 2021 ). Several models have implicated heightened attention towards food cues as a mechanism of binge‐eating pathology. In these models, heightened reward response towards food gives rise to attentional biases to food, leading to craving and loss‐of‐control eating (Berridge, 2009 ). Heightened attention towards weight/shape is suggested to contribute to dietary restriction in AN and BN. Here, the overvaluation of weight/shape is hypothesized to drive excessive attention towards disliked parts of the body, worsen body dissatisfaction, and encourage dietary restriction (Fairburn, Cooper, & Shafran, 2003 ). In another model, faulty beliefs about weight are proposed to drive selective attention towards the body, resulting in a confirmation bias that heightens disordered eating behavior (Vitousek & Hollon, 1990 ). Social processes Social processes cover social reasoning, perception, and understanding of self and others, and affiliation and attachment. These constructs facilitate interaction with others and the formation of social relationships. Social perception The set of brain regions that allow for representing, perceiving, and predicting others' behavior in the service of successfully communicating and interacting is widely distributed. This network overlaps with the DMN, frontoparietal, and cingulo‐opercular networks and includes the medial PFC, cingulate cortex, temporoparietal junction, precuneus, fusiform regions, insula, amygdala, and thalamus (Pintos Lobo et al., 2023 ). However, specific social functions uniquely involve particular regions and circuits. For example, understanding the self is associated with ACC activation (Pintos Lobo et al., 2023 ), whereas understanding others (e.g., predicting the mental state of another person and their consequent behavior) is associated with parietal and temporal activation (Molenberghs, Johnson, Henry, & Mattingley, 2016 ). Social communication also involves subregions of the parietal and temporal cortices, specifically those implicated in facial recognition and emotion interpretation (Redcay et al., 2010 ). Neurocognitive tasks measuring social attachment and affiliation wherein images of close others are viewed (Taylor et al., 2008 ) or tasks are performed on behalf of close others (Braams et al., 2014 ; Schreuders, Klapwijk, Will, & Güroğlu, 2018 ) are associated more with insular and temporoparietal junction activation than neurocognitive tasks evaluating alternative social processes (Pintos Lobo et al., 2023 ). Application to EDs Several models have proposed alterations in social processing and communication as key mechanisms of EDs (e.g., Fairburn et al., 2003 ; McAdams, Efseroff, McCoy, Ford, & Timko, 2022 ; Treasure & Schmidt, 2013 ). Broadly, these models postulate that EDs may arise and/or be maintained by social difficulties. In AN, difficulty understanding the self and experienced emotions (alexithymia), in combination with challenges in understanding others, is hypothesized to contribute to problems engaging with others and low self‐esteem. One model suggests low self‐esteem and poor self‐image result from alterations, specifically hypo‐functioning, of the medial PFC during self‐reflection (McAdams et al., 2022 ). Poor self‐image could render individuals sensitive to potential judgments from others (particularly surrounding weight) and a desire to belong and attain social norms (e.g., thin‐ideal internalization) (Treasure & Schmidt, 2013 ). In binge‐eating pathology, perceived or actual negative evaluation from others has been hypothesized to lead to low self‐esteem and negative affect that is managed via disordered eating (Ruan et al., 2022 ). Negative evaluations from others are also proposed to lead to poor body image among individuals who already overvalue weight and shape. This may, in turn, heighten the desire to engage in disordered eating, particularly when these behaviors are expected to improve self‐esteem or social standing (Rieger et al., 2010 ). Arousal/regulatory systems The arousal/regulatory systems domain encompasses sensitivity to internal and external events. Constructs within this domain include interoception, the ability to sense internal bodily signals (e.g., hunger/satiety, heart rate). Interoception is related to homeostasis, the process by which a constant internal state is maintained despite perturbations. Sensory neurons detect changes in body states and transmit signals to the central nervous system via vagal nerve ganglia that innervate the brainstem and dorsal root ganglia (located along the spinal nerve pathway) that project information toward the spinal cord (for an overview, see Saper ( 2002 )). Interoceptive information is processed in the central nervous system by brainstem regions (the nucleus of the solitary tract, parabrachial nucleus, and periaqueductal gray) and the thalamus, which project to other downstream brain regions implicated in processing internal signals (Hansen et al., 2024 ; Ran, Boettcher, Kaye, Gallori, & Liberles, 2022 ). These brain regions include the hypothalamus, hippocampus, amygdala, insula, and somatosensory cortices (Eickhoff et al., 2006 ; Jarrahi et al., 2015 ; Joyce & Barbas, 2018 ). Efferent (or outgoing) projections from the brain to internal organs permit the regulation of internal activity (Levinthal & Strick, 2020 ). Interoceptive brain circuits have explicitly been implicated in regulating food intake (Münzberg, Qualls‐Creekmore, Yu, Morrison, & Berthoud, 2016 ). The gustatory cortex is located in the same insular region involved in processing visceral signals from the body, potentially allowing information about bodily states to motivate feeding behavior (Avery et al., 2017 ). It has been proposed that the brain may use interoceptive cues to guide behavior by allocating reward value to actions that restore the body to a stable state (Simmons & DeVille, 2017 ). Application to EDs Several models have hypothesized altered responses to hunger and satiety cues among those with EDs. For example, individuals with AN are thought to be less sensitive to the effects of hunger, leading to decreased food‐seeking behavior in the presence of hunger (Wierenga et al., 2015 ). Other models suggest difficulty interpreting hunger and fullness signals, which may result in restrictive or binge eating (Jenkinson, Taylor, & Laws, 2018 ). A disturbance in interoceptive circuits, particularly the insula, may lead to challenges in using internal signals to update predictions about bodily experiences, causing a mismatch between expected and experienced internal states or misinterpretation of bodily signals. This is also believed to contribute to the persistence of illness symptoms, potentially by fostering mistrust in internal signals (Khalsa, Berner, & Anderson, 2022 ) or inducing negative emotional states (Frank