Schizophrenia Bulletin vol. 47 no. 4 pp. 1018–1028, 2021 doi:10.1093/schbul/sbaa187 Advance Access publication 17 February 2021 Obsessive-Compulsive Symptoms and Other Symptoms of the At-risk Mental State for Psychosis: A Network Perspective Hui Lin Ong1,29, Adela-Maria Isvoranu*,1,29, Frederike Schirmbeck2,3, Philip McGuire4, Lucia Valmaggia5, Matthew J Kempton4, Mark van der Gaag6, Anita Riecher-Rössler7, Rodrigo A. Bressan8, Neus Barrantes-Vidal9,10, Barnaby Nelson11,12, G Paul Amminger11, Patrick McGorry11,12, Christos Pantelis13, , Marie-Odile Krebs14, Merete Nordentoft15, Birte Glenthøj16, Stephan Ruhrmann17, Gabriele Sachs18, Bart P.F. Rutten19, Jim van Os4,19,20, Lieuwe de Haan2,3, Denny Borsboom1, and EU-GEI High Risk Study† 1 Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands; 2Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands; 3Arkin, Institute for Mental Health, Amsterdam, the Netherlands; 4Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England; 5Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK; 6Amsterdam Public Mental Health Research Institute, Department of Clinical Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; 7Medizinische Fakultät, Universität Basel, Basel, Switzerland; 8 LiNC-Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil; 9Departament de Psicologia Clínica i de la Salut, Universitat Autònoma de Barcelona, Barcelona, Spain; 10Fundació Sanitària Sant Pere Claver, Spanish Mental Health Research Network (CIBERSAM), Spain; 11Orygen, Parkville, Victoria, Australia; 12Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia; 13Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Carlton South, Victoria, Australia; 14 University of Paris, GHU-Paris, Sainte-Anne, C’JAAD, Inserm U1266, Institut de Psychiatrie (CNRS 3557), Paris, France; 15Mental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Copenhagen, Denmark; 16 Centre for Neuropsychiatric Schizophrenia Research (CNSR) & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark; 17Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany; 18Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; 19Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands; 20Department of Psychiatry, Brain Centre Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; 21University of Basel, Basel, Switzerland; 22Department of Psychiatry, Escola Paulista de Medicina, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil; 23CONACYT-Dirección de Investigaciones Epidemiológicas y Psicosociales, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz, México; 24Department of Psychology, University of Illinois at Urbana-Champaign, IL, USA; 25Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany; 26Psyberlin, Berlin, Germany; 27Icahn School of Medicine at Mount Sinai, department of Psychiatry, 1425 Madison Ave, New York, NY 10029, USA; 28 Mondriaan Mental health Trust, P.O. Box 4436 CX Heerlen, The Netherlands 29 These authors contributed equally to the article. *To whom correspondence should be addressed; Department of Psychology, Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129B, 1018 WT Amsterdam, the Netherlands; tel: +31 (0)20 8913639, email: isvoranu.adela@gmail.com † EU-GEI High Risk Study Group [not mentioned in main author list]: Maria Calem4, Stefania Tognin4, Gemma Modinos4, Sara Pisani4, Emily Hedges4, Eva Velthorst2,27, Tamar C. Kraan2, Daniella S. van Dam2, Nadine Burger7, Athena Politis11, Joanne Goodall11, Stefan Borgwardt21, Erich Studerus21, Ary Gadelha8, Elisa Brietzke22, Graccielle Asevedo8, Elson Asevedo8, Andre Zugman8, Tecelli Domínguez- Martínez23, Manel Monsonet9, Lidia Hinojosa9, Anna Racioppi9, Thomas R. Kwapil24, Mathilde Kazes14, Claire Daban14, Julie Bourgin14, Olivier Gay14, Célia Mam-Lam-Fook14, Dorte Nordholm15, Lasse Randers15, Kristine Krakauer15, Louise Birkedal Glenthøj15, Dominika Gebhard25, Julia Arnhold26, Joachim Klosterkötter25, Iris Lasser18, Bernadette Winklbaur18, Philippe A Delespaul19,28 Background: The high prevalence of obsessive-compulsive Mental States (CAARMS) has not yet been investigated. symptoms (OCS) among subjects at Ultra-High Risk This article aimed to use a network approach to investigate (UHR) for psychosis is well documented. However, the the associations between OCS and CAARMS symptoms network structure spanning the relations between OCS in a large sample of individuals with different levels of risk and symptoms of the at risk mental state for psychosis as for psychosis. Method: Three hundred and forty-one UHR assessed with the Comprehensive Assessment of At Risk and 66 healthy participants were included, who participated © The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 1018 Obsessive-Compulsive Symptoms in the EU-GEI study. Data