Journal of Abnormal PsychologyThe Core Symptoms of Bulimia Nervosa, Anxiety, andDepression: A Network AnalysisCheri A. Levinson, Stephanie Zerwas, Benjamin Calebs, Kelsie Forbush, Hans Kordy, HunnaWatson, Sara Hofmeier, Michele Levine, Ross D. Crosby, Christine Peat, Cristin D. Runfola,Benjamin Zimmer, Markus Moesner, Marsha D. Marcus, and Cynthia M. BulikOnline First Publication, March 9, 2017. http://dx.doi.org/10.1037/abn0000254

CITATIONLevinson, C. A., Zerwas, S., Calebs, B., Forbush, K., Kordy, H., Watson, H., Hofmeier, S., Levine, M.,Crosby, R. D., Peat, C., Runfola, C. D., Zimmer, B., Moesner, M., Marcus, M. D., & Bulik, C. M. (2017,March 9). The Core Symptoms of Bulimia Nervosa, Anxiety, and Depression: A Network Analysis.Journal of Abnormal Psychology. Advance online publication.http://dx.doi.org/10.1037/abn0000254

The Core Symptoms of Bulimia Nervosa, Anxiety, and Depression:A Network Analysis

Cheri A. LevinsonUniversity of Louisville

Stephanie ZerwasUniversity of North Carolina at Chapel Hill

Benjamin CalebsUniversity of Louisville

Kelsie ForbushThe University of Kansas

Hans KordyUniversity of Heidelberg

Hunna WatsonUniversity of North Carolina at Chapel Hill, The University of

Western Australia, and Curtin University

Sara HofmeierVeritas Collaborative, Durham, North Carolina

Michele LevineUniversity of Pittsburgh Medical Center

Ross D. CrosbyNeuropsychiatric Research Institute, Fargo, North Dakota, and

University of North Dakota School of Medicine and HealthSciences

Christine PeatUniversity of North Carolina at Chapel Hill

Cristin D. RunfolaUniversity of North Carolina at Chapel Hill and Stanford

University School of Medicine

Benjamin Zimmer and Markus MoesnerUniversity of Heidelberg

Marsha D. MarcusUniversity of Pittsburgh Medical Center and Curtin University

Cynthia M. BulikUniversity of North Carolina at Chapel Hill, Curtin University,

and Karolinska Institutet, Stockholm, Sweden

Cheri A. Levinson, Department of Psychological and Brain Sciences,University of Louisville; Stephanie Zerwas, Department of Psychiatry, Uni-versity of North Carolina at Chapel Hill; Benjamin Calebs, Department ofPsychological and Brain Sciences, University of Louisville; Kelsie Forbush,Department of Psychology, The University of Kansas; Hans Kordy, Center forPsychotherapy Research, University of Heidelberg; Hunna Watson, Depart-ment of Psychiatry, University of North Carolina at Chapel Hill, School ofPaediatrics and Child Health, Faculty of Medicine, Dentistry, and HealthSciences, The University of Western Australia, and School of Psychology andSpeech Pathology, Faculty of Health Sciences, Curtin University; SaraHofmeier, Veritas Collaborative, Durham, North Carolina; Michele Levine,Department of Psychiatry, Western Psychiatric Institute and Clinic, Universityof Pittsburgh Medical Center; Ross D. Crosby, Neuropsychiatric ResearchInstitute, Fargo, North Dakota, and Department of Psychiatry and BehavioralScience, University of North Dakota School of Medicine and Health Sciences;Christine Peat, Department of Psychiatry and Neurosurgery, University ofNorth Carolina at Chapel Hill; Cristin D. Runfola, Department of Psychiatry,University of North Carolina at Chapel Hill, and Department of Psychiatry andBehavioral Sciences, Stanford University School of Medicine; Benjamin Zim-mer and Markus Moesner, Center for Psychotherapy Research, University ofHeidelberg; Marsha D. Marcus, Department of Psychiatry, Western Psychiat-ric Institute and Clinic, University of Pittsburgh Medical Center, and School ofPsychology and Speech Pathology, Faculty of Health Sciences, Curtin Uni-versity; Cynthia M. Bulik, Department of Psychiatry and Nutrition, Universityof North Carolina at Chapel Hill, School of Psychology and Speech Pathology,Faculty of Health Sciences, Curtin University, and Department of MedicalEpidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

This research was supported by a National Institute of Mental Health(NIMH) grant (R01MH080065), a Clinical Translational Science Award(UL1TR000083), and the Alexander von Humboldt-Stiftung. Stephanie Zer-was is supported by a NIMH career development grant (K01MH100435).Cheri A. Levinson was supported by a NIH postdoctoral training grant(5T32DA007261). Christine Peat and Cristin D. Runfola were supported by aNIMH postdoctoral training grant (T32MH076694). Cristin D. Runfola wassupported by the Global Foundation for Eating Disorders (PIs: Bulik andBaucom; www.gfed.org). Benjamin Zimmer was supported by a Fellowshipfor Postdoctoral Researchers from the German Academic Exchange Service(DAAD). Kelsie Forbush is a recipient of a grant from Recovery Record Inc.Cynthia M. Bulik acknowledges support from the Swedish Research Council(VR Dnr: 538-2013-8864). This work has been presented at the Eating Dis-order Research Society in October 2016. Marsha D. Marcus and Cynthia M.Bulik contributed equally.

Cynthia M. Bulik is a recipient of a grant from Shire Pharmaceuticalsand a contract from RTI and Shire Pharmaceuticals and has consulted forIronshore. Marsha D. Marcus is on the Scientific Advisory Board ofWeight Watchers International, Inc. Christine Peat is recipient of a contractfrom RTI and has consulted for Sunovion Pharmaceuticals, Shire Pharma-ceuticals, and L.E.K consulting.