et al., 2019 ; Kaye et al., 2009 ). Heightened attention to or aversion towards bodily signals is proposed in AN, which may contribute to anxiety in anticipation of eating and consequent dietary restriction (Khalsa et al., 2015 ). It may also lead to discomfort after eating. The three‐dimensional neurobiological model of ARFID (Thomas et al., 2017 ) indicates that the lack of interest presentation may stem from inadequate responsiveness to hunger cues (due to disruptions in the arousal/regulatory systems), particularly in the anterior insula and hypothalamus, resulting in poor appetite regulation. Conversely, the sensory sensitivity presentation is believed to arise from heightened sensitivity to specific food attributes due to perturbations in the perceptual systems, including tastes and textures. Other models suggest interoceptive changes in EDs. Lower anterior insula function may contribute to distorted body perceptions and self‐image in AN (Nunn, Frampton, Fuglset, Törzsök‐Sonnevend, & Lask, 2011 ). In BN, changes in insular function are believed to affect pain thresholds, enabling greater tolerance of stomach distension caused by binge eating or discomfort due to purging and maladaptive exercise (Klabunde, Collado, & Bohon, 2017 ). Key findings In this section, we review the literature on the neural correlates of EDs in samples of youth from birth to those with a mean age of 24, aligning with contemporary perspectives that define the upper boundary of adolescence at the age of 24 (Sawyer, Azzopardi, Wickremarathne, & Patton, 2018 ). Findings are organized based on diagnostic context and methodology. We note any papers that were preprints at the time of this writing. Subcortical and cortical morphology ED risk Psychiatry has been interested in establishing biomarkers for disorder risk, including EDs. Aligned with this initiative, researchers examined morphological correlates of genetic ED risk in children of European ancestry enrolled in the Adolescent Brain Cognitive Development (ABCD) study ( n≈ 4,500, M age [ SD ] = 9.94 [0.62], ~50% female). They found that cortical thickness, surface area, and subcortical GMV were associated with polygenic risk scores for AN and high BMI (Westwater et al., 2023 ). Moreover, higher BMI polygenic risk scores were uniquely linked to increased self‐reported eating pathology, widespread increases in cortical thickness, and reductions in surface area within this subgroup. In contrast, higher AN polygenic risk scores were associated with lower caudate volume. This work suggests that neurobiological factors influence disordered eating specific to AN earlier in development than previously thought and highlights the link between high weight and ED risk. AN Research on brain morphology in AN has primarily focused on the acute and partially weight‐restored stages. This distinction is meaningful, as acute AN is characterized by low weight and low food intake, which could exacerbate morphological disruptions. To investigate brain morphology in AN on a large scale, the Enhancing Neuro Imaging Genetics through Meta Analysis (ENGIMA) ED workgroup aggregated data from 22 research sites, resulting in a sample of 685 female participants with AN (251 partially weight‐restored; M age = 21, range 15–27) and 963 female controls (Walton et al., 2022 ). Results indicated sizable and widespread reductions in cortical thickness, subcortical volumes, and, to a lesser degree, cortical surface area among the acute and partially weight‐restored AN groups compared to controls. These reductions were correlated with lower BMI in acute AN, but this correlation strength was less pronounced among partially weight‐restored AN. Atypical AN Although consensus on the nosology of atypical AN has not been reached, research on the neural alterations in atypical AN compared to AN and controls may guide nosology. To our knowledge, only two such studies exist. In one of these studies, GMV was compared between 22 girls with atypical AN ( M age [ SD ] = 14.9 [1.6]) and 38 female controls, and no significant differences were found. In the second study, volumes were compared among female adolescents and young adults (aged 10–22) with AN ( n = 37), atypical AN ( n = 23), and controls ( n = 41), with results indicating lower GMV in frontal, temporal, and parietal areas in atypical AN and AN compared to controls, but no differences between atypical AN and AN (Lyall et al., 2024 ). These preliminary findings suggest that restriction and weight loss similarly influence brain morphology, regardless of BMI. However, high weight (e.g., obesity) is associated with lower brain volumes, and some adolescents with atypical AN have high weight. As such, future work is needed to disentangle the potential confounding effect of BMI on brain morphology in this population. Changes with AN recovery Most studies on this topic have examined partial (vs. full) weight restoration, and it is unclear whether complete morphological normalization occurs. However, the existing research suggests positive long‐term improvements in brain morphology with recovery from AN in adults (Seitz, Herpertz‐Dahlmann, & Konrad, 2016 ); although other research suggests persistent differences with 1 year of weight restoration (Arold et al., 2023 ). Further, morphological recovery may depend on age rather than AN illness duration (Kaufmann et al., 2020 ). Additional longitudinal research is needed to further establish weight restoration's impacts on morphological normalization in AN. Comparing AN and ARFID As AN and ARFID are associated with low weight, one preprint investigated neural differences between the disorders (Moreau et al., 2024 ). Results suggested that children <13 years ( M age [ SD ] = 11.5 [1.18], 85% female) with AN ( n = 124) or ARFID ( n = 50) showed different neurostructural alterations despite similar BMIs. Specifically, although half of the regional brain measures correlated with BMI in AN, there were no regional correlates of BMI in ARFID, suggesting diagnostically distinct and BMI‐independent correlates. Further, this study found that youth with AN showed widespread cortical thinning and lower GMV relative to typically developing youth ( n = 112). ARFID To our knowledge, one study has examined morphological differences specific to ARFID (Sader et al., 2024 ). This study leveraged data from the Dutch population‐based Generation R Study ( n = 1,977, age ~10 at scanning, 50% female) to compare cortical thickness and brain volumes between those with ( n = 121, 6.1% of the sample) and without ARFID‐like symptoms. Results indicated significantly greater cortical thickness in the frontal and superior frontal cortex among children with ARFID symptoms. BN and BED BN Research on structural alterations in BN is limited, and findings have been mixed, revealing