{"paper_id":"455173fc-7535-438d-a4ec-23d2e2306185","body_text":"Neuroanatomical Substrates of emotional Dysregulation in Bulimia Nervosa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Neuroanatomical Substrates of emotional Dysregulation in Bulimia Nervosa Lan Zhang, Xin Zhao, Liqiong Liu, Wenxin Bao, Meiou Wang, Yidan Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9025261/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective We aimed to elucidate the neuroanatomical alterations in drug-free young females with BN and their relationship to core illness severity and comorbid emotional symptoms. Methods A total of 53 adult female patients with BN and 52 age- and sex- matched healthy controls (HCs) were included in the study and underwent high-resolution T1-weighted MRI scans. We applied both voxel-based morphometry (VBM) and surface-based morphometry (SBM) methods to comprehensively explore gray matter (GM) alterations in BN patients. Correlations and mediation analyses were further performed to assess the relationships among morphological alterations, depression or anxiety symptoms and eating disorder symptoms in BN patients. Results Compared to HCs, BN patients exhibited reduced GMV/CV in the bilateral lingual and calcarine gyri, right superior temporal gyrus, and bilateral posterior cerebellum. Among these regions, the right lingual gyrus volume correlated negatively with shape concern scores for eating disorder, while bilateral posterior cerebellar volumes correlated negatively with depression and anxiety symptoms. We also found that depression and anxiety symptoms fully mediated the association between left posterior cerebellar volume and eating disorder severity, with depression demonstrating a significantly stronger mediating effect than anxiety. Conclusions Our study identifies convergent gray matter reductions in cerebellar, occipital, and temporal regions in BN. Notably, we demonstrated that comorbid depression and anxiety symptoms fully mediate the link between posterior cerebellar volume and eating disorder severity. Our finding emphasized the cerebellum as a neurobiological locus where emotional dysregulation converges to exacerbate BN, offering a novel target for pathophysiology-informed treatment strategies. Biological sciences/Neuroscience Health sciences/Diseases Bulimia nervosa Structural magnetic resonance imaging Cerebellum Depression Anxiety. Figures Figure 1 Figure 2 Introduction Bulimia Nervosa (BN) is a debilitating eating disorder characterized by recurrent binge eating, compensatory behaviors to prevent weight gain, and self-evaluation unduly influenced by body shape and weight[ 1 ]. Critically, emotional dysregulation, particularly depression and anxiety, is increasingly recognized as a core pathological driver that fuels these maladaptive behaviors as dysfunctional coping mechanisms[ 2 – 4 ].The disorder poses a significant global public health challenge, with lifetime prevalence estimates ranging from 0.3% to 4.6%[ 5 ]and peak incidence among young females[ 6 ]. As a major subtype of eating disorders, the neurobiological underpinnings of BN remain less thoroughly investigated than those of anorexia nervosa (AN), and existing structural findings are often inconsistent. Structural MRI studies have reported gray matter (GM) abnormalities in BN, including reduced gray matter volume (GMV) and cortical thinning in the temporal and parietal lobes[ 7 ] as well as increased GMV in the orbitofrontal cortex and insula and decreased GMV in the frontal lobe and cingulate gyrus [ 8 – 10 ]. Moreover, most prior studies have predominantly focused on cortical regions, with limited attention to the potential contribution of the cerebellum, despite its established involvement in the cerebellar-cognitive-affective network[ 11 ]and its putative role in cognitive, emotional, and reward-related processes implicated in BN. More importantly, previous studies usually only used voxel-based morphometry (VBM) or surface-based morphometry (SBM) methods to identify GM alterations in BN patients. While VBM is robust for detecting volumetric changes across the entire brain (including subcortex), and SBM offers superior sensitivity to cortical thickness and surface area, neither method alone captures the full spectrum of GM pathology. Therefore, we utilized both to achieve a more integrative understanding of brain structure in BN. Beyond the behavioral diagnostic criteria in BN, emotional dysregulation, particularly depressive and anxiety symptoms, is increasingly conceptualized as a core dimension of BN pathology rather than a secondary comorbidity[ 2 , 3 ]. These affective disturbances serve as sustaining factors that perpetuate the disorder, often precipitating binge eating as a maladaptive coping mechanism for emotional distress[ 4 , 12 , 13 ].While the clinical association between negative affect and BN symptomatology is well established, and previous studies have separately linked emotional disturbances and structural brain alterations to BN symptomatology[ 14 , 15 ], the mediating pathways through which specific brain abnormalities influence BN symptom severity via affective states remain unexplored. Specifically, it remains unclear whether and how negative affections constitutes a mechanistic pathway through which emotional dysregualtion mediates the interaction between brain structure and eating disorder symptoms. Thus, present study aims to disentangle the unique roles of depressive and anxiety symptoms in linking structural deficits to BN severity by comprehensively exploring brain structural alterations and examine their clinical relevance in BN patients combining VBM and SBM analyses. More importantly, we employed mediation models to determine whether depressive and anxiety symptoms independently mediate the association between structural alterations and BN symptom severity. We hypothesized that patients should exhibit abnormal GM morphology, and that these abnormalities would influence BN symptom severity through the mediating effects of depressive and anxiety symptoms. Materials and Methods Participants The study was approved by the Research Ethics Committee of West China Hospital, Sichuan University, and written informed consent was obtained from all participants. A total of 53 adult female patients with BN and 52 healthy controls (HCs) were included in the study. All participants were between 18 and 45 years of age and of Han Chinese descent. Patients with BN were recruited from the Mental Health Center of West China Hospital, Sichuan University, and were diagnosed by two independent psychiatrists (XZ and MW) using the DSM-5 Structured Clinical Interview for Disorders (SCID-5)[ 1 ]. To minimize the potential confounding effects of therapeutic interventions on brain morphometry, all patients were required to be free from psychotropic medications, physical therapies (e.g., electroconvulsive therapy or repetitive transcranial magnetic stimulation), and systematic psychotherapy for at least three months prior to enrollment. HCs were recruited through advertisements in local newspapers. All individuals were screened to exclude any history of neurological disorders, seizures, head injuries with loss of consciousness, intellectual disability, or pervasive developmental disorders. Importantly, the HC participants had no lifetime history of any Axis I disorders, and the BN participants did not meet the diagnostic criteria for any comorbid Axis I disorders at enrollment. Clinical psychological assessment The Eating disorder Inventory (EDI) is a widely used self-reporting measure of ED symptoms and consists of 8 standardized subscales representing dimensions that are clinically relevant to ED[ 16 ]. The first 3 dimensions deal with attitudes and behavior concerning eating, weight, and body shape, namely, Drive for Thinness (DT), Bulimia (B), and Body Dissatisfaction (BD). The remaining 5 dimensions tap more general psychological and relational constructs relevant to ED, namely, Ineffectiveness (I), Perfectionism (P), Interpersonal Distrust (ID), Interoceptive Awareness (IA), and Maturity Fears (MF). The Eating Disorder Examination Questionnaire (EDE-Q) assesses a variety of behaviors and cognitive features relevant to eating pathology and is summarized by four subscale scores: Restraint, Eating Concern, Shape Concern, and Weight obtained from item scores[ 17 ]. The Patient Health Questionnaire-9 (PHQ-9) is a nine-item questionnaire designed to screen for depression in primary care and other medical settings. The standard cut-off score for screening to identify possible major depression is 10 or above[ 18 ]. The seven items of the Generalized Anxiety Disorder scale (GAD-7) describe the most important diagnostic criteria for GAD according to DSM-V, namely the Criterion A (fear and anxiety related to a series of events or activities), Criterion B (difficulties in controlling concerns) and Criterion C (anxiety and worry are accompanied by at least three additional symptoms such as restlessness, mild fatigue, difficulty concentrating, irritability, muscle tension and sleep problems)[ 19 ]. MRI data acquisition All participants were scanned using uMR790 3.0T system with a 32-channel phased‐array head coil at the West China Hospital of Sichuan University. The high-resolution 3-Dimensional T1-Weighted (T1W) imaging was obtained with the axial fast spoiled gradient recalled sequence with the following parameters: repetition time/echo time/inversion time (TR/TE/TI) of 8.5/3.1/1100 ms, flip angle of 8°, field of view (FOV) of 240 × 256 mm, slice thickness of 0.8 mm, and a matrix size of 300 × 320, yielding 208 contiguous slices covering the entire brain. Participants were positioned supine, and foam padding was utilized to minimize the head movement and reduce motion artifacts. Soft earplugs were used to reduce scanner noise. They were instructed to maintain stillness throughout the scanning session. Images were visually inspected by two radiologists (LL and YW) immediately after acquisition, and individuals with visible head movement artifact were immediately rescanned. MRI data preprocessing VBM analysis VBM analysis was performed using the Computational Anatomy Toolbox (CAT12; http://dbm.neuro.unijena.de/cat12/ ), integrated within the Statistical Parametric Mapping (SPM12; http://www.fil.ion.ucl.ac.uk/spm/ ) toolboxes. Specifically, the data preprocessing procedure consisted of four steps: 1) normalizing T1 images to the standard Montreal Neurological Institute (MNI) space