analysis consisted of con- constructs. However, phenomenologically distinguishing structing a network of CAARMS symptoms, investigating OCS from APSs, eg, intrusive thoughts from overvalued central items in the network, and identifying the shortest ideas is challenging.12 In accordance, the Comprehensive pathways between OCS and positive symptoms. Results: Assessment of At-Risk Mental State (CAARMS) for psy- Strong associations between OCS and anxiety, social iso- chosis, the standard instrument for assessing prodromal lation and blunted affect were identified. Depression was psychopathology and identification of the UHR status, the most central symptom in terms of the number of con- assesses OCS as part of psychopathology dimensions, nections, and anxiety was a key item in bridging OCS to thought to be associated with an imminent psychotic dis- other symptoms. Shortest paths between OCS and positive order.13 Hence, exploring associations between OCS and symptoms revealed that unusual thought content and per- symptoms of the at-risk mental state for psychosis may ceptual abnormalities were connected mainly via anxiety, uncover novel perspectives on the patterns and interrela- while disorganized speech was connected via blunted af- tions between them. fect and cognitive change. Conclusions: Findings provide The network approach to psychopathology14,15 al- valuable insight into the central role of depression and the lows for the investigation of such direct relations and potential connective component of anxiety between OCS interactions on the symptom level, without a priori as- and other symptoms of the network. Interventions specifi- sumptions regarding the adequacy of the diagnostic cally aimed to reduce affective symptoms might be crucial categories constructed out of these symptoms. Classical for the development and prospective course of symptom psychometric practices are typically based on latent co-occurrence. variable theory, where psychopathological symptoms are caused by a hypothetical underlying mental dis- Key words: network analysis/clinical high risk/psychosis/ order (eg, OCD)—implying that symptoms cannot be obsessive-compulsive/anxiety/depression/ultra-high risk directly related to each other if the disorder is absent (ie, local independence).14,15 Within a network model, however, symptoms are viewed as active causal agents Introduction that can influence one another. Hence, a disorder can Obsessive-compulsive symptoms (OCS) have a prevalence be seen as emerging from a system of interacting com- rate of 30.7% and comorbid obsessive-compulsive dis- ponents. As a result, within a network structure, we can orders (OCD) of 12.3% in patients with a psychotic dis- investigate associations between individual symptoms, order.1 Accordingly, a review of the literature reported as well as potential bridging elements (eg, symptoms or increased prevalence rates of OCS (13.7%) and OCD other factors) connecting clusters of (other) interacting (5.5%) in subjects at Ultra-High Risk (UHR) for psy- symptoms.15 chosis2,3 compared to the general population. Recent To this end, the purpose of the current study was to ex- studies reported even higher rates in individuals meeting plore the interplay between OCS and other symptoms of UHR criteria.4 Subjects at UHR are defined by either ex- the CAARMS using a network model, in a sample with periencing brief intermittent psychotic symptoms (BLIPS), different levels of risk for psychosis, by including indi- or sub-threshold attenuated psychotic symptoms (APS), viduals meeting UHR criteria as well as healthy controls. or having a genetic risk for psychosis in combination with Including healthy control subjects helped to broaden the a significant decline in functioning.5 Of those individuals focus to subclinical symptoms in a sample of non-help meeting UHR criteria, 22% develop a psychotic disorder seeking individuals and also increased power of the con- within 3 years after presentation to clinical services.6 The ducted analyses. We aimed to identify aspects of symptom high OCS prevalence rates in UHR subjects have resulted co-occurrence, such as symptoms that may play an im- in increased research interest in investigating the effects portant role in facilitating the co-occurrence of OCS and of OCS on clinical outcomes, such as psychosocial func- psychotic symptoms. tioning and transition rates.2,4,7 The results have been in- conclusive. Some authors have found higher impairment of Method functioning and positive symptoms in UHR subjects with co-occurring OCS compared to those without7 whereas Participants and Procedure others have reported no associations or even better func- Data were collected in the European network of national tioning in the