Correspondence concerning this article should be addressed to Cheri A.Levinson, Department of Psychological and Brain Sciences, University ofLouisville, Life Sciences Building, Louisville, KY 40292. E-mail:cheri.levinson@louisville.edu or Stephanie Zerwas, Department of Psychi-atry, University of North Carolina at Chapel Hill, CB 7160, 101 ManningDrive, Chapel Hill, NC 27599-7160. E-mail: zerwas@med.unc.edu

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Journal of Abnormal Psychology © 2017 American Psychological Association2017, Vol. , No. , 0021-843X/17/$12.00 http://dx.doi.org/10.1037/abn0000254

1

Bulimia nervosa (BN) is characterized by symptoms of binge eating and compensatory behavior, andoverevaluation of weight and shape, which often co-occur with symptoms of anxiety and depression.However, there is little research identifying which specific BN symptoms maintain BN psychopathologyand how they are associated with symptoms of depression and anxiety. Network analyses represent anemerging method in psychopathology research to examine how symptoms interact and may becomeself-reinforcing. In the current study of adults with a Diagnostic and Statistical Manual for MentalDisorders-Fourth Edition (DSM–IV) diagnosis of BN (N � 196), we used network analysis to identifythe central symptoms of BN, as well as symptoms that may bridge the association between BN symptomsand anxiety and depression symptoms. Results showed that fear of weight gain was central to BNpsychopathology, whereas binge eating, purging, and restriction were less central in the symptomnetwork. Symptoms related to sensitivity to physical sensations (e.g., changes in appetite, feeling dizzy,and wobbly) were identified as bridge symptoms between BN, and anxiety and depressive symptoms. Wediscuss our findings with respect to cognitive–behavioral treatment approaches for BN. These findingssuggest that treatments for BN should focus on fear of weight gain, perhaps through exposure therapies.Further, interventions focusing on exposure to physical sensations may also address BN psychopathol-ogy, as well as co-occurring anxiety and depressive symptoms.

General Scientific SummaryFear of weight gain is a central symptom in a bulimia nervosa psychopathology network. Sensitivityto physical symptoms connect symptoms of bulimia nervosa with associated anxiety and depressionsymptoms.

Keywords: bulimia nervosa, anxiety, depression, network analysis

Supplemental materials: http://dx.doi.org/10.1037/abn0000254.supp

Models based on theories of psychopathology have the ability toenhance our understanding and treatment of mental disorders. Thefield of clinical psychology has traditionally relied on latent vari-able theory and has used these models to help understand whymental disorders develop and persist (e.g., Bollen, 2002; Bors-boom et al., 2016; Clark & Watson, 1991; Eaton, 2015; Haslam,Holland, & Kuppens, 2012). In latent variable theory it is assumedthat symptoms of mental disorders arise from a common cause(e.g., Borsboom et al., 2016; Clark & Watson, 1991). For example,in bulimia nervosa (BN) it is assumed that symptoms such as fearof weight gain, avoidance of food, binge eating, and purging eacharise from an underlying latent variable that represents BN. Inother words, BN is the cause that leads to each of these commonsymptoms. However, there are additional theories and modelingtechniques that are being increasingly applied to psychopathology.

One of these more recent theories is network theory. Networktheory proposes that symptoms of disorders cause each other(Borsboom & Cramer, 2013). In other words, symptoms are part ofa dynamic network of symptoms, which produce, sustain, andunderlie mental disorders (e.g., Kendler, Zachar, & Craver, 2011).For example, in BN, the symptoms fear of weight gain andavoidance of food may be highly correlated not because they stemfrom the same latent variable representing “bulimia,” but becausefear of weight gain directly leads to avoidance of food (for addi-tional examples with other disorders please see Borsboom &Cramer, 2013). These symptoms may then interact with each otherto strengthen (or weaken) the disorder. Network theory proposesthat there are causal paths between such symptoms. This type oftheory can be tested using network analysis.

Network analysis is a methodology drawn from network sciencethat allows for an unprecedented number of interacting factors tobe considered at once within a mathematical model (Borgatti,

Mehra, Brass, & Labianca, 2009). Applied to the study of psycho-pathology, network analysis allows for examination of how indi-vidual behaviors or symptoms are associated with other behaviorsor symptoms (e.g., Borsboom et al., 2016). Further, network anal-ysis allows for the characterization of core symptoms (e.g., centralfeatures of the disorder) within networks of psychopathology(using measures of centrality explained further in the analysessection). Thus, network analysis is one step toward understandingcausal pathways between symptoms of disorders (pending exper-imental and longitudinal research testing causal pathways). Byanalyzing symptoms as a network, we can examine not just howsymptoms of disorders are associated with each other, but howthey may interact and become mutually reinforcing (e.g., Cramer,Waldorp, van der Maas, & Borsboom, 2010).

Most recently in network analysis, methods of using partialcorrelation networks have been championed as the best way tounderstand psychopathology (see McNally, 2016 for a discussionof the different types of network analyses, for a comparisonbetween latent variable analysis and network analysis, as well aslimitations and strengths of both methods). Partial correlationnetworks control for each of the symptoms in the network. Ifsymptoms are overlapping, partial correlation networks accountfor that overlap. Consider the example of major depressive disor-der. A partial correlation network would allow researchers tounderstand how the symptoms difficulty sleeping, irritability, anddifficulty concentrating each uniquely interact with each other,while considering all relationships between these symptoms. Forexample, irritability and difficulty concentrating may be affectedby difficulty sleeping. A partial correlation network explains howthese symptoms relate while also considering the impact of diffi-culty sleeping on both irritability and difficulty concentrating. Thismethodology provides a way to understand which symptoms

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2 LEVINSON ET AL.

uniquely contribute to the development and maintenance of psy-chopathology.