and segmenting images into the gray matter, white matter, and cerebrospinal fluid using DARTEL algorithm[ 20 ]; 2) estimating the homogeneity of the samples and visually checking for heterogeneity of the images; 3) calculating the total intracranial volume (TIV) for each subject based on the unsmoothed images; 4) smoothing spatial images with an 8 mm Gaussian kernel of full-width at half maximum (FWHM) following the operation manual recommended. SBM analysis For SBM analysis, the Freesurfer ( http://surfer.nmr.mgh.harvard.edu , version 7.3.2) surface-based processing stream was used to estimate CT, cortical surface area (CSA), and cortical volume (CV) at each vertex on the cortical surface. This method has been described in detail elsewhere[ 21 – 23 ]. In brief, it involves: motion correction and conform, transformation to MNI space, intensity normalization, skull-stripping, segmentation of gray/white matter, tessellation of the white matter and gray matter boundary, topology correction, surface deformation and inflation, surface atlas registration and surface extraction. CT was defined as the closest straight-line distance between the pial surface and the gray/white matter boundary[ 23 ]. CSA was calculated as the average area of the surrounding tessellated triangles on the pial surface[ 24 ]. CV is the volume of gray matter that lies between the pial surface and the gray/white matter boundary[ 25 ]. The CT, CSA, and CV of each subject were calculated independently for the left and right hemispheres. Once computed, vertex-wise estimates of cortical surface architecture were registered to the Freesurfer average template and smoothed with a Gaussian kernel (FWHM = 10 mm) for statistical analysis. In addition, a measure of TIV was extracted. Quality control procedures All segmentation was visually verified following a recently published recommendations for cortical QC 2.0 by the Enhancing Neuro Imaging Genetics Through Meta-analysis (ENIGMA) consortium ( https://enigma.ini.usc.edu/protocols/imaging-protocols ). In brief, the segmentation of each subject was independently visually checked by two authors (LL and YW), and subjects with segmentation results judged to be incorrect (e.g., skull-strip errors and/or failure in the formation of the white matter mass) were excluded. No participant showed segmentation failure. Statistical analysis Statistical analyses were conducted with the R Statistics software (version 4.3.2). The Shapiro-Wilk normality test was used to test the normality of variable distribution. Mann-Whitney U test was used to compare continuous variables with non-normal distribution. The between-group differences in GM morphological parameters were assessed with two-sample t tests controlled for age, education years and TIV (age and education years for CT analyses). The thresholds were set as voxel-wise P < 0.001 and cluster-wise P < 0.05 (family-wise error (FWE) corrected) for VBM analysis and vertex-wise P < 0.001 and cluster-wise P < 0.05 (Monte Carlo simulation with 10,000 iterations corrected) for SBM analysis. Significant clusters identified in the statistical difference maps were neuroanatomically located using the Automated Anatomical Labeling atlas (for GMV) or the Desikan-Killiany atlas (for CT, CSA and CV). For regions that showed significant between-group differences, partial correlation analysis was used to examine the correlation between their mean values and clinical characteristics, controlling for age, BMI, education years and TIV (age, BMI and education years for CT analyses). The correlation method was chosen based on residual normality, using pearson or spearman as appropriately. Mediation analysis was used to explore the relationship among GM morphological alterations, affective symptoms, and BN severity. Models were estimated in R using the lavaan package (version 0.6.17). We first estimated simple mediation models with the morphometric index as the predictor (X), either the PHQ-9 or GAD-7 as the mediator (M), and the EDI total score as the outcome (Y). We then estimated a parallel mediation model in which PHQ-9 and GAD-7 were entered jointly as mediators, and their residual covariance was estimated. Age, education years, BMI, and TIV were included as covariates for all endogenous variables. For all analyses, a bootstrapping method (with 5000 bootstrap samples) was employed to estimate the bias-corrected 95% confidence interval (CI) to verify the mediating effects. Results Clinical and demographic characteristics There were no differences in age, TIV, or BMI between patients with BN and HCs. Education years were significantly lower in patients with BN than HCs ( P < 0.05). For BN patients, the mean age of onset was 20.84 ± 5.45 years, and the illness duration was 4.54 ± 5.38 years. The detailed demographic and clinical characteristics of all participants were shown in Table 1 . Table 1 Demographic and clinical characteristics of the participants Note : Data are presented as mean ± standard deviation unless otherwise indicated. Group differences between BN and HC were examined using Mann–Whitney U tests. BN, bulimia nervosa; HC, healthy controls; BMI, body mass index; PHQ-9, Patient Health Questionnaire-9; GAD-7, Generalized Anxiety Disorder-7; EDI, Eating Disorder Inventory; EDE-Q, Eating Disorder Examination Questionnaire. *indicated P < 0.05; **indicated P < 0.01; ***indicated P < 0.001. Age, (years) BN (n = 53) HC (n = 52) U P 25.30 ± 6.93 25.44 ± 5.77 -150 0.54 Education, (years) 15.49 ± 2.22 16.35 ± 2.37 -442.5 0.01* BMI, (kg/m2) 21.85 ± 2.54 20.97 ± 2.21 201.5 0.10 Smoking history, n (%) 7 (13.21%) - - - Alcohol consumption history, n (%) 7 (13.21%) - - - Onset age, (years) 20.84 ± 5.48 - - - The course of disease, (years) 4.54 ± 5.38 - - - PHQ-9 12.55 ± 5.57 - - - GAD-7 10.80 ± 5.10 - - - EDI total scores 240.93 ± 23.27 - - - Drive for thinness (DT) 29.11 ± 4.15 - - - Body dissatisfaction (BD) 27.41 ± 2.45 - - - Bulimia (B) 31.89 ± 5.59 - - - Perfectionism (P) 23.98 ± 5.21 - - - Interpersonal distrust (ID) 24.07 ± 3.24 - - - Maturity fears (MF) 26.89 ± 4.09 - - - Interoceptive awareness (IA) 39.23 ± 6.15 - - - Ineffectiveness 34.02 ± 4.40 - - - EDE-Q total scores 6.86 ± 1.47 - - - Restraint (R) 4.65 ± 1.44 - - - Eating concern (EC) 5.02 ± 1.64 - - - Shape concern (SC) 4.62 ± 0.99 - - - Weight concern (WC) 2.49 ± 0.55 - - - Structure alteration in BN patients VBM result Group differences in GMV are summarized in Fig. 1 A and Table 2 . Compared to HCs, BN patients manifested decreased GMV ( P < 0.05, FWE corrected) in the bilateral calcarine and lingual gyrus, right superior temporal gyrus (STG) and bilateral posterior cerebellum. Table 2 Brain regions showing gray matter volume reduction in BN patients compared with HCs in the whole-brain VBM analysis Cluster. No Anatomic region peak MNI coordinate Number of voxels P FWE T X Y Z 1 Bilateral lingual and calcarine gyrus 24 -48 0 3381 < 0.001*** 4.71 2 Right superior temporal gyrus 51 -12 -2 882 0.041* 4.19 3 Left posterior cerebellum -34 -50 -36 1864 0.003** 4.38 4 Right posterior cerebellum 24 -76 -34 943 0.033* 4.00 Notes : Statistical analyses of VBM were performed based on a whole-brain level (voxel-wise uncorrected p < 0.001 and cluster-wise p < 0.05, family-wise error corrected). Abbreviations : BN, bulimia nervosa; HCs, Health controls; VBM, voxel-based morphometry; MNI, Montreal Neurological Institute; L, left; R, right. *indicated P < 0.05; **indicated P < 0.01; ***indicated P < 0.001. SBM result Compared to HCs, BN patients manifested decreased CV in the right lingual ( P < 0.001, FWE corrected, Fig. 1 B). No significant group difference was found for CT and CSA. Detailed results are provided in Supplementary table S3. Correlation between brain morphometric alterations and clinical characteristics In the BN group, CV in the right lingual was negatively correlated with EDE-Q Shape Concern scores (r = -0.296, P = 0.039) (Fig. 1 B). GMV in left posterior cerebellum was negatively correlated with GAD-7 (r = -0.406, P = 0.004) (Fig. 1 A.c) and PHQ-9 scores (r = -0.288, P = 0.045) (Fig. 1 A.c). GMV in right posterior cerebellum was negatively correlated with GAD-7 scores (r = -0.306, P = 0.032) (Fig. 1 A.d). Mediation analysis in the BN group Simple Mediation Model Mediation analysis indicated that both PHQ-9 and GAD-7 scores were significant mediator of the relationship between GMV values in the left posterior cerebellum and EDI scores in BN patients. Specifically, The relationships between left posterior cerebellum GMV and EDI scores were significantly mediated by PHQ-9 (a = -52.40, P = 0.005; b = 2.32, P < 0.001; c’= 63.45, P = 0.364; indirect effect = -121.46, P = 0.009, 95% CI: -211.95, -30.62) and GAD-7 (a = -57.63, P < 0.001; b = 2.32, P < 0.001; c’= 75.70, P = 0.345; indirect effect = -133.71, P = 0.022, 95% CI: -260.87, -32.06. The schematic diagram of the mediation models is shown in Fig. 2 A-B. Parallel Mediation Model Parallel mediation analysis showed a significant indirect effect of GMV in left posterior cerebellum on EDI scores mediated by the combined PHQ-9 and GAD-7 (total indirect effect = -142.53, P = 0.012, 95% CI -259.66, -37.02). The PHQ-9 and GAD-7 shared residual variance (r = 15.27, P < 0.001). Specifically, after controlled one of the mediators, the effect of PHQ-9 (a 1 = -52.40, P = 0.005; b 1 = 1.85, P = 0.017; indirect effect = -96.74, P = 0.048, 95% CI -193.49, -3.31) was stronger than the GAD-7 ( a 2 = -57.63, P < 0.001; b 2 = 0.80, P = 0.369; indirect effect = -45.79, P = 0.437, 95% CI -189.92, 42.69). The schematic diagram of the mediation models is shown in Fig. 2 C. Discussion This study investigated brain structural alterations and their associations with eating disorder symptom severity and affective symptoms in young adult females with BN. Our whole-brain morphometric analyses (VBM/SBM) revealed convergent gray matter reductions in the bilateral calcarine and lingual, bilateral cerebellum posterior lobe, and right STG. The volume of right lingual was negatively correlated with shape concern symptoms in eating disorder and the volume of bilateral posterior cerebellum was negatively correlated with depression and anxiety symptoms in BN patients. Critically, Mediation analysis unveiled a novel \"cerebello-affective\" pathway, demonstrating that reduced GMV in the left cerebellum posterior lobe indirectly exacerbates BN severity specifically through the mediation of depressive symptoms, rather than anxiety. These findings provide neuroanatomical evidence that