A Framework for Parsing Psychopathological Heterogeneity: Initial Application in a Large-Scale Unselected Community Sample

A Framework for Parsing Psychopathological Heterogeneity: Initial Application in a Large-Scale Unselected Community Sample | 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 A Framework for Parsing Psychopathological Heterogeneity: Initial Application in a Large-Scale Unselected Community Sample Chaoyi Wu, Chenyu Yuan, Yinqing Fan, Ang Hong, Zhiling Wu, Zhen Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5694361/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Psychiatry → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Traditional descriptive nosology arbitrarily distinguishes between mental illness and health, hindering the progress of scientific research and clinical practice. Building on recent advancements in psychiatric conceptualization, this study proposes an innovative phased framework for deconstructing psychopathological heterogeneity. The framework involves four key steps: extraction of symptom dimensions, identification of psychopathological subtypes, characterization of symptom interaction patterns using a network approach, and validation of their incremental validity through links to neurobehavioral functions. This framework is preliminarily applied to a large, non-selective community sample ( N = 4102) to explore its utility and potential for deconstructing psychopathological heterogeneity. Methods: Data on comprehensive psychopathology and RDoC negative valence constructs were collected from the sample. Factor analysis and exploratory graph analysis were used to extract symptom dimensions. Latent profile analysis based on these dimensions was applied to identify psychopathological profiles. Partial correlation networks were estimated for each profile, and symptom network characteristics were compared across profiles. Finally, hierarchical multiple regression was applied to assess incremental validity. Results: The first step of the phased framework involves extracting homogeneous dimensions based on symptom co-occurrence patterns, yielding seven distinct dimensions: Obsessive-Compulsive , Emotional Distress , Eating-Related , Substance-Related , Aggressive , Psychotic , and Somatoform dimensions. The second step involves applying a person-centered approach to identify latent subgroups based on these symptom dimensions. Four profiles were identified, namely Substance Use Group , Moderate Symptomatology Group , Disengaged from Symptomatology Group , and Severe Symptomatology Group . The third step involves characterizing symptom interaction patterns across subgroups. Using a network approach, the Severe Symptomatology Group exhibited the densest interconnections and the highest global network strength, with Aggressive and Psychotic dimensions serving as core issuescompared to other profiles. Finally, incremental validity was assessed through associations with neurobehavioral functions. Results showed that these profiles provided unique predictive value for RDoC negative valence constructs beyond both dichotomousdiagnostic status and purely dimensional approach. Conclusions: This study introduces a fine-grained framework for deconstructing psychopathological heterogeneity, providing a comprehensive approach to parsing psychopathology. While the framework is preliminarily applied to a large sample from the Chinese population, further validation is needed across diverse cultural and regional contexts. psychopathology heterogeneity dimensional approach network approach person-centered approach Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Over the last three decades, the incidence of mental disorders has risen. An epidemiological study revealed a lifetime prevalence of mental disorders in China is 16.6% [ 1 ]. However, research and clinical treatment of mental disorders are constrained by challenges in identifying biomarkers of mental illness, which limits the development of effective treatment strategies [ 2 , 3 ]. This is largely due to the over-reliance on traditional psychiatric nosology, such as the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [ 4 ] and the 11th revision of the International Classification of Diseases (ICD-11) [ 5 ]. The purely descriptive nosology inevitably confounds multiple entities from an etiological standpoint, leading to drawbacks such as low diagnostic reliability [ 6 ], heterogeneity problems within the same diagnosis [ 7 ], and widespread comorbidity across diagnoses [ 8 ]. Based on official nosology, most psychiatric research relied on case-control designs. This approach oversimplified mental health status into health or disease, ignoring the nuanced heterogeneity of traits or symptoms within both patient and healthy control groups [ 9 ]. Indeed, a significant portion of individuals exist in sub-health or subclinical states, challenging this binary classification. In other words, the official nosology is insufficient in capturing individual differences in mental health in a fine-grained way. Therefore, exploring empirical framework to deconstruct mental health heterogeneity in the general population is imperative. To address this limitation, this study proposes a phased framework for deconstructing psychopathological heterogeneity. It includes four steps: (1) extracting symptom dimensions to capture the core factors, (2) identifying psychopathological subtypes based on these dimensions, (3) characterizing these subtypes from a complex network perspective, and (4) linking these subtypes to neurobehavioral functions, and testing their incremental validity to assess their unique predictive value for mental health outcomes. 1.1 Extracting symptom dimensions using dimensional approach The first step involves extracting homogeneous dimensions based on symptom co-occurrence patterns using a dimensional approach. Official nosology is comprised almost exclusively of large sets of dichotomous (present/absent) diagnoses. Whether DSM and ICD diagnoses accurately reflect the nature of mental disorders is questionable. But it is undeniable that mental symptoms, such as depressive mood and obsessions, exist and cause suffering in the general population. The dimensional approach, represented by The Hierarchical Taxonomy of Psychopathology (HiTOP) [ 10 , 11 ] utilizes data-driven methods such as factor analysis to organize symptomatology. It integrates statistically related symptoms into homogeneous dimensions, while assigning unrelated symptoms to separate dimensions [ 12 ]. It is evident that most psychiatric problems are dimensional; thus, capturing symptomatology both above and below the diagnostic threshold aligns more closely with the true nature of mental health. Research has shown that dimensional models better capture the psychopathological patterns in the data compared to categorical ones [ 13 ]. The dimensional approach exhibit improved performance in risk prediction [ 14 ] and prognosis [ 15 ] of mental disorders. 1.2 Identifying psychopathological profiles using person-centered approach Although the dimensional approach provides theoretical insights beyond official nosology, purely dimensional frameworks have drawbacks in practical clinical applications. One prominent challenge is the curse of dimensionality [ 16 ], which requires exponentially larger sample sizes to accurately identify outliers as the number of dimensions increases. Failure to meet this requirement results in exponential degradation of the model performance [ 9 ]. Furthermore, a purely dimensional approach is challenging to apply in clinical settings. For any psychopathological nosology to be useful, it must effectively differentiate and categorize individuals; otherwise, it remains an ineffective clinical tool [ 17 ]. Therefore, the second step is to delineate psychopathological heterogeneity by integrating a person-centered approach, emphasizing the distinction between profiles, as opposed to the purely dimensional approach focused on outlier detection. Latent Variable Mixture Modelling (LVMM) is a person-centered statistical model that is effective in identifying latent subgroups in the populations [ 18 ]. Unlike the traditional variable-centered approach, LVMM does not assume homogeneity of the sample, but rather identifies different homogeneous subgroups through response patterns. LVMM offers advantages over variable-centered approaches in its ability to fit non-linear and complex interaction patterns among multiple indicators [ 19 ]. Recent studies using LVMM have started to explore psychopathological profiles, focusing on specific symptoms within selected clinical populations. These include post-traumatic stress symptoms [ 20 ], depressive and anxiety symptoms [ 21 ], and autism spectrum symptoms [ 22 ]. However, individual differences in transdiagnostic symptom dimensions in unselected general populations remain largely unexplored. According to the principles of computational factor modeling [ 23 ], it is crucial to deconstruct psychopathological heterogeneity using large-scale studies of unselected samples through remote, online, and “citizen science” efforts, rather than relying on small, diagnosed patient samples. This step aims to identify psychopathological profiles in a large, unselected community sample with diverse symptomatology. 1.3 Characterizing psychopathological profiles using network approach After identifying psychopathological profiles, researchers can apply a network approach to characterize symptom interaction patterns across profiles. The network approach views psychopathology as a dynamic and complex system, proposing that mental disorders arise from the complex interaction between symptoms rather than isolated events, variables, or traits[ 24 ]. Therefore, individual differences in psychopathology should be characterized through the overall network structure. According to the network theory, the activation of the symptom node can transmit to other connected nodes within the psychopathological systems. Excessive mutual reinforcement and feedback loops render the symptom network fragile, potentially leading to the transitions into mental disorders. Specifically, a minor external disturbance can trigger a dramatic activation of the fragile psychopathological systems, persisting for an extended duration even after the stimulus ceases [ 25 ]. Furthermore, network characteristics such as node centrality (structural importance) and density (the degree to which all nodes are interconnected) provide important insights into core dysregulation patterns across different phenotypes and potential individualized treatment targets [ 26 ]. The network approach facilitates the exploration of causal mechanisms between symptoms and advances in personalized prediction, such as identifying early warning signals of mental disorders [ 27 ]. 1.4 Linking to neurobehavioral function and testing the incremental validity Building on the characterization of distinct psychopathological profiles, the next step involves linking these profiles to neurobehavioral functions and testing incremental validity, which is crucial for advancing our understanding of the neurobiological bases of psychopathology. The Research Domain Criteria (RDoC) seeks to understand mental health through fundamental neurobehavioral functions [ 28 ]. This study uses self-reported RDoC negative valence constructs as indicators of neurobehavioral function. These constructs primarily regulate responses to aversive stimuli, such as fear, anxiety, and loss. The negative valence constructs in this study include Potential Threat , represented by intolerance of uncertainty and behavioral inhibition, and Sustained Threat , represented by childhood trauma. Based on Ockham’s principle of parsimony, effective profiles should add to the prediction of neurobehavioural functions beyond other existing approach [ 19 , 29 ]. In summary, this study proposes an innovative, phased framework that integrates dimensional, person-centered, and network approaches to comprehensively capture the heterogeneity of psychopathology. We also applied it to a large, unselected community sample for preliminary validation. It is structured around four key research questions, as outlined in the phased research framework (Fig. 1 ). RQ1 . Which homogeneous symptom dimensions can be extracted from the symptom co-occurrence patterns? RQ2 . Which psychopathological profiles can be identified based on transdiagnostic symptom dimensions? RQ3 . What are the differences in symptom network characteristics across these profiles? RQ4 . Does profile membership provide additional predictive value for RDoC negative valence constructs beyond purely descriptive and dimensional approaches? Given the pioneering nature of this study, we can only propose exploratory hypotheses for the above research questions. We hypothesize that the psychopathological profiles identified in this study will offer additional value over traditional approaches. 