group with OCS.2,4 schizophrenia networks studying Gene-Environment Up-to-date, the association between OCS/OCD and Interactions (EU-GEI study), which was conducted be- subthreshold symptoms of psychosis has solely been re- tween May 2010 and May 2015. The EU-GEI study was ported as co-occurring prevalence rates or associations designed to identify the interactive genetic, clinical, and on the syndrome level in the general population8 in environmental determinants involved in the develop- healthy relatives of patients9,10 and in UHR subjects.2,7,11 ment, severity, and outcome of psychosis. Participants The investigation of the effects of OCS/OCD in UHR were part of the High Risk Study of the EU-GEI cohort. subjects assumes a clear distinction between these A sample of UHR individuals were recruited from 11 1019 H. L. Ong et al health care institutions (Amsterdam, Barcelona, Basel, never), which would have had the potential to bias results Cologne, Copenhagen, London, Melbourne, Paris, Sao (eg, due to severe violations of the normality assump- Paulo, The Hague, and Vienna). Controls were recruited tion).22 Low variability items were selected based on the from the same geographical catchment area as the UHR full sample, but these were identical when investigated in group, but only from 4 centers.16 Inclusion and exclusion the UHR only sample. Each remaining CAARMS item criteria for UHR and control participants are detailed was represented as a node in the network. The regular- in Appendix 1 in the supplementary material. The pro- ized partial correlation between two nodes, after control- tocol of the EU-GEI study was approved by the Medical ling for all other nodes in the network, was represented Ethics Committees of all participating study sites. In the as an edge. current study, we included UHR individuals and healthy First, we constructed Pairwise Markov Random Fields subjects for whom CAARMS measures were available. (PMRFs) to estimate an undirected network structure (ie, a Gaussian Graphical Model),23 which allows us to Materials identify the conditional associations between variables.24 To prevent spurious connections and to estimate a sparse All participants completed a detailed sociodemographic model, we adopted the least absolute shrinkage and se- questionnaire which included questions on their age, lection operator (LASSO) statistical regularisation tech- gender, and ethnicity. Furthermore, participants completed nique. We chose the graphical LASSO, which uses the the CAARMS, a 28-item semi-structured interview devel- Extended Bayesian Information Criterion (EBIC) model oped to assess UHR criteria that is intensity, frequency, du- selection with a tuning hyperparameter set to 0.5, as com- ration, and recency of subthreshold psychotic symptoms, monly employed in previous research.25–27 To account for as well as other dimensions of psychopathology thought the non-normal distribution of some items and missing to be indicative of or associated with an imminent psy- data, we based our analyses on Spearman correlations chotic disorder.17,18 The CAARMS can be clustered into using pairwise complete observations.28 seven domains: positive symptoms, cognitive symptoms, Second, we assessed the importance of each node in emotional disturbance, negative symptoms, behavioral the resulting network by computing centrality indices change, motor changes, and general psychopathology. As of the network structure, using the R package qgraph.21 part of general psychopathology, one CAARMS item as- Specifically, we computed strength centrality for each sesses symptoms of OCD by asking participants whether node in the network (ie, the sum of the weights of the they repeatedly experience upsetting thoughts that they connections, in absolute value), as well as the node- cannot stop, whether they repeat actions they feel they specific predictive betweenness (ie, how often a node lies should do, whether they have to do things in a specific way on the pathways between two other nodes, of which one to prevent feeling extremely anxious, whether they need to is always the OCS node).29–31 We chose to investigate only check things repeatedly like light switches/gas/electrical ap- these two centrality measures because (1) previous re- pliances, or doors or whether they are doing something to search has reported that strength centrality is the most prevent unpleasant things from happening (rituals/super- stable and interpretable centrality measure,28 and (2) stitions). The intensity of reported OCS is measured on our interest was in bridging nodes that may play a role a 7-point Likert scale from 0: never present to 6: extreme, in linking OCS to other symptom clusters, and may thus with a higher score reflecting higher