BN involves a disruption of distributed networks regulating self-referential processing, visual integration, and emotion regulation. Abnormal morphology was found in the bilateral cerebellum posterior lobe, which are involved in cognitive-affective regulation and sensorimotor integration[ 26 ]. Consistent with previous observations of cerebellar deactivation in BN, which may be related to hyperphagia, we propose that the observed reduction in GMV could disrupt cerebellar-driven satiety networks. Moreover, we found that depressive and anxiety symptoms (PHQ-9 and GAD-7 scores) was negatively correlated with the GM volume of the posterior cerebellum. The cerebellum, particularly its posterior cognitive-affective regions, is integral to updating internal models that fine-tune behavior and affect, based on internal states and external feedback[ 26 ]. Such disruption may impair the normal devaluation of food rewards and, more broadly, diminish an individual's capacity to adaptively cope with emotional distress[ 9 , 27 ]. Consequently, patients may increasingly rely on rigid, behavior-centric, immediate relief strategies, manifested as binge-purge cycles, when faced with negative affect. Critically, our results demonstrate an indirect exacerbation of BN severity by reduced left cerebellar posterior lobe GMV, specifically mediated through depressive symptoms instead of anxiety. This finding suggests that cerebellar alterations are associated with a vulnerable state rather than directly causing binge-eating behavior[ 28 , 29 ]. Specifically, this suggests that trait-like components of depression, such as anhedonia, feelings of worthlessness, and a negative self-schema, may play a more central mechanistic role in linking cerebellar structure to BN severity than the state-like hypervigilance characteristic of anxiety[ 3 ]. Depression provides a negative and persistent internal framework for interpreting external events and self-expression. Anxiety (as a more “state-like” alertness directed toward external threats), on the other hand, finds it difficult to offer such a continuous, self-directed negative evaluation—especially feelings of helplessness and pessimism about the future, which may be the most direct clinical manifestations of this maladaptive emotional adjustment. When the cerebellum cannot effectively “update” internal models[ 30 ]to process negative emotions, this failure is “experienced” as the core symptom of depression. Subsequently, this depressive mood “drives” reliance on binge-purge behaviors in an attempt to seek immediate escape or comfort[ 31 , 32 ]. Abnormal outcomes in the cerebellum may further disrupt the normal devaluation of food rewards, making it difficult for food to provide satisfaction. This leads to a cycle of seeking comfort but never truly obtaining it[ 33 ], worsening depressive feelings and abnormal eating behavior patterns. The core psychopathology of BN, which involves overvaluation of body shape and weight, resonates deeply with self-directed negative affect and distorted self-perception, which are hallmarks of depression[ 34 , 35 ]. Therefore, the emotional dysregulation associated with cerebellar deficits may be most directly channeled into the pervasive negative self-evaluation seen in depression, which in turn fuels eating disorder pathology. In contrast, anxiety may represent a more generalized background factor, whose unique explanatory power for illness severity is reduced when considered alongside depression. Regarding cortical regions, we also found that volume of bilateral calcarine and lingual decreased in BN patients compared to HCs and volume of the right lingual gyrus was negatively correlated with \"Shape Concern\" scores of the EDE-Q in BN. The calcarine and lingual gyri are critical components of the ventral visual stream, supporting early visual processing and higher-level visual recognition[ 36 , 37 ]. Our results are consistent with the “perceptual deficit hypothesis” of eating disorders, which posits that body image disturbance may arise not only from top-down cognitive distortions but also from bottom-up visual processing anomalies in the brain[ 38 – 40 ]. Previous functional neuroimaging studies have reported altered occipito-temporal responses during body image tasks in BN[ 41 , 42 ], and the volumetric reductions we observed provide structural support for these functional inefficiencies. Deficits in primary visual integration may impair the updating of the body schema with real-time visual input, thereby reinforcing reliance on maladaptive internal representations and perpetuating the distortion of body image. \"Shape Concern\" reflects the degree of preoccupation and distress individuals experience regarding their body shape, a core symptom dimension in eating disorders. This specific association suggests that the lingual gyrus serves as a neuroanatomical substrate for the pathological preoccupation with physical appearance[ 43 – 45 ]. We propose that structural deficits in this region may destabilize the precise visual encoding of body details, creating perceptual ambiguity that facilitates the intrusion of negative cognitive distortions and emotional dysregulation. Finally, we observed a significant GMV reduction in the right STG in patients with BN compared to HCs. This finding is consistent with recent studies using diffusion tensor imaging-based machine learning in patients with bulimia nervosa, suggesting that structural changes in the right STG are key neuroimaging markers for distinguishing BN from other eating disorder subtypes[ 10 , 46 ]. The STG is a key component of the social cognitive network and is mainly involved in auditory processing, language comprehension, social cognition, bodily perception, and emotional processing[ 47 , 48 ]. GMV reduction in the STG may reflect impaired social interaction abilities, emotion recognition, or perception of bodily boundaries in patients with BN, which could exacerbate their interpersonal difficulties. Structural abnormalities in the STG may be associated with binge eating impulsivity in BN. Some studies have reported that the right STG of patients with BN is functionally connected to the ventral tegmental area (VTA, the reward center) of the midbrain[ 49 ]. We speculate that structural changes in the STG may provide a potential anatomical basis for enhanced STG-VTA circuit function, leading to abnormal salience attribution or excessive reward responses to food cues, which ultimately manifest as impulsivity and loss of control in binge-eating behavior. Limitations of this study should be acknowledged. First, despite recruiting a relatively large cohort of medication-free patients the cross-sectional design limits our ability to infer causality within the \"cerebello-affective\" pathway. Longitudinal studies are needed to determine whether these gray matter reductions represent neurobiological scars of the disease or pre-existing risk factors. Second, while this study provided a comprehensive characterization of regional gray matter alterations in BN, it did not examine how these regions are organized into large-scale structural networks. Future studies using morphometric similarity network analysis may extend these findings by characterizing network-level organization and system-level alterations in BN. Conclusions This study revealed that patients with bulimia nervosa exhibit reduced gray matter volume in the bilateral calcarine and lingual gyrus, right STG and bilateral posterior cerebellum. Notably, depressive symptoms play a key mediating role in the relationship between cerebellar structure and disease severity. Based on these findings, noninvasive neuromodulation targeting cerebellar function and medications aimed at improving mood may become potential adjunctive treatment approaches. Declarations Previous presentation None. Disclosures: The authors report no financial relationships with commercial interests. Ethics approval and consent to participate: All participants provided written informed consent. The study was approved by the Ethics Committee of West China Hospital of Sichuan University and was conducted according to the Helsinki Declaration. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author contributions LZ and XQH designed this study. XZ, LQL, WXB, MOW, YDW and XYH conducted this study. XZ, LQL and WXB conducted data analysis. XZ and LQL wrote the first draft of the paper, and LZ and XQH critically revised the manuscript. All authors participated in the data collection, and made contributions to critical revision of the manuscript. Acknowledgement This work as supported by the General Program of National Natural Science Foundation of China (No. 82271580) and the Key R&D Project of Sichuan Provincial Department of Science and Technology: Neuroimaging-based machine learning for the diagnosis of the eating disorders (No. 2022YFS0184). 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Kruithof ES, Klaus J, Schutter DJLG: The human cerebellum in reward anticipation and outcome processing: An activation likelihood estimation meta-analysis. Neuroscience and Biobehavioral Reviews. 2023;149. Arleo A, Bares M, Bernard JA, et al: Consensus Paper: Cerebellum and Ageing. Cerebellum. 2024;23(2):802–32. Rudolph S, Badura A, Lutzu S, et al: Cognitive-Affective Functions of the Cerebellum. Journal of Neuroscience. 2023;43(45):7554–64. Sader M, Waiter GD, Williams JHG: The cerebellum plays more than one role in the dysregulation of appetite: Review of structural evidence from typical and eating disorder populations. Brain and Behavior. 2023;13(12). Ito M: Opinion - Control of mental activities by internal models in the cerebellum. Nature Reviews Neuroscience. 2008;9(4):304–13. Beck AT: The evolution of the cognitive model of depression and its neurobiological correlates. American Journal of Psychiatry. 2008;165(8):969–77. Heatherton TF, Baumeister RF: Binge eating as escape from self-awareness. Psychological Bulletin. 1991;110(1):86–108. Fairburn CG, Cooper Z, Shafran R: Cognitive behaviour therapy for eating disorders: a \"transdiagnostic\" theory and treatment. Behaviour Research and Therapy. 2003;41(5):509–28. Romeo M, Cavaliere G, Traina G: Bulimia Nervosa and Depression, from the Brain to the Gut Microbiota and Back. Frontiers in Bioscience-Landmark. 2024;29(8). Polivy J, Herman CP: Causes of eating disorders. Annual Review of Psychology. 2002; 53:187–213. Grill-Spector K, Malach R: The human visual cortex. Annual Review of Neuroscience. 