Symptom and Bayesian network analyses of positive and negative symptoms in psychotic- like experiences: A multicenter cross-sectional study of Chinese students at 19 cities

Symptom and Bayesian network analyses of positive and negative symptoms in psychotic- like experiences: A multicenter cross-sectional study of Chinese students at 19 cities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Symptom and Bayesian network analyses of positive and negative symptoms in psychotic- like experiences: A multicenter cross-sectional study of Chinese students at 19 cities Fei Liu, Zhao-qi Wang, Jia-xin Wu, Xiang-yun Long, An-si Qi, Xiao-feng Guan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6215745/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Psychoticlike experiences (PLEs) are perceived as early indicators for the progression to schizophrenia spectrum disorders, a grave concern in psychiatric research. Historically, the interconnectedness between positive and negative symptoms in PLEs has remained enigmatic. In this multicenter cross-sectional study, we aim to investigate the relationship between positive and negative symptoms in PLEs – crucial indicators of the transition to schizophrenia spectrum disorders.Our sample includes 37,443 high school/college students from 19 cities across four Chinese provinces (September 2017 to November 2019). Participants completed multiple assessments, such as the Prodromal Questionnaire-Brief Version and the Questionnaire for Negative Symptoms, Disorganization Symptoms, and General Symptoms.The analysis of symptom networks reveals that delusions and general negative symptoms emerge as central nodes in the network. Interestingly, the network demonstrates a clear separation of positive and negative symptoms while highlighting their close interconnections. Additionally, schizotypal personality disorder serve as bridging elements in this network. Using Bayesian network analysis, we further establish that negative symptoms drive the development of positive symptoms.These findings underscore the significance of exploring negative symptoms in PLEs and suggest their potential importance in early identification and intervention of schizophrenia spectrum disorders. schizophrenia spectrum disorders positive symptoms negative symptoms symptom network network analysis Bayesian network analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Schizophrenia spectrum disorders and schizotypal personality disorder are precursors of schizophrenia(Maier et al., 1999 ). Schizophrenia spectrum disorders are schizophrenia-like disorders which are not fulfilling the diagnostic criteria for schizophrenia but which are sharing symptoms, causes and risk factors with schizophrenia, representing a collection of chronic and severe mental conditions affecting millions globally(Jauhar et al., 2022 ). Schizotypal personality disorder refers to some people who are vulnerable to schizophrenia as an enduring personality condition. The pathogenesis of these disorders is perceived as developmental; both the pre-psychotic prodromal phase (with an average duration of 4.8 years) and the pre-psychotic stage (lasting on average 1.3 years) have been recognized(Hfner & Maurer, 2006 ). Attenuated psychotic symptoms are a defining characteristic of the prodromal phase, and individuals exhibiting these symptoms are classified as being at Clinical High Risk (CHR) or Ultra-High Risk (UHR) for psychosis(Rosengard et al., 2019 ). The CHR/UHR states have garnered significant research attention in recent years, aiming to preemptively address schizophrenia spectrum disorders prior to their onset(Nelson et al., 2019 ). Yet, the effective diagnosis of the developmental trajectory of schizophrenia remains a formidable challenge. There is also a lack of nodes regarding wether symptom–like experiences be used as an effective means of diagnosing schizophrenia spectrum disorders. Symptoms stand as the most direct indicators in assessing the progression of schizophrenia spectrum disorders, including among these are Psychoticlike Experiences (PLEs) such as hallucinations and delusions, considered early markers for schizophrenia(Knoll & Dietz, 2023 ). However, the transition from PLEs in the general population to the CHR state remains inadequately understood. Generally, research on the prevalence of PLEs in the healthy population indicates that between 5–10% of individuals report experiencing PLEs(McGrath et al., 2015 ; Van Os et al., 2009 ). Analogous to psychotic symptoms, PLEs can be broadly categorized into positive and negative domains(Lee et al., 2016 ; Starzer et al., 2023 ). Positive PLEs encompass delusions and hallucinations, whereas negative PLEs pertain to subtle or transient deficits in normal functionality, manifesting as social difficulties, dysthymia, anhedonia, or avolition. Historically, research gravitated towards positive PLEs due to their perceived greater relevance to the subsequent development of schizophrenia. It is worth noting that PLEs can be seen as a sign of mental disorders among young adults and even juveniles in the general population(Isaksson et al., 2022 ; Lindgren et al., 2022 ). However, some studies have suggested that negative PLEs, like social difficulties and dysthymia, might play a more significant role in distress associated with the progression of schizophrenia(Dickson et al., 2014 ; Laurens et al., 2007 ). It's theorized that, over time, PLEs might exhibit reciprocal causality, interacting in a mutually reinforcing and feedback-driven manner. To sum up, gaining clarity on the nature and trajectory of this relationship is paramount for the early detection and prevention of schizophrenia spectrum disorders, and there may be differences in PLEs symptoms' prediction of schizotypal personality disorder among juveniles and adults. Yet, despite a plethora of studies on healthy populations, our grasp on the interrelationships within positive and negative symptoms of PLEs and the contrast between their relationship with schizotypal personality disorder in juveniles and adults remains unclear. One avenue to explore these complex symptom relationships is via network analysis. This method visualizes symptoms as nodes and their interrelations as edges in a graphical model(Borsboom, 2017 ), illuminating the structure and dynamics of symptom networks and highlighting the most central or influential symptoms within the network(van der Wal et al., 2021 ). Previous applications of the Least Absolute Shrinkage and Selection Operator (LASSO) predominantly focused on the general non-clinical population and often integrated other scales in relation to PLEs. Among the 22 published network analyses to date (as of September 2023), a mere three studies differentiated between positive and negative symptoms within their networks. For instance, a network analysis of positive and negative PLEs from 7141 participants across 13 countries highlighted auditory hallucinations and Capgras delusions as highly central positive symptoms(Wüsten et al., 2018 ). Another British study explaind the interplay between negative and positive symptoms and post-traumatic stress, however, the study did not include all negative symptoms in its analysis. Another British study revealed psychotic experiences (i.e., hallucinations and delusions) were central to the network—more so than negative symptoms of psychosis—and expressed high strength centrality, possibly highlighting the particularly debilitating nature of positive symptoms of psychosis(Astill Wright et al., 2023 ), In Slovakia, the negative symptoms and positive symptoms were examined more comprehensively in the research and revealed that negative symptoms, through impaired social functionality, were connected to positive symptoms (Hajdúk et al., 2023 ). However, network analyses rooted in correlation or regression methodologies are limited in inferring causal relationships between symptoms, constraining their explanatory scope. A promising alternative is Bayesian network analysis, a methodology estimating the most probable causal structure elucidating observed data, grounded in prior knowledge and probabilistic inference(Puga et al., 2015 ). With regard to cross-sectional Bayesian network research, studies remain scarce. An analysis of 6941 pre-clinical British participants, while measuring positive symptoms like grandiosity, paranoia, hallucinations, and cognitive disarray, also employed the Černis Felt Sense of Anomaly (ČEFSA) scale to gauge dissociation. This scale, predominantly detailing "subtraction" phenomena from normal perception or experience, leans more towards negative symptoms. Both LASSO and Bayesian networks revealed dissociation as the most central trait, mediating influences on paranoia, cognitive disarray, and grandiosity(Černis et al., 2021 ). However, a comprehensive elucidation of the interrelationships and influence directions between positive and negative PLEs remains elusive. In this study, our objective is to navigate the symptom network relationships of PLEs within non-clinical students, deemed a pivotal characteristic in the premorbid phase of schizophrenia spectrum disorders. Age, another potentially influential factor, will be considered across two groups: juveniles (12–18 years old) and emerging adults (19–35 years old). Our core aims encompass: 1) Describing the relationship of PLE symptoms. 2) Contrasting the symptom network structures of PLEs between juveniles and adults. 3) Inferring the causal interplay between positive and negative PLEs. To realize these aims, we conducted LASSO network analysis and Bayesian network analysis across nine dimensions of three questionnaires. These evaluated positive symptoms, negative symptoms, and schizotypal personality disorder within a sample of college students spanning 19 institutions across three Chinese cities. Methods Procedure To ensure the representativeness of the research samples, we selected four provinces (Shanghai, Jiangsu, Jiangxi, and Guangdong province) for this study from September 2017 to November 2019. We chose 19 universities from these provinces and randomly selected 45,420 college students by the class as a unit (see Table S1 for more details). We distributed paper or electronic versions of the questionnaire uniformly in the class. When taking online surveys, participants sometimes respond consistently to items without regard to their content. These types of responses, referred to as careless or insufficient effort responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing. careless data is deleted when at least one item in the test is marked as being consistent and significant by "careless package" in R. Considering that PLE is most prevalent in juveniles and early adulthood, 7944 subjects who answered carelessly and younger than 12 and older than 35 were eliminated, and the remaining subjects were divided into young group ( N = 3623 subjects 12–17 years old, M = 16.71, SD = 0.66) and old group ( N = 33820, 18–35 years old, M = 18.93, SD = 1.00). All participants fully understood the objectives, process, benefits and risks of this study before participating in the evaluation and signed paper or electronic informed consent forms. In addition, for minor participants who agreed to participate in this study, their parents or other legal guardians signed the informed consent forms. To protect the privacy of the participants, all survey results were only used for analysis and this study had not disclosed to the school counselors, students or other personnel. Insert Fig. 1 This study used a multicenter design to increase the generalizability of the results (see Table S2 for details). A total of 3 study centers, mainly located in the eastern region, were involved, involving more than 20 vocational, college, high school, and undergraduate students. Researchers at all centers received uniform training to ensure standardization of intervention delivery. The sample size for each center is estimated to be 8,000 individuals. Subject screening criteria and procedures were standardized across centers. Random assignment sequences for questionnaire collection were generated centrally at random and stratified by center. A uniform symptom assessment scale and questionnaire are used across centers, and a central database collects all data. Center effects will be assessed and adjusted in our analyses. Centers will be monitored and audited by the central team on a regular basis to ensure standardized implementation of the study. The use of a multicenter design increases the breadth of results, and randomized stratification ensures comparability of results across centers. Measurements Prodromal Questionnaire - Brief version Prodromal Questionnaire - Brief version (PQ-B) is a self-rating scale was used to assess positive symptoms and includes 21 items with a 5-level score based on yes answer (1 = strongly disagree, 5 = strongly agree, 0 = no)(Loewy et al., 2012 ). The scale consists of the following five dimensions: Delusions (P1) includes items 1, 4–5 and 11–17; Persecutory Ideas (P2) includes items 8 and 18; Grandiosity (P3) includes item 7, hallucinations (P4) includes items 2–3, 9–10 and 19–20, Disorganized Communication (P5) includes items 6 and 21 (see Table S3 for details). The score for each dimension is the average score for its corresponding items (e.g., the score of Persecutory Ideas (P2) is the mean of the items 8 and 18). In this study, the scale coefficient of internal consistency Cronbach’ s alpha was 0.93, indicating a good questionnaire reliability. Questionnaire for negative symptoms, disorganization symptoms and general symptoms This tool is a self-rating scale made by ourselves in this study, which is derived from some questions in the negative symptoms, disorganization symptom and general symptoms of Scale of Psychosis-risk Symptoms (SOPS)(Pontillo et al., 2021 ). The scale includes 19 items in total with a 3-point Likert scale ranging from 0 (no), 1 (uncertain) to 2 (yes). The negative symptoms (AN) consist of the following six dimensions: Social anhedonia (N1) includes items 1 and 2, Avolition (N2) includes items 3 and 4; Expression of emotion (N3) includes item 5, Experience of Emotions and Self (N4) includes item 6, Ideational Richness (N5) includes items 7 and 8; Occupational Functioning (N6) includes items 9 and 10. The disorganization symptom (AD) consists of the Personal Hygiene (D4) which includes item 11. The general symptoms (AG) consist of the following four dimensions: Sleep Disturbance (G1) includes item 12, Dysphoric Mood (G2) includes item 13–17, Motor Disturbances (G3) includes item 18, Impaired Tolerance to Normal Stress (G4). The score for each symptom is the average score for its corresponding items (e.g., the score of social anhedonia (N1) is the mean of the items 1 and 2, the score of negative symptoms (AN) is the total mean of the items N1-N5). In this study, coefficient of internal consistency Cronbach’ s α = 0.84, indicating a good questionnaire reliability. Exploratory factor analysis showed good structure validity, Kaiser-Meyer-Olkin (KMO)value of 0.708(The closer the KMO value is to 1, it means that the correlation between variables is stronger, and the original variable is more suitable for factor analysis), the total extract six common factor, can explain 65.6% of the total variance. Personality Diagnostic Questionnaire for Schizotypal Personality Disorder We used the Chinese version of the Personality Diagnostic Questionnaire for Schizotypal Personality Disorder (PDQ-SPD) to evaluate the schizotypal personality disorder(Yang et al., 2002 ). The includes 9 items with a 2-level score based on participant’ s answered “yes” (1 point) or “no” (0 point). The score is the sum of the scores for each item, the higher the total score, the more significant the symptoms of schizoid personality disorder. In this study, the internal consistency coefficient Cronbach’s α of this scale was 0.67. Data processing and statistical analysis We used R (v 4.2.3) for data processing and network comparison. We used the “usf” package (a new version of the 'userfriendlyscience' package which contains a number of basic functions to create higher level plots) to screen for response inattentiveness. For the data set of PLEs, we initially had 45420 participants (Fig. 1 .), but we excluded 4282 participants who did not fill in their age or were outside the age range of 12 to 35 years old. We also excluded 3695 participants who filled in the questionnaire carelessly, based on the reaction time incorporated into the careless package. Finally, we included 37443 internet users in the main analysis. Network construction We applied the Graphical lasso (Glasso) network (regularized partial correlation network) method to estimate the symptom networks, the quickNet R package is used in this process, which integrates bootnet, qgraph, and other packages. The symptom network analysis approach focuses on which symptom activation is more likely to activate other symptoms in the network. Four common measures of centrality are strength, closeness, betweenness and expected influence. We evaluate the stability of the symptom network by using subset bootstrap procedure (repeatedly correlate the centrality of a subset of the decreasing sample size with the centrality of the original sample)(Costenbader & Valente, 2003 ). To evaluated the stability of edge weights in each network, we firstly calculate non-parametric bootstrap confidence intervals and checking for differences in strength between samples by using R package. Second, we calculate the correlation stability coefficient (CS coefficient), which indicates that the maximum sample size can be reduced in the process of subset bootstrapping while maintaining 95% probability of the correlation between the property of interest and the property in the full data set. The CS coefficient of 0.7 or higher is recommended, while 0.25 or lower is discarded. To compare connectivity differences among symptoms between young (N = 3623) and old (N = 33820) group’ s networks, NCT would be used for statistically assessing the difference between 2 groups (young and old groups) by repeatedly (5000 times) for randomly regrouped individuals. Differences observed below the 0.05 threshold are considered significant. Bayesian network In order to ensure the stability of the Directed Acyclic Graph (DAG), we used the bootstrap method (50000 bootstrap samples from a single original sample, with replacement) to obtain the final DAG structure. Firstly, we apply the optimal cut-point approach for retaining edges to obtain a DAG network with both high specificity and sensitivity. If 51% or more of the edges in 50000 bootstrap DAG network are in the same direction (e.g., pointed from symptom A to symptom B), the directional edge will be represented in the final DAG network using an arrow pointing from symptom A to symptom B. The iamb.fdr algorithm may be more suitable for sparse network with fewer nodes. If the number of nodes exceeds 50 and the number of edges exceeds 200, then the hc algorithm can be considered, and if the actual number of edges is less than 5 and the edge density is less than 0.1, it can be regarded as a sparse network. In fact, our network actually has 32 connected edges, and the maximum edge density is (10 * 9) / 2 = 45 edges, involving 10 variables, which can be considered as a medium-sized small dense network suitable for hc algorithm. The conditional independence test is used to learn the skeleton of the network, and then the greedy mountain climbing method is used to optimize the direction of the edge. Applies to both discrete and continuous variables. Then, we fit the DAG network structure obtained from the data domain and estimate the arc intensity through bootstrap 50000 times; Finally, we visualized the average intensity using qgraph. Results Symptom network analysis results To explore the symptom network relationship of PLEs, we conducted network analysis on 9 dimensions of 3 questionnaires. The results showed that there was a significant symptom network among all symptoms (Fig. 2 a) and the z-scored centrality indices appear in Fig. 2 b and Table 1. Positive and negative PLEs were separated spatially and each closely connected internally. According to Fig. 2 b and 2 c, P1 (strength = 1.381) and AG (strength = 1.412) had the highest node strength. We found that there was a clear association relationship within the symptoms. In the negative domains, negative PLEs and AG were closely related, with an edge weight of 0.72. Among the positive domains, P1 and P4 were most closely related, with an edge weight of 0.52. Schizotypal personality disorder node also had extensive associations, but mainly with AG. It’ s worth noting that there seems to be a relationship between PLEs and age. Age node was only positively correlated with negative PLEs, and negatively correlated with positive PLEs and schizotypal personality disorder. The supplementary materials show that all networks were relatively stable, with the larger the sample size, betweenness, closeness and strength have stronger average correlation with the original sample (see Figure S1 ). In our person-dropping stability analysis, we found CS-coefficients of 0.75 for our betweenness, closeness, and strength centrality metrics, respectively. Each of these values is greater than the recommended minimum threshold of 0.25, suggesting that our centrality estimates are stable. Consistent with this finding, using the bootstrapped difference test in bootnet, we found that there were significant differences between node strength of the network (see Figure S2-S4). Insert Table 1 Insert Fig. 2 Age group symptom network differences We also compared the symptom networks between juveniles group (12–17 years old) and adults’ group (18–35 years old). Figure 3 shows that there were significant differences in the symptom networks between the two age groups. Specifically, P3, AG, P1, P5 and schizotypal personality disorder in juveniles group (Fig. 3 a) were more positively correlated, while P3, P4, P1 and P2 were more negatively correlated in adults group (Fig. 3 b). Insert Fig. 3 Bayesian network modeling results To further speculate on the possible relationship between negative and positive domains in PLEs, we used Bayesian network estimation method to conduct statistics. The learned Bayesian network (Fig. 4 a and 4 b) contained 10 nodes and 32 directed edges whereby edge thickness signifies confidence that direction of prediction (and potentially causation) flows in the direction depicted in the graph. Bayesian networks reveal that P1and AG (as from node 5 times) are more likely to be upstream, and P3 is more likely to be downstream (as to node 6 times) (see Table S4 for details). The network had an average Markov blanket size of 8.6 and average node degree of 6.6. In the network, the schizotypal personality disorder node had the highest degree of connections with multiple positive and negative symptoms. P1 and P2 emerged as core nodes among the positive symptoms. The network structure revealed that schizotypal personality disorder may play a central role in symptom development and can influence other positive and negative symptoms. This may reflect causal relationships and conduction effects between symptoms in schizophrenia. Specifically, schizotypal personality disorder had direct links to P1, P2, AN and AG, suggesting it may directly contribute to their occurrence. Meanwhile, schizotypal personality disorder also had indirect connections to symptoms like P4 and P3 through intermediary nodes like P1. Overall, the network topology delineates probabilistic dependencies and interactions between variables that have important clinical implications for understanding schizophrenia. The network was learned using the Hill-Climbing algorithm with BIC score, through a bootstrap approach (50000). Further research is warranted to validate the symptom relationships suggested by the network structure. The stability of the Bayesian network has passed the test, mean loss = 12.64, SD loss = 0.07 (see Figure S5-S6). Insert Fig. 4 Discussion The original aim of this study was to investigate the network relationships of PLEs in juveniles and emerging adults. We identified three pivotal findings: 1) A significant symptom network exists within PLEs, where positive and negative PLEs are spatially distinct but but interrelated. Notably, P1 and AG (includes G1, G2, G3, and G4) emerged as the most influential nodes within this network. 2) This network exhibited age-related variations. Distinct network structures were evident between juveniles and adult cohorts. In juveniles, P3 and P1 manifested a stronger association compared to adults. 3) Bayesian network analyses indicated potential symptom propagation directions, pinpointing schizotypal personality disorder as the central node. Notably, most negative PLEs were precursors to positive PLEs. In essence, our findings corroborated the initial hypotheses, elucidating the interplay of positive and negative PLEs during the prodromal phase, delineating age-dependent network structural variations, and inferring the reasonable interaction relationships among the symptoms. Further network analysis results found that within the PLEs symptom network, positive and negative PLEs, while spatially distinct, were interrelated. P1 and P4 symptoms emerged as the most influential nodes. Earlier studies have underscored networks of psychotic experiences in the general populace, indicating proximity between positive and negative symptoms(Wüsten et al., 2018 ). However, due to varying questionnaires employed across different networks, results regarding centrality have been inconsistent. For instance, a survey targeting juveniles' self-harm ideation underscored positive symptoms, like auditory hallucinations and persecutory delusions, as central within the network(Núñez et al., 2018 ). Conversely, another study spotlighted the pivotal role of depression, anxiety, negative affect, and loneliness within the network, connecting maladaptive cognitive-emotional regulation with both loneliness adversity and PLEs(Qiao et al., 2023 ). Delusions might be unstable, and a direct correlation has been identified between acute cocaine use and delusion(Karsinti et al., 2020 ). Pertaining to negative symptom network studies, our research is groundbreaking, emphasizing the pivotal role of general negative symptoms, including sleep disturbances, dysphoric mood, motor disturbances, and impaired stress tolerance. These dimensions are evidently highly correlated with negative affect, intuitively suggesting their heightened centrality. The extensive symptom network connections reiterate the significance of early identification and intervention for psychotic disorders, underscoring the potential influence of general psychopathological symptoms on positive PLEs. Our investigation revealed substantial differences in PLEs symptom networks between juveniles and adults. While grandiosity and delusions experiences were more interrelated in juveniles, this association attenuated in adults. Specifically, in comparison to adults, juveniles demonstrated robust positive correlations between P3, P1, P5, and schizotypal personality disorder. Conversely, stronger negative correlations were observed between P2, P3 and P4. Such developmental disparities might mirror age-associated symptom expression variations and schizophrenia spectrum disorder manifestations(Jalbrzikowski et al., 2019 ). Juveniles might cultivate inflated self-perceptions as coping mechanisms against atypical hallucinations, while adults may grapple with amplified self-doubt and negative affect. These insights wield significant implications for age-specific evaluations and interventions for high-risk individuals(Flett & Hewitt, 2014 ). Overall, our discoveries underscore the necessity to factor in developmental aspects when discerning and preempting mental disorders across diverse age brackets. Another pressing global concern is the escalating suicide risk among juveniles. PLEs in conjunction with trauma and suicide (including suicidal ideation and non-suicidal self-injury) remain pivotal concerns. Suicidal ideation predominantly correlates with PLE hallucinations delusions(Núñez et al., 2018 ). Another study pinpointed a direct influence of PLEs on the duration and severity of Nonsuicidal Self-Injury (Misiak, Szewczuk-Bogusławska, et al., 2023 ), while yet another study indicated PLEs influencing