2. Methods 2.1 Sample Participants were recruited through the Naodao Research Platform ( https://www.naodao.com/ ), an online platform known for its emphasis on sharing, transparency, and usability. Data collection took place from September 26, 2023, over a period of 17 days. Individuals over 18 years old and fluent in Chinese are eligible to participate, excluding those with serious medical conditions that impair self-insight (e.g., major neurocognitive disorders or intellectual disabilities). The final sample consisted of 4,102 individuals (2,152 men and 1,950 women) from 33 provinces in China. Table S1 in the Supplementary Materials summarizes the demographic information of the sample. Participant ages ranged from 18 to 76 years ( M = 27.08, SD = 7.78). Approximately 19.99% of participants self-reported having received a diagnosis of a mental disorder from a professional psychiatrist, similar to the lifetime prevalence reported in previous epidemiological studies (16.6%) [ 1 ]. 2.2 Measures This study used a comprehensive battery of measurement tools addressing symptomatology and RDoC negative valence constructs. Symptomatology measures encompassed obsessive-compulsive [ 30 ], depressive [ 31 ], anxiety [ 32 ], perceived stress [ 33 ], eating disorder [ 34 ], alcohol dependence [ 35 ], nicotine dependence [ 36 ], psychotic [ 37 ], hostility-related [ 38 ], outward irritability [ 39 ], insomnia [ 40 ], somatic [ 41 ], and problematic smartphone usage symptoms [ 42 ]. Some scales include subscales, resulting in a total of 23 specific symptoms. Besides, RDoC negative valence constructs comprised childhood trauma [ 43 ], behavioral inhibition system [ 44 ], and intolerance of uncertainty [ 45 ]. For a comprehensive overview of instruments, please refer to online Appendix 1 in the Supplementary Materials. The descriptive statistics, Cronbach’s a values and Pearson r coefficient values for specific individual symptoms were shown in Table S2 in the Supplementary Materials. 2.3 Statistical analysis An overview of the data analysis, based on the phased research framework, is shown in Fig. 2 . The specific analysis steps will be detailed below. 2.3.1 Extracting symptom dimensions ( RQ.1 ) To explore the dimensions of symptomatology, the exploratory factor analysis (EFA) with the robust maximum likelihood estimation method (MLR) and oblique rotation was conducted. We used the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett test of homogeneity of variances to determine whether the data was suit for EFA. KMO value greater than 0.8, and significant Barrett’s test result indicate that data are adequate for factor analysis. The number of factors was determined through parallel analysis. Parallel analysis is recommended as one of the best methods to identify the number of factors [ 46 ]. Besides, we refer to the model fit metrics to select the optimal model. The comparative fit index (CFI), Tucker-Lewis index (TLI), standardized root mean square residual (SRMSR) and the root mean square error of approximation (RMSEA) were used to assess model fit. The factor structure fits well when CFI and TCI exceed 0.9, and SRMR and RMSEA are below 0.08. Additionally, we performed exploratory graph analysis (EGA) with GLASSO method [ 47 ] to verify the robustness of the EFA results. EGA integrates the Gaussian graphical model (GGM) with walktrap algorithm for weighted networks [ 48 ] to produce a visual guide of the dimensionality assessment. The bootstrap exploratory graph analysis (bootstrap EGA) was used to test the stability of symptom communities [ 49 ]. We utilized the R function bootEGA to generate 5000 iterations of parametric bootstrap samples and applied EGA to these samples, forming the sampling distribution of EGA results. Item stability was evaluated to confirm the robustness of each item’s placement within the empirically derived dimension. Finally, we considered data-driven dimensions extracted from EFA and EGA, as well as the transdiagnostic theory to delineate the final symptom dimensions. Scores for each symptom dimension were quantified by factor scores that can be used in subsequent analyses. Specific symptoms were loaded on only one dimension and there was no cross-loading. 2.3.2 Identifying psychopathological profiles ( RQ.2 ) To identify the psychopathological profiles, we adopted latent profile analysis (LPA) based on standardized symptom dimension scores [ 50 ]. The LPA was conducted using 1000 random start values, and 500 iterations, retaining the 250 best solutions for final stage optimization to avoid local maxima. To determine the best fitting model for the dataset, the Akaïke information criterion (AIC), Bayesian information criterion (BIC), sample-size adjusted BIC (SABIC), entropy values, adjusted Lo, Mendell, and Rubin’s Likelihood Ratio Test (aLMR) and the bootstrap likelihood ratio test (BLRT) were used. Lower AIC, BIC and SABIC suggest a better-fitting model. Higher entropy values indicate greater model classification accuracy, with values above 0.8 generally considered acceptable. As for aLMR and BLRT, a significant p-value indicates that the k profile solution is a better than the k-1 profile solution. Besides, model complexity will increase when the number of profiles is increasing, so it is important to weigh the model complexity and the theoretical interpretability of the added profiles [ 50 ]. 2.3.3 Conceptualizing network characteristics across profiles ( RQ.3 ) Following profile identification, we estimated partial correlation networks for different profiles. Individual symptoms were allocated to the previously identified symptom dimensions. Networks were regularized using the graphical least absolute shrinkage and selection operator (GLASSO) with the extended bayes information criterion (γ = 0.5) to identify edges that are likely to be spurious and shrink these edge weights to zero [ 51 ]. For evaluating network estimation stability, we used 1,000 iterations of nonparametric bootstrapping to compute 95% confidence intervals (CIs) around edge weights [ 52 ]. We also used case-drop bootstrapping to estimate correlation stability coefficients, with coefficients above 0.5 indicating strong stability [ 52 ]. To illuminate the core symptoms across different profiles, we chose strength, expected influence centrality and corresponding bridging centrality as node centrality statistic. These indicators are more clearly defined and more widely used in psychometric networks [ 52 , 53 ]. Strength centrality, as an indicator of overall connectedness, calculates the sum of the absolute values of weights on edges connected to a node. However, expected influence centrality does not consider the absolute values of edges before summation. Consequently, expected influence centrality serves as an indicator reflecting overall positive connectivity within networks. Bridging strength and bridging expected influence centrality targets bridge symptoms in comorbidity development and maintenance. Furthermore, we compared network characteristics across these profiles using the NetworkComparisonTest R package. We explored network invariance, global strength invariance and centrality invariance based on 5000 permutations and a seed value of ‘123’. Bonferroni-Holm correction was used to access potential different edges [ 54 ]. Following previous studies [ 55 , 56 ], we also calculated spearman correlations among all edges, Jaccard Index for edge comparisons, and matches in rank-order centrality for centrality comparisons. 2.3.4 Linking to RDoC constructs and testing incremental validity ( RQ.4 ) We used hierarchical multiple regression (HMR) to verify the incremental validity [ 57 , 58 ]. In the initial regression model (M1), we used self-reported diagnostic status (Yes/No) as predictors for RDoC negative valence constructs. In the second model (M2), RDoC negative valence constructs were further regressed on symptom dimensions. Lastly, dummy-coded profile memberships were included as additional variables in the final model (M3). A statistically significant increase in variance between M2 and M3 in the model comparison suggests the incremental validity of profile memberships. 3. Results 3.1 Symptom dimensions ( RQ.1 ) The KMO value of 0.94 and the significant Bartlett’s test of sphericity ( Bartlett’s K-squared = 33822; p < .001) affirm the suitability of our data for factor analysis. Parallel analysis indicated that the eigenvalues of the seven factors derived from real data exceed the average eigenvalues of the simulated data (see online Fig. S1 in the Supplementary Materials), suggesting a potential of seven factors. The EFA results indicated excellent model fit for the seven-factor model (CFI = 0.988, TLI = 0.972, SRMR = 0.010, RMSEA = 0.036, 90% confidence interval of RMSEA = [0.034, 0.039]). We categorized the 23 specific individual symptoms into seven symptom dimensions, and the factor loading results are presented in Table 1 . Table 1 EFA Factor loadings on seven dimensions for 23 specific symptoms Obsessive-ompulsive Emotional distress Substance-related Eating-related Aggressive Psychotic Somatoform WASH 0.739* CHECK 0.813* NEU 0.836* OBS 0.480* 0.459* HOA 0.621* ORD 0.849* PSU 0.306* PHQ 0.697* 0.310* GAD 0.817* PSS 0.680* CONSU 0.837* DEPE 0.842* PROB 0.882* FTND 0.440* DIET 0.838* BULI 0.686* ORAL 0.449* HOS 0.734* OIR 0.944* DEL 0.306* 0.405* HAL 0.949* SSS 0.611* ISI 0.861* Note . * P < 0.05. Bold font highlights that the symptom holds the highest factor loading on a specific dimension. Only results with factor loadings exceeding 0.3 are presented. Abbreviations . EFA = exploratory factor analysis; WASH = compulsive washing; CHECK = compulsive checking; NEU = compulsive neutralizing; OBS = obsessive beliefs; HOA = hoarding; ORD = compulsive ordering; PHQ = depressive symptoms; GAD = anxiety symptoms ; DIET = dieting; BULI = bulimia and food preoccupation; ORAL = oral control; PSS = perceived stress; CONSU = alcohol consumption; DEPE = alcohol dependence symptoms; PROB = alcohol-related problems; HOS = hostility; OIR = outward irritability; DEL = delusion; HAL = hallucinations; SSS = somatic symptom; FTND = nicotine dependence; ISI = insomnia; PSU = problematic smartphone use. We used another non-redundant method, EGA, to confirm the number of symptom dimensions. The EGA also detected seven symptom communities, reproducing the results of EFA. The bootstrap EGA revealed stability for the seven symptom dimensions (median = 7, 95%CI [5.13, 8.87]), with the highest replication frequency (frequency = 0.612). Frequencies for four to six dimensions were 0.068, 0.136, and 0.184, respectively. Figure 3 displays the network estimated using EGA alongside the median network derived from bootstrap EGA. The congruence between the original network using EGA and the median network offers further support for the identified dimensions. Fig.S2 in the Supplementary Materials depicts symptom replication frequency across bootstraps. Structural stability exceeds 0.7 for all dimensions except the Aggressive dimension, indicating the overall robustness of the dimension structure. Overall, EFA and EGA exhibited substantial concurrence regarding the delineation of dimensions. Nonetheless, a noteworthy deviation emerged between the two methodologies. EGA delineated obsessive beliefs and problematic smartphone use as a distinct dimension, while EFA segregated somatic symptoms and insomnia into a separate dimension. Referring to the HiTOP framework, we argue for segregating somatic symptoms and insomnia into a distinct dimension, termed the Somatoform dimension. This differentiation enables a nuanced distinction between emotional distress and somatoform issues, aligning with the internalizing and somatoform spectra of HiTOP [ 10 , 11 ], respectively. Finally, we divided the 23 specific individual symptoms into seven symptom dimensions, namely Obsessive-Compulsive , Emotional Distress , Eating-Related , Substance-Related , Aggressive , Psychotic and Somatoform dimensions. Table S3 in the Supplementary Materials displays the final item factor loadings for symptom dimensions. 