Network theory has not yet been applied to BN. However,applying network theory to BN may elucidate several importantaspects of the illness. Several existing theories propose that symp-toms such as overevaluation of weight and shape may be central toBN psychopathology (e.g., Fairburn & Cooper, 1993; Fairburn,Cooper, & Shafran, 2003; Vitousek, & Brown, 2015). Networkanalysis is a methodology that allows researchers to test if thesesymptoms are central to BN psychopathology within individualsdiagnosed with BN and how such symptoms might interact withother symptoms in a BN psychopathology network. Thus, networkanalysis provides researchers with a nuanced understanding ofhow symptoms relate to each other and how they might maintainBN. Network analysis can also establish how symptoms of comor-bid disorders interact with symptoms of BN. For example, networkanalysis can identify which anxiety symptoms are most likely toimpact BN symptoms (and vice versa).

A network analysis has clinical utility. First, interventions fo-cusing on alleviating specific central symptoms (symptoms relatedto the most other symptoms) should theoretically also decreaserelated symptoms. Second, defining which symptoms lie at thecenter of a psychopathology network might lead to focused inter-ventions to target these core symptoms (symptoms which havehigh centrality in the network and may play a crucial role in thenetwork; Hayes, Yasinski, Ben Barnes, & Bockting, 2015). Froma cognitive–behavioral perspective, these core symptoms may becentral to the maintenance of the psychopathology network (e.g.,Clark & Wells, 1995; Fairburn, Cooper, & Shafran, 2003). There-fore, intervening on core symptoms that are highly related to mostother symptoms in the network should maximize the impact of theintervention on the other behaviors, thoughts, and emotions relatedto the core symptom. Third, by identifying the core symptoms ofcomorbid disorders (e.g., comorbid anxiety symptoms), treatmentscould be refined to focus on these symptoms. Focusing on corecomorbid symptoms could disrupt or weaken the cycle betweensymptoms of multiple psychopathologies (e.g., eating disorder andanxiety disorder symptoms) or weaken the connection betweenthem. Identification of these networks may ultimately lead tointerventions that are more finely honed to target core symptoms inthe network.

In the current study, in a clinical sample of individuals with BN,we first examined a psychopathology network of BN symptoms toidentify which symptoms are most central in a BN psychopathol-ogy network (i.e., identification of core symptoms). Next, weexamined which symptoms of anxiety and depression are moststrongly related to symptoms of BN. This study had two primaryaims: (a) to identify which symptoms of BN are central to thedisorder and (b) to test which symptoms of anxiety and depressionare most strongly related to symptoms of BN. We hypothesizedthat the hallmarks of BN (binge eating and purging behaviors, aswell as the undue influence of body weight or shape on self-evaluation, or overevaluation of weight and shape) would be morecentral to the BN network than other symptoms. Additionally, wehypothesized that physical sensation symptoms associated witheating and digestion, such as indigestion and appetite, may bebridge symptoms between BN symptoms and associated anxietyand depression.

Method

Design and Procedure

Data were drawn from a randomized controlled trial conductedat two eating disorder centers, the Center of Excellence for EatingDisorders (CEED) at the University of North Carolina at ChapelHill and the Center for Overcoming Problem Eating (COPE) atWestern Psychiatric Institute and Clinic, University of PittsburghMedical Center. The institutional review boards at both institutionsapproved the trial and all patients provided informed consent.Details regarding the design, methods, and treatment of the studyhave been published previously and the study is registered atClinicalTrials.gov (NCT00877786; Bulik et al., 2012). We usedbaseline data in the current study. BN, anxiety, and depressivedisorder diagnoses were based on the Eating Disorder Examina-tion interview (EDE; Fairburn & Cooper, 1993) and the Diagnosticand Statistical Manual for Mental Disorders-Fourth Edition(DSM–IV) SCID-I/P semistructured interviews.

Participants

In total, 196 patients participated in the baseline assessment.Participants were primarily female (n � 187, 95.4%) and ofEuropean ancestry (n � 165, 84.2%). Other ancestries reportedwere: African American (n � 11, 5.6%), Asian (n � 5, 2.6%),Native Hawaiian or Pacific Islander (n � 1, .5%), and other (n �10, 5.1%). Participants’ average age was 28.2 years (SD � 9.2,range � 18 to 65). Patients were recruited via clinical referrals andadvertisements in university listservs, print, radio, and social me-dia platforms (such as Facebook), and brochures at counselingcenters, physician offices, and mental health organizations. Pa-tients completed a telephone screen to assess inclusion and exclu-sion criteria before an in-person baseline assessment.

Inclusion criteria were DSM–IV diagnosis of BN (AmericanPsychiatric Association, 2000); � 18 years; body mass index(BMI) � 18.5 kg/m2; English speaking; and private access to theInternet. Exclusion criteria included any major medical conditionthat would interfere with treatment (e.g., Type 1 diabetes mellitus;N � 6); alcohol or drug dependence in the last three months (N �11); psychosis, including schizophrenia, and bipolar I disorder orcurrent significant suicidal ideation reported during the clinicalassessment (N � 10). A sizable proportion of participants had alifetime diagnosis of comorbid depression or anxiety, with n � 136(69.4%) meeting criteria for major depressive disorder and n � 75(38.3%) meeting criteria for any anxiety disorder, obsessive–compulsive disorder, or posttraumatic stress disorder.

Measures

Eating disorder symptoms. Eating disorder symptoms(e.g., binge eating, restriction, overevaluation of weight, fearsof fatness; for a full list of symptoms assessed see note forFigure 1) were assessed via the Eating Disorder Examinationinterview (Fairburn & Cooper, 1993) and the ADQ (Bauer,Winn, Schmidt, & Kordy, 2005). The ADQ is a follow-upmeasure developed from the The Short Evaluation of EatingDisorders (SEED; Bauer et al., 2005). The SEED is a briefinstrument, which assesses eating disorder symptoms on a

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3BULIMIA NERVOSA SYMPTOM NETWORKS

weekly basis during treatment participation. The ADQ wasspecifically designed to assess number of days and times aparticipant engaged in eating disorder behaviors over the pre-vious week. We used the symptoms vomiting, purging, andexcessive exercising from the ADQ before treatment.