both greater and lower GMV in functionally distinct ROIs in modestly sized samples. Larger‐scale studies suggest lower GMV and cortical thickness in frontoparietal, temporal, and cingulate cortices in girls and women with BN compared to controls (Berner et al., 2018 ; Westwater, Seidlitz, Diederen, Fischer, & Thompson, 2018 ). Other work in female adolescents and emerging adults with BN ( n = 62, M age [ SD ] = 18.7 [4.0]) found no volumetric differences in subcortical regions compared to controls; however, shape alterations in the pallidum, caudate, putamen, and amygdala were associated with binge‐eating and purging frequencies in BN (Berner et al., 2019 ). BED Like BN, few studies have examined brain morphology in youth with BED. One group applied voxel‐based morphometry (VBM) to examine neurofunctional differences among 71 children (aged 9–10) with BED and 66 BMI‐ and developmentally matched, typically developing controls participating in the ABCD Study (Murray et al., 2022 ). This study found elevated GM density in prefrontal, parietal, and temporal regions among youth with BED. Another study compared GMV in adolescents (12–18 years) with BED and obesity ( n = 26; 68% female), obesity without BED ( n = 25), and controls ( n = 27) (Turan et al., 2021 ). This study found that the BED/obesity group showed greater bilateral mOFC volumes compared to those with obesity without BED and that the BED group demonstrated greater left mOFC GMV compared to controls. However, a third study did not find structural alterations in cortical thickness or subcortical GMV in female adolescents and emerging adults with clinically significant binge eating ( n = 56, M age [ SD ] = 24 [6.38]), compared to controls ( n = 26) (Hagan & Bohon, 2021 ). Additionally, the BN group demonstrated greater ventral striatal GMV, which was positively correlated with BMI and purging severity. Overall, this nascent literature indicates greater GMV in binge eating and BED. Still, additional research is needed to determine whether some studies showing the opposite pattern (particularly among those with BN) may reflect age, weight status, symptom severity, and/or other factors. WM morphology WMV There is comparatively less research on WMV than GMV in adolescents and emerging adults with EDs. In general, WMV in acute AN appears to be lower than WMV in controls but may restore with recovery (Seitz, Herpertz‐Dahlmann, & Konrad, 2016 ). However, findings have been inconsistent, with some studies showing little to no reduction in WMV in acute AN or partially recovered AN compared to controls (Pfuhl et al., 2016 ). In BN, limited research has found no differences in WMV compared to controls in the acute BN (Chen, Sorenson, et al., 2023 ; Chen, Wang, et al., 2023 ) and those who have recovered (Wagner et al., 2006 ). To our knowledge, there are no published findings on WMV alterations in the study of youth with BED or ARFID. WM microstructure A preprint using a multivariate diffusion‐weighted imaging approach compared girls with transdiagnostic eating pathology ( n = 91, M age [ SD ] = 16.1 [1.38]) to controls ( n = 48) and found meaningful covariance (46.9%) between the microstructure that connects frontal, limbic, and thalamic regions, with temperament profiles (Makowski et al., 2024 ). In acute AN, DTI has generally identified widespread disruption in WM microstructure in several regions and tracts, particularly in the corpus callosum, cingulum, and fornix (King, Frank, Thompson, & Ehrlich, 2018 ). However, this literature has several inconsistencies, and some metrics may not provide a valid index of WM properties among weight‐suppressed individuals (Kaufmann et al., 2017 ). We identified just one study that investigated WM microstructure in youth with BN, finding that girls and young women show reductions in frontal and temporoparietal cortices compared to BMI‐matched youth (He, Stefan, Terranova, Steinglass, & Marsh, 2016 ). To our knowledge, WM microstructure has not been investigated in youth with BED or ARFID. WM connectivity Research has demonstrated pronounced alterations in WM connectivity among adolescents with acute AN. One study compared WM connectivity in control ( n = 119) and female youth with AN ( n = 147, M age [ SD ] = 20.34 [6.21]) and found lower connectivity of subcortical networks (that was associated with lower BMI), lower connectivity of the left hippocampus, and greater connectivity between frontal‐cortical regions in the AN group (Lloyd et al., 2023 ). Similarly, another study showed that female youth with AN ( n = 96, M age [ SD ] = 16.3 [3.3]) demonstrated greater fractional anisotropy primarily in parietal–occipital regions and lower radial diffusivity of the WM connectome relative to controls (Geisler et al., 2022 ). In BN, one large study of WM connectivity found that female emerging adults with BN ( n = 48, M age [ SD ] = 22.0 [3.4]) showed greater left‐lateralized nodal strength within mesocorticolimbic reward circuitry compared to controls ( n = 44) (Wang et al., 2019 ). These researchers also found greater left‐lateralized connections both within the OFC and between other mesocorticolimbic and lateral temporal‐occipital areas, and fewer right‐lateralized connections across the inferior frontal gyrus, insula, and lateral temporal cortex in youth with BN than controls. Research on WM volume, microstructure, and connectivity suggests widespread alterations among youth with AN; though knowledge of how WM morphology is affected by illness stage is nascent. Further, there is a dearth of research on WM alterations in BN, BED, and ARFID. Functional connectivity Resting‐state fMRI Acute AN Research on resting‐state functional connectivity (rsFC) in AN has been summarized in systematic ( k = 15 articles) (Gaudio, Wiemerslage, Brooks, & Schiöth, 2016 ) and meta‐analytic reviews ( k = 15 articles, 100% female) (Su et al., 2021 ). Although studies included in these reviews evidenced heterogeneity in rsFC analyses, results have consistently demonstrated rsFC alterations in regions and networks thought to underpin cognitive control and perceptual (e.g., body image) disturbance. For instance, several studies found lower rsFC connectivity between the insula and thalamus (Ehrlich et al., 2015 ; Geisler et al., 2016 ), with additional evidence of altered rsFC in thalamocortical circuits (Biezonski, Cha, Steinglass, & Posner, 2016 ). Meta‐analytic results have further suggested lower rsFC of the bilateral ACC (extending to superior frontal gyrus) and medial cingulate cortex, and greater rsFC of the right parahippocampal gyrus, extending to the right temporal pole, middle temporal gyrus, and amygdala in youth with AN relative to controls (Su et al., 2021 ). Altogether, differences in insular, thalamic, and ACC rsFC in AN suggest a potential