symptom severity. In pave the way to co-occurrence. addition, the frequency of symptoms is assessed also on Third, we used the pathways function from the R a 7-point Likert scale from 0: absent to 6: continuously package qgraph,21 as used in previous research,26 to ex- present and a question assessing whether OCS were mani- amine the shortest pathways between the OCS node and fested by substance abuse. In the current study, CAARMS each of the positive psychotic symptoms. This examina- intensity scores were included. tion allows us to identify the shortest path from point A to point B for specific symptoms of interest (eg, from OCS to each positive symptom) and is computed using Data Analysis Dijkstra’s algorithm.32 In the current analysis, we estimated a network model Finally, we used the R package bootnet 1.3. to conduct of OCS and ARMS symptoms, using the R statistical robustness analyses to ensure stability and accuracy of software version 3.6.3.,19 the R package bootnet version results.20,28 Appendix 2 in supplementary material details 1.3.,20 and we visualized the resulting network structure on these analyses. using the R package qgraph 1.6.5.21 Due to the relatively low sample size in relation to a large number of nodes, Results and in an attempt to ensure more stable network esti- mates, we chose to reduce the number of nodes, prior to Sample Characteristics running the analyses. We did so by excluding low varia- Of the 344 UHR subjects and 67 healthy controls orig- bility items (ie, items with more than 75% response of 0: inally included in the EU-GEI study, 341 UHR and 66 1020 Obsessive-Compulsive Symptoms healthy participants provided comprehensive data on focus on highlighting the stronger and thus likely robust the CAARMS and were included in the current study. edges in the network structure. For instance, strong con- Slightly more than half of the sample (52.7%) were male, nections were found between GP1 and GP2 (depression and the mean age was 22.5 years (SD = 4.79). Twenty- and suicidality), P1 and P2 (unusual thought and non- nine (8.2%) participants fulfilled the diagnostic criteria bizarre ideas), N1 and N2 (anhedonia and avolition), and for an obsessive-compulsive disorder (OCD), 53 (21.0%) B2 and B3 (social isolation and impaired role function). reported moderate to severe obsessions and 54 (14.4%) Across domains, a strong connection was found between moderate to severe compulsions, respectively. Table 1 GP1 and N1 (depression and anhedonia). Interestingly, shows sociodemographic and clinical characteristics for GP6 (OCS) was indirectly connected to other CAARMS the overall sample and the UHR and healthy controls items mainly via GP5 (anxiety), B2 (social isolation), and subgroup separately. In addition, supplementary table S1 E2 (observed blunted affect). presents the means and standard deviations of individual CAARMS items. Strength and Node-specific Predictive Betweenness Measures General Network Structure Figure 2 (left) illustrates the magnitude of the node- We identified and excluded the following seven symp- strength for each symptom. In the entire network, GP1 toms with low variability (ie, items with more than 75% (depression) was the most central symptom, significantly response of 0: never): GP3 (mania), E3 (observed inap- stronger than most other nodes in the network. GP6 propriate affect), B4 (disorganizing, odd, stigmatizing (OCS) was the least central symptom, indicating that, behavior), M1 (subjective motor change), M2 (observed as compared to other CAARMS symptoms, GP6 (OCS) motor change), M3 (impaired bodily sensation), and was not a well-connected node in the network. Figure 3 M4 (impaired autonomic functioning) from the network illustrates the node-specific predictive betweenness values structure. Figure 1 presents the network constellation for each node in the network—ie, how often a node lies on of the 21 remaining symptoms from the CAARMS. All the pathways between two other nodes, of which one is al- symptoms were connected, either directly or indirectly, ways the OCS. The white dots represent the node-specific with other symptoms. Overall, there were stronger con- predictive betweenness in the current sample, while the nections with items within the same domain than across black lines represent the variability of the measure across domains, as defined in table 2. While robustness analyses 1000 nonparametric bootstrap iterations. Item GP5 (anx- indicate some caution is needed when interpreting the iety) had the highest node-specific predictive betweenness strength and presence of weaker edges, our results here score, followed by item E2 (observed blunted affect). This Table 1. Sociodemographic and Clinical Characteristics for the Overall Sample, as