Beck Anxiety Inventory (BAI; Beck, Steer, & Brown, 1996).Anxiety symptoms were assessed via the BAI, a 21-item self-report measure of anxiety. Each symptom is rated for severity on

a scale from “not at all” to “severely-it bothered me a lot.” TheBAI had excellent internal consistency (� � .90).

Beck Depression Inventory II (BDI-II; Beck et al., 1996).Depression symptoms were assessed via the BDI-II, a 21-itemself-report measure of depression. Each symptom was rated forseverity based on endorsement of one of a series of statementsarranged in order from least to most symptomatic. The BDI-II hasbeen validated in psychiatric and nonpsychiatric samples (Steer,

BN

BN

preoc

loss control

weigh

shape import

lose wt wtdis

fear wtgain

flat stom

feel fat

shape dis

wt preoc

wt import

vomit

over eat

secret eat

guilt expo disc

body disc

binge

fast restrict

avoid eat

empty stom

social eat

diet rule

food avoid

exces exer

Figure 1. BN network. BN symptom label descriptions: avoideat � avoidance of eating; binge � bingeing;bodydisc � body discomfort; dietrule � dieting rules; emptystom � desire for an empty stomach; excesexer �excessive exercising; expodisc � discomfort with exposure; fast � fasting; fearwtgain � fear of weight gain;feelfat � feelings of fatness; flatstom � desire for flat stomach; foodavoid � food avoidance; guilt � guilt overeating; losewt � desire to lose weight; losscontrol � loss of control over eating; overeat � overeating; preoc �preoccupation with weight/shape; restrict � restriction; secreteat � eating in secret; shapedis � overevaluationof shape; shapeimport � shape importance; socialeat � avoiding eating in social settings; vomit � vomiting;weigh � reaction to prescribed weighing; wtdis � overevaluation of weight; wtimport � weight importance;wtpreoc � preoccupation with weight. Tie strength is indicated by line thickness between nodes with thickerlines representing stronger ties. See the online article for the color version of this figure.

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4 LEVINSON ET AL.

Ball, Ranieri, & Beck, 1997) and has excellent internal consis-tency.

Network Analyses

In psychopathology networks, edges or ties represent the corre-lation between two nodes. In the present study, the strength of a tiewas operationalized as the partial correlation coefficient betweentwo nodes (i.e., the correlation coefficient for two nodes aftercontrolling for the influence of all other nodes in the network withwhich they are connected). Networks can be weighted or un-weighted. Unweighted networks are those in which the relation-ship between nodes is either present or not (e.g., McNally, 2016).Weighted networks are those in which some operationalization ofthe strength of the tie exists (as in a partial correlation network;e.g., McNally, 2016). The operationalization of tie strength as apartial correlation coefficient made the network models in thepresent study weighted networks.

Network models were constructed and analyzed in R (Version3.2.4) using the qgraph package (Epskamp, Cramer, Waldorp,Schmittmann, & Borsboom, 2012). We used a portion of the scriptprovided in the supplemental materials from Borsboom and Cra-mer (2013), a script provided by Costantini and colleagues (2015),and a script from Epskamp (2014) to arrive at our network models.We created graphical lasso (Glasso) networks using the glassofunction in qgraph (Epskamp et al., 2012). Glasso networks in-clude a least absolute shrinkage and selection operator (i.e., lasso)penalty (Friedman, Hastie, & Tibshirani, 2008; Tibshirani, 1996),which functions to create a more parsimonious network by reduc-ing small correlations to zero.

Additionally, a glasso network arises from a partial correlationnetwork; thus, each pathway is the remaining relationship betweentwo variables after partialing all other relationships, which hasbeen proposed as a means of limiting spurious connections in anetwork (Costantini et al., 2015). The glasso network is a variantof the general lasso model that calculates differing penalty weightsfor the various correlation coefficients (Friedman, Hastie, & Tib-shirani, 2008). Because the literature suggests that glasso modelsaccount for both partial relationships and small relationships be-tween items, we present these models in the current study.

Interpreting the networks. In each visual network, a circlerepresents an individual node or symptom (one item from thesymptom measures). Associations between nodes are representedwith either green or red lines, in which green lines represent apositive relationship between the symptoms and red lines representa negative relationship between symptoms. Strongly related symp-toms in the network have thicker lines and items in the center ofthe network represent items are represented as core to the psycho-pathology network (when centrality networks also support thisconclusion). Bridge nodes are symptoms that link adjacent symp-toms together and are theorized to constitute pathways that couldcausally connect symptoms or behaviors (pending longitudinal andexperimental data analysis). For example, there may be either BNsymptoms or anxiety symptoms that connect (bridge) the BN andanxiety psychopathology networks.

Centrality measures. Both tie strength, as well as the positionof a node, within the network structure are used to determine therelative importance of the nodes in a psychopathology network.Core nodes are those that are considered most important to the

psychopathology network. To identify which items were at thecore of the networks we use statistical indices, called measures ofcentrality (Freeman, 1979; Opsahl, Agneessens, & Skvoretz,2010), which quantify certain features of the networks and nodes.These indices were calculated using the centrality function in theqgraph package (Epskamp et al., 2012) for R and include between-ness, closeness, and strength (McNally, 2016). Strength centralityrefers to the sum of the strengths of the ties between a node and allother nodes to which it is connected. For example, if binge eatinghad high centrality it would have many strong connections (e.g.,indicated by larger partial correlation coefficients) to other symp-toms in a network. Betweenness centrality typically refers to thenumber of times that a node lies along the shortest path betweentwo other nodes in the network. For example, if binge eating hadhigh betweenness centrality, binge eating would be passed throughmany times when moving between symptoms in a network. Close-ness centrality refers to the inverse sum of the paths between anode and all other nodes in the network. For example, if bingeeating has high closeness it would be connected to other symptomsin the network with fewer symptoms between it and all othersymptoms.