non-suicidal self-injury and self-harm via depression(Zhou et al., 2023 ). A cross-sectional study on Chilean pre-clinical juveniles highlighted depressive symptoms as partial mediators between psychotic experiences and suicidal ideation(Núñez et al., 2021 ). Insomnia emerges as another potential risk factor for suicide among PLE patients, particularly correlating with experiences of acquaintanceship, hallucinations, and paranoia(Misiak, Gawęda, et al., 2023 ). Preceding PLEs, trauma might influence PLEs via cognitive biases and depressive symptoms(Gawęda et al., 2021 ). Additionally, leveraging experience sampling methodologies, daily stressors amplify PLE experiences, with nodes of lack of control and suspicion being susceptible to external influences(Klippel et al.). Direct correlations have been identified between PLEs and symptoms of obsessive-compulsive disorder, depression disorder, and attention-deficit/hyperactivity disorder(Rejek & Misiak, 2023 ). Depression seemingly plays a pivotal role in this chain. A study on depression and PLE network interpretations, although closely intertwined, spotlighted them as distinct symptoms (Granö et al., 2023 ). Other non-clinical research also suggests elevated levels of external attribution, the necessity for thought control, social cognitive impediments, and fantasy-based emotional regulation strategies might positively correlate with narcissistic individuals' PLE development(Misiak, Kowalski, et al., 2023 ). Another symptom characteristic is the reciprocal causal influence, wherein external factors intensify PLEs, PLE experiences induce distress, and distress exacerbates other risk factors. Peer bullying and rejection also correlate positively with hallucinatory experiences(Steenkamp et al., 2021 ). Within positive PLE symptoms, bizarre experiences and persecutory ideation positively correlate with anxiety, depression, and mood, while hallucinations only positively correlate with mood(Yang et al., 2023 ). However, PLE experiences don't invariably induce distress; even if statistically highly correlated, the primary modulating factor remains the degree of paranoia(Murphy et al., 2018 ). Furthermore, self-compassion negatively correlates with delusions and hallucinations(Scheunemann et al., 2019 ). Hence, we ought to draw inferences cautiously; our Bayesian network results furnish us with novel insights. Our Bayesian network analysis identified schizotypal personality disorder as the pivotal node influencing positive PLEs, either directly or indirectly. There is a close network connection between positive and negative symptoms, but we specifically point out that the positive symptom yellow receives mainly the influence of the negative symptom, and only one arrow from P2 points to the AD consists of the Personal Hygiene. A Bayesian network study on 902 British patients with psychotic experiences delineated dissociation within positive symptoms, potentially influencing hallucinations, and self-efficacy might influence responses to dissociation, with paranoid thought and delusions influencing sleep(Černis et al., 2022 ). This resonates with our findings, underscoring the intrinsic logic within positive symptoms. Our results complement this notion, suggesting negative symptoms might manifest earlier, thereby triggering positive symptoms. In our introduction, we had also alluded to earlier Bayesian network studies that showcased dissociation, regarded as a negative symptom, holding a dominant position within the network 15 . Nevertheless, Bayesian networks furnish hypothetical rather than definitive causal models, warranting further validation. Although not conclusively validated, these findings lend credence to the hypothesis postulating negative symptoms potentially influencing and driving positive symptom evolution in schizophrenia and the large sample size and extensive survey scope ensure the reliability of the results.Future prospective research is imperative to authenticate the symptom relationships postulated by our Bayesian modeling. However, we must acknowledge our study's limitations. Primarily, the comprehensiveness of the network in encompassing all pertinent variables remains debatable. Undetected causal influences of pivotal omitted variables, if absent from the network, constitute a significant concern. First, networks inferred from cross-sectional data can't discern potential symptom feedback loops. Longitudinal research remains essential to validate and expand upon our discoveries. Second, our data, anchored in self-report questionnaires, might be susceptible to response bias or measurement inaccuracies. Objective evaluative tools, such as neuroimaging, biochemical markers, or behavioral indicators, might proffer more accurate and reliable data. Third, our sample demographic, comprising college students from four Chinese cities, potentially curtails the broader applicability of our findings. Subsequent studies must strive to replicate our findings within more heterogeneous and representative cohorts. Last but far from least, some organic diseases (eg. epilepsy, brain tumor, vitamin deficits), drug abuse, unipolar, bipolar disorders and other personality disorders (eg. paranoid personality disorder, borderline personality disorder) can also present with minor psychoticlike experiences(Marques, 2020 ; Marques, 2021 ; Marques & Ouakinin, 2021 ), which was not considered in this study, future studies can be further explored. It is important to clarify that some authors that believe that schizotypal personality disorder is amputated form of schizophrenia, that do not progress to schizophrenia(Downhill Jr et al., 2001 ), others believe that the personality disorder with higher risk of developing schizophrenia is the schizoid personality disorder(Siever et al., 1990 ). We recognize that the relationship between schizoid personality disorder and schizophrenia is complex, However, our research goal is not to explore the relationship between the two, but to explore the relationship between schizoid personality disorder and Psychoticlike Experiences (PLEs), because PLEs are perceived as early indicators for the progression to schizophrenia spectrum disorders, The exploration of PLEs and schizoid personality disorder helps to provide initial screening and understanding to a wider population at a lower cost, thus providing the basis for resource allocation and follow-up research. This study is intended to provide fundamental data and direction for further research in the future, not as a diagnostic tool. while a simple scale cannot provide the depth of information needed to identify high-risk patients, it can provide initial screening and understanding to a broader population at lower cost and resource consumption. This approach helps to identify groups that may need further assessment, thereby informing resource allocation and follow-up research. In conclusion, our research underscores the potential interplay between positive and negative PLEs, laying the groundwork for speculation on causality, suggesting that the manifestations of negative symptoms, general psychopathological symptoms, and their bearings on positive psychotic experiences necessitate significant attention in schizophrenia spectrum disorders' prevention. These revelations proffer fresh perspectives for comprehending and intervening in these complex and debilitating conditions. Declarations Funding Zheng Lu was supported by Ministry of Science and Technology of China (2016YFC1306805), the Science and Technology Commission of Shanghai Municipality (21Y21900700); Fei Liu was supported by Shanghai Municipal Commission of Health and Family Planning (20214Y0295); Qiang Hu was supported by Jiangsu University Medical Education Collaborative Innovation Fund (JDYY2023088), Zhenjiang social development guiding science and technology plan project (FZ2022116), Clinical Medical Research Conversion Special, Anhui Key Research and Development Program (202204295107020065), Scientific Research Project of Anhui Provincial Health Commission (AHWJ2022b096). Statement of Ethics This study protocol was reviewed and approved by Shanghai Tongji Hospital Ethics Committee, approval number [2020-031]. Conflict of Interest Statement The authors report no competing interests. Data availability The observation datasets and all code used for this study is available at open science framework (https://osf.io/um53p/). Acknowledge Thanks are due to all the collectors who participated in the collection of this study and to the participants who made suggestions for the study. Author Contributions Fei Liu: Data Curation, Writing- Reviewing, Editing and Funding acquisition. Zhao-Qi Wang: Visualization, Software, Writing - Original Draft. Jiaxin Wu: Data curation, Conceptualization, Writing - Review & Editing. Xiang-yun Long: Investigation and Resources. An-si Qi: Investigation and Resources. Xiao-feng Guan: Investigation and Resources. Xin-yi Hu: Investigation and Resources. Mao-rong Hu: Investigation and Resources. Shi-ping Xie: Investigation and Resources. Hui Zheng: Conceptualization, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Supervision. Qiang Hu: Project administration and Funding acquisition. Zheng Lu: Resources, Funding acquisition. References Astill Wright, L., McElroy, E., Barawi, K., Roberts, N. P., Simon, N., Zammit, S., & Bisson, J. I. (2023). Associations among psychosis, mood, anxiety, and posttraumatic stress symptoms: A network analysis. Journal of Traumatic Stress , 36 (2), 385-396. Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry , 16 (1), 5-13. Černis, E., Evans, R., Ehlers, A., & Freeman, D. (2021). Dissociation in relation to other mental health conditions: An exploration using network analysis. Journal of psychiatric research , 136 , 460-467. Černis, E., Molodynski, A., Ehlers, A., & Freeman, D. (2022). Dissociation in patients with non-affective psychosis: Prevalence, symptom associations, and maintenance factors. Schizophrenia Research , 239 , 11-18. Costenbader, E., & Valente, T. W. (2003). The stability of centrality measures when networks are sampled. Social Networks , 25 (4), 283-307. https://doi.org/10.1016/s0378-8733(03)00012-1 Dickson, H., Calkins, M. E., Kohler, C. G., Hodgins, S., & Laurens, K. R. (2014). Misperceptions of facial emotions among youth aged 9–14 years who present multiple antecedents of schizophrenia. Schizophrenia bulletin , 40 (2), 460-468. Downhill Jr, J. E., Buchsbaum, M. S., Hazlett, E. A., Barth, S., Roitman, S. L., Nunn, M., . . . Mitropoulou, V. (2001). Temporal lobe volume determined by magnetic resonance imaging in schizotypal personality disorder and schizophrenia. Schizophrenia Research , 48 (2-3), 187-199. Flett, G. L., & Hewitt, P. L. (2014). A proposed framework for preventing perfectionism and promoting resilience and mental health among vulnerable children and adolescents. Psychology in the Schools , 51 (9), 899-912. Gawęda, Ł., Pionke, R., Hartmann, J., Nelson, B., Cechnicki, A., & Frydecka, D. (2021). Toward a complex network of risks for psychosis: combining trauma, cognitive biases, depression, and psychotic-like experiences on a large sample of young adults. Schizophrenia bulletin , 47 (2), 395-404. Granö, N., Lintula, S., Therman, S., Marttunen, M., Lång, U., & Ranta, K. (2023). Factor and network analysis of psychosis‐like experiences and depressive symptoms in a sample of Finnish adolescents entering psychiatric services. Early Intervention in Psychiatry . Hajdúk, M., Straková, A., Januška, J., Ivančík, V., Dančík, D., Čavojská, N., . . . Abplanalp, S. J. (2023). Connections between and within extended psychosis and autistic phenotypes and social relationships in the general population. Journal of psychiatric research , 157 , 36-42. Hfner, H., & Maurer, K. (2006). Early detection of schizophrenia: Current evidence and future perspectives. World psychiatry: official journal of the World Psychiatric Association (WPA) , 5 (3), 130-138. Isaksson, J., Angenfelt, M., Frick, M. A., Olofsdotter, S., & Vadlin, S. (2022). Psychotic-like experiences from adolescence to adulthood: A longitudinal study. Schizophrenia Research , 248 , 1-7. Jalbrzikowski, M., Murty, V. P., Tervo-Clemmens, B., Foran, W., & Luna, B. (2019). Age associated deviations of amygdala functional connectivity in psychosis spectrum youth: relevance to psychotic symptoms. The American journal of psychiatry , 176 (3), 196. Jauhar, S., Johnstone, M., & Mckenna, P. J. (2022). Schizophrenia. The Lancet (10323), 473-486. Karsinti, E., Labaeye, M., Piani, K., Fortias, M., Brousse, G., Bloch, V., . . . Vorspan, F. (2020). Network analysis of psychotic manifestations among cocaine users. Journal of psychiatric research , 130 , 300-305. Klippel, A., Viechtbauer, W., Reininghaus, U., & Wigman, J. The cascade of stress: a network approach to explore differential dynamics in populations varying in risk for psychosis Running title: The cascade of stress–a network approach. Knoll, J., & Dietz, P. (2023). Mass violence and the complex spectrum of mental illness and mental functioning. JAMA psychiatry , 80 (2), 186-188. Laurens, K. R., Hodgins, S., Maughan, B., Murray, R. M., Rutter, M. L., & Taylor, E. A. (2007). Community screening for psychotic-like experiences and other putative antecedents of schizophrenia in children aged 9–12 years. Schizophrenia Research , 90 (1-3), 130-146. Lee, K. W., Chan, K. W., Chang, W. C., Lee, E. H. M., Hui, C. L. M., & Chen, E. Y. H. (2016). A systematic review on definitions and assessments of psychotic‐like experiences. Early Intervention in Psychiatry , 10 (1), 3-16. Lindgren, M., Numminen, L., Holm, M., Therman, S., & Tuulio-Henriksson, A. (2022). Psychotic-like experiences of young adults in the general population predict mental disorders. Psychiatry Research , 312 , 114543. Loewy, R., Pearson, R., Bearden, C., Zinberg, J., Vinogradov, S., & Cannon, T. (2012). Longitudinal follow-up of screening for clinical-high-risk for psychosis with the Prodromal Questionnaire, Brief Version (PQ-B) [Meeting Abstract]. Early Intervention in Psychiatry , 6 , 2-2. ://WOS:000308580100006 Maier, W., Falkai, P., & Wagner, M. (1999). Schizophrenia spectrum disorders: a review. Schizophrenia , 2 , 311-405. Marques, J. G. (2020). Organic