well as Subjects at Ultra-High Risk (UHR) and Healthy Controls (HC) Apart Overall Sample UHR HC N = 407 N = 341 N = 66 Comparison between UHR and HC Age 22.52 (4.79) 22.42 (4.91) 22.91 (4.09) T = −0.760, P = .447 Gender (male %) 53.3 53.4 53.0 X = 0.003, P = .959 Ethnicity (Caucasian %) 69.7 71.2 62.1 X = 15.856, P = .007 Years of education 14.68 (3.08) 14.37 (3.07) 16.15 (2.72) T = −4.346, P < .001 Current employment (yes %) 65.6 60.5 90.7 X = 36.8091, P < .001 Current living (alone %) 13.3 14.8 6.1 X = 25.171, P < .001 DSM comorbid diagnosis (yes %) Depression 27.8 33.0 0 X = 28.815, P < .001 OCD 8.2 9.7 0 X = 5.582, P = .018 Anxiety disorder 38.8 45.1 6.3 X = 34.085, P < .001 CAARMS Positive 8.63 (5.01) 10.09 (3.96) 1.12 (2.54) T = 17.692, P < .001 Negative 6.02 (3.87) 6.94 (3.41) 1.35 (2.47) T = 15.685, P < .001 Cognitive 2.70 (1.83) 3.14 (1.65) 0.48 (.91) T = 17.920, P < .001 Emotional 2.71 (2.45) 3.16 (2.40) 0.39 (.95) T = 15.616, P < .001 Social 6.92 (4.51) 8.02 (3.96) 1.39 (2.70) T = 16.580, P < .001 Motor 1.90 (2.46) 2.24 (2.55) 0.25 (.89) T = 10.671, P < .001 General 12.83 (7.59) 14.96 (6.33) 2.52 (3.71) T = 21.556, P < .001 Medication use Antidepressant (yes %) 29.8 Antipsychotics (yes %) 9.6 Anxiolytics (yes %) 9.2 1021 H. L. Ong et al Fig. 1. Network structure of 21 CAARMS symptoms. Item groups are differentiated by color. The color of the edge indicates the size of the association (blue for positive associations; red for negative associations). For a color version, see this figure online. finding suggests that anxiety may be the main connector betweenness is not yet implemented in the bootnet R between OCS and other ARMS symptoms. package,20 the bootstrap results show node GP5 (anxiety) was consistently identified as the most central bridging Robustness Analysis node, followed by node E2 (observed blunted affect). Furthermore, in the additional case-drop bootstrap anal- Robustness analyses (see supplementary appendix 2) revealed ysis carried out (see supplementary figure S4), node GP5 some relatively wide bootstrapped confidence intervals (CIs). remains visibly the main and most stable bridging node in We, therefore, recommend caution when interpreting the net- the network. Markedly, in general and aligned with pre- work structure, especially when interpreting the strength and vious research,28 the node-specific predictive betweenness presence of weaker edges. Nonetheless, the bootstrap mean is unstable and shows high variability; despite this, node was generally close to the sample mean, indicating interpret- GP5 (anxiety) stands out as a stable bridging node both able results (see supplementary figure S1). in the nonparametric and case-drop bootstraps. The strength centrality stability plot is shown in figure 2 (right), while figure 3 includes the variability of the node-specific predictive betweenness across 1000 non- Shortest Pathways: OCS to Positive Symptoms parametric bootstrap iterations (ie, black lines). The CS Overall, all positive symptoms of psychosis (P1, P2, P3, coefficient obtained for strength was CS = 0.59. This is and P4) were indirectly connected to GP6 (OCS) via other above the preferred 0.5 cut-off, indicating stable results. symptoms (figure 4, left). Results of shortest pathways For significance difference testing, the node with the analyses showed that the main connective pathways were highest strength centrality, GP1 (depression), showed sig- through node GP5 (anxiety) and through nodes E2 (ob- nificantly higher node strength than most of the other served blunted affect) and C2 (observed cognitive change), nodes (refer to supplementary figure S3 for detail). Of directly linked to P4 (disorganized speech). Our robust- note, correcting for multiple testing when carrying out ness analysis (figure 4, right) showed that these were the significance difference testing is not feasible, as elab- most stable pathways. The pathways from GP5 (anxiety) orately explained in the cited paper.28 While the func- to the remaining positive symptoms P1, P2, and P3 were tionality for calculating the CS coefficient and running less stable and should thus be interpreted with caution, significance difference testing for node-specific predictive with multiple potential connective components, most often 1022 Obsessive-Compulsive Symptoms Table 2. Node Labels and Corresponding Symptom Names of the 28-CAARMS Symptoms Item Label Item Description Domains P1 Unusual thought Positive P2 Non-bizarre ideas Positive P3 Perceptual abnormalities Positive P4 Disorganized speech Positive N1 Anhedonia Negative N2 Avolition Negative N3 Alogia Negative C1 Subjective cognitive change Cognitive C2 Observed cognitive change Cognitive E1 Emotional disturbance Emotional E2 Observed blunted