neural mechanism of cognitive inflexibility and/or body‐image disturbance. Other research in adolescent AN has examined FC using novel techniques, including amplitude of low‐frequency fluctuations (i.e., baseline intensity of spontaneous neural oscillations) (Lai et al., 2020 ; Seidel et al., 2019 ). Compared to controls, these studies demonstrate alterations in frontal, hippocampal, and ventral visual stream regions in adolescent AN. Another large‐scale study applied dynamic rsFC (which supposes that FC changes with brain states) to data collected from girls with AN ( n = 99, M age [ SD ] = 15.19 [1.60]) and controls ( n = 99), finding lower connectivity and fewer transitions between brain states in AN (Boehm et al., 2024 ). rsFC changes with AN recovery There has been interest in understanding whether rsFC changes are a state or trait marker of AN. To this end, researchers have examined changes in FC with inpatient treatment for girls with AN ( n = 22, M age [ SD ] = 15.3 [1.9]) and FC in those who had been recovered from AN for at least 1 year ( n = 21, M age [ SD ] = 22.3 [3.3]), finding that FC alterations observed in acute AN largely resolved with treatment and were nearly absent after a year of recovery (Lotter et al., 2021 ). In addition, a longitudinal study of female youth (aged 12–24) with AN ( n = 87 scanned when underweight and again after ≥12% BMI increase) and 87 controls found that four rsFC parameters normalized with weight gain among the AN group (Seidel et al., 2024 ). However, the speed and degree to which these four parameters normalized varied, and some did not linearly improve with weight gain. These findings suggest that some rsFC alterations observed in acute AN may be state‐based. This possibility is also highlighted by discrepancies within the broader literature comparing DMN rsFC between acute and recovered AN. For example, research has shown greater anterior insula‐DMN connectivity among acute AN compared to controls and greater connectivity between angular gyrus and other frontoparietal network regions (Boehm et al., 2014 ). However, other work found no differences in DMN connectivity in acute AN and instead noted evidence of aberrant FC in somatosensory and visual areas (Phillipou et al., 2016 ). Notably, findings from a sample with acute AN (Boehm et al., 2014 ) were replicated in a sample of individuals who had recovered from AN (Cowdrey, Filippini, Park, Smith, & McCabe, 2014 ). In this study, female emerging adults recovered from AN for 1 year ( n = 16, M age [ SD ] = 23.06 [3.55]) demonstrated greater FC from the DMN to the precuneus, dorsolateral PFC, and inferior frontal gyrus compared to controls ( n = 15). In contrast, other research comparing female young adults who had been recovered from AN for 6 months ( n = 31, M age [ SD ] = 22.27 [3.08]) to controls ( n = 31) showed lower dorsolateral PFC to frontoparietal network rsFC in the recovered AN group than the control group and found no differences in DMN rsFC (Boehm et al., 2016 ). More research is needed to investigate whether rsFC restores with ED recovery and the time course over which these changes might occur. BN and BED rsFC has been less characterized in BN and BED. One study found greater DMN‐right ventral supramarginal gyral and right ventrolateral PFC‐left lateral parietal rsFC in girls with BN ( n = 33, M age [ SD ] = 17.5 [1.6]) compared to controls ( n = 37) (Domakonda, He, Lee, Cyr, & Marsh, 2019 ). Another group leveraged ABCD Study data and found that children with BED ( n = 58, M age [ SD ] = 9.9 [0.6], 96.7% female) show lower dorsolateral PFC‐amygdalar and ACC‐OFC connectivity compared to age‐ and BMI‐matched children without BED (Murray et al., 2023 ), suggesting the potential role of altered connectivity between reward and inhibitory control networks versus disruptions within these networks in BED. Task‐based fMRI Task‐based fMRI is commonly used to investigate patterns of neural activity in response to stimuli (e.g., food), which is inferred through changes in blood oxygen in response to stimuli (e.g., blood‐oxygen level‐dependent response). Task‐based fMRI has been a popular method in the ED field because it offers a way for researchers to probe responses to disorder‐specific stimuli (e.g., food and exercise cues). Recent methodological developments have enabled the examination of specific neural circuits underlying maladaptive behavior (e.g., repeatedly selecting a low‐fat vs. high‐fat food) by using paradigms that link brain and behavioral response. This approach shows promise in identifying biomarkers of EDs that may be targeted in translational therapeutics. AN and atypical AN In AN, many studies have used paradigms whereby participants passively view highly palatable food images, with contrasts to nonfood stimuli. Meta‐analytic findings suggest that those with AN demonstrate lower activation of emotion and reward‐related regions (e.g., amygdala, insula) and greater activation of cognitive control‐related regions (e.g., ACC and PFC) when viewing food‐related stimuli compared to noneating‐disorder controls (Bronleigh, Baumann, & Stapleton, 2022 ). Extending these findings, another literature review demonstrated that greater activation of frontostriatal networks was positively correlated with restrictive eating in AN (Steward, Menchon, Jiménez‐Murcia, Soriano‐Mas, & Fernandez‐Aranda, 2018 ). Beyond passive viewing of food stimuli, a recently developed food‐choice task paradigm provides insight into the neural underpinnings of how individuals with EDs decide what to eat. Research that administered this food‐choice task concurrent with fMRI scanning has shown greater activation of the dorsal striatum in young women with AN ( n = 21, M age [ SD ] = 26.1 [6.5]) during food choice, suggesting restrictive eating may be driven by reward and habit‐based processes (Foerde, Steinglass, Shohamy, & Walsh, 2015 ). This finding was not replicated in girls with AN (low‐weight AN: n = 66, atypical AN: n = 10, M age [ SD ] = 15.4 [1.5]); however, greater choice‐related activation of the anterior caudate was associated with fewer high‐fat food choices in AN (this correlation was not observed in controls) (Lloyd et al., 2024 ). These results suggest that the caudate plays a role in restrictive eating and demonstrate that altered neural activation observed during food choice in adult AN may not be as extensive in adolescent AN. Other work has used a tasting paradigm (i.e., sucrose solutions administered during fMRI scanning) to probe neural prediction errors in response to anticipation or receipt of sweet stimuli. This research found exaggerated prediction‐error responses in the striatum and insula in female adolescents with AN ( n = 56, M age [ SD ] = 15.9 [0.9]), compared to controls ( n = 52), which were cross‐sectionally