3.2 Psychopathological profiles ( RQ.2 ) We compared various person-centered model specifications ranging from one-profile to seven-profile models based on standardized scores of symptom dimensions (see Table S4 in the Supplementary Materials). AIC, BIC and SABIC tend to decrease as the number of profiles increases, and aLMR became nonsignificant at the seven-profile solution, revealing that seven-profile model are not improving fit than six-profile model. As for five or six-profile models, the additional profiles resemble those of the four-profile model in symptom dimension patterns, and the entropy values were smaller compared to the four-profile model, yielding no additional informative value. Considering model interpretability and statistical metrics, we ultimately selected the four-profile model as the best-fitting model. Figure 4 presented the profile-specific scores of symptom dimensions relative to the overall population. Four psychopathological profiles were identified. Profile 1, constituting 18.70% of the total sample (n = 767), demonstrates above-average scores on Substance-Related dimension, exceeding profiles 2 and 3 by approximately 0.6 standard deviations. This profile exhibits a tendency towards moderate tobacco dependence and frequent alcohol consumption, leading us to designate profile 1 as the Substance Use Group ( SUG ). The SUG is characterized by the highest proportion of males (72.8%). Profile 2, representing 29.64% of the sample (n = 1216), displayed above-average scores across most dimensions and symptoms, except for Substance-Related dimension. It ranked second in the intensity, following profile 4. Hence, profile 2 was labeled as Moderate Symptomatology group ( MSG ). The MSG features the youngest age demographic (Mean = 26.07) and the highest rate of unmarried individuals (80.2%). Profile 3 constituted 28.18% of the total participants (n = 1156), characterized by scores more than half a standard deviation below the mean across all symptom dimensions. Therefore, profile 3 was called Disengaged from Symptomatology Group ( DSG ). The DSG is marked by the oldest age distribution (Mean = 28.27), the highest proportion of females (61.1%) and the highest proportion of undiagnosed mental disorders (92.0%). Finally, profile 4, comprising 23.48% of the sample (n = 963), exhibited scores exceeding a standard deviation above the population mean across all symptom dimensions. Thus, profile 4 was labeled as the Severe Symptomatology Group ( SSG ). The SSG exhibits the highest prevalence of obesity (13.1%) and the highest prevalence of diagnosed mental disorders (42.2%). Tables S5 and S6 in the Supplementary Materials summarize the differences in symptom intensity and demographics across the profiles, respectively. 3.3 Symptom network characteristics of profiles ( RQ.3 ) We adopted an identical layout based on averaged node positions across networks, facilitating visual comparison of edge strength magnitude between different networks via edge thickness (see Fig.5). The symptom networks exhibited good performance in accuracy and stability across profiles. The general bootstrapped CIs around the edge weights were small, and the correlation stability coefficients for centrality indices exceeded the recommended cutoff of 0.5 (see Fig.S3 and Fig.S4 in the Supplementary Materials). INSERT-Fig.5 Global network characteristics and network comparison results across profiles were summarized in Tables S7 and S8 in the Supplementary Materials. The SSG exhibited the densest interconnections and the highest global network strength, while thenetwork connectivity of DSG was lowest. Omnibus tests of network structure invariance showed significant differences in network structure across all pairs except for the SUG versus the MSG . Besides, the global strength invariance test showed a significantly greater network connectivity in the SSG compared to the remaining three profiles. The edge invariance test also revealed significant differences, each ranging from 8 to 11 edges, between the SSG and each of the remaining three profiles, after Holm-Bonferroni correction. Furthermore, the findings from edge and centrality comparisons also revealed differences in symptom network characteristics across profiles (see Table S8 in the Supplementary Materials). Additionally, the nodes in the SSG manifested distinctive patterns in centrality metrics, whereas centrality metrics exhibited a degree of congruence across nodes in the remaining three profiles (see Table S7 and Fig.S5 in the Supplementary Materials). As for overall network connectivity (strength and expected influence centrality), hostility in the Aggressive dimension was one of the most central nodes in the SSG , whereas symptoms in the Obsessive-Compulsive (such as obsessive beliefs) and Distress dimension (such as depressive symptom) were mainly influential in the remaining three profiles. Regarding bridge network connectivity (bridge strength and bridge expected influence centrality), delusion in the Psychotic dimension acted as the core bridge symptoms in the SSG , while symptoms in the Distress dimension (such as depressive symptom and perceived stress) were mainly influential in the remaining profiles. 3.4 Incremental validity of profile memberships ( RQ.4 ) Simple regression models revealed a distinct relationship between profile memberships and RDoC negative valence constructs (see Table S9 in the Supplementary Materials). Hierarchical multiple regression models further confirmed the additional predictive value of utilizing profile memberships as a predictor of RDoC negative valence constructs (see Table 2). Model 2, including symptom dimensions and self-reported diagnostic status as predictors, displayed a statistically significant improvement in fit over Model 1. The relationships between different symptom dimensions and RDoC negative valence constructs exhibited specificity (see Fig.S6 in the Supplementary Materials). Furthermore, Model 3, which additionally integrated profile memberships as predictors, demonstrated a statistically significant enhancement in fit compared to Model 2 ( F = 19.899, p = .000 for childhood trauma; F = 3.939, p = .008 for intolerance of uncertainty; F = 12.298, p = .000 for behavioral inhibition). Profile membership remained significant predictors of RDoC negative valence constructs, even after adjusting for diagnostic status and seven symptom dimensions. Table 2 Hierarchical multiple regression comparing models using self-reported diagnosis of mental disorder as predictors (M1), models also include symptom dimensions as predictors (M2), and models further included profile memberships as additional predictor variables (M3) CTQS IUS BIS Predictor variable M1 M2 M3 M1 M2 M3 M1 M2 M3 Diagnosis 0.267** 0.054** 0.057** 0.183** 0.001 0.003 0.165** 0.033* 0.036* Dimensions OC − 0.151** − 0.129** 0.449** 0.445** 0.165** 0.168** Distress 0.107* 0.130* 0.680** 0.663** 0.686** 0.668** Substance 0.060** 0.106** − 0.040* − 0.023 − 0.102** − 0.058* Eating 0.044* 0.055* 0.031 0.030 0.117** 0.121** Somatoform 0.159** 0.169** − 0.303** − 0.309** − 0.222** − 0.225** Aggressive 0.215** 0.227** − 0.077** − 0.080** − 0.042 − 0.042 Psychotic 0.204** 0.234** − 0.075* − 0.067* − 0.156** − 0.131** Memberships (relative to DSG ) SUG − 0.087** − 0.001 − 0.044* MSG − 0.066** 0.048* 0.041* SSG − 0.185** 0.008 − 0.068* R 2 0.071 0.354 0.364 0.034 0.448 0.450 0.027 0.268 0.275 ΔR 2 0.071 0.283 0.009 0.034 0.415 0.002 0.027 0.241 0.007 Δ F p 313.544 .000 256.608 .000 19.899 .000 142.454 .000 439.641 .000 3.939 .008 115.022 .000 192.483 .000 12.298 .000 Note . * P < 0.05, ** P < 0.001. We performed dummy coding on profile memberships and the DSG was set as the baseline group. Abbreviations . CTQS = childhood trauma; IUS = intolerance of uncertainty; BIS = behavioral inhibition. SUG = Substance Use Group ; MSG = Moderate Symptomatology Group ; DSG = Disengaged from Symptomatology Group ; SSG = Severe Symptomatology Group . INSERT-Table 2 4. Discussion To our knowledge, this study is the first to systematically propose a phased framework for deconstructing psychopathological heterogeneity and applying it to a large, non-selective community sample. We deconstructed the heterogeneity of psychopathology into seven symptom dimensions and four distinct profiles, each exhibiting unique symptom network characteristics. Rigorous test also confirmed the incremental validity of these profiles. Phased research outcomes are discussed as follows. 4.1 Extracting homogeneous symptom dimensions with dimensional approach This study synthesized latent variable and network models to deconstruct heterogeneity at the symptom level rather than the diagnostic level. Both methods supported a robust seven-dimension symptom structure. Recently, a growing number of researchers have emphasized the equivalence between factor and network models, advocating for a focus on how these sophisticated tools can complement each other in capturing complex phenomena such as mental disorders [59,60]. It is notable that the dimensions identified in Chinese populations cover all three superspectra of HiTOP theory (Emotional Dysfunction, Psychosis and Externalizing), corroborating previous dimensional models obtained in Western populations [10,11]. The Obsessive-Compulsive , Emotional Distress , and Eating-Related dimensions correspond to the HiTOP Fear, Distress, and Eating Pathology subfactors within the internalizing spectrum, respectively. The Substance-Related dimension aligns with the HiTOP Substance Abuse subfactor within the disinhibiting externalizing spectrum, while the Aggressive dimension corresponds to the HiTOP Antisocial Behavior subfactor. The Psychotic and Somatoform dimensions align with the HiTOP Thought Disorder and Somatoform spectra, respectively. Beyond the original HiTOP framework, which does not yet include internet addiction symptoms, this study suggests that problematic smartphone use falls within the Obsessive-Compulsive dimension. As an emerging psychopathological phenomenon in the 21st century, problematic smartphone use is characterized by stereotypical excessive use of mobile phones [61,62], exhibiting obsessive-compulsive tendencies [63]. In sum, this study reveals the multidimensional structure of symptomatology in a large, non-selective community sample using factor and network models. These dimensions extend beyond traditional diagnostic boundaries and may reflect distinct neurobehavioral mechanisms. For example, intolerance of uncertainty was positively associated with the Obsessive-Compulsive and Emotional Distress dimensions but negatively associated with the Somatoform , Aggressive , and Psychotic dimensions. As the first step in deconstructing psychopathological heterogeneity, our study highlights the multidimensional structure of symptomatology as the foundation for identifying psychopathological subtypes and biomarkers. 4.2 Identifying psychopathological profiles with person-centered approach An essential contribution of this study is its novel identification of psychopathological profiles using person-centered approach. Notably, while previous study revealed differences only in term of symptom severity among profiles [64], psychopathological profiles in this large community sample exhibited variations in both overall intensity and specific types of dimensions. In terms of overall severity, the DSG showed minimal psychopathology severity, while the SSG exhibited maximal severity. Consistent with traditional nosology, the DSG had the highest proportion (92%) without a diagnosed mental disorder. Within the SSG , despite the majority having a diagnosed mental disorder (42.2%), a significant portion of individuals did not report such a diagnosis. Our findings may indicate the limitations of traditional diagnostic frameworks, particularly the rigid diagnostic thresholds that may fail to adequately capture psychopathological issues within the general population. A study using data from the China National Health and Wellness Survey found that the prevalence of generalized anxiety disorder in urban China was 5.3%, with only 0.5% of those reporting a formal diagnosis. This suggests, as our study also found, that traditional psychiatric nosology may overlook and underestimate mental health issues in the general population. In terms of specific dimensions, this study categorizes the SUG as exhibiting elevated levels exclusively in Substance-Related dimension and being predominantly composed of males (72.8%). Alcohol and tobacco consumption are significant public health concerns in China, increasing multiple disease risks, such as mental disorders and cardiovascular conditions [65]. Our findings highlight the necessity for healthcare professionals to collaborate in bolstering screening and treatment of Substance-Related dimension, particularly among Chinese men. Unlike the SUG , the MSG showed below-average intensity solely on Substance-Related dimensions, while demonstrating average to above-average intensity across all other dimensions. The MSG may represent a population experiencing psychological stress and heightened vulnerability to mental disorders, suggesting a high-risk subclinical group. Current diagnostic frameworks have predominantly concentrated on individuals with severe mental disorders [66]. Early monitoring and intervention for this high-risk group are crucial aspects of future mental health initiatives [67], helping prevent the deterioration toward severe disorders. This study highlights that deconstructing psychopathological heterogeneity requires consideration of both dimensional and categorical solutions. This step in the phased framework identified four psychopathology profiles in a non-selective population, reflecting differences in both overall severity and specific dimensions. These profiles span clinical populations above the diagnostic threshold, subclinical populations, and individuals with optimal mental health, collectively providing a comprehensive representation of the general population along the psychopathology continuum. The differentiation of psychopathological subtypes provides a foundation for conceptualizing symptomatological characteristics, ultimately guiding the development of targeted intervention strategies. 