affect Emotional E3* Observed inappropriate affect Emotional B1 Aggression Behavioral B2 Social isolation Behavioral B3 Impaired role function Behavioral B4* Disorganizing, odd, stigmatizing behavior Behavioral M1* Subjective motor change Motor M2* Observed motor change Motor M3* Impaired bodily sensation Motor M4* Impaired autonomic functioning Motor GP1 Depression General Psychopathology GP2 Suicidality General Psychopathology GP3* Mania General Psychopathology GP4 Mood swings General Psychopathology GP5 Anxiety General Psychopathology GP6 OCD symptoms General Psychopathology GP7 Dissociative symptoms General Psychopathology GP8 Impaired tolerance to normal stress General Psychopathology Note: *Seven symptoms were excluded from the network analysis due to low variability. Fig. 2. (Left) Node-strength centrality for each CAARMS symptom, and (right) stability of centrality indices. Centrality measures are shown as standardized z-scores. The right panel indicates the average correlation with the original sample after reducing the sample size through case-dropping bootstrapping. For a color version, see this figure online. including GP8 (impaired tolerance to normal stress) and Other potential pathways could also be identified, but since B1 (aggression) to P3 (perceptual abnormalities), and di- these were very small and, in most cases, present in less than rect pathways from GP5 (anxiety) to P1 (unusual thought). 20% of the bootstraps, we do not highlight these. 1023 H. L. Ong et al Fig. 3. Node-specific predictive betweenness. The white dots represent the node-specific predictive betweenness in the current sample, while the black lines represent the variability of node-specific betweenness across 1,000 nonparametric bootstrap iterations. Fig. 4. (Left) Network showing the shortest paths between the OCS and the positive symptoms of the CAARMS scale (P1, P2, P3, and P4) and stability analyses (right). The right panel displays the extent to which the pathways were identified across 1,000 non-parametric bootstraps (eg, the 0.85 edge between GP6 and GP5 indicated the edges was identified in 85% of the cases). For a color version, see this figure online. Discussion node linking OCS to other symptom clusters. While OCS did not occupy a central position in the network, they The current study was the first to examine symptom- were directly connected to social isolation, anxiety, and level associations of OCS and other symptoms of the blunted affect, and via anxiety and blunted affect to pos- ARMS in individuals with different levels of risk for psy- itive CAARMS symptoms. In general, connections be- chosis, and the first to evaluate the centrality measures tween symptoms within each domain were stronger than and bridging characteristics of symptoms. Overall, de- connections between domains. pression was found to be the most central symptom in In the current study, comorbid depression (27.8%) terms of direct connectivity, while anxiety was the main and anxiety disorders (38.8%) frequently occurred. The 1024 Obsessive-Compulsive Symptoms high centrality of depression and bridging characteristic emphasized feelings of uncertainty accompanied with of anxiety suggests that these variables might be essen- feelings of insecurity (disturbances in the so-called se- tial in their influence on other symptoms of the net- curity motivation system) as important underlying mech- work. The role of affective symptoms in UHR subjects anisms of OCS and OCD as an attempt to gain control has been of increased research interest. Meta-analyses and reduce distress.41 Another aspect of the aberrant reported that over 50% of UHR individuals fulfilled salience model namely failing to appropriately respond the criteria for a co-occurring depression or anxiety dis- to meaningful reward cues and diminished reward proc- order.33,34 Both depression and anxiety have been found essing has been directly linked to anhedonia and blunted associated with higher symptom severity of UHR symp- affect.42 These negative symptoms are closely associated toms and poorer global functioning.35,36 Fusar-Poli and with cognitive deficits,43,44 which again are conceptually colleagues concluded that affective disorders are likely related to disorganization in psychotic disorders.45 More to impact the overall longitudinal outcome of UHR recently studies suggest that reward dysfunctions and subjects albeit no associations have been found with a associated blunted affect might also constitute a neuro- transition to psychosis.34,37 Co-occurring OCD in 9.7% pathological mechanism in OCD.46 These findings might of the current UHR subjects exceed previously reported explain the connecting role of blunted affect in the net- mean estimates of 5.5% in subjects at