psychosis causing secondary schizophrenia in one-fourth of a cohort of 200 patients previously diagnosed with primary schizophrenia. The primary care companion for CNS disorders , 22 (2), 27065. Marques, J. G. (2021). Revisiting the Concepts of Secondary Schizophrenia and Pseudoschizophrenia. Ordem dos Medicos . Marques, J. G., & Ouakinin, S. (2021). Schizophrenia–schizoaffective–bipolar spectra: an epistemological perspective. CNS Spectr (3). McGrath, J. J., Saha, S., Al-Hamzawi, A., Alonso, J., Bromet, E. J., Bruffaerts, R., . . . Fayyad, J. (2015). Psychotic experiences in the general population: a cross-national analysis based on 31 261 respondents from 18 countries. JAMA psychiatry , 72 (7), 697-705. Misiak, B., Gawęda, Ł., Moustafa, A. A., & Samochowiec, J. (2023). Insomnia moderates the association between psychotic-like experiences and suicidal ideation in a non-clinical population: a network analysis. European Archives of Psychiatry and Clinical Neuroscience , 1-9. Misiak, B., Kowalski, K., Jaworski, A., Świrkosz, G., Szyszka, M., & Piotrowski, P. (2023). Understanding pathways from narcissistic grandiosity to psychotic-like experiences: Insights from the network analysis. Journal of psychiatric research . Misiak, B., Szewczuk-Bogusławska, M., Samochowiec, J., Moustafa, A. A., & Gawęda, Ł. (2023). Unraveling the complexity of associations between a history of childhood trauma, psychotic-like experiences, depression and non-suicidal self-injury: a network analysis. Journal of psychiatric research (166), 122-129. Murphy, J., McBride, O., Fried, E., & Shevlin, M. (2018). Distress, impairment and the extended psychosis phenotype: a network analysis of psychotic experiences in an US general population sample. Schizophrenia bulletin , 44 (4), 768-777. Nelson, B., Amminger, G. P., Bechdolf, A., French, P., & Mcgorry, P. D. (2019). Evidence for preventive treatments in young patients at clinical high risk of psychosis: the need for context. The Lancet Psychiatry , 7 (5), 378-380. Núñez, D., Fresno, A., Van Borkulo, C., Courtet, P., Arias, V., Garrido, V., & Wigman, J. (2018). Examining relationships between psychotic experiences and suicidal ideation in adolescents using a network approach. Schizophrenia Research , 201 , 54-61. Núñez, D., Monjes, P., Campos, S., & Wigman, J. T. (2021). Evidence for specific associations between depressive symptoms, psychotic experiences, and suicidal ideation in Chilean adolescents from the general population. Frontiers in Psychiatry , 11 , 552343. Pontillo, M., Averna, R., Tata, M. C., Chieppa, F., Pucciarini, M. L., & Vicari, S. (2021). Neurodevelopmental Trajectories and Clinical Profiles in a Sample of Children and Adolescents With Early- and Very-Early-Onset Schizophrenia [Article]. Frontiers in Psychiatry , 12 , Article 662093. https://doi.org/10.3389/fpsyt.2021.662093 Puga, J. L., Krzywinski, M., & Altman, N. (2015). Points of Significance: Bayesian networks. Nature Methods , 12 (9), 799-800. Qiao, Z., Lafit, G., Lecei, A., Achterhof, R., Kirtley, O. J., Hiekkaranta, A. P., . . . Reininghaus, U. (2023). Childhood Adversity and Emerging Psychotic Experiences: A Network Perspective. Schizophrenia bulletin , sbad079. Rejek, M., & Misiak, B. (2023). Dimensions of psychopathology associated with psychotic-like experiences: Findings from the network analysis in a nonclinical sample. European Psychiatry , 66 (1), e56. Rosengard, R. J., Malla, A., Mustafa, S., Iyer, S. N., Joober, R., Bodnar, M., . . . Shah, J. L. (2019). Association of Pre-onset Subthreshold Psychotic Symptoms With Longitudinal Outcomes During Treatment of a First Episode of Psychosis. JAMA psychiatry , 76 (1), 61-70. https://doi.org/10.1001/jamapsychiatry.2018.2552 Scheunemann, J., Schlier, B., Ascone, L., & Lincoln, T. M. (2019). The link between self‐compassion and psychotic‐like experiences: A matter of distress? Psychology and Psychotherapy: Theory, Research and Practice , 92 (4), 523-538. Siever, L. J., Silverman, J. M., Horvath, T. B., Klar, H., Coccaro, E., Keefe, R. S., . . . Davis, K. L. (1990). Increased morbid risk for schizophrenia related disorders in relatives of schizotypal personality disordered patients. Archives of General Psychiatry , 47 (7), 634-640. Starzer, M., Hansen, H. G., Hjorthøj, C., Albert, N., Nordentoft, M., & Madsen, T. (2023). 20‐year trajectories of positive and negative symptoms after the first psychotic episode in patients with schizophrenia spectrum disorder: results from the OPUS study. World Psychiatry , 22 (3), 424-432. Steenkamp, L. R., Tiemeier, H., Bolhuis, K., Hillegers, M. H., Kushner, S. A., & Blanken, L. M. (2021). Peer‐reported bullying, rejection and hallucinatory experiences in childhood. Acta Psychiatrica Scandinavica , 143 (6), 503-512. van der Wal, J. M., van Borkulo, C. D., Deserno, M. K., Breedvelt, J. J., Lees, M., Lokman, J. C., . . . Smidt, M. P. (2021). Advancing urban mental health research: From complexity science to actionable targets for intervention. The Lancet Psychiatry , 8 (11), 991-1000. Van Os, J., Linscott, R. J., Myin-Germeys, I., Delespaul, P., & Krabbendam, L. (2009). A systematic review and meta-analysis of the psychosis continuum: evidence for a psychosis proneness–persistence–impairment model of psychotic disorder. Psychological medicine , 39 (2), 179-195. Wüsten, C., Schlier, B., Jaya, E. S., Fonseca-Pedrero, E., Peters, E., Verdoux, H., . . . Lincoln, T. M. (2018). Psychotic experiences and related distress: a cross-national comparison and network analysis based on 7141 participants from 13 countries. Schizophrenia bulletin , 44 (6), 1185-1194. Yang, X.-H., Zhang, J.-w., Li, Y., Zhou, L., & Sun, M. (2023). Psychotic-like experiences as a co-occurring psychopathological indicator of multi-dimensional affective symptoms: Findings from a cross-sectional survey among college students. Journal of affective disorders , 323 , 33-39. Yang, Y., Shen, D., Wang, J., & Yang, J. (2002). The Reliability and Validity of PDQ-4+ in China. Chinese Journal of Clinical Psychology (03), 165-168. Zhou, H.-Y., Luo, Y.-H., Shi, L.-J., & Gong, J. (2023). Exploring psychological and psychosocial correlates of non-suicidal self-injury and suicide in college students using network analysis. Journal of affective disorders . Tables Table 1. The degree, closeness and betweenness of variable nodes in the present network and bridge strength, bridge betweenness and bridge closeness between variable nodes and communities. Nodes Degree Closeness Betweenness Communities Bridge. Strength Bridge. Betweenness Bridge. Closeness P1 1.40 0.01 32 PDQ 0.05 0.42 0.07 P2 0.89 0.01 2 PDQ 0.04 0 0.05 P3 0.69 0.01 4 PDQ 0.04 0 0.05 P4 0.93 0.01 0 PDQ 0.01 0 0.06 P5 0.82 0.01 0 PDQ 0.03 0 0.05 PDQ 0.68 0.01 0 PDQ 0.05 0 0.06 AN 0.93 0.01 0 Neg 0.03 0 0.08 AD 0.34 0.01 4 Neg 0.02 0.13 0.06 AG 1.43 0.01 26 Neg 0.07 0.29 0.08 Age 0.26 0 0 Age 0.03 0 0.03 Additional Declarations No competing interests reported. Supplementary Files Supplementmaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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02:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6215745/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6215745/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78732680,"identity":"183be66f-c990-49bd-90fd-7c76ae3d4b22","added_by":"auto","created_at":"2025-03-18 07:50:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":318165,"visible":true,"origin":"","legend":"\u003cp\u003eMulticenter cross-sectional study collection flowchart. First, we collected 45420 subjects from Jiangxi, Jiangsu, Shanghai and other regions participated in the questionnaire survey; Then, 7,944 subjects who answered carelessly and younger than 12 and older than 35 were eliminated, and the remaining subjects were divided into young group (N= 3623, aged 12-17 years old) and old group (N= 33820, 18-35 years old); Next, all the symptoms that predicted schizotypal personality disorder were divided into age, negative symptoms and positive symptoms; Finally, the network model was used to statistic how each symptom predicted schizotypal personality disorder and its correlation coefficient.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6215745/v1/3581726a124689dbd78d7654.png"},{"id":78732682,"identity":"c6c8f70e-d426-484f-b8cc-7073c361c7c2","added_by":"auto","created_at":"2025-03-18 07:50:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":358216,"visible":true,"origin":"","legend":"\u003cp\u003eThe Symptom network of psychotic-like experiences. (a). Network structure of positive symptoms (P1-P5), Negative symptoms (AN), Disorganization symptom (AD), General symptoms (AG), Age, and Schizotypal Personality Disorder. Blue edges represent positive correlations, and red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. The white circle is the predictable value of each node. The cut-off value is set to be 0.05. (b). Network centrality plot of Psychosis-Risk and Personality Diagnostic symptoms depicts the strength, closeness, betweenness, and expected influence of variables selected in the present network (z-score). Strength, as the primary centrality measure, represents the sum of edge weights of each node, reflecting the possibility of one symptom activating another; Closeness measures the average distance between a node and all other nodes in the network by calculating the reciprocal of all shortest path lengths between the node and all other nodes; Betweenness is the calculation of the shortest path length of any two symptoms, and symptoms with high intermediation can be thought of as Bridges to other symptoms; Expected influence represents the percentage of variance in a given node that can be predicted by its adjacent edges. (c). Network Bridge plot of Psychosis-Risk and Personality Diagnostic symptoms depicts the strength, betweenness and closeness between different communities (Age, AN, AD, AG and Schizotypal Personality Disorder) and different symptom node. Note: P1 is delusions, P2 is Persecutory Ideas, P3 is Grandiosity, P4 is hallucinations, P5 is Disorganized Communication\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6215745/v1/b1a3d72f064ada9c031674ab.png"},{"id":78732692,"identity":"407fd791-68ed-45f6-b493-489a0a33d47b","added_by":"auto","created_at":"2025-03-18 07:50:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166112,"visible":true,"origin":"","legend":"\u003cp\u003eThe difference of psychoticlike experiences symptom network between juveniles group (a) and adults group (b) in positive symptoms (P1-P5), Negative symptoms (AN), Disorganization symptom (AD), General symptoms (AG), Age, and Schizotypal Personality Disorder. There were significant differences in the symptom networks between the juveniles group (12-17 years old) and adults group (18-35 years old). Blue edges represent positive correlations, and red edges represent negative correlations.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6215745/v1/1a908f2f682b1d661b335c26.png"},{"id":78734314,"identity":"50b19f8f-491c-4e39-8e55-5f90e7fd4c17","added_by":"auto","created_at":"2025-03-18 07:58:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":475341,"visible":true,"origin":"","legend":"\u003cp\u003eThe Symptom Bayesian network of PLE. (a). Network structure of positive symptoms (P1-P5), Negative symptoms (AN), Disorganization symptom (AD), General symptoms (AG), Age, and Schizotypal Personality Disorder. The arrows indicate the direction of potential causation. (b). The thickness of the green arrows reflects the magnitude of the correlation.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6215745/v1/25be2fc36dcb2cc6c0e28880.png"},{"id":86659809,"identity":"a3da0aa4-846c-4b9d-83a7-20677a5302d7","added_by":"auto","created_at":"2025-07-14 10:32:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1835393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6215745/v1/30091123-2ab2-44af-94a2-ef8b59ad0869.pdf"},{"id":78732687,"identity":"3bc02858-7f3b-4eed-8e8e-dfaefa781060","added_by":"auto","created_at":"2025-03-18 07:50:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":325145,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6215745/v1/309919a2cc36160750a81692.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Symptom and Bayesian network analyses of positive and negative symptoms in psychotic- like experiences: A multicenter cross-sectional study of Chinese students at 19 cities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSchizophrenia spectrum disorders and schizotypal personality disorder are precursors of schizophrenia(Maier et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Schizophrenia spectrum disorders are schizophrenia-like disorders which are not fulfilling the diagnostic criteria for schizophrenia but which are sharing symptoms, causes and risk factors with schizophrenia, representing a collection of chronic and severe mental conditions affecting millions globally(Jauhar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Schizotypal personality disorder refers to some people who are vulnerable to schizophrenia as an enduring personality condition. The pathogenesis of these disorders is perceived as developmental; both the pre-psychotic prodromal phase (with an average duration of 4.8 years) and the pre-psychotic stage (lasting on average 1.3 years) have been recognized(Hfner \u0026amp; Maurer, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Attenuated psychotic symptoms are a defining characteristic of the prodromal phase, and individuals exhibiting these symptoms are classified as being at Clinical High Risk (CHR) or Ultra-High Risk (UHR) for psychosis(Rosengard et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The CHR/UHR states have garnered significant research attention in recent years, aiming to preemptively address schizophrenia spectrum disorders prior to their onset(Nelson et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Yet, the effective diagnosis of the developmental trajectory of schizophrenia remains a formidable challenge. There is also a lack of nodes regarding wether symptom\u0026ndash;like experiences be used as an effective means of diagnosing schizophrenia spectrum disorders.