4.3 Conceptualizing symptomatological characteristics through network approach Instead of focusing solely on the average intensity of symptoms, we explored the interaction patterns among underlying symptoms through network perspective. It is essential for evidence-based personalized medicine, which advocates for tailored interventions based on person-specific symptom networks to improve treatment outcomes and healthcare efficiency [26]. This study found substantial differences in network characteristics among the four profiles, with the SSG notably diverging from other profiles by displaying the highest overall network connectivity. Consistent with dynamic models of mental disorder [68], the densely interconnected symptom network observed in the SSG , characterized by heightened mutual reinforcement and feedback loops among symptoms, signals that the psychopathological system is in a fragile state with low resilience. Additionally, core symptom discrepancies emerged between the SSG and the other three profiles. In the SSG , hostility in the Aggressive dimension and delusions in the Psychotic dimension played pivotal roles in their associations with other symptoms. However, the Emotional Distress and Obsessive-Compulsive dimension exerted central influences for the other psychopathological profiles. According to the centrality hypothesis, improvements in core symptom dimensions specific to each profile will foster overall mental health improvements [69]. This study suggests that interventions tailored for individuals with severe mental illness should emphasize Psychotic and Aggressive dimensions, while those with mild to moderate symptoms might respond better to interventions targeting Emotional Distress and Obsessive-Compulsive dimensions. In summary, this study reveals the heterogeneity of symptom network characteristics across psychopathology profiles. The network approach provides a more precise characterization of the complexity of psychopathology, providing unique insights into profile heterogeneity from a systemic and holistic perspective. The findings in this step also validate the effectiveness of the first two steps for deconstructing heterogeneity and providing supportive evidence for the incremental validity in the final step of the phased framework. 4.4 Assessing incremental validity through neurobehavioral function association To deepen the understanding of the neurobiological basis of psychopathological profiles and examine their incremental validity, this study established links between the identified profiles and neurobehavioral functioning within the RDoC domain [70,71]. Our findings revealed differences in neurobehavioral functioning among psychopathological profiles. For instance, the SUG exhibited diminished levels of behavioral inhibition, whereas the MSG manifested elevated levels of behavioral inhibition compared to the DSG . Therefore, intervention strategies for different profiles may be markedly divergent. Most importantly, profile memberships add unique information to the prediction of neurobehavioural functioning, even under overly rigorous testing conditions. This suggests that the profiles offer unique theoretical value beyond descriptive and dimensional approaches, while also demonstrating the preliminary feasibility of the phased framework. 4.5 Limitations and future research directions Although we have ensured the robustness of our results through empirical conceptualization and advanced statistical models, this study has some limitations. Firstly, this study utilized snap-shot cross-sectional designs. The temporal dynamics represent important aspects of psychopathology [68]. The next phase of research could adopt ecological momentary assessment to extract time-series features at the intra-individual level, thus validating the phased framework from dynamic perspectives [72]. Secondly, this study validated psychopathological profiles using self-reported RDoC constructs. Future studies could take multimodal perspectives to enhance validation by incorporating neurological unit of analysis such as genes, molecules, or brain circuits. Exploring computationally well-defined neurocognitive processes could also offer deeper insights into the neurophysiological mechanisms underpinning these transdiagnostic phenotypes [23]. Furthermore, future research should validate the replicability and generalizability of the phased framework using independent samples. Additionally, exploring its manifestation across diverse cultural contexts represents an important cross-cultural research avenue. 5. Conclusion Building on novel conceptualizations of psychopathology, this study proposes a phased framework for deconstructing psychopathological heterogeneity. Using a large-scale unselected sample, we applied a dimensional approach to deconstruct symptom heterogeneity into seven dimensions, a person-centered approach to identify four psychopathological profiles, and a network approach to characterize the symptom patterns of these profiles. Finally, we verified their incremental validity within the RDoC neurobehavioral framework. By using this framework, this study established a comprehensive psychopathological map of the Chinese population. The proposed phased framework for deconstructing psychopathological heterogeneity requires further validation with independent samples from diverse cultural and regional contexts. Declarations Ethics approval and consent to participate This study was ethically cleared by the Shanghai Mental Health Center’s Ethics Committee (Ref. No. 2023-54). The study design was preregistered on the Protocol Registration and Results System (NCT06105970) on September 26, 2023, prior to data collection. This study was conducted in accordance with the ethical standards of the Declaration of Helsinki. The questionnaire was administered online after informed consent was obtained. Consent for publication Not applicable. Data availability The dataset and analysis code are publicly available on OSF (DOI: 10.17605/OSF.IO/AWV6F, https://osf.io/awv6f/files/osfstorage). Competing interests The authors declare no competing interests. Funding This work was supported by the grants from Shanghai Municipal Health Commission (2019ZB0201). Authors’ contributions CYW, CYY, YQF, and ZW designed the study and conducted data collection. CYW, CYY, YQF, and AH handled data preprocessing. CYW developed the research ideas, led data analysis, and wrote the manuscript. ZW provided critical feedback on the research design, analysis methods and the manuscript. All authors participated in the revision of the manuscript and approved the manuscript’s content. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5694361","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":395924272,"identity":"5f2b9daa-998c-432f-be4a-66b0f2387cb7","order_by":0,"name":"Chaoyi Wu","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Chaoyi","middleName":"","lastName":"Wu","suffix":""},{"id":395924273,"identity":"323905ba-f02e-44c0-b8c5-1f9af6c1bc03","order_by":1,"name":"Chenyu Yuan","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Chenyu","middleName":"","lastName":"Yuan","suffix":""},{"id":395924274,"identity":"c7e25eb7-60c7-4b46-9e5e-9a017ca667fa","order_by":2,"name":"Yinqing Fan","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yinqing","middleName":"","lastName":"Fan","suffix":""},{"id":395924275,"identity":"d1111938-8925-48ef-b180-382042321ccd","order_by":3,"name":"Ang Hong","email":"","orcid":"","institution":"Shanghai Mental Health Center, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Ang","middleName":"","lastName":"Hong","suffix":""},{"id":395924276,"identity":"51bec011-8d1e-4ef2-98b4-686da6a5036a","order_by":4,"name":"Zhiling Wu","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zhiling","middleName":"","lastName":"Wu","suffix":""},{"id":395924277,"identity":"35bb42d1-62f0-4292-9bd6-4239eb7620a7","order_by":5,"name":"Zhen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYLACxgYGBn4GBjYStUg2kKzF4ACxWgyOnz388ucOmzzj86fTHvxgsMmXd2B+9gCvljN5aRaSZ9KKzW7kbjfsYUiz3HiAzdwAnxazAzlmBoZthxO33eDdJs3AcNjAsIGHTQKvlvNvzAwS2/4nbu4/S6yWGznGDw62HUjcwJAL0SLPQECL/Y03ZoyNbcmJM27kbpPsMUgzMGBmM8OrRbI/x/jjzza7xH6gwyR+VNgYyLc3P8OrBQiQnQEMKoPDBNQDAfMHFK58A2Eto2AUjIJRMLIAALakSMBBRKscAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Mental Health Center, Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-12-22 14:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5694361/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5694361/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-025-06960-8","type":"published","date":"2025-07-01T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72752120,"identity":"eaa060e3-ce9c-4f91-bcc2-de52fdfd0318","added_by":"auto","created_at":"2025-01-01 15:25:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104162,"visible":true,"origin":"","legend":"\u003cp\u003ePhased research framework for precisely deconstructing mental health heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. This study proposes a phased framework for deconstructing psychopathological heterogeneity, integrating dimensional, person-centered, and network approaches. The first step involves extracting homogeneous dimensions based on symptom co-occurrence patterns. The second step involves applying person-centered approach to identify latent subgroups based on symptom dimensions. The third step involves characterizing symptom interaction patterns across subgroups. Finally, incremental validity should be assessed through neurobehavioral function association.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5694361/v1/110b0b665d282c66cb57a581.png"},{"id":72752220,"identity":"c2149c2e-605b-4389-be46-4d881bab96bf","added_by":"auto","created_at":"2025-01-01 15:33:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":247955,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of data analysis within the phased research framework.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Panel A illustrates the extraction of final symptom dimensions through factor and exploratory graph analysis, combined with transdiagnostic theory. Panel B shows latent profile analysis, based on these dimensions, to identify psychopathological profiles. In Panel C, partial correlation networks were estimated for each profile, and network characteristics were compared. Panel D presents hierarchical multiple regression to assess incremental validity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5694361/v1/a4d68d91f92dd9311bb16739.png"},{"id":72752219,"identity":"c3f3da29-d3cf-4295-9a59-629c81ca2ff4","added_by":"auto","created_at":"2025-01-01 15:33:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":341328,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork model estimated using EGA (left) and the median network derived from bootstrap EGA (right).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Cluster 1 includes compulsive washing (WASH), checking (CHECK), neutralizing (NEU), ordering (ORD), and hoarding (HOA). Cluster 2 consists of obsessive beliefs (OBS) and problematic smartphone use (PSU). Cluster 3 encompasses depressive (PHQ) and anxiety (GAD) symptoms, perceived stress (PSS), insomnia (ISI), and somatic symptoms (SSS). Cluster 4 involves eating behaviors (DIET), bulimia and food preoccupation (BULI), and oral control (ORAL). Cluster 5 includes alcohol consumption (CONSU), dependence symptoms (DEPE), alcohol-related problems (PROB), and nicotine dependence (FTND). Cluster 6 covers hostility (HOS) and outward irritability (OIR). Cluster 7 consists of hallucinations (HAL) and delusions (DEL).