increased risk for work and account for the frequent co-occurrence of OCS psychosis,2 but high variability between studies has been and psychotic symptoms.47 reported.34 Some previous studies reported associations In addition to shared underlying neurobiological al- between OCS and severity of psychotic symptoms on terations, causal interactions on the symptom level might the syndrome level in UHR subjects7 and psychotic ex- also be assumed. Psychotic-like experiences could lead periences in individuals from the general population.8 to increased anxiety, rumination, worries, and doubt, However, the majority of studies do not find associ- which are strongly related to obsessive thoughts and ations with positive symptom severity2,4,38 and a review might be associated with a subsequent attempt to reduce of the literature does not show an increased or decreased the resulting anxiety through compulsive behavior. In risk for transition to psychosis in UHR subjects with this line, early psychopathological concepts speculated OCS/OCD.34 In the current CAARMS network, OCS that sometimes OCS might be seen as an attempt to con- were not identified as central symptoms, nor directly trol for the feeling of self-disintegration.48 In support of linked to positive symptoms. Low centrality might be this hypothesis, one study found inverse associations be- explained by the content of the item, which is a more tween OCS and disorganization.49 However, more recent standalone item, compared to most other items that research associated co-occurring OCS with dysfunc- are part of clearly structured subdomains and show tional coping tendencies,50 increased general psychopa- stronger within-domain associations. These associ- thology and does not support direct associations with ations might thus drive other items to be more central, increased or decreased severity of positive symptoms, including other general psychopathology items (eg, de- including disorganization.51 The link between increased pression and suicidality are very strongly related to each anxiety, worry, and fear and the co-occurrence of OCS other). In addition, while other items are associated and psychosis has also been reported on the trait level. with a wide array of items from multiple subdomains, Schreuders and colleagues found increased neuroticism the OCS node has less and more well-defined associ- in patients with psychosis who reported comorbid OCS ations, resulting in lower centrality, but suggesting more compared to those without OCS. Exploratory ana- clear pathways. Direct associations with social isolation lyses suggested a mediating effect of neuroticism on can be explained by often observed avoidance and social the course of OCS severity over time.52 To disentangle withdrawal in individuals with OCD and social isolation hypothesized causal pathways, analyses of prospective has also been identified as a predictor for later devel- data are necessary. opment of OCD.39 Shortest paths analyses furthermore Taken together, current findings indicate the impor- showed that OCS were rather indirectly related to symp- tance of affective symptoms and their direct relations toms of the positive cluster, via anxiety and blunted af- and interactions with other symptoms of the CAARMS. fect coupled with observed cognitive change. Noteworthy, in a previous network study, anxiety was One possible interpretation of these interrelations found to be the main connective component between suggests that neurobiological alterations—such as aber- experienced traumatic events and psychotic symp- rant activation of the dopamine system and associated toms.26 Based on these findings, it seems advisable to not aberrant salience assignment to irrelevant stimuli causes only focus treatment on psychotic experiences in UHR uncertainty and consequently anxiety and emotional subjects but extend the focus to emotional disturbances. distress.40 In an attempt to make sense of these experi- Interventions aimed to reduce experienced anxiety and ences and in combination with cognitive alterations such distress could be important treatment targets as they as a tendency to jump to conclusion, psychotic symp- seem to play a central role in the network and might be toms might develop. Similarly, Szechtman and Woody crucial for the development of symptoms. 