associated with ED severity (Frank et al., 2018 ). Another study that used this paradigm in female emerging adults with AN ( N = 35, M age [ SD ] = 23 [7]) demonstrated that greater OFC response to expecting caloric stimuli (e.g., an anxiety‐related response to sweet taste) correlated with lower BMI 4 years posttreatment (Gorrell, Shott, Pryor, & Frank, 2024 ). Another research group studied the neural mechanisms of food‐cue reversal learning – or learning that a previously rewarded choice is no longer rewarded – among adolescents with AN. This study found that adolescents with AN ( n = 15) and controls ( n = 14) did not show behavioral differences in reversal learning; however, adolescents with AN showed greater activation of the DLPFC during food‐cue learning acquisition and reversal, suggesting a higher reliance on prefrontal regions during learning compared to controls (Hildebrandt et al., 2018 ). Other task‐based fMRI research in AN has examined neural responses to viewing different body shapes. This shows that individuals with AN exhibit greater activation in threat‐processing regions (e.g., amygdala) when viewing larger bodies, and greater activity in reward‐related regions when viewing underweight bodies (Haynos, Lavender, Nelson, Crow, & Peterson, 2020 ). Preclinical models (Beeler et al., 2021 ) and a review of the initial human work (Gorrell, Collins, Le Grange, & Yang, 2020 ) suggest dopamine signaling and brain regions linked to inhibitory control and reward are involved in exercise, a key symptom of EDs. One study administered a go/no‐go task with food and exercise images during fMRI scanning to young women with AN ( n = 12, M age [ SD ] = 23.3 [4.7]), healthy athletes, and nonathlete controls. The AN group showed lower response inhibition in the putamen for food images but heightened PFC and cerebellar responses to exercise cues compared to athletes and nonathletes, indicating a reward response to disorder‐specific cues (Kullmann et al., 2014 ). Recent studies of adolescents with AN using fMRI assessed neural responses to anxiety‐provoking, ED‐related words (e.g., diet and fat). No significant differences were found between girls with AN ( n = 25, M age [ SD ] = 14.7 [1.8]) and controls with mild anxiety ( n = 22) in anxiety‐circuit analyses. However, within the AN group, significant differences emerged in prefrontal regions for anxiety versus neutral cues (Seiger et al., 2023 ). Longitudinally, higher self‐reported anxiety symptoms predicted less BMI change over time in girls with AN ( n = 33, M age [ SD ] = 14.6 [1.9]), but brain activation and connectivity in anxiety‐related regions did not predict changes in BMI or eating pathology at 6‐month follow‐up (Derissen et al., 2023 ). Beyond ED‐related stimuli, fMRI studies have explored neural responses to emotional stimuli, like faces. For instance, girls with AN ( n = 11, M age [ SD ] = 16.4 [1.4]) exhibited lower PFC activation in facial and emotion recognition areas compared to controls ( n = 11) (Lulé, Müller, Fladung, Uttner, & Schulze, 2021 ) and greater amygdalar and dorsolateral prefrontal activation when viewing aversive emotional faces in a larger group of adolescent girls with AN ( n = 36, M age [ SD ] = 16.6 [3.9]) and 36 controls (Seidel et al., 2018 ). These emotion‐related neural changes appear to resolve with weight restoration (Seidel et al., 2022 ). Still others have investigated the neural underpinnings of reward‐related constructs in AN, such as delay and effort discounting. Delay discounting is the tendency to select smaller rewards that are received sooner over larger rewards that are delivered later. Adults with AN have typically shown reduced delay discounting, or a preference for larger‐later rewards, which has been interpreted as a mechanism for AN maintenance (e.g., forgoing food in pursuit of weight loss). One study of adolescent girls with AN ( n = 31, M age [ SD ] = 15.7 [2.5]) showed no behavioral differences from controls ( n = 31) in preferences for smaller‐sooner versus larger‐later monetary rewards; but girls with AN made decisions faster than controls (King et al., 2016 ). Moreover, the groups showed similar neural correlates; however, decision‐related neural activation was lower in frontoparietal regions in the AN group than controls, suggesting a mechanism of faster and potentially more efficient decision‐making in AN. A longitudinal follow‐up on a subset of adolescents with AN included in this study ( n = 22) found that DMN regions (including the medial prefrontal cortex, posterior cingulate/precuneus, and inferior parietal lobe) showed less deactivation following partial weight restoration; though there were no changes in delay discounting rate or decision‐making speed (Doose et al., 2020 ). The authors inferred that this strengthening of DMN activation in the context of treatment may reflect relaxation of overcontrol in AN with weight restoration. This research group also interrogated the neurocognitive mechanisms of cognitive effort discounting among female patients with acute AN ( n = 48, M age [ SD ] = 15.9 [2.2]) and 48 age‐matched controls (King et al., 2025 ). Effort discounting refers to the degree to which rewards are devalued as a function of the effort required to obtain them. Researchers have posited that effort discounting may be altered in AN due to persistent engagement in overly effortful behaviors (e.g., extreme caloric restriction and excessive exercise). Results suggested that patients with AN did not experience effort as less costly, or more rewarding in and of itself, than controls. However, patients with AN demonstrated greater frontoparietal activation (specifically in the lateral prefrontal cortex and intraparietal sulcus) during effort‐based decision‐making compared to controls, suggesting elevated cognitive control among those with AN. ARFID The three‐dimensional neurobiological model of ARFID has provided a strong framework for research in this area. In brief, this model hypothesizes that alterations in perceptual systems underpin three presentations: sensory sensitivity, lack of interest (rooted in perturbations to the arousal/regulatory system), and fear of aversive consequences (linked to disruptions in the negative valence system) (Thomas et al., 2017 ). Combined ARFID presentations (e.g., with sensory sensitivity and lack of interest) are believed to result from alterations in multiple systems. One study interrogated this model by administering a food‐viewing paradigm concurrent with fMRI scanning to examine potential sex differences in youth with ARFID ( n = 62, M age [ SD ] = 16.1 [3.7], 50% female), finding no significant differences in whole‐brain nor region‐of‐interest (lateral PFC, OFC, and hippocampus) analyses between boys and girls (Getachew et al., 2021 ). Another study