Opsahl et al. (2010) suggested a way to incorporate both thenumber of nodes and the strength of the relationship betweennodes into centrality indices when working with weighted net-works. Opsahl et al. (2010) used Dijkstra’s algorithm (Dijkstra,1959) to identify the least costly path between nodes, which takesinto account the number of nodes that are passed through, as wellas the strengths of the ties between the nodes that are being passedthrough (Opsahl et al., 2010). Thus, betweenness and closenesscentrality can account for the fact that a path between two nodesthat involves a small number of nodes and strong ties will be moreimportant in a network than a path that involves the same numberof nodes but weaker ties. The present study used Opsahl andcolleagues’ (2010) definitions of betweenness and closeness givenfor weighted networks. Overall, symptoms that have higher cen-trality are more central to the network and have more frequent andstronger relationships with other symptoms than do symptomswith lower centrality. These measures are important for identifyingwhich symptoms may possibly drive the psychopathology net-work.

Stability analyses. We also tested the stability of these net-works using the R package bootnet (Epskamp, Borsboom, & Fried,2016). When creating network models, the stability of the mea-sures (e.g., tie strength, centrality) must be considered so that onehas adequate support for any inferences made about the networks.To test the stability of the edge weights, confidence intervals wereconstructed using a nonparametric bootstrapping technique inbootnet as recommended by Epskamp and colleagues (2016). Thestability of the order of nodes in terms of centrality was also testedusing the nodedropping function in bootnet (Epskamp et al., 2016).

Results

Model 1: BN Psychopathology

Fear of weight gain, desire to lose weight, and feelings offatness fell at the center of the model, suggesting that thesesymptoms may be core to BN psychopathology (see Figure 1).Binge eating and vomiting have a strong relationship with each

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5BULIMIA NERVOSA SYMPTOM NETWORKS

other, but are located at the periphery of the network. Similarly,restriction and overeating have a strong relationship with eachother but are again, located somewhat at the periphery of thenetwork (see supplementary material Table 1 that reports thepartial correlation coefficients between symptoms). The indices ofcentrality support the finding that fear of weight gain (between-ness � 1.79, closeness � 1.23), desire to lose weight (between-ness � 2.09, closeness � 1.42, strength � 1.45), preoccupationwith weight (betweenness � 1.91, strength � 1.57), and over-evaluation of weight (closeness � 1.21, strength � 1.22) arecentral (core symptoms) to this network (see Figure 2). Othersymptoms with high centrality were dieting rules (betweenness �1.73), feelings of fatness (closeness � 1.28), and overeating(strength � 1.24).

Model 2: BN and Associated Anxiety Psychopathology

There are two clusters of symptoms (anxiety and BN symptoms)that are bridged by several symptoms (see Figure 3). The anxietysymptoms that are closest to BN symptoms are as follows (BNsymptoms in parentheses): feelings of wobbliness (located closestto avoidance of eating), fear of losing control (located closest toguilt over eating and eating in secret), feelings of unsteadiness(located close to guilt over eating and avoidance of social eating),and terrified (located close to avoidance of social eating and eatingin secret; see supplementary material Table 2 that reports thepartial correlation coefficients between symptoms). The anxietysymptoms with the highest centrality are feelings of choking(betweenness � 2.39, strength � 1.54), unsteady (betweenness �1.69, closeness � .84, strength � 1.32), terrified (betweenness �1.89, closeness � .87), and worries about losing control (between-ness � 1.91, closeness � .95), whereas the BN symptoms with thehighest centrality are overevaluation of shape (betweenness �2.03, closeness � .87, strength � 1.30), avoiding eating (between-ness � 1.96, closeness � .64), and preoccupation with weight(betweenness � 1.28, closeness � .65, strength � 1.09; see Figure4). Other symptoms with high centrality were (anxiety) handstrembling (closeness � .85), dizzy (strength � 1.08), shaky(strength � 1.70), and (BN) fears of weight gain (betweenness �.89), avoidance of social eating (closeness � .63), overevaluationof weight (strength � 1.03), and desire to lose weight (strength �1.23).

Model 3: BN and Associated Depression Psychopathology

There are two clusters of symptoms (depression and BN symp-toms) that are bridged by several symptoms (see Figure 5). Thedepression symptoms that fall closest to BN symptoms are feelinglike crying, lack of interest in sex, self-dislike, self-criticalness,change in appetite, thinking of suicide, and guilty feelings, fallingclose to the BN symptoms overevaluation of weight, overevalua-tion of shape, preoccupation with weight, desire to have an emptystomach, preoccupation with shape, and binge eating (see supple-mentary material Table 3 that reports the partial correlation coef-ficients between symptoms). Irritability (betweenness � .81,closeness � .81), feelings of sadness (betweenness � 1.05,strength � 2.14), and concentration difficulties (betweenness �1.63, closeness � .98, strength � 1.20) were the depressionsymptoms with the highest centrality, whereas fear of weight gain

(betweenness � 2.60, closeness � 1.95), avoiding food (between-ness � 2.52, closeness � 1.47), and preoccupation with weight(betweenness � 2.24, closeness � 1.29, strength � 1.41) were theBN symptoms with the highest centrality (see Figure 6). Othersymptoms with high centrality were (depression) self-dislike (be-tweenness � 1.00), agitation (closeness � .75), difficulty sleeping(closeness � .69), loss of interest (strength � 1.00), feelings ofworthlessness (strength � 1.35) and (BN) dieting rules (between-ness � 1.96), feelings of loss of control (closeness � 1.36), desireto lose weight (strength � 1.19), dissatisfaction with shape(strength � 1.22), and overeating (strength � .94).

Stability Analyses

The results from the stability analyses showed that the networkmodels were stable. Specifically, the results from the edge weightstability analyses suggested that the tie strengths were reliablyestimated. Also, the results from nodedropping stability analysessuggested that the order of the nodes in terms of centrality wasstable even after dropping up to 50% of the nodes in each network.Strength centrality appeared to be most stable among the centralitymeasures. Additionally, the BN network was most stable and theBN and depression network was least stable, though all wereoverall stable. Depictions of the results from stability analyses areavailable upon request from the first author.