1025 H. L. Ong et al Limitations and Future Directions as mania or motor symptoms, which could have contrib- The current findings should be interpreted considering uted to knowledge on the interdependence of mixed af- some limitations. First, while our robustness analysis in- fective symptoms and motor symptoms with symptoms dicates that the resulting network and centrality indices of obsession, compulsion, anxiety, and psychosis. For the are interpretable, this should be done with some caution, interested reader, we included an extended network struc- especially for weaker links in the network and centrality ture with all 28 CAARMS items in supplementary ap- measures. pendix 4. Of note, the network structure is for reference Second, the current study is exploratory, and inter- only and should be interpreted with the utmost caution pretation of results is hypothetical in need of further in- and replication in larger samples is needed. All the main vestigations. Moreover, the current analysis was based links identified and discussed in the current manuscript on cross-sectional data; thus, it is not possible to infer are retained in the extended network structure, including causality, such as the direction of edges. Future research the associations between OCS and other symptoms. should consider longitudinal data to specify symptoms that actively trigger other symptoms as indicators of Conclusions the prospective development of the at-risk mental state We aimed to expand on our understanding of the for psychosis. Unfortunately, due to missing data and symptom-level association between OCS and other the relatively small sample size, we were not able to ex- symptoms of the ARMS, in a large sample of individuals plore prospective symptom interaction. Along this line, with different levels of risk for psychosis, using novel net- we also chose to only include CAARMS intensity scores work models. Our results did not reveal a central role of in the current study. Future studies in a larger sample OCS in the network, nor direct associations with positive should integrate information on frequencies of occurring symptoms, but indirect associations mainly via anxiety. symptoms. In addition, results suggested a central role of depressed Third, we included both healthy and UHR subjects mood in the symptom network. In sum, these findings in our analysis to evaluate subclinical symptom net- point to the critical role of affective symptoms in the net- works across the risk spectrum for psychosis. This work of the ARMS in individuals with different levels of approach led to increased power and accounted for a risk for psychosis. broad distribution of symptomatic expression at the subclinical level. Nonetheless, we included within sup- plementary appendix 3 a network structure constructed Supplementary Material in the UHR sample only, which shows comparable Supplementary material is available at https://academic. results, with the note that many of the connections oup.com/schizophreniabulletin/. were weaker or missing, indicating a significant loss in power. The edges in the UHR sample only network were mainly—and as expected—a subset of the edges Funding in the overall network structure. Sample sizes were The European Network of National Schizophrenia too small to incorporate network comparison tests, Networks Studying Gene-Environment Interactions investigating whether network structure or centrality (EU-GEI) Project is funded by grant agreement measures would be different between groups. Analyses HEALTH-F2-2010–241909 (Project EU-GEI) from the with large enough samples of non-help-seeking con- European Community’s Seventh Framework Programme. trols, UHR subjects, as well as first-episode patients Additional support was provided by a Medical Research would be desirable. Council Fellowship to M Kempton (grant MR/J008915/1). Fourth, findings are limited by OCS assessment with A.M.I was supported by the Netherlands Organization one item of the CAARMS. Future studies should include for Scientific Research (NWO), grant number 406.16.516. a state-of-the-art instrument to assess OCS such as the Yale Brown Obsessive and Compulsive Scale. Fifth, the majority of individuals included in the cur- Conflict of interest rent study is Caucasian/White. Replication in a more ra- The authors have declared that there are no conflicts of cially diverse sample is necessary to investigate to which interest in relation to the subject of this study. extent findings generalize to non-Caucasian populations. Finally, in an attempt to ensure more stable network estimates and avoid potential biased results (eg, due to se- References vere violations of normality assumption), we reduced the 1. Swets M, Dekker J, van Emmerik-van Oortmerssen K, et al. number of nodes in the network structure by excluding The obsessive compulsive spectrum in schizophrenia, a meta- low variability items from the network structures. This led analysis and meta-regression exploring prevalence rates. to the exclusion of clinically interesting dimensions such Schizophr Res. 2014;152(2–3):458–468. 1026 Obsessive-Compulsive Symptoms 2. 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