administered a food‐viewing paradigm during fMRI scanning to youth with ARFID ( n = 23, M age = 16.92, 65% female), 12 of whom had “healthy” weight and 11 of whom had high weight (Kerem et al., 2022 ). Region‐of‐interest analyses contrasting high‐calorie and nonfood images found that high‐weight youth with ARFID showed hyperactivation in the OFC and bilateral anterior insula compared to healthy‐weight youth with ARFID. Similarly, whole‐brain analyses found that high‐weight youth with ARFID demonstrated elevated OFC, anterior insula, striatal, and ACC activation while viewing high‐calorie food images compared to healthy‐weight youth. This nascent task‐based fMRI work suggests that the OFC and insula may play a role in ARFID symptomatology. As ARFID commonly co‐occurs with neurodevelopmental disorders like autism spectrum disorder and other psychiatric illnesses (e.g., anxiety, obsessive‐compulsive disorder), future research on the neural mechanisms of ARFID should interrogate shared features of these conditions (e.g., disgust, executive‐function inefficiencies), and features that may be unique to ARFID. BN and BED Task‐based fMRI research in BN and BED has primarily focused on reward‐system function, finding evidence for elevated reward‐related neural response during reward anticipation and receipt (monetary and food‐related), more model‐free reinforcement learning (i.e., more dependency on in‐the‐moment feedback), and more habitual behavior among those who binge eat (Leenaerts, Jongen, Ceccarini, Van Oudenhove, & Vrieze, 2022 ). Specifically, in studies investigating neural response to reward‐based learning and decision‐making tasks, those with BN or BED evidence hypoactivation of emotional, reward‐processing, and cognitive control regions (e.g., ventromedial PFC, striatum, insula) (Cyr et al., 2016 ; Reiter, Heinze, Schlagenhauf, & Deserno, 2017 ). Research has found that girls with BN ( n = 18, M age [ SD ] = 18.4 [2.1]) show lower activation of frontostriatal regions and the cingulate cortex during a learning task compared to controls (Marsh et al., 2011 ). Longitudinal studies found different neural activation trajectories during self‐regulatory control for girls with BN ( n = 32, M age [ SD ] = 16.7 [1.3]) vs. controls ( n = 28). Engagement of the inferior frontal gyrus, ACC, insula, and dorsal striatum decreases over time in controls but not in adolescents with BN. Additionally, those who remitted from BN showed increased engagement of these regions. Other fMRI research highlights dysregulation in attentional networks among adolescents with BN (Seitz et al., 2016 ). Researchers have studied neural activation during the anticipation and consumption of chocolate milkshakes in female youth with BN ( n = 13, M age [ SD ] = 20.3 [1.9]) and controls ( n = 13) (Bohon & Stice, 2011 ). The study found that higher prescan negative affect correlated with greater anticipation‐related activation in the putamen, caudate, and pallidum in the BN group, while no correlation was found in controls. This suggests negative affect may amplify reward responsivity in BN. Task‐based fMRI research in youth with BED is also limited. For instance, an investigation using ABCD Study data showed that compared with age‐ and BMI‐matched controls ( n = 68), youth with BED ( n = 58, M age [ SD ] = 10 [0.6], 48% female) do not show differential brain activation or abnormal neural activity in reward or inhibitory control circuitry during monetary incentive delay or stop‐signal tasks (Murray, Zhang, Duval, Nagata, & Jann, 2024 ). Altogether, evidence suggests that youth with BN or BED may simultaneously have difficulties dampening reward circuits and engaging self‐control‐related circuits, but this literature is nascent. Neurometabolism Neurometabolic research in adolescent EDs is relatively limited. In AN, a systematic review summarized research using proton magnetic resonance spectroscopy ( 1 H‐MRS), and nearly all participants in the seven included studies were female (98%) young adults ( M age [ SD ] = 22.20 [3.75], range = 11–41) (Mitchell, Anijärv, Levenstein, Hermens, & Lagopoulos, 2023 ). Overall, those with AN evidenced lower glutamate concentration in the ACC and occipital cortex and lower Glx (combination of glutamate and glutamine) concentrations in the ACC compared to controls. No differences in GABA concentrations were found between those with AN and controls. However, few studies investigated each of these neurotransmitters, and results should be interpreted with caution. An early 1 H‐MRS study showed lower myoinositol and lipid levels in the frontal white matter of a combined AN and BN sample ( n = 20, M age [ SD ] = 19 [9], 95% female) compared to controls; these reductions correlated with lower BMI in the ED group (Roser et al., 1999 ). Additionally, this research found lower lipid signals in occipital GM and higher metabolite concentrations (except lipids) in the cerebellum of the ED group versus controls. A more recent study compared young women ( M age [ SD ] = 23.96 [3.98]) with AN‐BP ( n = 22) to those with BN ( n = 33) and controls ( n = 33), revealing that the AN‐BP, but not BN, group had lower myo ‐inositol and N ‐acetyl aspartate concentrations in the right inferior lateral prefrontal and occipital cortices relative to controls (Westwater et al., 2022 ). Researchers have also used positron emission tomography (PET) and single photon emission computed tomography (SPECT) to study neurochemicals in EDs. Yet, this research is also limited and has been primarily conducted in adults with EDs (Gianni, De Donatis, Valente, De Ronchi, & Atti, 2020 ). One study of young women with BN ( n = 16, M age [ SD ] = 23.8 [7.1]), AN ( n = 14; M age [ SD ] = 20.5 [3.6]) and controls ( n = 19) found significant greater availability of type 1 cannabinoid receptor (CB1R) in the insula in the AN and BN groups versus controls, implicating reward processing in AN and BN (Gérard, Pieters, Goffin, Bormans, & Van Laere, 2011 ). SPECT has primarily been used in AN, and findings suggest lower blood perfusion in acute AN in several regions, including the basal ganglia, insula, posterior cingulate, and prefrontal, temporal, parietal, and occipital lobes (Gianni et al., 2020 ). Although some work in adolescents (Komatsu et al., 2010 ) and emerging adults (Matsumoto et al., 2006 ) with AN has shown resolution of cerebral blood flow alterations with weight restoration, other work has shown continued hypoperfusion, including in the temporal, parietal, and occipital lobes, and ACC (Kojima et al., 2005 ). A handful of SPECT studies have included emerging adults with BN (Goethals et al., 2007 ; Jiménez‐Bonilla et al., 2009 ), and no studies have been published on young people with BED. Overall, the understanding of neurometabolic alterations and their impact