Supplemental Analyses

In the online supplement we include a fourth model (see Figure 1)that includes all three constructs in one model: BN, anxiety, anddepression, as well as the indices of centrality for this model(Figure 2). As can be seen in this supplement, the primary findingsremained the same. We also include tables that show the tiestrength between each of the related symptoms (partial correlationsbetween symptoms: supplementary material Table 4).

Discussion

We identified several core symptoms of BN, as well as bridgesymptoms between BN and anxiety and depression symptoms.Overall, two common themes emerged in these networks. First,fear of weight gain and overevaluation of weight and shapeemerged as core BN symptoms throughout each network. Second,when concentrating on the overlap between BN and anxiety anddepression symptoms, physical sensations such as dizziness, wob-bliness in legs, changes in appetite, and lack of interest in sex,appear to be possible bridge symptoms that connect anxiety ordepression symptoms to symptoms of BN.

BN Psychopathology Network

Fear of weight gain, overevaluation of weight and shape, andfeeling fat were identified as highly central to BN psychopa-thology in the BN network model. These findings supporttheory and research that suggest that overevaluation of weightgain and fears of weight gain are central to BN psychopathol-ogy (Fairburn & Cooper, 1993; Fairburn, Cooper, & Shafran,2003; Vitousek, & Brown, 2015). These symptoms anchoredthe center of the network and had the strongest and most

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6 LEVINSON ET AL.

Figure 2. Centrality indices for BN network. Higher numbers indicate that the item is more central to thenetwork; highest four values are indicated within each index by a red dot; symptoms with at least two of thehighest centralities are bolded. Values shown on the x-axis are standardized z-scores. See the online article forthe color version of this figure.

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7BULIMIA NERVOSA SYMPTOM NETWORKS

exces exer

fast

empty stom

weigh restrict over eat

avoid eat

diet rule

flat stom

food avoid

lose wt

fear wtgain

feel fat

expo disc guilt

shape dis body

disc social

eat wt

preoc

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loss control

secret eat

preoc

vomit

binge

fear worst

ngle terrified

lose control nervous

scared

cant relax

fear die

choking

un steady

shaky hand

tremble

wobble

dizzy faint face flush

heart pound

di

indiges on

sweat

hot

Figure 3. BN and anxiety network. BN symptom label descriptions: avoideat � avoidance of eating;binge � bingeing; bodydisc � body discomfort; dietrule � dieting rules; emptystom � desire for an emptystomach; excesexer � excessive exercising; expodisc � discomfort with exposure; fast � fasting;fearwtgain � fear of weight gain; feelfat � feelings of fatness; flatstom � desire for flat stomach;foodavoid � food avoidance; guilt � guilt over eating; losewt � desire to lose weight; losscontrol � lossof control over eating; overeat � overeating; preoc � preoccupation with weight/shape; restrict �restriction; secreteat � eating in secret; shapedis � overevaluation of shape; shapeimport � shapeimportance; socialeat � avoiding eating in social settings; vomit � vomiting; weigh � reaction toprescribed weighing; wtdis � overevaluation of weight; wtimport � weight importance; wtpreoc �preoccupation with weight. Anxiety symptom label descriptions: cantrelax � unable to relax; choking �feeling of choking; diffb � difficulty breathing, dizzy � dizzy or lightheaded; faceflush � face flushed;faint � feeling faint/lightheaded; feardie � fear of dying; fearworst � fear of worst happening;handtremble � hands trembling; heartpound � heart pounding/racing; hot � feeling hot; indigestion �indigestion; losecontrol � fear of losing control; nervous � feeling nervous; scared � feeling scared;shaky � feeling shaky; sweat � hot/cold sweats; terrified � terrified/afraid; tingle � numbness or tingling;unsteady � unsteady; wobble � wobbliness in legs. Tie strength is indicated by line thickness betweennodes with thicker lines representing stronger ties. See the online article for the color version of this figure.

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8 LEVINSON ET AL.

Figure 4. Centrality indices for BN and anxiety network. BN symptoms are denoted in red and anxietysymptoms in green; higher numbers indicate that the item is more central to the network; highest four values foreach disorder are indicated within each index by a red dot for BN and a green dot for anxiety; symptoms withat least two of the highest centralities are bolded. Values shown on the x-axis are standardized z-scores. See theonline article for the color version of this figure.

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9BULIMIA NERVOSA SYMPTOM NETWORKS

exces exer

fast weigh

empty stom

restrict over eat

food avoid

flat stom diet

rule

avoid eat

social eat

fear wtgain

lose wt

wtdis

feel fat

shape dis

expo disc

body disc

wt preoc

preoc

binge

vomit

loss control

secret eat

wt import

shape import

guilt

appe te pessim

suicide

sex

con centrate

fa gue

low energy

loss interest

sleep

agita on

irritable

loss pleasure

sad

past fail

self dislike

cri al

worth less

inde cisive

crying punish guilty

Figure 5. BN and depression network. BN symptom label descriptions: avoideat � avoidance of eating;binge � bingeing; bodydisc � body discomfort; dietrule � dieting rules; emptystom � desire for an emptystomach; excesexer � excessive exercising; expodisc � discomfort with exposure; fast � fasting;fearwtgain � fear of weight gain; feelfat � feelings of fatness; flatstom � desire for flat stomach;foodavoid � food avoidance; guilt � guilt over eating; losewt � desire to lose weight; losscontrol � lossof control over eating; overeat � overeating; preoc � preoccupation with weight/shape; restrict �restriction; secreteat � eating in secret; shapedis � overevaluation of shape; shapeimport � shapeimportance; socialeat � avoiding eating in social settings; vomit � vomiting; weigh � reaction toprescribed weighing; wtdis � overevaluation of weight; wtimport � weight importance; wtpreoc �preoccupation with weight. Depression symptom label descriptions: agitation � feeling agitated andrestless; appetite � changes in appetite; concentrate � concentration difficulties; critical � self-criticalness; crying � crying too much or too little; fatigue � tiredness or fatigue; guilty � feeling guilty;indecisive � difficulty making decisions; irritable � irritability; lossinterest � loss of interest; lossplea-sure � loss of pleasure; lowenergy � loss of energy; pastfail � past feels like a failure; pessim �pessimism; punish � punishment feelings; sad � sadness; selfdislike � disliking self; sex � loss of interestin sex; sleep � changes in sleeping pattern; suicide � suicidal ideation; worthless � feelings ofworthlessness. Tie strength is indicated by line thickness between nodes with thicker lines representingstronger ties. See the online article for the color version of this figure.