2004; 27:649–77. Peelen MV, Downing PE: The neural basis of visual body perception. Nature Reviews Neuroscience. 2007;8(8):636–48. Riva G: The neuroscience of body memory: From the self through the space to the others. Cortex. 2018; 104:241–60. Lee S, Kim KR, Ku J, et al: Resting-state synchrony between anterior cingulate cortex and precuneus relates to body shape concern in anorexia nervosa and bulimia nervosa. Psychiatry Research-Neuroimaging. 2014;221(1):43–8. Li W, Lai TM, Bohon C, et al: Anorexia nervosa and body dysmorphic disorder are associated with abnormalities in processing visual information. Psychological Medicine. 2015;45(10):2111–22. Uher R, Murphy T, Brammer MJ, et al: Medial prefrontal cortex activity associated with symptom provocation in eating disorders. American Journal of Psychiatry. 2004;161(7):1238–46. van den Eynde F, Giampietro V, Simmons A, et al: Brain responses to body image stimuli but not food are altered in women with bulimia nervosa. Bmc Psychiatry. 2013;13. Liu S, Yu L, Ren J, et al: The neural representation of body orientation and emotion from biological motion. Neuroimage. 2025;310. Spagna A, Heidenry Z, Miselevich M, et al: Visual mental imagery: Evidence for a heterarchical neural architecture. Physics of Life Reviews. 2024; 48:113–31. Wang J-n, Wang M, Wu G-w, et al: Uncovering neural pathways underlying bulimia nervosa: resting-state neural connectivity disruptions correlate with maladaptive eating behaviors. Eating and Weight Disorders-Studies on Anorexia Bulimia and Obesity. 2023;28(1). Zheng L, Wang Y, Ma J, et al: Machine learning research based on diffusion tensor images to distinguish between anorexia nervosa and bulimia nervosa. Frontiers in Psychiatry. 2024;14. Adolphs R: Cognitive neuroscience of human social behaviour. Nature Reviews Neuroscience. 2003;4(3):165–78. Bigler ED, Mortensen S, Neeley ES, et al: Superior temporal gyrus, language function, and autism. Developmental Neuropsychology. 2007;31(2):217–38. Li W, Wang M, Wu G, et al: Exploration of the relationships between clinical traits and functional connectivity based on surface morphology abnormalities in bulimia nervosa. Brain and Behavior. 2023;13(4). Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryBN.docx Neuroanatomical Substrates of emotional Dysregulation in Bulimia Nervosa Cite Share Download PDF Status: Under Review Version 1 posted Review # 2 received at journal 16 Apr, 2026 Reviewer # 2 agreed at journal 26 Mar, 2026 Reviewer # 1 agreed at journal 12 Mar, 2026 Reviewers invited by journal 08 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 03 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9025261\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":602717665,\"identity\":\"d924b850-a344-4eb6-ab76-24492e3706cf\",\"order_by\":0,\"name\":\"Lan Zhang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACAxCRwHAASDI3MDBUSMjxk6CFEajljIWxZAMxWhhgWhjbKhI3ENJiLpH8TOLBnzvy5vwLGz8XzpNg3MDA/PDRDTxaLGekGRsk8Dwz3DnjYbP0zG0SzOYMbMbGOfgcdiPB8EGCxGHGDTcONkjzbpNgs2zgYZPGryX9w4EEg8P2QC3Nv3nnSPAYHCCoJQdoS8LhxA3nG9ukeRskJAhrOfOm2CDhwOHkDTcY26x5jkkYSDYT8svx9G2SP/4ctt1w/vDh2zw1dfX97M0PH+PTggASCVAGM1HKQYD/ANFKR8EoGAWjYIQBAAN3U50h2SgdAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"West China Hospital of Sichuan University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Lan\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":602717666,\"identity\":\"f79b68d6-b6b0-4d9b-8923-30baaac7a6f3\",\"order_by\":1,\"name\":\"Xin Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xin\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":602717667,\"identity\":\"3dba225a-b16b-4633-9d38-499dcac42261\",\"order_by\":2,\"name\":\"Liqiong Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Liqiong\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":602717668,\"identity\":\"71c9e146-91d9-4770-a5ab-619bd099850d\",\"order_by\":3,\"name\":\"Wenxin Bao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Wenxin\",\"middleName\":\"\",\"lastName\":\"Bao\",\"suffix\":\"\"},{\"id\":602717669,\"identity\":\"ea753621-462b-47bc-98d4-6ecec29278f4\",\"order_by\":4,\"name\":\"Meiou Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Meiou\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":602717670,\"identity\":\"44a67332-85a8-4f95-9a05-cbac17e175b6\",\"order_by\":5,\"name\":\"Yidan Wang\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-0952-5386\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yidan\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":602717671,\"identity\":\"63a8f2aa-0d43-4af4-8f4d-67930b6d0fcc\",\"order_by\":6,\"name\":\"Xinyue Hu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xinyue\",\"middleName\":\"\",\"lastName\":\"Hu\",\"suffix\":\"\"},{\"id\":602717672,\"identity\":\"44b83346-b7c7-4e05-b55f-1aaac9e69b17\",\"order_by\":7,\"name\":\"Xiaoqi Huang\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0001-8686-5010\",\"institution\":\"Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology and Medical Imaging. West China Hospital of Sichuan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaoqi\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-04 03:26:22\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9025261/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9025261/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":104548127,\"identity\":\"872f78ff-89d1-4049-9b40-a4b3b4b8d676\",\"added_by\":\"auto\",\"created_at\":\"2026-03-13 07:41:08\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":246974,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eBrain regions showing abnormal structural abnormalities in BN patients compared with HCs in the whole-brain VBM and SBM analysis. (A) Brain regions showing abnormal GMV in BN patients compared with HCs in the whole-brain VBM analysis (voxel-wise uncorrected p \\u0026lt; 0.001 and cluster-wise p \\u0026lt; 0.05, family-wise error corrected). (B) Brain region showing decreased cortical volume in BN patients compared with HCs in the whole-brain SBM analysis (vertex-wise p \\u0026lt; 0.001 and cluster-wise p \\u0026lt; 0.05, Monte-Carlo simulations corrected). Group difference results are shown as a bar charts with individual data points and a smoothed distribution. Correlation results are displayed as scatterplots using residualized values, with linear fits and 95% confidence intervals. The color-bar for \\u003cem\\u003eT\\u003c/em\\u003e values ranges from 1 to 5; The color-bar for \\u003cem\\u003eP\\u003c/em\\u003e values was on a logarithmic scale (log10) with a range of 1.67-3.00. BN, bulimia nervosa; HCs, healthy controls; VBM, voxel-based morphometry; SBM, surface-based morphometry; GMV, gray matter volume; FWE, family-wise error corrected; GMV, gray-matter volume; CV, cortical volume; TIV, total intracranial volume; PHQ-9, Patient Health Questionnaire-9; GAD-7, Generalized Anxiety Disorder-7; EDE-Q, Eating Disorder Examination-Questionnaire; SC, Shape Concern. *indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eFWE\\u003c/em\\u003e\\u003c/sub\\u003e \\u0026lt; 0.05; **indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eFWE\\u003c/em\\u003e\\u003c/sub\\u003e \\u0026lt; 0.01; ***indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eFWE\\u003c/em\\u003e\\u003c/sub\\u003e \\u0026lt; 0.001.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAlt text:\\u003c/strong\\u003e Multi-panel brain maps and plots show reduced gray matter volumes in bulimia nervosa versus controls, with group bars and correlation scatterplots.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9025261/v1/90a86c37eefaa7a41e2ddd3a.png\"},{\"id\":104548170,\"identity\":\"8053f862-716c-4711-b8bc-a7c8a5be43a4\",\"added_by\":\"auto\",\"created_at\":\"2026-03-13 07:41:14\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":292810,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMediation of PHQ-9 and GAD-7 on the association between GMV of left posterior cerebellum and eating disorder severity. (A) Simple mediation model with PHQ-9 as the mediator; (B) Simple mediation model with GAD-7 as the mediator; (C) Parallel mediation model including PHQ-9 and GAD-7 as concurrent mediators. All paths are adjusted for age, education, BMI, and total intracranial volume (TIV). Unstandardized coefficients are displayed on the arrows. Indirect effects were estimated using non-parametric bootstrapping (5,000 samples; bias-corrected and accelerated 95% CIs)；a, the effect of the independent variable on the mediator variable. b, the effect of the mediator on the dependent variable. c’, the effect of the independent variable on the dependent variable. a*b, the indirect effect. GMV = gray matter volume; PHQ-9 = Patient Health Questionnaire-9; GAD-7 = Generalized Anxiety Disorder-7; EDI = Eating Disorder Inventory. *indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt; 0.05; **indicated \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.01; ***indicated \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAlt text:\\u003c/strong\\u003e Three mediation diagrams show gray matter volume in the left posterior cerebellum linked to eating disorder symptoms via PHQ-9 and GAD-7.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9025261/v1/8b3f7426e8efa8fcf476f238.png\"},{\"id\":104548612,\"identity\":\"3ea9ed40-c487-4837-a304-40a4386380d5\",\"added_by\":\"auto\",\"created_at\":\"2026-03-13 07:43:04\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1445196,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9025261/v1/df6da283-b458-48ba-8306-dc2ece25bcc5.pdf\"},{\"id\":104548485,\"identity\":\"a76ad8fc-fb82-49bd-876e-5e3619559fad\",\"added_by\":\"auto\",\"created_at\":\"2026-03-13 07:42:38\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":28760,\"visible\":true,\"origin\":\"\",\"legend\":\"Neuroanatomical Substrates of emotional Dysregulation in Bulimia Nervosa\",\"description\":\"\",\"filename\":\"SupplementaryBN.