\u003c/p\u003e \u003cp\u003eSymptoms stand as the most direct indicators in assessing the progression of schizophrenia spectrum disorders, including among these are Psychoticlike Experiences (PLEs) such as hallucinations and delusions, considered early markers for schizophrenia(Knoll \u0026amp; Dietz, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the transition from PLEs in the general population to the CHR state remains inadequately understood. Generally, research on the prevalence of PLEs in the healthy population indicates that between 5\u0026ndash;10% of individuals report experiencing PLEs(McGrath et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Van Os et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Analogous to psychotic symptoms, PLEs can be broadly categorized into positive and negative domains(Lee et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Starzer et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Positive PLEs encompass delusions and hallucinations, whereas negative PLEs pertain to subtle or transient deficits in normal functionality, manifesting as social difficulties, dysthymia, anhedonia, or avolition. Historically, research gravitated towards positive PLEs due to their perceived greater relevance to the subsequent development of schizophrenia. It is worth noting that PLEs can be seen as a sign of mental disorders among young adults and even juveniles in the general population(Isaksson et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lindgren et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, some studies have suggested that negative PLEs, like social difficulties and dysthymia, might play a more significant role in distress associated with the progression of schizophrenia(Dickson et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Laurens et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). It's theorized that, over time, PLEs might exhibit reciprocal causality, interacting in a mutually reinforcing and feedback-driven manner. To sum up, gaining clarity on the nature and trajectory of this relationship is paramount for the early detection and prevention of schizophrenia spectrum disorders, and there may be differences in PLEs symptoms' prediction of schizotypal personality disorder among juveniles and adults. Yet, despite a plethora of studies on healthy populations, our grasp on the interrelationships within positive and negative symptoms of PLEs and the contrast between their relationship with schizotypal personality disorder in juveniles and adults remains unclear.\u003c/p\u003e \u003cp\u003eOne avenue to explore these complex symptom relationships is via network analysis. This method visualizes symptoms as nodes and their interrelations as edges in a graphical model(Borsboom, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), illuminating the structure and dynamics of symptom networks and highlighting the most central or influential symptoms within the network(van der Wal et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous applications of the Least Absolute Shrinkage and Selection Operator (LASSO) predominantly focused on the general non-clinical population and often integrated other scales in relation to PLEs. Among the 22 published network analyses to date (as of September 2023), a mere three studies differentiated between positive and negative symptoms within their networks. For instance, a network analysis of positive and negative PLEs from 7141 participants across 13 countries highlighted auditory hallucinations and Capgras delusions as highly central positive symptoms(W\u0026uuml;sten et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Another British study explaind the interplay between negative and positive symptoms and post-traumatic stress, however, the study did not include all negative symptoms in its analysis. Another British study revealed psychotic experiences (i.e., hallucinations and delusions) were central to the network\u0026mdash;more so than negative symptoms of psychosis\u0026mdash;and expressed high strength centrality, possibly highlighting the particularly debilitating nature of positive symptoms of psychosis(Astill Wright et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), In Slovakia, the negative symptoms and positive symptoms were examined more comprehensively in the research and revealed that negative symptoms, through impaired social functionality, were connected to positive symptoms (Hajd\u0026uacute;k et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, network analyses rooted in correlation or regression methodologies are limited in inferring causal relationships between symptoms, constraining their explanatory scope. A promising alternative is Bayesian network analysis, a methodology estimating the most probable causal structure elucidating observed data, grounded in prior knowledge and probabilistic inference(Puga et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). With regard to cross-sectional Bayesian network research, studies remain scarce. An analysis of 6941 pre-clinical British participants, while measuring positive symptoms like grandiosity, paranoia, hallucinations, and cognitive disarray, also employed the Černis Felt Sense of Anomaly (ČEFSA) scale to gauge dissociation. This scale, predominantly detailing \"subtraction\" phenomena from normal perception or experience, leans more towards negative symptoms. Both LASSO and Bayesian networks revealed dissociation as the most central trait, mediating influences on paranoia, cognitive disarray, and grandiosity(Černis et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, a comprehensive elucidation of the interrelationships and influence directions between positive and negative PLEs remains elusive.\u003c/p\u003e \u003cp\u003eIn this study, our objective is to navigate the symptom network relationships of PLEs within non-clinical students, deemed a pivotal characteristic in the premorbid phase of schizophrenia spectrum disorders. Age, another potentially influential factor, will be considered across two groups: juveniles (12\u0026ndash;18 years old) and emerging adults (19\u0026ndash;35 years old). Our core aims encompass: 1) Describing the relationship of PLE symptoms. 2) Contrasting the symptom network structures of PLEs between juveniles and adults. 3) Inferring the causal interplay between positive and negative PLEs. To realize these aims, we conducted LASSO network analysis and Bayesian network analysis across nine dimensions of three questionnaires. These evaluated positive symptoms, negative symptoms, and schizotypal personality disorder within a sample of college students spanning 19 institutions across three Chinese cities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eProcedure\u003c/p\u003e \u003cp\u003eTo ensure the representativeness of the research samples, we selected four provinces (Shanghai, Jiangsu, Jiangxi, and Guangdong province) for this study from September 2017 to November 2019. We chose 19 universities from these provinces and randomly selected 45,420 college students by the class as a unit (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for more details). We distributed paper or electronic versions of the questionnaire uniformly in the class. When taking online surveys, participants sometimes respond consistently to items without regard to their content. These types of responses, referred to as careless or insufficient effort responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing. careless data is deleted when at least one item in the test is marked as being consistent and significant by \"careless package\" in R. Considering that PLE is most prevalent in juveniles and early adulthood, 7944 subjects who answered carelessly and younger than 12 and older than 35 were eliminated, and the remaining subjects were divided into young group (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3623 subjects 12\u0026ndash;17 years old, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.71, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66) and old group (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33820, 18\u0026ndash;35 years old, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18.93, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00). All participants fully understood the objectives, process, benefits and risks of this study before participating in the evaluation and signed paper or electronic informed consent forms. In addition, for minor participants who agreed to participate in this study, their parents or other legal guardians signed the informed consent forms. To protect the privacy of the participants, all survey results were only used for analysis and this study had not disclosed to the school counselors, students or other personnel.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study used a multicenter design to increase the generalizability of the results (see Table S2 for details). A total of 3 study centers, mainly located in the eastern region, were involved, involving more than 20 vocational, college, high school, and undergraduate students. Researchers at all centers received uniform training to ensure standardization of intervention delivery. The sample size for each center is estimated to be 8,000 individuals. Subject screening criteria and procedures were standardized across centers. Random assignment sequences for questionnaire collection were generated centrally at random and stratified by center. A uniform symptom assessment scale and questionnaire are used across centers, and a central database collects all data. Center effects will be assessed and adjusted in our analyses. Centers will be monitored and audited by the central team on a regular basis to ensure standardized implementation of the study. The use of a multicenter design increases the breadth of results, and randomized stratification ensures comparability of results across centers.\u003c/p\u003e \u003cp\u003eMeasurements\u003c/p\u003e \u003cp\u003eProdromal Questionnaire - Brief version\u003c/p\u003e \u003cp\u003eProdromal Questionnaire - Brief version (PQ-B) is a self-rating scale was used to assess positive symptoms and includes 21 items with a 5-level score based on yes answer (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree, 0\u0026thinsp;=\u0026thinsp;no)(Loewy et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The scale consists of the following five dimensions: Delusions (P1) includes items 1, 4\u0026ndash;5 and 11\u0026ndash;17; Persecutory Ideas (P2) includes items 8 and 18; Grandiosity (P3) includes item 7, hallucinations (P4) includes items 2\u0026ndash;3, 9\u0026ndash;10 and 19\u0026ndash;20, Disorganized Communication (P5) includes items 6 and 21 (see Table S3 for details). The score for each dimension is the average score for its corresponding items (e.g., the score of Persecutory Ideas (P2) is the mean of the items 8 and 18). In this study, the scale coefficient of internal consistency Cronbach\u0026rsquo; s alpha was 0.93, indicating a good questionnaire reliability.\u003c/p\u003e \u003cp\u003eQuestionnaire for negative symptoms, disorganization symptoms and general symptoms\u003c/p\u003e \u003cp\u003eThis tool is a self-rating scale made by ourselves in this study, which is derived from some questions in the negative symptoms, disorganization symptom and general symptoms of Scale of Psychosis-risk Symptoms (SOPS)(Pontillo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The scale includes 19 items in total with a 3-point Likert scale ranging from 0 (no), 1 (uncertain) to 2 (yes). The negative symptoms (AN) consist of the following six dimensions: Social anhedonia (N1) includes items 1 and 2, Avolition (N2) includes items 3 and 4; Expression of emotion (N3) includes item 5, Experience of Emotions and Self (N4) includes item 6, Ideational Richness (N5) includes items 7 and 8; Occupational Functioning (N6) includes items 9 and 10. The disorganization symptom (AD) consists of the Personal Hygiene (D4) which includes item 11. The general symptoms (AG) consist of the following four dimensions: Sleep Disturbance (G1) includes item 12, Dysphoric Mood (G2) includes item 13\u0026ndash;17, Motor Disturbances (G3) includes item 18, Impaired Tolerance to Normal Stress (G4). The score for each symptom is the average score for its corresponding items (e.g., the score of social anhedonia (N1) is the mean of the items 1 and 2, the score of negative symptoms (AN) is the total mean of the items N1-N5). In this study, coefficient of internal consistency Cronbach\u0026rsquo; s α\u0026thinsp;=\u0026thinsp;0.84, indicating a good questionnaire reliability. Exploratory factor analysis showed good structure validity, Kaiser-Meyer-Olkin (KMO)value of 0.708(The closer the KMO value is to 1, it means that the correlation between variables is stronger, and the original variable is more suitable for factor analysis), the total extract six common factor, can explain 65.6% of the total variance.\u003c/p\u003e \u003cp\u003ePersonality Diagnostic Questionnaire for Schizotypal Personality Disorder\u003c/p\u003e \u003cp\u003eWe used the Chinese version of the Personality Diagnostic Questionnaire for Schizotypal Personality Disorder (PDQ-SPD) to evaluate the schizotypal personality disorder(Yang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The includes 9 items with a 2-level score based on participant\u0026rsquo; s answered \u0026ldquo;yes\u0026rdquo; (1 point) or \u0026ldquo;no\u0026rdquo; (0 point). The score is the sum of the scores for each item, the higher the total score, the more significant the symptoms of schizoid personality disorder. In this study, the internal consistency coefficient Cronbach\u0026rsquo;s α of this scale was 0.67.\u003c/p\u003e \u003cp\u003eData processing and statistical analysis\u003c/p\u003e \u003cp\u003eWe used R (v 4.2.3) for data processing and network comparison. We used the \u0026ldquo;usf\u0026rdquo; package (a new version of the 'userfriendlyscience' package which contains a number of basic functions to create higher level plots) to screen for response inattentiveness. For the data set of PLEs, we initially had 45420 participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.), but we excluded 4282 participants who did not fill in their age or were outside the age range of 12 to 35 years old. We also excluded 3695 participants who filled in the questionnaire carelessly, based on the reaction time incorporated into the careless package. Finally, we included 37443 internet users in the main analysis.