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10 LEVINSON ET AL.

Figure 6. Centrality indices for BN and depression network. BN symptoms are denoted in red and depressionsymptoms in blue; higher numbers indicate that the item is more central to the network; highest four values foreach disorder are indicated within each index by a red dot for BN and a blue dot for depression; symptoms withat least two of the highest centralities are bolded. Values shown on the x-axis are standardized z-scores. See theonline article for the color version of this figure.

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11BULIMIA NERVOSA SYMPTOM NETWORKS

frequent relationships with other symptoms of BN. An interest-ing find was that other symptoms, such as binge eating, vom-iting, restriction, and overeating fell on the periphery of thenetwork and had lower centrality. Consistent with research thatimplicates restriction in a cycle of risk for overeating, restric-tion was very strongly related to overeating (e.g., Polivy, 1996;Mathes, Brownley, Mo, & Bulik, 2009). Similarly, binge eatingand vomiting were very highly linked, as found in other re-search (Striegel-Moore et al., 2005).

Our findings support the importance of overevaluation of weightand shape as central in a cognitive–behavioral model of BN(Fairburn & Cooper, 1993; Fairburn, Cooper, & Shafran, 2003;Vitousek, & Brown, 2015). In our data, overevaluation of weightand shape were highly central. Researchers have found that weightand shape concerns do not necessarily decrease after cognitive–behavioral therapy (CBT; for a review see Anderson & Maloney,2001). Our findings further support the idea that these are centralBN maintaining symptoms and that treatments need to addresssuch concerns.

Our findings help clarify individual differences within individ-uals diagnosed with BN. While binge eating and purging areimportant indicator symptoms of BN, our findings show that theyare located on the periphery of the BN psychopathology networkin individuals with BN and are less highly central. Our findingssuggest that while these hallmark symptoms may be critical fordiagnosis, binge eating and purging may not play as strong of amaintaining role in the disorder as does fear of weight gain.Alternatively, fear of weight gain may be the core maintainingsymptom once the disorder develops. In other words, when com-paring individuals with and without BN, the symptoms bingeeating and purging are highly important. However, our data con-sisted of individuals who all engaged in binge eating and compen-satory behaviors (e.g., purging). Within individuals with BN, fearsof weight gain and overevaluation of weight and shape play a keyrole, rather than binge eating and purging.

Network theory suggests that treatments focused on core main-taining symptoms should have the maximal effect in decreasing allsymptoms within a psychopathology network (Borsboom & Cra-mer, 2013). Therefore, our findings using network analysis supportthe idea that our treatments should focus on targeting shape andweight concerns, as well as fears of weight gain (Fairburn, 2008).Our findings also bring to light several areas that should be testedwith future treatment research. In addition to concentrating onbalancing eating habits and decreasing episodes of binge eatingand purging (Fairburn, 2008), researchers could test if integratingadditional and novel specific interventions focused on fears ofweight gain into existing treatments would improve treatmentoutcomes. Interventions specifically enhanced to focus on fears ofweight gain (such as routine weighing) may lead to subsequentdecline in binge eating and purging episodes, in addition to thetraditional focus on binge eating, purging, and restrictive behav-iors.

Existing treatments should continue to include componentswhich focus on fears of weight gain. For example, CBT for BNalready includes a weekly weighing component (Fairburn, 2008),and this research supports the idea that weighing once weekly maybe necessary to combat fears and reactions related to weighing.However, some research suggests that while routine weighing is awidely supported empirical intervention, clinicians often drift

away from employing this practice as suggested in most CBT forBN approaches (Waller, Stringer, & Meyer, 2012). Additionalresearch suggests that clinicians who do weigh patients weekly,often practice blind weighing practices (Forbush, Richardson, &Bohrer, 2015). Our findings, which suggest that fear of weight gainis a core symptom of BN and may drive other symptoms in thenetwork, augment the existing literature suggesting the importanceof including routine, weekly, open weighing in CBT treatments. Itmay be that combatting fear of weight gain through weekly weigh-ing is a central mechanism by which CBT leads to successfultreatment outcomes.

Although fear of weight gain is frequently discussed as animportant component of CBT for BN, it may be necessary to craftadditional interventions (beyond routine weekly weighing) thatspecifically address this core symptom. For example, it may beuseful to integrate imaginal exposures related to fear of weightgain (as described in Levinson, Rapp, & Riley, 2014). Suchinterventions hold promise for enacting maximal change on thegreatest number of BN symptoms, since they exhibit the highestlevels of centrality (i.e., they are the most connected symptoms inthe network). Research testing exposure therapy with BN hasfocused on exposing individuals to binge and purge cues (Bulik etal., 1998). This research has suggested that adding this type ofexposure component did not lead to better outcomes versus CBTwithout exposure. It may be that exposures focused on fears ofweight gain would produce longer lasting effects than exposuresfocused on binge eating and purging cues. Our findings suggestthat exposures that target fear of weight gain would address core(fear of weight gain) rather than peripheral (binge eating andpurging) fears. Further, additional evidence from network analysissuggests that body checking may be a key symptom in eatingdisorder psychopathology (Forbush, Siew, & Vitevitch, 2016). It ispossible that a combination of body checking exposures and ima-ginal exposures addressing fears of weight gain may be optimal foraddressing core symptoms of BN. We, therefore, need further pilotexperimental and clinical intervention research testing the theorythat including novel interventions focused on fear of weight gainmight lead to the greatest change in BN symptomatology.