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5694361/v1/33d0f57b7cd93db14b97435a.png"},{"id":72752134,"identity":"0c70d341-d669-4653-b0ed-234cd00a3e01","added_by":"auto","created_at":"2025-01-01 15:25:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182795,"visible":true,"origin":"","legend":"\u003cp\u003eThe pattern of the standardized symptom dimension scores across four profiles.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations\u003c/em\u003e. OC = \u003cem\u003eObsessive-Compulsive\u003c/em\u003e; DISTRESS = \u003cem\u003eEmotional Distress; \u003c/em\u003eSUBSTANC = \u003cem\u003eSubstance-Related\u003c/em\u003e; EATING = \u003cem\u003eEating-Related\u003c/em\u003e; SOMATOFO = \u003cem\u003eSomatoform\u003c/em\u003e; AGGRESSI = \u003cem\u003eAggressive\u003c/em\u003e; PSYCHOSI = \u003cem\u003ePsychotic\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5694361/v1/dcf998a037c7385479ead057.png"},{"id":72752223,"identity":"a041b4b1-10aa-4da1-a39b-9b35719a514a","added_by":"auto","created_at":"2025-01-01 15:33:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":437305,"visible":true,"origin":"","legend":"\u003cp\u003eSymptom networks for each psychopathological profile.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Edge thickness indicates the strength of partial correlation. Solid blue edges represent positive relationships, while dashed red edges represent negative relationships. \u003cem\u003eAbbreviations\u003c/em\u003e. \u003cem\u003eSUG\u003c/em\u003e= \u003cem\u003eSubstance Use Group\u003c/em\u003e; \u003cem\u003eMSG\u003c/em\u003e = \u003cem\u003eModerate Symptomatology Group\u003c/em\u003e; \u003cem\u003eDSG\u003c/em\u003e = \u003cem\u003eDisengaged from Symptomatology Group\u003c/em\u003e; \u003cem\u003eSSG \u003c/em\u003e=\u003cem\u003eSevere Symptomatology Group\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5694361/v1/4b2b1fe6fd3fa2b2d353c4d1.png"},{"id":86179347,"identity":"d2695c29-bee2-4c68-a23c-0b301a533c3a","added_by":"auto","created_at":"2025-07-07 16:17:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2612084,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5694361/v1/ae497f9a-9d5a-4bc4-89dc-6f184d4330e0.pdf"},{"id":72752124,"identity":"84742ea5-d039-4200-9ac9-2873d2102576","added_by":"auto","created_at":"2025-01-01 15:25:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1652549,"visible":true,"origin":"","legend":"","description":"","filename":"BMCPsychiatrySupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5694361/v1/273cf3c63598c0620f4cc218.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Framework for Parsing Psychopathological Heterogeneity: Initial Application in a Large-Scale Unselected Community Sample","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the last three decades, the incidence of mental disorders has risen. An epidemiological study revealed a lifetime prevalence of mental disorders in China is 16.6% [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, research and clinical treatment of mental disorders are constrained by challenges in identifying biomarkers of mental illness, which limits the development of effective treatment strategies [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. This is largely due to the over-reliance on traditional psychiatric nosology, such as the fifth edition of the \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders\u003c/em\u003e (DSM-5) [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e] and the 11th revision of the \u003cem\u003eInternational Classification of Diseases\u003c/em\u003e (ICD-11) [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. The purely descriptive nosology inevitably confounds multiple entities from an etiological standpoint, leading to drawbacks such as low diagnostic reliability [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e], heterogeneity problems within the same diagnosis [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e], and widespread comorbidity across diagnoses [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eBased on official nosology, most psychiatric research relied on case-control designs. This approach oversimplified mental health status into health or disease, ignoring the nuanced heterogeneity of traits or symptoms within both patient and healthy control groups [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Indeed, a significant portion of individuals exist in sub-health or subclinical states, challenging this binary classification. In other words, the official nosology is insufficient in capturing individual differences in mental health in a fine-grained way. Therefore, exploring empirical framework to deconstruct mental health heterogeneity in the general population is imperative. To address this limitation, this study proposes a phased framework for deconstructing psychopathological heterogeneity. It includes four steps: (1) extracting symptom dimensions to capture the core factors, (2) identifying psychopathological subtypes based on these dimensions, (3) characterizing these subtypes from a complex network perspective, and (4) linking these subtypes to neurobehavioral functions, and testing their incremental validity to assess their unique predictive value for mental health outcomes.\u003c/p\u003e\n\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e1.1 Extracting symptom dimensions using dimensional approach\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThe first step involves extracting homogeneous dimensions based on symptom co-occurrence patterns using a dimensional approach. Official nosology is comprised almost exclusively of large sets of dichotomous (present/absent) diagnoses. Whether DSM and ICD diagnoses accurately reflect the nature of mental disorders is questionable. But it is undeniable that mental symptoms, such as depressive mood and obsessions, exist and cause suffering in the general population. The dimensional approach, represented by The Hierarchical Taxonomy of Psychopathology (HiTOP) [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] utilizes data-driven methods such as factor analysis to organize symptomatology. It integrates statistically related symptoms into homogeneous dimensions, while assigning unrelated symptoms to separate dimensions [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. It is evident that most psychiatric problems are dimensional; thus, capturing symptomatology both above and below the diagnostic threshold aligns more closely with the true nature of mental health. Research has shown that dimensional models better capture the psychopathological patterns in the data compared to categorical ones [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. The dimensional approach exhibit improved performance in risk prediction [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] and prognosis [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] of mental disorders.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1.2 Identifying psychopathological profiles using person-centered approach\u003c/h2\u003e\n \u003cp\u003eAlthough the dimensional approach provides theoretical insights beyond official nosology, purely dimensional frameworks have drawbacks in practical clinical applications. One prominent challenge is the curse of dimensionality [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], which requires exponentially larger sample sizes to accurately identify outliers as the number of dimensions increases. Failure to meet this requirement results in exponential degradation of the model performance [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, a purely dimensional approach is challenging to apply in clinical settings. For any psychopathological nosology to be useful, it must effectively differentiate and categorize individuals; otherwise, it remains an ineffective clinical tool [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTherefore, the second step is to delineate psychopathological heterogeneity by integrating a person-centered approach, emphasizing the distinction between profiles, as opposed to the purely dimensional approach focused on outlier detection. Latent Variable Mixture Modelling (LVMM) is a person-centered statistical model that is effective in identifying latent subgroups in the populations [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Unlike the traditional variable-centered approach, LVMM does not assume homogeneity of the sample, but rather identifies different homogeneous subgroups through response patterns. LVMM offers advantages over variable-centered approaches in its ability to fit non-linear and complex interaction patterns among multiple indicators [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recent studies using LVMM have started to explore psychopathological profiles, focusing on specific symptoms within selected clinical populations. These include post-traumatic stress symptoms [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], depressive and anxiety symptoms [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], and autism spectrum symptoms [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, individual differences in transdiagnostic symptom dimensions in unselected general populations remain largely unexplored. According to the principles of computational factor modeling [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], it is crucial to deconstruct psychopathological heterogeneity using large-scale studies of unselected samples through remote, online, and \u0026ldquo;citizen science\u0026rdquo; efforts, rather than relying on small, diagnosed patient samples. This step aims to identify psychopathological profiles in a large, unselected community sample with diverse symptomatology.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e1.3 Characterizing psychopathological profiles using network approach\u003c/h2\u003e\n \u003cp\u003eAfter identifying psychopathological profiles, researchers can apply a network approach to characterize symptom interaction patterns across profiles. The network approach views psychopathology as a dynamic and complex system, proposing that mental disorders arise from the complex interaction between symptoms rather than isolated events, variables, or traits[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, individual differences in psychopathology should be characterized through the overall network structure. According to the network theory, the activation of the symptom node can transmit to other connected nodes within the psychopathological systems. Excessive mutual reinforcement and feedback loops render the symptom network fragile, potentially leading to the transitions into mental disorders. Specifically, a minor external disturbance can trigger a dramatic activation of the fragile psychopathological systems, persisting for an extended duration even after the stimulus ceases [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, network characteristics such as node centrality (structural importance) and density (the degree to which all nodes are interconnected) provide important insights into core dysregulation patterns across different phenotypes and potential individualized treatment targets [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. The network approach facilitates the exploration of causal mechanisms between symptoms and advances in personalized prediction, such as identifying early warning signals of mental disorders [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e1.4 Linking to neurobehavioral function and testing the incremental validity\u003c/h2\u003e\n \u003cp\u003eBuilding on the characterization of distinct psychopathological profiles, the next step involves linking these profiles to neurobehavioral functions and testing incremental validity, which is crucial for advancing our understanding of the neurobiological bases of psychopathology. The Research Domain Criteria (RDoC) seeks to understand mental health through fundamental neurobehavioral functions [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. This study uses self-reported RDoC negative valence constructs as indicators of neurobehavioral function. These constructs primarily regulate responses to aversive stimuli, such as fear, anxiety, and loss. The negative valence constructs in this study include \u003cem\u003ePotential Threat\u003c/em\u003e, represented by intolerance of uncertainty and behavioral inhibition, and \u003cem\u003eSustained Threat\u003c/em\u003e, represented by childhood trauma. Based on Ockham\u0026rsquo;s principle of parsimony, effective profiles should add to the prediction of neurobehavioural functions beyond other existing approach [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eIn summary, this study proposes an innovative, phased framework that integrates dimensional, person-centered, and network approaches to comprehensively capture the heterogeneity of psychopathology. We also applied it to a large, unselected community sample for preliminary validation. It is structured around four key research questions, as outlined in the phased research framework (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRQ1\u003c/strong\u003e. Which homogeneous symptom dimensions can be extracted from the symptom co-occurrence patterns?