\u003c/p\u003e \u003cp\u003eNetwork construction\u003c/p\u003e \u003cp\u003eWe applied the Graphical lasso (Glasso) network (regularized partial correlation network) method to estimate the symptom networks, the quickNet R package is used in this process, which integrates bootnet, qgraph, and other packages. The symptom network analysis approach focuses on which symptom activation is more likely to activate other symptoms in the network. Four common measures of centrality are strength, closeness, betweenness and expected influence. We evaluate the stability of the symptom network by using subset bootstrap procedure (repeatedly correlate the centrality of a subset of the decreasing sample size with the centrality of the original sample)(Costenbader \u0026amp; Valente, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). To evaluated the stability of edge weights in each network, we firstly calculate non-parametric bootstrap confidence intervals and checking for differences in strength between samples by using R package. Second, we calculate the correlation stability coefficient (CS coefficient), which indicates that the maximum sample size can be reduced in the process of subset bootstrapping while maintaining 95% probability of the correlation between the property of interest and the property in the full data set. The CS coefficient of 0.7 or higher is recommended, while 0.25 or lower is discarded.\u003c/p\u003e \u003cp\u003eTo compare connectivity differences among symptoms between young (N\u0026thinsp;=\u0026thinsp;3623) and old (N\u0026thinsp;=\u0026thinsp;33820) group\u0026rsquo; s networks, NCT would be used for statistically assessing the difference between 2 groups (young and old groups) by repeatedly (5000 times) for randomly regrouped individuals. Differences observed below the 0.05 threshold are considered significant.\u003c/p\u003e \u003cp\u003eBayesian network\u003c/p\u003e \u003cp\u003eIn order to ensure the stability of the Directed Acyclic Graph (DAG), we used the bootstrap method (50000 bootstrap samples from a single original sample, with replacement) to obtain the final DAG structure. Firstly, we apply the optimal cut-point approach for retaining edges to obtain a DAG network with both high specificity and sensitivity. If 51% or more of the edges in 50000 bootstrap DAG network are in the same direction (e.g., pointed from symptom A to symptom B), the directional edge will be represented in the final DAG network using an arrow pointing from symptom A to symptom B. The iamb.fdr algorithm may be more suitable for sparse network with fewer nodes. If the number of nodes exceeds 50 and the number of edges exceeds 200, then the hc algorithm can be considered, and if the actual number of edges is less than 5 and the edge density is less than 0.1, it can be regarded as a sparse network. In fact, our network actually has 32 connected edges, and the maximum edge density is (10 * 9) / 2\u0026thinsp;=\u0026thinsp;45 edges, involving 10 variables, which can be considered as a medium-sized small dense network suitable for hc algorithm. The conditional independence test is used to learn the skeleton of the network, and then the greedy mountain climbing method is used to optimize the direction of the edge. Applies to both discrete and continuous variables. Then, we fit the DAG network structure obtained from the data domain and estimate the arc intensity through bootstrap 50000 times; Finally, we visualized the average intensity using qgraph.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSymptom network analysis results\u003c/p\u003e \u003cp\u003eTo explore the symptom network relationship of PLEs, we conducted network analysis on 9 dimensions of 3 questionnaires. The results showed that there was a significant symptom network among all symptoms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) and the z-scored centrality indices appear in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and Table\u0026nbsp;1. Positive and negative PLEs were separated spatially and each closely connected internally. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, P1 (strength\u0026thinsp;=\u0026thinsp;1.381) and AG (strength\u0026thinsp;=\u0026thinsp;1.412) had the highest node strength. We found that there was a clear association relationship within the symptoms. In the negative domains, negative PLEs and AG were closely related, with an edge weight of 0.72. Among the positive domains, P1 and P4 were most closely related, with an edge weight of 0.52. Schizotypal personality disorder node also had extensive associations, but mainly with AG. It\u0026rsquo; s worth noting that there seems to be a relationship between PLEs and age. Age node was only positively correlated with negative PLEs, and negatively correlated with positive PLEs and schizotypal personality disorder. The supplementary materials show that all networks were relatively stable, with the larger the sample size, betweenness, closeness and strength have stronger average correlation with the original sample (see Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In our person-dropping stability analysis, we found CS-coefficients of 0.75 for our betweenness, closeness, and strength centrality metrics, respectively. Each of these values is greater than the recommended minimum threshold of 0.25, suggesting that our centrality estimates are stable. Consistent with this finding, using the bootstrapped difference test in bootnet, we found that there were significant differences between node strength of the network (see Figure S2-S4).\u003c/p\u003e\n\u003ch3\u003eInsert Table 1\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eAge group symptom network differences\u003c/p\u003e \u003cp\u003eWe also compared the symptom networks between juveniles group (12\u0026ndash;17 years old) and adults\u0026rsquo; group (18\u0026ndash;35 years old). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that there were significant differences in the symptom networks between the two age groups. Specifically, P3, AG, P1, P5 and schizotypal personality disorder in juveniles group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) were more positively correlated, while P3, P4, P1 and P2 were more negatively correlated in adults group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInsert Fig. 3\u003c/h3\u003e\n\u003cp\u003eBayesian network modeling results\u003c/p\u003e \u003cp\u003eTo further speculate on the possible relationship between negative and positive domains in PLEs, we used Bayesian network estimation method to conduct statistics. The learned Bayesian network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) contained 10 nodes and 32 directed edges whereby edge thickness signifies confidence that direction of prediction (and potentially causation) flows in the direction depicted in the graph. Bayesian networks reveal that P1and AG (as from node 5 times) are more likely to be upstream, and P3 is more likely to be downstream (as to node 6 times) (see Table S4 for details). The network had an average Markov blanket size of 8.6 and average node degree of 6.6. In the network, the schizotypal personality disorder node had the highest degree of connections with multiple positive and negative symptoms. P1 and P2 emerged as core nodes among the positive symptoms. The network structure revealed that schizotypal personality disorder may play a central role in symptom development and can influence other positive and negative symptoms. This may reflect causal relationships and conduction effects between symptoms in schizophrenia. Specifically, schizotypal personality disorder had direct links to P1, P2, AN and AG, suggesting it may directly contribute to their occurrence. Meanwhile, schizotypal personality disorder also had indirect connections to symptoms like P4 and P3 through intermediary nodes like P1. Overall, the network topology delineates probabilistic dependencies and interactions between variables that have important clinical implications for understanding schizophrenia. The network was learned using the Hill-Climbing algorithm with BIC score, through a bootstrap approach (50000). Further research is warranted to validate the symptom relationships suggested by the network structure. The stability of the Bayesian network has passed the test, mean loss\u0026thinsp;=\u0026thinsp;12.64, SD loss\u0026thinsp;=\u0026thinsp;0.07 (see Figure S5-S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/h2\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe original aim of this study was to investigate the network relationships of PLEs in juveniles and emerging adults. We identified three pivotal findings: 1) A significant symptom network exists within PLEs, where positive and negative PLEs are spatially distinct but but interrelated. Notably, P1 and AG (includes G1, G2, G3, and G4) emerged as the most influential nodes within this network. 2) This network exhibited age-related variations. Distinct network structures were evident between juveniles and adult cohorts. In juveniles, P3 and P1 manifested a stronger association compared to adults. 3) Bayesian network analyses indicated potential symptom propagation directions, pinpointing schizotypal personality disorder as the central node. Notably, most negative PLEs were precursors to positive PLEs. In essence, our findings corroborated the initial hypotheses, elucidating the interplay of positive and negative PLEs during the prodromal phase, delineating age-dependent network structural variations, and inferring the reasonable interaction relationships among the symptoms.\u003c/p\u003e \u003cp\u003eFurther network analysis results found that within the PLEs symptom network, positive and negative PLEs, while spatially distinct, were interrelated. P1 and P4 symptoms emerged as the most influential nodes. Earlier studies have underscored networks of psychotic experiences in the general populace, indicating proximity between positive and negative symptoms(W\u0026uuml;sten et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, due to varying questionnaires employed across different networks, results regarding centrality have been inconsistent. For instance, a survey targeting juveniles' self-harm ideation underscored positive symptoms, like auditory hallucinations and persecutory delusions, as central within the network(N\u0026uacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, another study spotlighted the pivotal role of depression, anxiety, negative affect, and loneliness within the network, connecting maladaptive cognitive-emotional regulation with both loneliness adversity and PLEs(Qiao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Delusions might be unstable, and a direct correlation has been identified between acute cocaine use and delusion(Karsinti et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Pertaining to negative symptom network studies, our research is groundbreaking, emphasizing the pivotal role of general negative symptoms, including sleep disturbances, dysphoric mood, motor disturbances, and impaired stress tolerance. These dimensions are evidently highly correlated with negative affect, intuitively suggesting their heightened centrality. The extensive symptom network connections reiterate the significance of early identification and intervention for psychotic disorders, underscoring the potential influence of general psychopathological symptoms on positive PLEs.\u003c/p\u003e \u003cp\u003eOur investigation revealed substantial differences in PLEs symptom networks between juveniles and adults. While grandiosity and delusions experiences were more interrelated in juveniles, this association attenuated in adults. Specifically, in comparison to adults, juveniles demonstrated robust positive correlations between P3, P1, P5, and schizotypal personality disorder. Conversely, stronger negative correlations were observed between P2, P3 and P4. Such developmental disparities might mirror age-associated symptom expression variations and schizophrenia spectrum disorder manifestations(Jalbrzikowski et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Juveniles might cultivate inflated self-perceptions as coping mechanisms against atypical hallucinations, while adults may grapple with amplified self-doubt and negative affect. These insights wield significant implications for age-specific evaluations and interventions for high-risk individuals(Flett \u0026amp; Hewitt, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Overall, our discoveries underscore the necessity to factor in developmental aspects when discerning and preempting mental disorders across diverse age brackets.