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9025261/v1/f267a0ac4d5f4617612c2407.docx\"}],\"financialInterests\":\"The authors have declared there is \\u003cb\\u003eNO\\u003c/b\\u003e conflict of interest to disclose\",\"formattedTitle\":\"Neuroanatomical Substrates of emotional Dysregulation in Bulimia Nervosa\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eBulimia Nervosa (BN) is a debilitating eating disorder characterized by recurrent binge eating, compensatory behaviors to prevent weight gain, and self-evaluation unduly influenced by body shape and weight[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Critically, emotional dysregulation, particularly depression and anxiety, is increasingly recognized as a core pathological driver that fuels these maladaptive behaviors as dysfunctional coping mechanisms[\\u003cspan additionalcitationids=\\\"CR3\\\" citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].The disorder poses a significant global public health challenge, with lifetime prevalence estimates ranging from 0.3% to 4.6%[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]and peak incidence among young females[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAs a major subtype of eating disorders, the neurobiological underpinnings of BN remain less thoroughly investigated than those of anorexia nervosa (AN), and existing structural findings are often inconsistent. Structural MRI studies have reported gray matter (GM) abnormalities in BN, including reduced gray matter volume (GMV) and cortical thinning in the temporal and parietal lobes[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e] as well as increased GMV in the orbitofrontal cortex and insula and decreased GMV in the frontal lobe and cingulate gyrus [\\u003cspan additionalcitationids=\\\"CR9\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Moreover, most prior studies have predominantly focused on cortical regions, with limited attention to the potential contribution of the cerebellum, despite its established involvement in the cerebellar-cognitive-affective network[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]and its putative role in cognitive, emotional, and reward-related processes implicated in BN. More importantly, previous studies usually only used voxel-based morphometry (VBM) or surface-based morphometry (SBM) methods to identify GM alterations in BN patients. While VBM is robust for detecting volumetric changes across the entire brain (including subcortex), and SBM offers superior sensitivity to cortical thickness and surface area, neither method alone captures the full spectrum of GM pathology. Therefore, we utilized both to achieve a more integrative understanding of brain structure in BN.\\u003c/p\\u003e \\u003cp\\u003eBeyond the behavioral diagnostic criteria in BN, emotional dysregulation, particularly depressive and anxiety symptoms, is increasingly conceptualized as a core dimension of BN pathology rather than a secondary comorbidity[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. These affective disturbances serve as sustaining factors that perpetuate the disorder, often precipitating binge eating as a maladaptive coping mechanism for emotional distress[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].While the clinical association between negative affect and BN symptomatology is well established, and previous studies have separately linked emotional disturbances and structural brain alterations to BN symptomatology[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], the mediating pathways through which specific brain abnormalities influence BN symptom severity via affective states remain unexplored. Specifically, it remains unclear whether and how negative affections constitutes a mechanistic pathway through which emotional dysregualtion mediates the interaction between brain structure and eating disorder symptoms.\\u003c/p\\u003e \\u003cp\\u003eThus, present study aims to disentangle the unique roles of depressive and anxiety symptoms in linking structural deficits to BN severity by comprehensively exploring brain structural alterations and examine their clinical relevance in BN patients combining VBM and SBM analyses. More importantly, we employed mediation models to determine whether depressive and anxiety symptoms independently mediate the association between structural alterations and BN symptom severity. We hypothesized that patients should exhibit abnormal GM morphology, and that these abnormalities would influence BN symptom severity through the mediating effects of depressive and anxiety symptoms.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eParticipants\\u003c/h2\\u003e \\u003cp\\u003eThe study was approved by the Research Ethics Committee of West China Hospital, Sichuan University, and written informed consent was obtained from all participants. A total of 53 adult female patients with BN and 52 healthy controls (HCs) were included in the study. All participants were between 18 and 45 years of age and of Han Chinese descent. Patients with BN were recruited from the Mental Health Center of West China Hospital, Sichuan University, and were diagnosed by two independent psychiatrists (XZ and MW) using the DSM-5 Structured Clinical Interview for Disorders (SCID-5)[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. To minimize the potential confounding effects of therapeutic interventions on brain morphometry, all patients were required to be free from psychotropic medications, physical therapies (e.g., electroconvulsive therapy or repetitive transcranial magnetic stimulation), and systematic psychotherapy for at least three months prior to enrollment. HCs were recruited through advertisements in local newspapers. All individuals were screened to exclude any history of neurological disorders, seizures, head injuries with loss of consciousness, intellectual disability, or pervasive developmental disorders. Importantly, the HC participants had no lifetime history of any Axis I disorders, and the BN participants did not meet the diagnostic criteria for any comorbid Axis I disorders at enrollment.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eClinical psychological assessment\\u003c/h3\\u003e\\n\\u003cp\\u003eThe Eating disorder Inventory (EDI) is a widely used self-reporting measure of ED symptoms and consists of 8 standardized subscales representing dimensions that are clinically relevant to ED[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. The first 3 dimensions deal with attitudes and behavior concerning eating, weight, and body shape, namely, Drive for Thinness (DT), Bulimia (B), and Body Dissatisfaction (BD). The remaining 5 dimensions tap more general psychological and relational constructs relevant to ED, namely, Ineffectiveness (I), Perfectionism (P), Interpersonal Distrust (ID), Interoceptive Awareness (IA), and Maturity Fears (MF).\\u003c/p\\u003e \\u003cp\\u003eThe Eating Disorder Examination Questionnaire (EDE-Q) assesses a variety of behaviors and cognitive features relevant to eating pathology and is summarized by four subscale scores: Restraint, Eating Concern, Shape Concern, and Weight obtained from item scores[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe Patient Health Questionnaire-9 (PHQ-9) is a nine-item questionnaire designed to screen for depression in primary care and other medical settings. The standard cut-off score for screening to identify possible major depression is 10 or above[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe seven items of the Generalized Anxiety Disorder scale (GAD-7) describe the most important diagnostic criteria for GAD according to DSM-V, namely the Criterion A (fear and anxiety related to a series of events or activities), Criterion B (difficulties in controlling concerns) and Criterion C (anxiety and worry are accompanied by at least three additional symptoms such as restlessness, mild fatigue, difficulty concentrating, irritability, muscle tension and sleep problems)[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003ch3\\u003eMRI data acquisition\\u003c/h3\\u003e\\n\\u003cp\\u003eAll participants were scanned using uMR790 3.0T system with a 32-channel phased‐array head coil at the West China Hospital of Sichuan University. The high-resolution 3-Dimensional T1-Weighted (T1W) imaging was obtained with the axial fast spoiled gradient recalled sequence with the following parameters: repetition time/echo time/inversion time (TR/TE/TI) of 8.5/3.1/1100 ms, flip angle of 8\\u0026deg;, field of view (FOV) of 240 \\u0026times; 256 mm, slice thickness of 0.8 mm, and a matrix size of 300 \\u0026times; 320, yielding 208 contiguous slices covering the entire brain. Participants were positioned supine, and foam padding was utilized to minimize the head movement and reduce motion artifacts. Soft earplugs were used to reduce scanner noise. They were instructed to maintain stillness throughout the scanning session. Images were visually inspected by two radiologists (LL and YW) immediately after acquisition, and individuals with visible head movement artifact were immediately rescanned.\\u003c/p\\u003e\\n\\u003ch3\\u003eMRI data preprocessing\\u003c/h3\\u003e\\n\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eVBM analysis\\u003c/h2\\u003e \\u003cp\\u003eVBM analysis was performed using the Computational Anatomy Toolbox (CAT12; \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://dbm.neuro.unijena.de/cat12/\\u003c/span\\u003e\\u003cspan address=\\\"http://dbm.neuro.unijena.de/cat12/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), integrated within the Statistical Parametric Mapping (SPM12; \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.fil.ion.ucl.ac.uk/spm/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.fil.ion.ucl.ac.uk/spm/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) toolboxes. Specifically, the data preprocessing procedure consisted of four steps: 1) normalizing T1 images to the standard Montreal Neurological Institute (MNI) space and segmenting images into the gray matter, white matter, and cerebrospinal fluid using DARTEL algorithm[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]; 2) estimating the homogeneity of the samples and visually checking for heterogeneity of the images; 3) calculating the total intracranial volume (TIV) for each subject based on the unsmoothed images; 4) smoothing spatial images with an 8 mm Gaussian kernel of full-width at half maximum (FWHM) following the operation manual recommended.