\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRQ2\u003c/strong\u003e. Which psychopathological profiles can be identified based on transdiagnostic symptom dimensions?\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRQ3\u003c/strong\u003e. What are the differences in symptom network characteristics across these profiles?\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRQ4\u003c/strong\u003e. Does profile membership provide additional predictive value for RDoC negative valence constructs beyond purely descriptive and dimensional approaches?\u003c/p\u003e\n \u003cp\u003eGiven the pioneering nature of this study, we can only propose exploratory hypotheses for the above research questions. We hypothesize that the psychopathological profiles identified in this study will offer additional value over traditional approaches.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Sample\u003c/h2\u003e\n \u003cp\u003eParticipants were recruited through the Naodao Research Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.naodao.com/\u003c/span\u003e\u003c/span\u003e), an online platform known for its emphasis on sharing, transparency, and usability. Data collection took place from September 26, 2023, over a period of 17 days. Individuals over 18 years old and fluent in Chinese are eligible to participate, excluding those with serious medical conditions that impair self-insight (e.g., major neurocognitive disorders or intellectual disabilities).\u003c/p\u003e\n \u003cp\u003eThe final sample consisted of 4,102 individuals (2,152 men and 1,950 women) from 33 provinces in China. Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e in the Supplementary Materials summarizes the demographic information of the sample. Participant ages ranged from 18 to 76 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27.08, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.78). Approximately 19.99% of participants self-reported having received a diagnosis of a mental disorder from a professional psychiatrist, similar to the lifetime prevalence reported in previous epidemiological studies (16.6%) [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Measures\u003c/h2\u003e\n \u003cp\u003eThis study used a comprehensive battery of measurement tools addressing symptomatology and RDoC negative valence constructs. Symptomatology measures encompassed obsessive-compulsive [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], depressive [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], anxiety [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], perceived stress [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], eating disorder [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], alcohol dependence [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e], nicotine dependence [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e], psychotic [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e], hostility-related [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], outward irritability [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e], insomnia [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], somatic [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], and problematic smartphone usage symptoms [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Some scales include subscales, resulting in a total of 23 specific symptoms. Besides, RDoC negative valence constructs comprised childhood trauma [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e], behavioral inhibition system [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e], and intolerance of uncertainty [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. For a comprehensive overview of instruments, please refer to online Appendix 1 in the Supplementary Materials. The descriptive statistics, Cronbach\u0026rsquo;s \u003cem\u003ea\u003c/em\u003e values and Pearson \u003cem\u003er\u003c/em\u003e coefficient values for specific individual symptoms were shown in Table S2 in the Supplementary Materials.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eAn overview of the data analysis, based on the phased research framework, is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The specific analysis steps will be detailed below.\u003c/p\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e\u003cem\u003e2.3.1 Extracting symptom dimensions\u003c/em\u003e (\u003cem\u003eRQ.1\u003c/em\u003e)\u003c/h2\u003e\n \u003cp\u003eTo explore the dimensions of symptomatology, the exploratory factor analysis (EFA) with the robust maximum likelihood estimation method (MLR) and oblique rotation was conducted. We used the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett test of homogeneity of variances to determine whether the data was suit for EFA. KMO value greater than 0.8, and significant Barrett\u0026rsquo;s test result indicate that data are adequate for factor analysis. The number of factors was determined through parallel analysis. Parallel analysis is recommended as one of the best methods to identify the number of factors [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. Besides, we refer to the model fit metrics to select the optimal model. The comparative fit index (CFI), Tucker-Lewis index (TLI), standardized root mean square residual (SRMSR) and the root mean square error of approximation (RMSEA) were used to assess model fit. The factor structure fits well when CFI and TCI exceed 0.9, and SRMR and RMSEA are below 0.08.\u003c/p\u003e\n \u003cp\u003eAdditionally, we performed exploratory graph analysis (EGA) with GLASSO method [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e] to verify the robustness of the EFA results. EGA integrates the Gaussian graphical model (GGM) with walktrap algorithm for weighted networks [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e] to produce a visual guide of the dimensionality assessment. The bootstrap exploratory graph analysis (bootstrap EGA) was used to test the stability of symptom communities [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. We utilized the R function bootEGA to generate 5000 iterations of parametric bootstrap samples and applied EGA to these samples, forming the sampling distribution of EGA results. Item stability was evaluated to confirm the robustness of each item\u0026rsquo;s placement within the empirically derived dimension.\u003c/p\u003e\n \u003cp\u003eFinally, we considered data-driven dimensions extracted from EFA and EGA, as well as the transdiagnostic theory to delineate the final symptom dimensions. Scores for each symptom dimension were quantified by factor scores that can be used in subsequent analyses. Specific symptoms were loaded on only one dimension and there was no cross-loading.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e\u003cem\u003e2.3.2 Identifying psychopathological profiles\u003c/em\u003e (\u003cem\u003eRQ.2\u003c/em\u003e)\u003c/h2\u003e\n \u003cp\u003eTo identify the psychopathological profiles, we adopted latent profile analysis (LPA) based on standardized symptom dimension scores [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]. The LPA was conducted using 1000 random start values, and 500 iterations, retaining the 250 best solutions for final stage optimization to avoid local maxima.\u003c/p\u003e\n \u003cp\u003eTo determine the best fitting model for the dataset, the Aka\u0026iuml;ke information criterion (AIC), Bayesian information criterion (BIC), sample-size adjusted BIC (SABIC), entropy values, adjusted Lo, Mendell, and Rubin\u0026rsquo;s Likelihood Ratio Test (aLMR) and the bootstrap likelihood ratio test (BLRT) were used. Lower AIC, BIC and SABIC suggest a better-fitting model. Higher entropy values indicate greater model classification accuracy, with values above 0.8 generally considered acceptable. As for aLMR and BLRT, a significant p-value indicates that the k profile solution is a better than the k-1 profile solution. Besides, model complexity will increase when the number of profiles is increasing, so it is important to weigh the model complexity and the theoretical interpretability of the added profiles [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e\u003cem\u003e2.3.3 Conceptualizing network characteristics across profiles\u003c/em\u003e (\u003cem\u003eRQ.3\u003c/em\u003e)\u003c/h2\u003e\n \u003cp\u003eFollowing profile identification, we estimated partial correlation networks for different profiles. Individual symptoms were allocated to the previously identified symptom dimensions. Networks were regularized using the graphical least absolute shrinkage and selection operator (GLASSO) with the extended bayes information criterion (\u0026gamma;\u0026thinsp;=\u0026thinsp;0.5) to identify edges that are likely to be spurious and shrink these edge weights to zero [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. For evaluating network estimation stability, we used 1,000 iterations of nonparametric bootstrapping to compute 95% confidence intervals (CIs) around edge weights [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. We also used case-drop bootstrapping to estimate correlation stability coefficients, with coefficients above 0.5 indicating strong stability [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTo illuminate the core symptoms across different profiles, we chose strength, expected influence centrality and corresponding bridging centrality as node centrality statistic. These indicators are more clearly defined and more widely used in psychometric networks [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. Strength centrality, as an indicator of overall connectedness, calculates the sum of the absolute values of weights on edges connected to a node. However, expected influence centrality does not consider the absolute values of edges before summation. Consequently, expected influence centrality serves as an indicator reflecting overall positive connectivity within networks. Bridging strength and bridging expected influence centrality targets bridge symptoms in comorbidity development and maintenance.\u003c/p\u003e\n \u003cp\u003eFurthermore, we compared network characteristics across these profiles using the NetworkComparisonTest R package. We explored network invariance, global strength invariance and centrality invariance based on 5000 permutations and a seed value of \u0026lsquo;123\u0026rsquo;. Bonferroni-Holm correction was used to access potential different edges [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. Following previous studies [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e], we also calculated spearman correlations among all edges, Jaccard Index for edge comparisons, and matches in rank-order centrality for centrality comparisons.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e\u003cem\u003e2.3.4 Linking to RDoC constructs and testing incremental validity\u003c/em\u003e (\u003cem\u003eRQ.4\u003c/em\u003e)\u003c/h2\u003e\n \u003cp\u003eWe used hierarchical multiple regression (HMR) to verify the incremental validity [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. In the initial regression model (M1), we used self-reported diagnostic status (Yes/No) as predictors for RDoC negative valence constructs. In the second model (M2), RDoC negative valence constructs were further regressed on symptom dimensions. Lastly, dummy-coded profile memberships were included as additional variables in the final model (M3). A statistically significant increase in variance between M2 and M3 in the model comparison suggests the incremental validity of profile memberships.