\u003c/p\u003e \u003cp\u003eAnother pressing global concern is the escalating suicide risk among juveniles. PLEs in conjunction with trauma and suicide (including suicidal ideation and non-suicidal self-injury) remain pivotal concerns. Suicidal ideation predominantly correlates with PLE hallucinations delusions(N\u0026uacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Another study pinpointed a direct influence of PLEs on the duration and severity of Nonsuicidal Self-Injury (Misiak, Szewczuk-Bogusławska, et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while yet another study indicated PLEs influencing non-suicidal self-injury and self-harm via depression(Zhou et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A cross-sectional study on Chilean pre-clinical juveniles highlighted depressive symptoms as partial mediators between psychotic experiences and suicidal ideation(N\u0026uacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Insomnia emerges as another potential risk factor for suicide among PLE patients, particularly correlating with experiences of acquaintanceship, hallucinations, and paranoia(Misiak, Gawęda, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Preceding PLEs, trauma might influence PLEs via cognitive biases and depressive symptoms(Gawęda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, leveraging experience sampling methodologies, daily stressors amplify PLE experiences, with nodes of lack of control and suspicion being susceptible to external influences(Klippel et al.). Direct correlations have been identified between PLEs and symptoms of obsessive-compulsive disorder, depression disorder, and attention-deficit/hyperactivity disorder(Rejek \u0026amp; Misiak, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Depression seemingly plays a pivotal role in this chain. A study on depression and PLE network interpretations, although closely intertwined, spotlighted them as distinct symptoms (Gran\u0026ouml; et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other non-clinical research also suggests elevated levels of external attribution, the necessity for thought control, social cognitive impediments, and fantasy-based emotional regulation strategies might positively correlate with narcissistic individuals' PLE development(Misiak, Kowalski, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another symptom characteristic is the reciprocal causal influence, wherein external factors intensify PLEs, PLE experiences induce distress, and distress exacerbates other risk factors. Peer bullying and rejection also correlate positively with hallucinatory experiences(Steenkamp et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Within positive PLE symptoms, bizarre experiences and persecutory ideation positively correlate with anxiety, depression, and mood, while hallucinations only positively correlate with mood(Yang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, PLE experiences don't invariably induce distress; even if statistically highly correlated, the primary modulating factor remains the degree of paranoia(Murphy et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, self-compassion negatively correlates with delusions and hallucinations(Scheunemann et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, we ought to draw inferences cautiously; our Bayesian network results furnish us with novel insights.\u003c/p\u003e \u003cp\u003eOur Bayesian network analysis identified schizotypal personality disorder as the pivotal node influencing positive PLEs, either directly or indirectly. There is a close network connection between positive and negative symptoms, but we specifically point out that the positive symptom yellow receives mainly the influence of the negative symptom, and only one arrow from P2 points to the AD consists of the Personal Hygiene. A Bayesian network study on 902 British patients with psychotic experiences delineated dissociation within positive symptoms, potentially influencing hallucinations, and self-efficacy might influence responses to dissociation, with paranoid thought and delusions influencing sleep(Černis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This resonates with our findings, underscoring the intrinsic logic within positive symptoms. Our results complement this notion, suggesting negative symptoms might manifest earlier, thereby triggering positive symptoms. In our introduction, we had also alluded to earlier Bayesian network studies that showcased dissociation, regarded as a negative symptom, holding a dominant position within the network \u003csup\u003e15\u003c/sup\u003e. Nevertheless, Bayesian networks furnish hypothetical rather than definitive causal models, warranting further validation. Although not conclusively validated, these findings lend credence to the hypothesis postulating negative symptoms potentially influencing and driving positive symptom evolution in schizophrenia and the large sample size and extensive survey scope ensure the reliability of the results.Future prospective research is imperative to authenticate the symptom relationships postulated by our Bayesian modeling.\u003c/p\u003e \u003cp\u003eHowever, we must acknowledge our study's limitations. Primarily, the comprehensiveness of the network in encompassing all pertinent variables remains debatable. Undetected causal influences of pivotal omitted variables, if absent from the network, constitute a significant concern. First, networks inferred from cross-sectional data can't discern potential symptom feedback loops. Longitudinal research remains essential to validate and expand upon our discoveries. Second, our data, anchored in self-report questionnaires, might be susceptible to response bias or measurement inaccuracies. Objective evaluative tools, such as neuroimaging, biochemical markers, or behavioral indicators, might proffer more accurate and reliable data. Third, our sample demographic, comprising college students from four Chinese cities, potentially curtails the broader applicability of our findings. Subsequent studies must strive to replicate our findings within more heterogeneous and representative cohorts. Last but far from least, some organic diseases (eg. epilepsy, brain tumor, vitamin deficits), drug abuse, unipolar, bipolar disorders and other personality disorders (eg. paranoid personality disorder, borderline personality disorder) can also present with minor psychoticlike experiences(Marques, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Marques, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marques \u0026amp; Ouakinin, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which was not considered in this study, future studies can be further explored. It is important to clarify that some authors that believe that schizotypal personality disorder is amputated form of schizophrenia, that do not progress to schizophrenia(Downhill Jr et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), others believe that the personality disorder with higher risk of developing schizophrenia is the schizoid personality disorder(Siever et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). We recognize that the relationship between schizoid personality disorder and schizophrenia is complex, However, our research goal is not to explore the relationship between the two, but to explore the relationship between schizoid personality disorder and Psychoticlike Experiences (PLEs), because PLEs are perceived as early indicators for the progression to schizophrenia spectrum disorders, The exploration of PLEs and schizoid personality disorder helps to provide initial screening and understanding to a wider population at a lower cost, thus providing the basis for resource allocation and follow-up research. This study is intended to provide fundamental data and direction for further research in the future, not as a diagnostic tool. while a simple scale cannot provide the depth of information needed to identify high-risk patients, it can provide initial screening and understanding to a broader population at lower cost and resource consumption. This approach helps to identify groups that may need further assessment, thereby informing resource allocation and follow-up research.\u003c/p\u003e \u003cp\u003eIn conclusion, our research underscores the potential interplay between positive and negative PLEs, laying the groundwork for speculation on causality, suggesting that the manifestations of negative symptoms, general psychopathological symptoms, and their bearings on positive psychotic experiences necessitate significant attention in schizophrenia spectrum disorders' prevention. These revelations proffer fresh perspectives for comprehending and intervening in these complex and debilitating conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZheng Lu was supported by Ministry of Science and Technology of China (2016YFC1306805), the Science and Technology Commission of Shanghai Municipality (21Y21900700);\u003c/p\u003e\n\u003cp\u003eFei Liu was supported by Shanghai Municipal Commission of Health and Family Planning (20214Y0295);\u003c/p\u003e\n\u003cp\u003eQiang Hu was supported by Jiangsu University Medical Education Collaborative Innovation Fund (JDYY2023088), Zhenjiang social development guiding science and technology plan project (FZ2022116), Clinical Medical Research Conversion Special, Anhui Key Research and Development Program (202204295107020065), Scientific Research Project of Anhui Provincial Health Commission (AHWJ2022b096).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was reviewed and approved by Shanghai Tongji Hospital Ethics Committee, approval number [2020-031].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe observation datasets and all code used for this study is available at open science framework \u0026nbsp; (https://osf.io/um53p/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks are due to all the collectors who participated in the collection of this study and to the participants who made suggestions for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFei Liu: Data Curation, Writing- Reviewing, Editing and Funding acquisition. Zhao-Qi Wang: Visualization, Software, Writing - Original Draft. Jiaxin Wu: Data curation, Conceptualization, Writing - Review \u0026amp; Editing. Xiang-yun Long: Investigation and Resources. An-si Qi: Investigation and Resources. Xiao-feng Guan: Investigation and Resources. Xin-yi Hu: Investigation and Resources. Mao-rong Hu: Investigation and Resources. Shi-ping Xie: Investigation and Resources. Hui Zheng: Conceptualization, Formal analysis, Writing - Original Draft, Writing - Review \u0026amp; Editing, Supervision. Qiang Hu: Project administration and Funding acquisition. Zheng Lu: Resources, Funding acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAstill Wright, L., McElroy, E., Barawi, K., Roberts, N. P., Simon, N., Zammit, S., \u0026amp; Bisson, J. I. (2023). Associations among psychosis, mood, anxiety, and posttraumatic stress symptoms: A network analysis. \u003cem\u003eJournal of Traumatic Stress\u003c/em\u003e,\u003cem\u003e 36\u003c/em\u003e(2), 385-396.\u003c/li\u003e\n\u003cli\u003eBorsboom, D. (2017). A network theory of mental disorders. \u003cem\u003eWorld Psychiatry\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(1), 5-13.\u003c/li\u003e\n\u003cli\u003eČernis, E., Evans, R., Ehlers, A., \u0026amp; Freeman, D. (2021). Dissociation in relation to other mental health conditions: An exploration using network analysis. \u003cem\u003eJournal of psychiatric research\u003c/em\u003e,\u003cem\u003e 136\u003c/em\u003e, 460-467.\u003c/li\u003e\n\u003cli\u003eČernis, E., Molodynski, A., Ehlers, A., \u0026amp; Freeman, D. (2022). Dissociation in patients with non-affective psychosis: Prevalence, symptom associations, and maintenance factors. \u003cem\u003eSchizophrenia Research\u003c/em\u003e,\u003cem\u003e 239\u003c/em\u003e, 11-18.\u003c/li\u003e\n\u003cli\u003eCostenbader, E., \u0026amp; Valente, T. W. (2003). The stability of centrality measures when networks are sampled. \u003cem\u003eSocial Networks\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e(4), 283-307. https://doi.org/10.1016/s0378-8733(03)00012-1\u003c/li\u003e\n\u003cli\u003eDickson, H., Calkins, M. E., Kohler, C. G., Hodgins, S., \u0026amp; Laurens, K. R. (2014). Misperceptions of facial emotions among youth aged 9\u0026ndash;14 years who present multiple antecedents of schizophrenia. \u003cem\u003eSchizophrenia bulletin\u003c/em\u003e,\u003cem\u003e 40\u003c/em\u003e(2), 460-468.\u003c/li\u003e\n\u003cli\u003eDownhill Jr, J. E., Buchsbaum, M. S., Hazlett, E. A., Barth, S., Roitman, S. L., Nunn, M., . . . Mitropoulou, V. (2001). Temporal lobe volume determined by magnetic resonance imaging in schizotypal personality disorder and schizophrenia. \u003cem\u003eSchizophrenia Research\u003c/em\u003e,\u003cem\u003e 48\u003c/em\u003e(2-3), 187-199.\u003c/li\u003e\n\u003cli\u003eFlett, G. L., \u0026amp; Hewitt, P. L. (2014). A proposed framework for preventing perfectionism and promoting resilience and mental health among vulnerable children and adolescents. \u003cem\u003ePsychology in the Schools\u003c/em\u003e,\u003cem\u003e 51\u003c/em\u003e(9), 899-912.\u003c/li\u003e\n\u003cli\u003eGawęda, Ł., Pionke, R., Hartmann, J., Nelson, B., Cechnicki, A., \u0026amp; Frydecka, D. (2021). Toward a complex network of risks for psychosis: combining trauma, cognitive biases, depression, and psychotic-like experiences on a large sample of young adults. \u003cem\u003eSchizophrenia bulletin\u003c/em\u003e,\u003cem\u003e 47\u003c/em\u003e(2), 395-404.\u003c/li\u003e\n\u003cli\u003eGran\u0026ouml;, N., Lintula, S., Therman, S., Marttunen, M., L\u0026aring;ng, U., \u0026amp; Ranta, K. (2023). Factor and network analysis of psychosis‐like experiences and depressive symptoms in a sample of Finnish adolescents entering psychiatric services. \u003cem\u003eEarly Intervention in Psychiatry\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eHajd\u0026uacute;k, M., Strakov\u0026aacute;, A., Janu\u0026scaron;ka, J., Ivanč\u0026iacute;k, V., Danč\u0026iacute;k, D., Čavojsk\u0026aacute;, N., . . . Abplanalp, S. J. (2023). Connections between and within extended psychosis and autistic phenotypes and social relationships in the general population. \u003cem\u003eJournal of psychiatric research\u003c/em\u003e,\u003cem\u003e 157\u003c/em\u003e, 36-42.\u003c/li\u003e\n\u003cli\u003eHfner, H., \u0026amp; Maurer, K. (2006). Early detection of schizophrenia: Current evidence and future perspectives. \u003cem\u003eWorld psychiatry: official journal of the World Psychiatric Association (WPA)\u003c/em\u003e,\u003cem\u003e 5\u003c/em\u003e(3), 130-138.\u003c/li\u003e\n\u003cli\u003eIsaksson, J., Angenfelt, M., Frick, M. A., Olofsdotter, S., \u0026amp; Vadlin, S. (2022). Psychotic-like experiences from adolescence to adulthood: A longitudinal study. \u003cem\u003eSchizophrenia Research\u003c/em\u003e,\u003cem\u003e 248\u003c/em\u003e, 1-7.\u003c/li\u003e\n\u003cli\u003eJalbrzikowski, M., Murty, V. P., Tervo-Clemmens, B., Foran, W., \u0026amp; Luna, B. (2019). Age associated deviations of amygdala functional connectivity in psychosis spectrum youth: relevance to psychotic symptoms. \u003cem\u003eThe American journal of psychiatry\u003c/em\u003e,\u003cem\u003e 176\u003c/em\u003e(3), 196.\u003c/li\u003e\n\u003cli\u003eJauhar, S., Johnstone, M., \u0026amp; Mckenna, P. J. (2022). Schizophrenia. \u003cem\u003eThe Lancet\u003c/em\u003e(10323), 473-486.