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSBM analysis\\u003c/h2\\u003e \\u003cp\\u003eFor SBM analysis, the Freesurfer (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://surfer.nmr.mgh.harvard.edu\\u003c/span\\u003e\\u003cspan address=\\\"http://surfer.nmr.mgh.harvard.edu\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, version 7.3.2) surface-based processing stream was used to estimate CT, cortical surface area (CSA), and cortical volume (CV) at each vertex on the cortical surface. This method has been described in detail elsewhere[\\u003cspan additionalcitationids=\\\"CR22\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. In brief, it involves: motion correction and conform, transformation to MNI space, intensity normalization, skull-stripping, segmentation of gray/white matter, tessellation of the white matter and gray matter boundary, topology correction, surface deformation and inflation, surface atlas registration and surface extraction. CT was defined as the closest straight-line distance between the pial surface and the gray/white matter boundary[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. CSA was calculated as the average area of the surrounding tessellated triangles on the pial surface[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. CV is the volume of gray matter that lies between the pial surface and the gray/white matter boundary[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. The CT, CSA, and CV of each subject were calculated independently for the left and right hemispheres. Once computed, vertex-wise estimates of cortical surface architecture were registered to the Freesurfer average template and smoothed with a Gaussian kernel (FWHM\\u0026thinsp;=\\u0026thinsp;10 mm) for statistical analysis. In addition, a measure of TIV was extracted.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eQuality control procedures\\u003c/h3\\u003e\\n\\u003cp\\u003eAll segmentation was visually verified following a recently published recommendations for cortical QC 2.0 by the Enhancing Neuro Imaging Genetics Through Meta-analysis (ENIGMA) consortium (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://enigma.ini.usc.edu/protocols/imaging-protocols\\u003c/span\\u003e\\u003cspan address=\\\"https://enigma.ini.usc.edu/protocols/imaging-protocols\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). In brief, the segmentation of each subject was independently visually checked by two authors (LL and YW), and subjects with segmentation results judged to be incorrect (e.g., skull-strip errors and/or failure in the formation of the white matter mass) were excluded. No participant showed segmentation failure.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eStatistical analyses were conducted with the R Statistics software (version 4.3.2). The Shapiro-Wilk normality test was used to test the normality of variable distribution. Mann-Whitney U test was used to compare continuous variables with non-normal distribution.\\u003c/p\\u003e \\u003cp\\u003eThe between-group differences in GM morphological parameters were assessed with two-sample t tests controlled for age, education years and TIV (age and education years for CT analyses). The thresholds were set as voxel-wise \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 and cluster-wise \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (family-wise error (FWE) corrected) for VBM analysis and vertex-wise \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 and cluster-wise \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (Monte Carlo simulation with 10,000 iterations corrected) for SBM analysis. Significant clusters identified in the statistical difference maps were neuroanatomically located using the Automated Anatomical Labeling atlas (for GMV) or the Desikan-Killiany atlas (for CT, CSA and CV). For regions that showed significant between-group differences, partial correlation analysis was used to examine the correlation between their mean values and clinical characteristics, controlling for age, BMI, education years and TIV (age, BMI and education years for CT analyses). The correlation method was chosen based on residual normality, using pearson or spearman as appropriately.\\u003c/p\\u003e \\u003cp\\u003eMediation analysis was used to explore the relationship among GM morphological alterations, affective symptoms, and BN severity. Models were estimated in R using the lavaan package (version 0.6.17). We first estimated simple mediation models with the morphometric index as the predictor (X), either the PHQ-9 or GAD-7 as the mediator (M), and the EDI total score as the outcome (Y). We then estimated a parallel mediation model in which PHQ-9 and GAD-7 were entered jointly as mediators, and their residual covariance was estimated. Age, education years, BMI, and TIV were included as covariates for all endogenous variables. For all analyses, a bootstrapping method (with 5000 bootstrap samples) was employed to estimate the bias-corrected 95% confidence interval (CI) to verify the mediating effects.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eClinical and demographic characteristics\\u003c/h2\\u003e \\u003cp\\u003eThere were no differences in age, TIV, or BMI between patients with BN and HCs. Education years were significantly lower in patients with BN than HCs (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). For BN patients, the mean age of onset was 20.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.45 years, and the illness duration was 4.54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.38 years. The detailed demographic and clinical characteristics of all participants were shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDemographic and clinical characteristics of the participants Note\\u003c/b\\u003e: Data are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation unless otherwise indicated. Group differences between BN and HC were examined using Mann\\u0026ndash;Whitney U tests. BN, bulimia nervosa; HC, healthy controls; BMI, body mass index; PHQ-9, Patient Health Questionnaire-9; GAD-7, Generalized Anxiety Disorder-7; EDI, Eating Disorder Inventory; EDE-Q, Eating Disorder Examination Questionnaire. *indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; **indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; ***indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eAge, (years)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBN (n\\u0026thinsp;=\\u0026thinsp;53)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHC (n\\u0026thinsp;=\\u0026thinsp;52)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eU\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25.30\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.93\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.44\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.77\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-150\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.54\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEducation, (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.49\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.35\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-442.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.01*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI, (kg/m2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e201.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmoking history, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7 (13.21%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAlcohol consumption history, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7 (13.21%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOnset age, (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eThe course of disease, (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePHQ-9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.55\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGAD-7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10.80\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEDI total scores\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e240.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;23.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDrive for thinness (DT)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e29.11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBody dissatisfaction (BD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBulimia (B)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePerfectionism (P)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInterpersonal distrust (ID)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMaturity fears (MF)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInteroceptive awareness (IA)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e39.23\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIneffectiveness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e34.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEDE-Q total scores\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRestraint (R)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEating concern (EC)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eShape concern (SC)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.62\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.99\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWeight concern (WC)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.49\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStructure alteration in BN patients\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eVBM result\\u003c/h2\\u003e \\u003cp\\u003eGroup differences in GMV are summarized in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA and Table \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Compared to HCs, BN patients manifested decreased GMV (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, \\u003cem\\u003eFWE\\u003c/em\\u003e corrected) in the bilateral calcarine and lingual gyrus, right superior temporal gyrus (STG) and bilateral posterior cerebellum.