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cem\u003e3.1 Symptom dimensions\u003c/em\u003e (\u003cem\u003eRQ.1\u003c/em\u003e)\u003c/h2\u003e\n \u003cp\u003eThe KMO value of 0.94 and the significant Bartlett\u0026rsquo;s test of sphericity (\u003cem\u003eBartlett\u0026rsquo;s K-squared\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33822; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) affirm the suitability of our data for factor analysis. Parallel analysis indicated that the eigenvalues of the seven factors derived from real data exceed the average eigenvalues of the simulated data (see online Fig.\u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e in the Supplementary Materials), suggesting a potential of seven factors. The EFA results indicated excellent model fit for the seven-factor model (CFI\u0026thinsp;=\u0026thinsp;0.988, TLI\u0026thinsp;=\u0026thinsp;0.972, SRMR\u0026thinsp;=\u0026thinsp;0.010, RMSEA\u0026thinsp;=\u0026thinsp;0.036, 90% confidence interval of RMSEA = [0.034, 0.039]). We categorized the 23 specific individual symptoms into seven symptom dimensions, and the factor loading results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEFA Factor loadings on seven dimensions for 23 specific symptoms\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eObsessive-ompulsive\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEmotional distress\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSubstance-related\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEating-related\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAggressive\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePsychotic\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSomatoform\u003c/em\u003e\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\u003eWASH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.739*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHECK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.813*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.836*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.480*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.459*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n 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align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.306*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.697*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.310*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.817*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.680*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCONSU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.837*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.842*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePROB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.882*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFTND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.440*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDIET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.838*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBULI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.686*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eORAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.449*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.734*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.944*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.306*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.405*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.949*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.611*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.861*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Bold font highlights that the symptom holds the highest factor loading on a specific dimension. Only results with factor loadings exceeding 0.3 are presented. \u003cem\u003eAbbreviations\u003c/em\u003e. EFA\u0026thinsp;=\u0026thinsp;exploratory factor analysis; WASH\u0026thinsp;=\u0026thinsp;compulsive washing; CHECK\u0026thinsp;=\u0026thinsp;compulsive checking; NEU\u0026thinsp;=\u0026thinsp;compulsive neutralizing; OBS\u0026thinsp;=\u0026thinsp;obsessive beliefs; HOA\u0026thinsp;=\u0026thinsp;hoarding; ORD\u0026thinsp;=\u0026thinsp;compulsive ordering; PHQ\u0026thinsp;=\u0026thinsp;depressive symptoms; GAD\u0026thinsp;=\u0026thinsp;anxiety symptoms ; DIET\u0026thinsp;=\u0026thinsp;dieting; BULI\u0026thinsp;=\u0026thinsp;bulimia and food preoccupation; ORAL\u0026thinsp;=\u0026thinsp;oral control; PSS\u0026thinsp;=\u0026thinsp;perceived stress; CONSU\u0026thinsp;=\u0026thinsp;alcohol consumption; DEPE\u0026thinsp;=\u0026thinsp;alcohol dependence symptoms; PROB\u0026thinsp;=\u0026thinsp;alcohol-related problems; HOS\u0026thinsp;=\u0026thinsp;hostility; OIR\u0026thinsp;=\u0026thinsp;outward irritability; DEL\u0026thinsp;=\u0026thinsp;delusion; HAL\u0026thinsp;=\u0026thinsp;hallucinations; SSS\u0026thinsp;=\u0026thinsp;somatic symptom; FTND\u0026thinsp;=\u0026thinsp;nicotine dependence; ISI\u0026thinsp;=\u0026thinsp;insomnia; PSU\u0026thinsp;=\u0026thinsp;problematic smartphone use.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eWe used another non-redundant method, EGA, to confirm the number of symptom dimensions. The EGA also detected seven symptom communities, reproducing the results of EFA. The bootstrap EGA revealed stability for the seven symptom dimensions (median\u0026thinsp;=\u0026thinsp;7, 95%CI [5.13, 8.87]), with the highest replication frequency (frequency\u0026thinsp;=\u0026thinsp;0.612). Frequencies for four to six dimensions were 0.068, 0.136, and 0.184, respectively. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e displays the network estimated using EGA alongside the median network derived from bootstrap EGA. The congruence between the original network using EGA and the median network offers further support for the identified dimensions. Fig.S2 in the Supplementary Materials depicts symptom replication frequency across bootstraps. Structural stability exceeds 0.7 for all dimensions except the \u003cem\u003eAggressive\u003c/em\u003e dimension, indicating the overall robustness of the dimension structure.\u003c/p\u003e\n \u003cp\u003eOverall, EFA and EGA exhibited substantial concurrence regarding the delineation of dimensions. Nonetheless, a noteworthy deviation emerged between the two methodologies. EGA delineated obsessive beliefs and problematic smartphone use as a distinct dimension, while EFA segregated somatic symptoms and insomnia into a separate dimension. Referring to the HiTOP framework, we argue for segregating somatic symptoms and insomnia into a distinct dimension, termed the \u003cem\u003eSomatoform\u003c/em\u003e dimension. This differentiation enables a nuanced distinction between emotional distress and somatoform issues, aligning with the internalizing and somatoform spectra of HiTOP [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], respectively. Finally, we divided the 23 specific individual symptoms into seven symptom dimensions, namely \u003cem\u003eObsessive-Compulsive\u003c/em\u003e, \u003cem\u003eEmotional Distress\u003c/em\u003e, \u003cem\u003eEating-Related\u003c/em\u003e, \u003cem\u003eSubstance-Related\u003c/em\u003e, \u003cem\u003eAggressive\u003c/em\u003e, \u003cem\u003ePsychotic\u003c/em\u003e and \u003cem\u003eSomatoform\u003c/em\u003e dimensions. Table S3 in the Supplementary Materials displays the final item factor loadings for symptom dimensions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cem\u003e3.2 Psychopathological profiles\u003c/em\u003e (\u003cem\u003eRQ.2\u003c/em\u003e)\u003c/h2\u003e\n \u003cp\u003eWe compared various person-centered model specifications ranging from one-profile to seven-profile models based on standardized scores of symptom dimensions (see Table S4 in the Supplementary Materials). AIC, BIC and SABIC tend to decrease as the number of profiles increases, and aLMR became nonsignificant at the seven-profile solution, revealing that seven-profile model are not improving fit than six-profile model. As for five or six-profile models, the additional profiles resemble those of the four-profile model in symptom dimension patterns, and the entropy values were smaller compared to the four-profile model, yielding no additional informative value. Considering model interpretability and statistical metrics, we ultimately selected the four-profile model as the best-fitting model.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presented the profile-specific scores of symptom dimensions relative to the overall population. Four psychopathological profiles were identified. Profile 1, constituting 18.70% of the total sample (n\u0026thinsp;=\u0026thinsp;767), demonstrates above-average scores on \u003cem\u003eSubstance-Related\u003c/em\u003e dimension, exceeding profiles 2 and 3 by approximately 0.6 standard deviations. This profile exhibits a tendency towards moderate tobacco dependence and frequent alcohol consumption, leading us to designate profile 1 as the \u003cem\u003eSubstance Use Group\u003c/em\u003e (\u003cem\u003eSUG\u003c/em\u003e). The \u003cem\u003eSUG\u003c/em\u003e is characterized by the highest proportion of males (72.8%). Profile 2, representing 29.64% of the sample (n\u0026thinsp;=\u0026thinsp;1216), displayed above-average scores across most dimensions and symptoms, except for \u003cem\u003eSubstance-Related\u003c/em\u003e dimension. It ranked second in the intensity, following profile 4. Hence, profile 2 was labeled as \u003cem\u003eModerate Symptomatology group\u003c/em\u003e (\u003cem\u003eMSG\u003c/em\u003e). The \u003cem\u003eMSG\u003c/em\u003e features the youngest age demographic (Mean\u0026thinsp;=\u0026thinsp;26.07) and the highest rate of unmarried individuals (80.2%). Profile 3 constituted 28.18% of the total participants (n\u0026thinsp;=\u0026thinsp;1156), characterized by scores more than half a standard deviation below the mean across all symptom dimensions. Therefore, profile 3 was called \u003cem\u003eDisengaged from Symptomatology Group\u003c/em\u003e (\u003cem\u003eDSG\u003c/em\u003e). The \u003cem\u003eDSG\u003c/em\u003e is marked by the oldest age distribution (Mean\u0026thinsp;=\u0026thinsp;28.27), the highest proportion of females (61.1%) and the highest proportion of undiagnosed mental disorders (92.0%). Finally, profile 4, comprising 23.48% of the sample (n\u0026thinsp;=\u0026thinsp;963), exhibited scores exceeding a standard deviation above the population mean across all symptom dimensions. Thus, profile 4 was labeled as the \u003cem\u003eSevere Symptomatology Group\u003c/em\u003e (\u003cem\u003eSSG\u003c/em\u003e). The \u003cem\u003eSSG\u003c/em\u003e exhibits the highest prevalence of obesity (13.1%) and the highest prevalence of diagnosed mental disorders (42.2%). Tables S5 and S6 in the Supplementary Materials summarize the differences in symptom intensity and demographics across the profiles, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 Symptom network characteristics of profiles\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(\u003cem\u003eRQ.3\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe adopted an identical layout based on averaged node positions across networks, facilitating visual comparison of edge strength magnitude between different networks via edge thickness (see Fig.5). The symptom networks exhibited good performance in accuracy and stability across profiles. The general bootstrapped CIs around the edge weights were small, and the correlation stability coefficients for centrality indices exceeded the recommended cutoff of 0.5 (see Fig.S3 and Fig.S4 in the Supplementary Materials).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eINSERT-Fig.5\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobal network characteristics and network comparison results across profiles were summarized in Tables S7 and S8 in the Supplementary Materials. The \u003cem\u003eSSG\u003c/em\u003e exhibited the densest interconnections and the highest global network strength, while thenetwork connectivity of \u003cem\u003eDSG\u003c/em\u003e was lowest. Omnibus tests of network structure invariance showed significant differences in network structure across all pairs except for the \u003cem\u003eSUG\u003c/em\u003e versus the \u003cem\u003eMSG\u003c/em\u003e. Besides, the global strength invariance test showed a significantly greater network connectivity in the \u003cem\u003eSSG\u003c/em\u003e compared to the remaining three profiles. The edge invariance test also revealed significant differences, each ranging from 8 to 11 edges, between the \u003cem\u003eSSG\u0026nbsp;\u003c/em\u003eand each of the remaining three profiles, after Holm-Bonferroni correction. Furthermore, the findings from edge and centrality comparisons also revealed differences in symptom network characteristics across profiles (see Table S8 in the Supplementary Materials).