on eating pathology in youth is limited. Synthesis and future directions In this Annual Research Review, we have described adolescent brain development, the major neural models of EDs, and the existing neural research on youth EDs. To put it succinctly, research on the neural mechanisms of EDs in youth is nascent relative to other forms of childhood psychopathology (e.g., depression and anxiety), and studies have mainly employed cross‐sectional designs that predominantly include all‐female samples and focused almost exclusively on low‐weight AN. This is problematic, given that most people with an ED are not underweight and the fact that boys also experience EDs, especially ARFID and binge eating. Moreover, many of the studies featured have been underpowered, which has likely contributed to inconsistent results and nonreplicable findings. Further, our review highlighted that many neural models of EDs have not been empirically tested in adolescent EDs, despite empirical evidence. Our in‐depth overview of these areas has highlighted gaps in the literature and growth opportunities, which we delineate below. Growth opportunity: Interdisciplinary collaboration As there are inherent limitations to noninvasive neuroimaging in pinpointing the causal mechanisms of EDs, future research would benefit from interdisciplinary science that integrates neuroimaging with genetic and hormonal approaches. Interdisciplinary efforts that link hormonal profiles and pubertal timing with neuroimaging data and gene expression patterns could offer novel insights into the processes that confer vulnerability to EDs during adolescence. For example, research integrating transcriptomic atlases with neuroimaging data could help researchers identify which gene pathways are enriched in brain regions implicated in EDs and how these patterns may change over puberty or with fluctuations in gonadal hormones. Moreover, EDs are unique within the psychiatric disorders given that nutrition is central to both pathology and treatment. To this end, incorporation of nutrigenetics (i.e., the study of genetic variations that alter individual physiologic processing of nutrient intake) and research centered on altered gut microbiome and the function of microbial genetics into neuroimaging investigations may illuminate and clarify new disease pathways. Together, such interdisciplinary approaches are essential to building more mechanistic accounts of ED emergence and entrenchment and have proven fruitful in the context of other disorders. Growth opportunity: Longitudinal research Longitudinal neuroimaging research in adolescent EDs could significantly advance knowledge of how neural mechanisms of EDs change naturally with time or treatment and identify neurobiological heralds of EDs in youth. As noted in our narrative review, scant evidence exists for how brain structure and function change throughout treatment and the pace at which such changes may occur. Further, existing research has almost exclusively occurred in adolescents with AN who were undergoing intensive inpatient treatment for their ED. Many adolescents with AN (and other EDs) do not receive inpatient treatment for their ED and instead receive outpatient specialty or nonspecialty care (e.g., via a generalist mental‐health clinician or pharmacology). For instance, a first‐line outpatient treatment for adolescent AN is family‐based treatment (FBT), and a first‐line treatment for adolescent BN is cognitive behavioral therapy (or FBT). It has been well documented that early response (e.g., reductions in ED behaviors) within the first few weeks of treatment is associated with better treatment outcomes. Neural changes within the first few weeks of treatment may, in part, underpin this durable change; however, to our knowledge, no published research has examined these changes. Thus, future research could examine neural changes that occur alongside outpatient treatment. Moreover, many adolescents with an ED do not receive care, and understanding the neural mechanisms by which symptoms may persist or remit without care would be fruitful. Furthermore, neural alterations may render the patients less ready or suited for certain treatments; examining this may help to explain who will benefit most from certain interventions. Another area of opportunity is to study youth who have not yet developed an ED over time to elucidate the neural factors that promote risk versus resilience to an ED. For instance, neurofunctional and neurostructural markers of depression in those at familial risk (e.g., by having a parent with depression) have been well‐characterized (Nazarova, Schmidt, Cookey, & Uher, 2022 ), and research has identified neural signatures of resilience to developing depression in adolescents (Fischer, Camacho, Ho, Whitfield‐Gabrieli, & Gotlib, 2018 ). This research in EDs is nascent, with only one study to our knowledge comparing neural function in high familial risk of eating pathology compared to those without (Stice, Yokum, Rohde, Cloud, & Desjardins, 2021 ). Extending this research to EDs is critical for understanding the neural mechanisms of resilience and risk to developing an ED and will help to inform early detection and prevention efforts. Further, understanding how neurodevelopment relates to ED emergence, regardless of family history, would advance understanding of how brain development may be linked to ED development. Growth opportunity: Leveraging novel methods and statistical approaches The adoption of novel neuroimaging methods and statistical approaches in the ED field, particularly to youth EDs, has been relatively limited to date, representing a key growth opportunity for advancing understanding of neural mechanisms of youth EDs. For instance, psychiatric researchers have recently implemented neuromelanin‐sensitive MRI (NM‐MRI) to noninvasively interrogate the proxy function of neurochemicals implicated in psychopathology, such as dopamine and noradrenaline. NM‐MRI is an attractive alternative to traditional methods for imaging dopamine function, such as PET, which are invasive and not well‐suited for use in pediatric samples due to IV placement and radiation exposure. NM‐MRI has been used to measure proxy dopamine function in pediatric obsessive‐compulsive disorder (Pagliaccio et al., 2023 ). Although there is no published work yet for EDs, a study protocol for NM‐MRI in adolescent AN has been recently published (Murray et al., 2023 ), suggesting that research is underway, at least in this diagnostic group. Further, we recommend researchers adopt methodological and statistical approaches that help to account for the heterogeneity that is inherent to EDs. For example, diagnostic crossover between EDs (e.g., from AN to BN, from BN to BED) is common in EDs and may muddle the neurobiological distinction