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBrain regions showing gray matter volume reduction in BN patients compared with HCs in the whole-brain VBM analysis\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCluster. No\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAnatomic region\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003epeak MNI coordinate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNumber of voxels\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eFWE\\u003c/em\\u003e\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eT\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eX\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eY\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eZ\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBilateral lingual and calcarine gyrus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3381\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRight superior temporal gyrus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e882\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.041*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLeft posterior cerebellum\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1864\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.003**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRight posterior cerebellum\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e943\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.033*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003e\\u003cb\\u003eNotes\\u003c/b\\u003e: Statistical analyses of VBM were performed based on a whole-brain level (voxel-wise uncorrected p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 and cluster-wise p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, family-wise error corrected). \\u003cb\\u003eAbbreviations\\u003c/b\\u003e: BN, bulimia nervosa; HCs, Health controls; VBM, voxel-based morphometry; MNI, Montreal Neurological Institute; L, left; R, right. *indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; **indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; ***indicated \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSBM result\\u003c/h2\\u003e \\u003cp\\u003eCompared to HCs, BN patients manifested decreased CV in the right lingual (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, \\u003cem\\u003eFWE\\u003c/em\\u003e corrected, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). No significant group difference was found for CT and CSA. Detailed results are provided in Supplementary table S3.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCorrelation between brain morphometric alterations and clinical characteristics\\u003c/h2\\u003e \\u003cp\\u003eIn the BN group, CV in the right lingual was negatively correlated with EDE-Q Shape Concern scores (r = -0.296, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.039) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). GMV in left posterior cerebellum was negatively correlated with GAD-7 (r = -0.406, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA.c) and PHQ-9 scores (r = -0.288, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.045) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA.c). GMV in right posterior cerebellum was negatively correlated with GAD-7 scores (r = -0.306, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.032) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA.d).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMediation analysis in the BN group\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eSimple Mediation Model\\u003c/h2\\u003e \\u003cp\\u003eMediation analysis indicated that both PHQ-9 and GAD-7 scores were significant mediator of the relationship between GMV values in the left posterior cerebellum and EDI scores in BN patients. Specifically, The relationships between left posterior cerebellum GMV and EDI scores were significantly mediated by PHQ-9 (a = -52.40, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.005; b\\u0026thinsp;=\\u0026thinsp;2.32, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; c\\u0026rsquo;= 63.45, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.364; indirect effect = -121.46, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.009, 95% CI: -211.95, -30.62) and GAD-7 (a = -57.63, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; b\\u0026thinsp;=\\u0026thinsp;2.32, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; c\\u0026rsquo;= 75.70, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.345; indirect effect = -133.71, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.022, 95% CI: -260.87, -32.06. The schematic diagram of the mediation models is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA-B.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eParallel Mediation Model\\u003c/h2\\u003e \\u003cp\\u003eParallel mediation analysis showed a significant indirect effect of GMV in left posterior cerebellum on EDI scores mediated by the combined PHQ-9 and GAD-7 (total indirect effect = -142.53, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.012, 95% CI -259.66, -37.02). The PHQ-9 and GAD-7 shared residual variance (r\\u0026thinsp;=\\u0026thinsp;15.27, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Specifically, after controlled one of the mediators, the effect of PHQ-9 (a\\u003csub\\u003e1\\u003c/sub\\u003e = -52.40, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.005; b\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;1.85, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.017; indirect effect = -96.74, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.048, 95% CI -193.49, -3.31) was stronger than the GAD-7 ( a\\u003csub\\u003e2\\u003c/sub\\u003e = -57.63, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; b\\u003csub\\u003e2\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.80, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.369; indirect effect = -45.79, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.437, 95% CI -189.92, 42.69). The schematic diagram of the mediation models is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study investigated brain structural alterations and their associations with eating disorder symptom severity and affective symptoms in young adult females with BN. Our whole-brain morphometric analyses (VBM/SBM) revealed convergent gray matter reductions in the bilateral calcarine and lingual, bilateral cerebellum posterior lobe, and right STG. The volume of right lingual was negatively correlated with shape concern symptoms in eating disorder and the volume of bilateral posterior cerebellum was negatively correlated with depression and anxiety symptoms in BN patients. Critically, Mediation analysis unveiled a novel \\\"cerebello-affective\\\" pathway, demonstrating that reduced GMV in the left cerebellum posterior lobe indirectly exacerbates BN severity specifically through the mediation of depressive symptoms, rather than anxiety. These findings provide neuroanatomical evidence that BN involves a disruption of distributed networks regulating self-referential processing, visual integration, and emotion regulation.\\u003c/p\\u003e \\u003cp\\u003eAbnormal morphology was found in the bilateral cerebellum posterior lobe, which are involved in cognitive-affective regulation and sensorimotor integration[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Consistent with previous observations of cerebellar deactivation in BN, which may be related to hyperphagia, we propose that the observed reduction in GMV could disrupt cerebellar-driven satiety networks. Moreover, we found that depressive and anxiety symptoms (PHQ-9 and GAD-7 scores) was negatively correlated with the GM volume of the posterior cerebellum. The cerebellum, particularly its posterior cognitive-affective regions, is integral to updating internal models that fine-tune behavior and affect, based on internal states and external feedback[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Such disruption may impair the normal devaluation of food rewards and, more broadly, diminish an individual's capacity to adaptively cope with emotional distress[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Consequently, patients may increasingly rely on rigid, behavior-centric, immediate relief strategies, manifested as binge-purge cycles, when faced with negative affect.\\u003c/p\\u003e \\u003cp\\u003eCritically, our results demonstrate an indirect exacerbation of BN severity by reduced left cerebellar posterior lobe GMV, specifically mediated through depressive symptoms instead of anxiety. This finding suggests that cerebellar alterations are associated with a vulnerable state rather than directly causing binge-eating behavior[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Specifically, this suggests that trait-like components of depression, such as anhedonia, feelings of worthlessness, and a negative self-schema, may play a more central mechanistic role in linking cerebellar structure to BN severity than the state-like hypervigilance characteristic of anxiety[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Depression provides a negative and persistent internal framework for interpreting external events and self-expression. Anxiety (as a more \\u0026ldquo;state-like\\u0026rdquo; alertness directed toward external threats), on the other hand, finds it difficult to offer such a continuous, self-directed negative evaluation\\u0026mdash;especially feelings of helplessness and pessimism about the future, which may be the most direct clinical manifestations of this maladaptive emotional adjustment. When the cerebellum cannot effectively \\u0026ldquo;update\\u0026rdquo; internal models[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]to process negative emotions, this failure is \\u0026ldquo;experienced\\u0026rdquo; as the core symptom of depression. Subsequently, this depressive mood \\u0026ldquo;drives\\u0026rdquo; reliance on binge-purge behaviors in an attempt to seek immediate escape or comfort[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Abnormal outcomes in the cerebellum may further disrupt the normal devaluation of food rewards, making it difficult for food to provide satisfaction. This leads to a cycle of seeking comfort but never truly obtaining it[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e], worsening depressive feelings and abnormal eating behavior patterns. The core psychopathology of BN, which involves overvaluation of body shape and weight, resonates deeply with self-directed negative affect and distorted self-perception, which are hallmarks of depression[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. Therefore, the emotional dysregulation associated with cerebellar deficits may be most directly channeled into the pervasive negative self-evaluation seen in depression, which in turn fuels eating disorder pathology. In contrast, anxiety may represent a more generalized background factor, whose unique explanatory power for illness severity is reduced when considered alongside depression.