\u003c/li\u003e\n\u003cli\u003eKarsinti, E., Labaeye, M., Piani, K., Fortias, M., Brousse, G., Bloch, V., . . . Vorspan, F. (2020). Network analysis of psychotic manifestations among cocaine users. \u003cem\u003eJournal of psychiatric research\u003c/em\u003e,\u003cem\u003e 130\u003c/em\u003e, 300-305.\u003c/li\u003e\n\u003cli\u003eKlippel, A., Viechtbauer, W., Reininghaus, U., \u0026amp; Wigman, J. The cascade of stress: a network approach to explore differential dynamics in populations varying in risk for psychosis Running title: The cascade of stress\u0026ndash;a network approach.\u003c/li\u003e\n\u003cli\u003eKnoll, J., \u0026amp; Dietz, P. (2023). Mass violence and the complex spectrum of mental illness and mental functioning. \u003cem\u003eJAMA psychiatry\u003c/em\u003e,\u003cem\u003e 80\u003c/em\u003e(2), 186-188.\u003c/li\u003e\n\u003cli\u003eLaurens, K. R., Hodgins, S., Maughan, B., Murray, R. M., Rutter, M. L., \u0026amp; Taylor, E. A. (2007). Community screening for psychotic-like experiences and other putative antecedents of schizophrenia in children aged 9\u0026ndash;12 years. \u003cem\u003eSchizophrenia Research\u003c/em\u003e,\u003cem\u003e 90\u003c/em\u003e(1-3), 130-146.\u003c/li\u003e\n\u003cli\u003eLee, K. W., Chan, K. W., Chang, W. C., Lee, E. H. M., Hui, C. L. M., \u0026amp; Chen, E. Y. H. (2016). A systematic review on definitions and assessments of psychotic‐like experiences. \u003cem\u003eEarly Intervention in Psychiatry\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(1), 3-16.\u003c/li\u003e\n\u003cli\u003eLindgren, M., Numminen, L., Holm, M., Therman, S., \u0026amp; Tuulio-Henriksson, A. (2022). Psychotic-like experiences of young adults in the general population predict mental disorders. \u003cem\u003ePsychiatry Research\u003c/em\u003e,\u003cem\u003e 312\u003c/em\u003e, 114543.\u003c/li\u003e\n\u003cli\u003eLoewy, R., Pearson, R., Bearden, C., Zinberg, J., Vinogradov, S., \u0026amp; Cannon, T. (2012). Longitudinal follow-up of screening for clinical-high-risk for psychosis with the Prodromal Questionnaire, Brief Version (PQ-B) [Meeting Abstract]. \u003cem\u003eEarly Intervention in Psychiatry\u003c/em\u003e,\u003cem\u003e 6\u003c/em\u003e, 2-2. \u0026lt;Go to ISI\u0026gt;://WOS:000308580100006\u003c/li\u003e\n\u003cli\u003eMaier, W., Falkai, P., \u0026amp; Wagner, M. (1999). Schizophrenia spectrum disorders: a review. \u003cem\u003eSchizophrenia\u003c/em\u003e,\u003cem\u003e 2\u003c/em\u003e, 311-405.\u003c/li\u003e\n\u003cli\u003eMarques, J. G. (2020). Organic psychosis causing secondary schizophrenia in one-fourth of a cohort of 200 patients previously diagnosed with primary schizophrenia. \u003cem\u003eThe primary care companion for CNS disorders\u003c/em\u003e,\u003cem\u003e 22\u003c/em\u003e(2), 27065.\u003c/li\u003e\n\u003cli\u003eMarques, J. G. (2021). Revisiting the Concepts of Secondary Schizophrenia and Pseudoschizophrenia. \u003cem\u003eOrdem dos Medicos\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eMarques, J. G., \u0026amp; Ouakinin, S. (2021). Schizophrenia\u0026ndash;schizoaffective\u0026ndash;bipolar spectra: an epistemological perspective. \u003cem\u003eCNS Spectr\u003c/em\u003e(3).\u003c/li\u003e\n\u003cli\u003eMcGrath, J. J., Saha, S., Al-Hamzawi, A., Alonso, J., Bromet, E. J., Bruffaerts, R., . . . Fayyad, J. (2015). Psychotic experiences in the general population: a cross-national analysis based on 31 261 respondents from 18 countries. \u003cem\u003eJAMA psychiatry\u003c/em\u003e,\u003cem\u003e 72\u003c/em\u003e(7), 697-705.\u003c/li\u003e\n\u003cli\u003eMisiak, B., Gawęda, Ł., Moustafa, A. A., \u0026amp; Samochowiec, J. (2023). Insomnia moderates the association between psychotic-like experiences and suicidal ideation in a non-clinical population: a network analysis. \u003cem\u003eEuropean Archives of Psychiatry and Clinical Neuroscience\u003c/em\u003e, 1-9.\u003c/li\u003e\n\u003cli\u003eMisiak, B., Kowalski, K., Jaworski, A., Świrkosz, G., Szyszka, M., \u0026amp; Piotrowski, P. (2023). Understanding pathways from narcissistic grandiosity to psychotic-like experiences: Insights from the network analysis. \u003cem\u003eJournal of psychiatric research\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eMisiak, B., Szewczuk-Bogusławska, M., Samochowiec, J., Moustafa, A. A., \u0026amp; Gawęda, Ł. (2023). Unraveling the complexity of associations between a history of childhood trauma, psychotic-like experiences, depression and non-suicidal self-injury: a network analysis. \u003cem\u003eJournal of psychiatric research\u003c/em\u003e(166), 122-129.\u003c/li\u003e\n\u003cli\u003eMurphy, J., McBride, O., Fried, E., \u0026amp; Shevlin, M. (2018). Distress, impairment and the extended psychosis phenotype: a network analysis of psychotic experiences in an US general population sample. \u003cem\u003eSchizophrenia bulletin\u003c/em\u003e,\u003cem\u003e 44\u003c/em\u003e(4), 768-777.\u003c/li\u003e\n\u003cli\u003eNelson, B., Amminger, G. P., Bechdolf, A., French, P., \u0026amp; Mcgorry, P. D. (2019). Evidence for preventive treatments in young patients at clinical high risk of psychosis: the need for context. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(5), 378-380.\u003c/li\u003e\n\u003cli\u003eN\u0026uacute;\u0026ntilde;ez, D., Fresno, A., Van Borkulo, C., Courtet, P., Arias, V., Garrido, V., \u0026amp; Wigman, J. (2018). Examining relationships between psychotic experiences and suicidal ideation in adolescents using a network approach. \u003cem\u003eSchizophrenia Research\u003c/em\u003e,\u003cem\u003e 201\u003c/em\u003e, 54-61.\u003c/li\u003e\n\u003cli\u003eN\u0026uacute;\u0026ntilde;ez, D., Monjes, P., Campos, S., \u0026amp; Wigman, J. T. (2021). Evidence for specific associations between depressive symptoms, psychotic experiences, and suicidal ideation in Chilean adolescents from the general population. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e, 552343.\u003c/li\u003e\n\u003cli\u003ePontillo, M., Averna, R., Tata, M. C., Chieppa, F., Pucciarini, M. L., \u0026amp; Vicari, S. (2021). Neurodevelopmental Trajectories and Clinical Profiles in a Sample of Children and Adolescents With Early- and Very-Early-Onset Schizophrenia [Article]. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e, Article 662093. https://doi.org/10.3389/fpsyt.2021.662093\u003c/li\u003e\n\u003cli\u003ePuga, J. L., Krzywinski, M., \u0026amp; Altman, N. (2015). Points of Significance: Bayesian networks. \u003cem\u003eNature Methods\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(9), 799-800.\u003c/li\u003e\n\u003cli\u003eQiao, Z., Lafit, G., Lecei, A., Achterhof, R., Kirtley, O. J., Hiekkaranta, A. P., . . . Reininghaus, U. (2023). Childhood Adversity and Emerging Psychotic Experiences: A Network Perspective. \u003cem\u003eSchizophrenia bulletin\u003c/em\u003e, sbad079.\u003c/li\u003e\n\u003cli\u003eRejek, M., \u0026amp; Misiak, B. (2023). Dimensions of psychopathology associated with psychotic-like experiences: Findings from the network analysis in a nonclinical sample. \u003cem\u003eEuropean Psychiatry\u003c/em\u003e,\u003cem\u003e 66\u003c/em\u003e(1), e56.\u003c/li\u003e\n\u003cli\u003eRosengard, R. J., Malla, A., Mustafa, S., Iyer, S. N., Joober, R., Bodnar, M., . . . Shah, J. L. (2019). Association of Pre-onset Subthreshold Psychotic Symptoms With Longitudinal Outcomes During Treatment of a First Episode of Psychosis. \u003cem\u003eJAMA psychiatry\u003c/em\u003e,\u003cem\u003e 76\u003c/em\u003e(1), 61-70. https://doi.org/10.1001/jamapsychiatry.2018.2552\u003c/li\u003e\n\u003cli\u003eScheunemann, J., Schlier, B., Ascone, L., \u0026amp; Lincoln, T. M. (2019). The link between self‐compassion and psychotic‐like experiences: A matter of distress? \u003cem\u003ePsychology and Psychotherapy: Theory, Research and Practice\u003c/em\u003e,\u003cem\u003e 92\u003c/em\u003e(4), 523-538.\u003c/li\u003e\n\u003cli\u003eSiever, L. J., Silverman, J. M., Horvath, T. B., Klar, H., Coccaro, E., Keefe, R. S., . . . Davis, K. L. (1990). Increased morbid risk for schizophrenia related disorders in relatives of schizotypal personality disordered patients. \u003cem\u003eArchives of General Psychiatry\u003c/em\u003e,\u003cem\u003e 47\u003c/em\u003e(7), 634-640.\u003c/li\u003e\n\u003cli\u003eStarzer, M., Hansen, H. G., Hjorth\u0026oslash;j, C., Albert, N., Nordentoft, M., \u0026amp; Madsen, T. (2023). 20‐year trajectories of positive and negative symptoms after the first psychotic episode in patients with schizophrenia spectrum disorder: results from the OPUS study. \u003cem\u003eWorld Psychiatry\u003c/em\u003e,\u003cem\u003e 22\u003c/em\u003e(3), 424-432.\u003c/li\u003e\n\u003cli\u003eSteenkamp, L. R., Tiemeier, H., Bolhuis, K., Hillegers, M. H., Kushner, S. A., \u0026amp; Blanken, L. M. (2021). Peer‐reported bullying, rejection and hallucinatory experiences in childhood. \u003cem\u003eActa Psychiatrica Scandinavica\u003c/em\u003e,\u003cem\u003e 143\u003c/em\u003e(6), 503-512.\u003c/li\u003e\n\u003cli\u003evan der Wal, J. M., van Borkulo, C. D., Deserno, M. K., Breedvelt, J. J., Lees, M., Lokman, J. C., . . . Smidt, M. P. (2021). Advancing urban mental health research: From complexity science to actionable targets for intervention. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e,\u003cem\u003e 8\u003c/em\u003e(11), 991-1000.\u003c/li\u003e\n\u003cli\u003eVan Os, J., Linscott, R. J., Myin-Germeys, I., Delespaul, P., \u0026amp; Krabbendam, L. (2009). A systematic review and meta-analysis of the psychosis continuum: evidence for a psychosis proneness\u0026ndash;persistence\u0026ndash;impairment model of psychotic disorder. \u003cem\u003ePsychological medicine\u003c/em\u003e,\u003cem\u003e 39\u003c/em\u003e(2), 179-195.\u003c/li\u003e\n\u003cli\u003eW\u0026uuml;sten, C., Schlier, B., Jaya, E. S., Fonseca-Pedrero, E., Peters, E., Verdoux, H., . . . Lincoln, T. M. (2018). Psychotic experiences and related distress: a cross-national comparison and network analysis based on 7141 participants from 13 countries. \u003cem\u003eSchizophrenia bulletin\u003c/em\u003e,\u003cem\u003e 44\u003c/em\u003e(6), 1185-1194.\u003c/li\u003e\n\u003cli\u003eYang, X.-H., Zhang, J.-w., Li, Y., Zhou, L., \u0026amp; Sun, M. (2023). Psychotic-like experiences as a co-occurring psychopathological indicator of multi-dimensional affective symptoms: Findings from a cross-sectional survey among college students. \u003cem\u003eJournal of affective disorders\u003c/em\u003e,\u003cem\u003e 323\u003c/em\u003e, 33-39.\u003c/li\u003e\n\u003cli\u003eYang, Y., Shen, D., Wang, J., \u0026amp; Yang, J. (2002). The Reliability and Validity of PDQ-4+ in China. \u003cem\u003eChinese Journal of Clinical Psychology\u003c/em\u003e(03), 165-168.\u003c/li\u003e\n\u003cli\u003eZhou, H.-Y., Luo, Y.-H., Shi, L.-J., \u0026amp; Gong, J. (2023). Exploring psychological and psychosocial correlates of non-suicidal self-injury and suicide in college students using network analysis. \u003cem\u003eJournal of affective disorders\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. The degree, closeness and betweenness of variable nodes in the present network and bridge strength, bridge betweenness and bridge closeness between variable nodes and communities.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNodes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCloseness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBetweenness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCommunities\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBridge.\u003c/p\u003e\n \u003cp\u003eStrength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBridge.\u003c/p\u003e\n \u003cp\u003eBetweenness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBridge.\u003c/p\u003e\n \u003cp\u003eCloseness\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"schizophrenia spectrum disorders, positive symptoms, negative symptoms, symptom network, network analysis, Bayesian network analysis","lastPublishedDoi":"10.21203/rs.3.rs-6215745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6215745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePsychoticlike experiences (PLEs) are perceived as early indicators for the progression to schizophrenia spectrum disorders, a grave concern in psychiatric research. Historically, the interconnectedness between positive and negative symptoms in PLEs has remained enigmatic. In this multicenter cross-sectional study, we aim to investigate the relationship between positive and negative symptoms in PLEs \u0026ndash; crucial indicators of the transition to schizophrenia spectrum disorders.Our sample includes 37,443 high school/college students from 19 cities across four Chinese provinces (September 2017 to November 2019). Participants completed multiple assessments, such as the Prodromal Questionnaire-Brief Version and the Questionnaire for Negative Symptoms, Disorganization Symptoms, and General Symptoms.The analysis of symptom networks reveals that delusions and general negative symptoms emerge as central nodes in the network. Interestingly, the network demonstrates a clear separation of positive and negative symptoms while highlighting their close interconnections. Additionally, schizotypal personality disorder serve as bridging elements in this network. Using Bayesian network analysis, we further establish that negative symptoms drive the development of positive symptoms.These findings underscore the significance of exploring negative symptoms in PLEs and suggest their potential importance in early identification and intervention of schizophrenia spectrum disorders.\u003c/p\u003e","manuscriptTitle":"Symptom and Bayesian network analyses of positive and negative symptoms in psychotic- like experiences: A multicenter cross-sectional study of Chinese students at 19 cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-18 07:50:48","doi":"10.21203/rs.3.rs-6215745/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"654ce312-1603-4fbd-8f5d-0a960c430f99","owner":[],"postedDate":"March 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T10:24:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-18 07:50:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6215745","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6215745","identity":"rs-6215745","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}