BN and Associated Anxiety and DepressionPsychopathology Networks

We found that physical sensations may bridge the associationsbetween BN and anxiety/depression symptoms. Specifically, inboth the BN and anxiety symptom network and the BN anddepression symptom network, forms of sensitivity to physicalsensations emerged as the closest connections between BN symp-toms and anxiety and depression. For example, in the anxiety andBN network, the anxiety symptoms feelings of wobbliness in one’slegs, unsteadiness, and dizziness fell very close to the BN symp-toms and were highly central. Similarly, in the depression and BNnetwork, lack of interest in sex and changes in appetite were highlycentral. These findings are important because they further contrib-ute to our understanding of how anxiety, depression, and BNsymptoms might interact with each other. Such identification hasthe potential to lead to interventions targeted at maintenancesymptoms, which could disrupt the link between BN and associ-ated anxiety and depression.

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12 LEVINSON ET AL.

A number of studies have implicated a potential role of theinsula in eating disorders specifically in AN, with some researchfinding an exaggerated insula response in individuals with BN ascompared with individuals without the disorder (e.g., Kim et al.,2012; Oberndorfer et al., 2013). The insula is thought to beassociated with sensitivity to physical sensations and hyperaware-ness of interoceptive sensations. Other research has suggested thatwithin BN specifically there may be interoceptive deficits, such asinaccuracies in perceiving the number of heartbeats during aspecified interval (Fassino, Pierò, Gramaglia, & Abbate-Daga,2004; Klabunde et al., 2013). Interoceptive exposure has beenfound to be an effective transdiagnostic intervention across anxietydisorders (Boswell, Farchione, Sauer-Zavala, & Murray, 2013). Itis possible that extending this type of intervention to BN could behelpful in addressing comorbid anxiety and depression, as well asBN symptoms. Of course, it is also possible that BN behaviorsthemselves may alter such anxiety and depressive symptoms.

Several additional symptoms in the BN and anxiety/depressionnetworks were highly central. In the anxiety and BN network,feelings of choking was a highly central symptom. This findingcould be indicative of overall difficulties with physical sensationsthat occur during eating, the somatization of anxiety in one’sthroat, and may help explain why individuals with BN oftenstruggle during meals. Additionally, in the depression and BNnetwork, the depression symptom irritability emerged as highlycentral to the network on all three measures of centrality, meaningthat irritability may be an important symptom across disorders.Finally, though appetite dysregulation did not emerge as a centralsymptom, it was related to symptoms associated with eating, suchas binge eating and purging.

Limitations of Our Data

There are certain limitations of this work that must be consid-ered. First, our data are cross-sectional. Although network theoryhypothesizes that these analyses reflect potential dynamic relation-ships between symptoms, longitudinal and/or experimental dataare needed to test if the associations found in the present studyaffect each other across time and in causal experiments. We cannotmake any causal claims about the data presented here. Second, oursample was treatment seeking and therefore we do not know ifthese results would generalize to nontreatment seeking samples,although we should note that our results are consistent with the oneprior paper using network analysis in a nontreatment seekingeating disorder sample (Forbush, Siew, & Vitevitch, 2016). Addi-tionally, although the sample was somewhat representative, itconsisted primarily of women of European ancestry and may notgeneralize to men and other racial and ethnic groups. Finally,because our sample consisted of individuals diagnosed with BN,we had less variance than if a healthy control population was alsoincluded. Future research should test these network models in asample including a range of participants with and without BN.

Limitations of Network Analysis

Any methodology has inherent limitations, as is true for networkanalysis. Our data are limited by the symptoms and behaviors wechose to include and to define as BN symptoms in our networks.For example, inclusion of additional symptoms (e.g., body check-

ing) might produce a different psychopathology network. Thislimitation is especially relevant because binge eating and purgingwere each measured with only one item. This may limit thereliability of the measurement of these symptoms, and couldpossibly impact why we found binge eating and purging to beperipheral to the network. We await future research with additionalmeasurement of these symptoms to test if our results replicate.Relatedly, we hope future research will test if these results arestable across samples and to explore which centrality indices aremost stable. We also chose to include symptoms and behaviors thatare not specific to a DSM diagnosis of BN, given that research hasnot shown a general convergence between models of DSM symp-toms and non-DSM network models (Fried, Epskamp, Nesse,Tuerlinckx, & Borsboom, 2016). Network analysis as applied topsychopathology is a relatively new enterprise. Therefore, meth-ods such as tests of reliability and establishment of fit indices arestill in development. While network analyses may have the abilityto lead to important insights in clinical psychology, it is alsonecessary to consider other traditional methods such as latentvariable analysis and multidimensional models of psychopathol-ogy (e.g., Flett, Vredenburg, & Krames, 1997; Prisciandaro &Roberts, 2009). However, the results presented here demonstratehow network analysis can be used to better define psychopathol-ogy and represent a step forward in our understanding of how BNsymptoms may interact.

Conclusions

Overall, we found that fear of weight gain was a core symptomof the BN network of psychopathology, whereas binge eating,purging, and restriction were less central symptoms. We alsofound that physical sensation symptoms might link BN with asso-ciated anxiety and depression. Consistent with a CBT framework,fear of weight gain may represent a core belief or fear that in turnengenders subsequent behaviors, thoughts, and emotions, such asbinge eating, purging, restriction, and guilt. Future research isneeded to test if interventions targeting the core symptom of fearof weight gain may maximize change on other behaviors, thoughts,and emotions. Additionally, further research is needed to test iftargeting physical sensation symptoms can disrupt the link be-tween BN and associated anxiety and depression. The develop-ment of targeted interventions that augment current treatments forfear of weight gain and physical sensation symptoms should beconsidered.

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Received April 19, 2016Revision received December 8, 2016

Accepted December 8, 2016 �

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15BULIMIA NERVOSA SYMPTOM NETWORKS