\\u003c/p\\u003e \\u003cp\\u003eRegarding cortical regions, we also found that volume of bilateral calcarine and lingual decreased in BN patients compared to HCs and volume of the right lingual gyrus was negatively correlated with \\\"Shape Concern\\\" scores of the EDE-Q in BN. The calcarine and lingual gyri are critical components of the ventral visual stream, supporting early visual processing and higher-level visual recognition[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. Our results are consistent with the \\u0026ldquo;perceptual deficit hypothesis\\u0026rdquo; of eating disorders, which posits that body image disturbance may arise not only from top-down cognitive distortions but also from bottom-up visual processing anomalies in the brain[\\u003cspan additionalcitationids=\\\"CR39\\\" citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Previous functional neuroimaging studies have reported altered occipito-temporal responses during body image tasks in BN[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e], and the volumetric reductions we observed provide structural support for these functional inefficiencies. Deficits in primary visual integration may impair the updating of the body schema with real-time visual input, thereby reinforcing reliance on maladaptive internal representations and perpetuating the distortion of body image. \\\"Shape Concern\\\" reflects the degree of preoccupation and distress individuals experience regarding their body shape, a core symptom dimension in eating disorders. This specific association suggests that the lingual gyrus serves as a neuroanatomical substrate for the pathological preoccupation with physical appearance[\\u003cspan additionalcitationids=\\\"CR44\\\" citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. We propose that structural deficits in this region may destabilize the precise visual encoding of body details, creating perceptual ambiguity that facilitates the intrusion of negative cognitive distortions and emotional dysregulation.\\u003c/p\\u003e \\u003cp\\u003eFinally, we observed a significant GMV reduction in the right STG in patients with BN compared to HCs. This finding is consistent with recent studies using diffusion tensor imaging-based machine learning in patients with bulimia nervosa, suggesting that structural changes in the right STG are key neuroimaging markers for distinguishing BN from other eating disorder subtypes[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. The STG is a key component of the social cognitive network and is mainly involved in auditory processing, language comprehension, social cognition, bodily perception, and emotional processing[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. GMV reduction in the STG may reflect impaired social interaction abilities, emotion recognition, or perception of bodily boundaries in patients with BN, which could exacerbate their interpersonal difficulties. Structural abnormalities in the STG may be associated with binge eating impulsivity in BN. Some studies have reported that the right STG of patients with BN is functionally connected to the ventral tegmental area (VTA, the reward center) of the midbrain[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. We speculate that structural changes in the STG may provide a potential anatomical basis for enhanced STG-VTA circuit function, leading to abnormal salience attribution or excessive reward responses to food cues, which ultimately manifest as impulsivity and loss of control in binge-eating behavior.\\u003c/p\\u003e \\u003cp\\u003eLimitations of this study should be acknowledged. First, despite recruiting a relatively large cohort of medication-free patients the cross-sectional design limits our ability to infer causality within the \\\"cerebello-affective\\\" pathway. Longitudinal studies are needed to determine whether these gray matter reductions represent neurobiological scars of the disease or pre-existing risk factors. Second, while this study provided a comprehensive characterization of regional gray matter alterations in BN, it did not examine how these regions are organized into large-scale structural networks. Future studies using morphometric similarity network analysis may extend these findings by characterizing network-level organization and system-level alterations in BN.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study revealed that patients with bulimia nervosa exhibit reduced gray matter volume in the bilateral calcarine and lingual gyrus, right STG and bilateral posterior cerebellum. Notably, depressive symptoms play a key mediating role in the relationship between cerebellar structure and disease severity. Based on these findings, noninvasive neuromodulation targeting cerebellar function and medications aimed at improving mood may become potential adjunctive treatment approaches.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003ePrevious presentation\\u003c/h2\\u003e\\n\\u003cp\\u003eNone.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDisclosures:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors report no financial relationships with commercial interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll participants provided written informed consent. The study was approved by the Ethics Committee of West China Hospital of Sichuan University and was conducted according to the Helsinki Declaration.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003ch2\\u003eAuthor contributions\\u003c/h2\\u003e\\n\\u003cp\\u003eLZ and XQH designed this study. XZ, LQL, WXB, MOW, YDW and XYH conducted this study. XZ, LQL and WXB conducted data analysis. XZ and LQL wrote the first draft of the paper, and LZ and XQH critically revised the manuscript. All authors participated in the data collection, and made contributions to critical revision of the manuscript.\\u003c/p\\u003e\\n\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\n\\u003cp\\u003eThis work as supported by the General Program of National Natural Science Foundation of China (No. 82271580) and the Key R\\u0026amp;D Project of Sichuan Provincial Department of Science and Technology: Neuroimaging-based machine learning for the diagnosis of the eating disorders (No. 2022YFS0184).\\u003c/p\\u003e\\n\\u003ch2\\u003eData availability\\u003c/h2\\u003e\\n\\u003cp\\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eKocsis RN: Diagnostic and Statistical Manual of Mental Disorders: Fifth Edition (DSM-5). 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Brain and Behavior. 2023;13(4).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"translational-psychiatry\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"tp\",\"sideBox\":\"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)\",\"snPcode\":\"41398\",\"submissionUrl\":\"https://mts-tp.nature.com/cgi-bin/main.plex\",\"title\":\"Translational Psychiatry\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Bulimia nervosa, Structural magnetic resonance imaging, Cerebellum, Depression, Anxiety.\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9025261/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9025261/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eObjective\\u003c/h2\\u003e \\u003cp\\u003eWe aimed to elucidate the neuroanatomical alterations in drug-free young females with BN and their relationship to core illness severity and comorbid emotional symptoms.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eA total of 53 adult female patients with BN and 52 age- and sex- matched healthy controls (HCs) were included in the study and underwent high-resolution T1-weighted MRI scans. We applied both voxel-based morphometry (VBM) and surface-based morphometry (SBM) methods to comprehensively explore gray matter (GM) alterations in BN patients. Correlations and mediation analyses were further performed to assess the relationships among morphological alterations, depression or anxiety symptoms and eating disorder symptoms in BN patients.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eCompared to HCs, BN patients exhibited reduced GMV/CV in the bilateral lingual and calcarine gyri, right superior temporal gyrus, and bilateral posterior cerebellum. Among these regions, the right lingual gyrus volume correlated negatively with shape concern scores for eating disorder, while bilateral posterior cerebellar volumes correlated negatively with depression and anxiety symptoms. We also found that depression and anxiety symptoms fully mediated the association between left posterior cerebellar volume and eating disorder severity, with depression demonstrating a significantly stronger mediating effect than anxiety.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eOur study identifies convergent gray matter reductions in cerebellar, occipital, and temporal regions in BN. Notably, we demonstrated that comorbid depression and anxiety symptoms fully mediate the link between posterior cerebellar volume and eating disorder severity. Our finding emphasized the cerebellum as a neurobiological locus where emotional dysregulation converges to exacerbate BN, offering a novel target for pathophysiology-informed treatment strategies.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Neuroanatomical Substrates of emotional Dysregulation in Bulimia Nervosa\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-03-13 07:38:29\",\"doi\":\"10.21203/rs.3.rs-9025261/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2026-04-16T16:55:19+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2026-03-26T11:02:14+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2026-03-12T12:57:09+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-03-08T22:49:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-03-05T17:30:12+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-03-05T17:25:59+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Translational Psychiatry\",\"date\":\"2026-03-04T03:24:50+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"translational-psychiatry\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"tp\",\"sideBox\":\"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)\",\"snPcode\":\"41398\",\"submissionUrl\":\"https://mts-tp.nature.com/cgi-bin/main.plex\",\"title\":\"Translational Psychiatry\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"393e0d20-6e28-4788-ac07-70014425a5fe\",\"owner\":[],\"postedDate\":\"March 13th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":64132802,\"name\":\"Biological sciences/Neuroscience\"},{\"id\":64132803,\"name\":\"Health sciences/Diseases\"}],\"tags\":[],\"updatedAt\":\"2026-03-13T07:38:30+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-03-13 07:38:29\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9025261\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9025261\",\"identity\":\"rs-9025261\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}