between disorders. However, this confound may be mitigated by studying behavior transdiagnostically (e.g., studying binge eating across diagnostic groups), an approach that has not yet gained traction in EDs. Further, low and high weight alter neurostructure and neurofunction, which may introduce confounds for studying the neural correlates of EDs (e.g., restrictive eating in higher‐weight youth). To address this confound, researchers could carefully index variables such as hydration status or other body composition markers (e.g., fat mass) and control for these variables in analyses. In addition, we encourage the adoption of novel statistical approaches that move beyond traditional univariate approaches, which elucidate one‐to‐one correspondences between discrete brain regions and behaviors, to multivariate approaches that are better suited to capture the complex nature of brain‐behavior associations in EDs. For instance, multivoxel pattern analysis (MVPA) and partial least squares (PLS) regression enable the identification of spatially distributed patterns of neural activation with ED behaviors and cognitions. Moreover, network‐based approaches (e.g., graph theory, dynamic functional connectivity) permit the interrogation of brain network organization and flexibility, potentially yielding insights into systems‐level alterations that underlie ED symptoms. Additionally, approaches like connectome‐based predictive modeling (CPM) and machine‐learning frameworks can be used to predict ED outcomes based on patterns of brain connectivity (using resting‐state or task‐based fMRI). Taken together, although traditional univariate approaches have provided foundational knowledge of brain‐behavior associations in EDs, sophisticated multivariate approaches are likely necessary for more precise neural signatures of EDs across illness stages, as well as treatment‐selection biomarkers and prediction of individual differences in illness course. In parallel, advances in computational approaches allow for measuring latent cognitive and neural processes (e.g., value updating, prediction‐error signaling, reward sensitivity) that underlie developmental changes in reward and punishment learning and have been implicated in adolescent eating pathology. Thus, leveraging these advances and linking them directly to disturbances in eating behavior offers a promising means of identifying mechanistic targets for intervention. Growth opportunity: Empirical tests of theoretical models Our review suggested a misalignment between theoretical models of the neural mechanisms of EDs and empirical work conducted, such that much of the published work did not directly test the proposed models. Moreover, although some processes have been robustly implicated in certain EDs, little work has tested how these processes impact neural function and potentially relate to ED symptom expression. For example, much of the task‐based fMRI work in adolescent BN has focused on reward processing and self‐regulation, with relatively less attention to negative affect, despite research demonstrating that negative affect is a strong antecedent of binge‐eating pathology. Further, few studies have directly interrogated the neural underpinnings of actual eating behavior (e.g., restrictive eating, binge eating). Tests of the neural underpinnings of eating behavior could help to inform therapeutics (e.g., neuromodulation, pharmacotherapy) that directly target these neural underpinnings and improve outcomes. Growth opportunity: Enhanced inclusivity EDs affect all sexes, and rates of EDs are increasing faster in boys than in girls. Despite this, boys have been mostly excluded from ED neuroimaging research. Though there has been scientific justification for this exclusion (e.g., gonadal hormone differences), it is important to understand how EDs affect brain structure and function in males and whether there are sex‐specific differences. There is also an opportunity to expand inclusivity regarding ED diagnoses and behaviors. Although insights into the neural mechanisms of all EDs are limited, they are particularly limited for binge‐eating pathology and nonlow‐weight EDs. For instance, most of our knowledge about the neural mechanisms of adolescent BN comes from data collected by one research group, and such research in adolescent BED is virtually nonexistent. Additionally, little is known about the neural markers of adolescent atypical AN. Similarly, little is known about the neural mechanisms of adolescent ARFID. The paucity of the neural mechanisms of these EDs could be explained by assessment challenges (e.g., binge eating in youth) and/or the relative newness of some diagnostic labels (e.g., atypical AN and ARFID); however, without such research, our understanding of the etiological and maintaining factors of these pernicious disorders remains incomplete. Conclusions Knowledge of the neural mechanisms of EDs remains in its infancy relative to other forms of adolescent psychopathology. To date, the majority of neurobiological research in adolescent EDs has focused on characterizing neurostructural alterations primarily among underpowered samples of female participants with low‐weight AN and has employed cross‐sectional designs. These limitations highlight exciting future avenues for research on the neural mechanisms of EDs, which can provide nuanced insights into etiologic and maintenance models of EDs and inform mechanistic, developmentally appropriate treatments for these pernicious disorders. Ethical information Ethical approval was not applicable for this manuscript, as it was a narrative review that did not involve data collection from human subjects. Key points. What's known? Eating disorders often emerge during adolescence, a time of substantial neurofunctional and neurostructural change. This review summarizes adolescent brain development and its theorized links to eating disorders, and provides a narrative review of neural correlates of eating disorders in young persons. What's new? Our narrative review underscores the paucity of neurobiological research on adolescent eating disorders and highlights the specific focus on low‐weight anorexia nervosa and the recruitment of predominantly female participants. What's relevant? We highlight growth opportunities for neurobiological research in adolescent eating disorders, underscoring the need for longitudinal research across illness stages and consideration of the neural correlates of transdiagnostic behaviors (e.g., binge eating), as well as incorporation of novel approaches to advance knowledge on these correlates. Acknowledgements K.H. (K12AR084233, K23MH137567), E.L. (T32MH096678), and S.G. (K23MH126201, R21MH131787) are supported by the National Institutes of Health. 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