\u003c/p\u003e\n\u003cp\u003eAdditionally, the nodes in the \u003cem\u003eSSG\u003c/em\u003e manifested distinctive patterns in centrality metrics, whereas centrality metrics exhibited a degree of congruence across nodes in the remaining three profiles (see Table S7 and Fig.S5 in the Supplementary Materials). As for overall network connectivity (strength and expected influence centrality), hostility in the \u003cem\u003eAggressive\u003c/em\u003e dimension was one of the most central nodes in the \u003cem\u003eSSG\u003c/em\u003e, whereas symptoms in the \u003cem\u003eObsessive-Compulsive\u003c/em\u003e (such as obsessive beliefs) and \u003cem\u003eDistress\u0026nbsp;\u003c/em\u003edimension (such as depressive symptom) were mainly influential in the remaining three profiles. Regarding bridge network connectivity (bridge strength and bridge expected influence centrality), delusion in the \u003cem\u003ePsychotic\u003c/em\u003e dimension acted as the core bridge symptoms in the \u003cem\u003eSSG\u003c/em\u003e, while symptoms in the \u003cem\u003eDistress\u0026nbsp;\u003c/em\u003edimension (such as depressive symptom and perceived stress) were mainly influential in the remaining profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4 Incremental validity of profile memberships\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(\u003cem\u003eRQ.4\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimple regression models revealed a distinct relationship between profile memberships and RDoC negative valence constructs (see Table S9 in the Supplementary Materials). Hierarchical multiple regression models further confirmed the additional predictive value of utilizing profile memberships as a predictor of RDoC negative valence constructs (see Table 2). Model 2, including symptom dimensions and self-reported diagnostic status as predictors, displayed a statistically significant improvement in fit over Model 1. The relationships between different symptom dimensions and RDoC negative valence constructs exhibited specificity (see Fig.S6 in the Supplementary Materials). Furthermore, Model 3, which additionally integrated profile memberships as predictors, demonstrated a statistically significant enhancement in fit compared to Model 2 (\u003cem\u003eF\u0026nbsp;\u003c/em\u003e= 19.899,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = .000 for childhood trauma; \u003cem\u003eF\u0026nbsp;\u003c/em\u003e= 3.939,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = .008 for intolerance of uncertainty; \u003cem\u003eF\u0026nbsp;\u003c/em\u003e= 12.298,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = .000 for behavioral inhibition). Profile membership remained significant predictors of RDoC negative valence constructs, even after adjusting for diagnostic status and seven symptom dimensions.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHierarchical multiple regression comparing models using self-reported diagnosis of mental disorder as predictors (M1), models also include symptom dimensions as predictors (M2), and models further included profile memberships as additional predictor variables (M3)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCTQS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eIUS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBIS\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\u003ePredictor variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003eM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.057**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.183**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDimensions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.151**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.129**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e0.449**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e0.445**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.168**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.130*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e0.680**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e0.663**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.686**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.668**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.106**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.102**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.058*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.055*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.117**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSomatoform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.169**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.303**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.309**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.222**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.225**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAggressive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.215**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.227**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.077**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.080**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.234**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.075*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.067*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.156**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.131**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMemberships\u003c/p\u003e\n \u003cp\u003e(relative to \u003cem\u003eDSG\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.4656%;\"\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.6984%;\"\u003e\u003cbr\u003e\u003cbr\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.2741%;\" colspan=\"3\"\u003e\u003cbr\u003e\u003cbr\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 1.3761%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.945%;\" colspan=\"2\"\u003e\u003cbr\u003e\u003cbr\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.8405%;\"\u003e\u003cbr\u003e\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.1659%;\" colspan=\"3\"\u003e\u003cbr\u003e\u003cbr\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 1.1009%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.8075%;\"\u003e\u003cbr\u003e\u003cbr\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0642%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.0459%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSUG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.087**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMSG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.066**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e0.048*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSSG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.185**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.068*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313.544\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256.608\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.899\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e142.454\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.7159%;\"\u003e\n \u003cp\u003e439.641\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 9.1659%;\"\u003e\n \u003cp\u003e3.939\u003c/p\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.022\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.483\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.298\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003e\u003cem\u003eNote\u003c/em\u003e. *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. We performed dummy coding on profile memberships and the \u003cem\u003eDSG\u003c/em\u003e was set as the baseline group. \u003cem\u003eAbbreviations\u003c/em\u003e. CTQS\u0026thinsp;=\u0026thinsp;childhood trauma; IUS\u0026thinsp;=\u0026thinsp;intolerance of uncertainty; BIS\u0026thinsp;=\u0026thinsp;behavioral inhibition. \u003cem\u003eSUG\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eSubstance Use Group\u003c/em\u003e; \u003cem\u003eMSG\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eModerate Symptomatology Group\u003c/em\u003e; \u003cem\u003eDSG\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eDisengaged from Symptomatology Group\u003c/em\u003e; \u003cem\u003eSSG\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eSevere Symptomatology Group\u003c/em\u003e.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eINSERT-Table 2\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this study is the first to systematically propose a phased framework for deconstructing psychopathological heterogeneity and applying it to a large, non-selective community sample. We deconstructed the heterogeneity of psychopathology into seven symptom dimensions and four distinct profiles, each exhibiting unique symptom network characteristics. Rigorous test also confirmed the incremental validity of these profiles. Phased research outcomes are discussed as follows.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1 Extracting homogeneous symptom dimensions with dimensional approach\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study synthesized latent variable and network models to deconstruct heterogeneity at the symptom level rather than the diagnostic level. Both methods supported a robust seven-dimension symptom structure. Recently, a growing number of researchers have emphasized the equivalence between factor and network models, advocating for a focus on how these sophisticated tools can complement each other in capturing complex phenomena such as mental disorders [59,60]. It is notable that the dimensions identified in Chinese populations cover all three superspectra of HiTOP theory (Emotional Dysfunction, Psychosis and Externalizing), corroborating previous dimensional models obtained in Western populations [10,11]. The \u003cem\u003eObsessive-Compulsive\u003c/em\u003e, \u003cem\u003eEmotional Distress\u003c/em\u003e, and \u003cem\u003eEating-Related\u003c/em\u003e dimensions correspond to the HiTOP Fear, Distress, and Eating Pathology subfactors within the internalizing spectrum, respectively. The \u003cem\u003eSubstance-Related\u003c/em\u003e dimension aligns with the HiTOP Substance Abuse subfactor within the disinhibiting externalizing spectrum, while the \u003cem\u003eAggressive\u003c/em\u003e dimension corresponds to the HiTOP Antisocial Behavior subfactor. The \u003cem\u003ePsychotic\u003c/em\u003e and \u003cem\u003eSomatoform\u003c/em\u003e dimensions align with the HiTOP Thought Disorder and Somatoform spectra, respectively. Beyond the original HiTOP framework, which does not yet include internet addiction symptoms, this study suggests that problematic smartphone use falls within the \u003cem\u003eObsessive-Compulsive\u003c/em\u003e dimension. As an emerging psychopathological phenomenon in the 21st century, problematic smartphone use is characterized by stereotypical excessive use of mobile phones [61,62], exhibiting obsessive-compulsive tendencies [63]. \u003c/p\u003e\n\u003cp\u003eIn sum, this study reveals the multidimensional structure of symptomatology in a large, non-selective community sample using factor and network models. These dimensions extend beyond traditional diagnostic boundaries and may reflect distinct neurobehavioral mechanisms. For example, intolerance of uncertainty was positively associated with the \u003cem\u003eObsessive-Compulsive\u003c/em\u003e and \u003cem\u003eEmotional Distress \u003c/em\u003edimensions but negatively associated with the \u003cem\u003eSomatoform\u003c/em\u003e, \u003cem\u003eAggressive\u003c/em\u003e, and \u003cem\u003ePsychotic\u003c/em\u003e dimensions. As the first step in deconstructing psychopathological heterogeneity, our study highlights the multidimensional structure of symptomatology as the foundation for identifying psychopathological subtypes and biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.2 Identifying psychopathological profiles with person-centered approach\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn essential contribution of this study is its novel identification of psychopathological profiles using person-centered approach. Notably, while previous study revealed differences only in term of symptom severity among profiles [64], psychopathological profiles in this large community sample exhibited variations in both overall intensity and specific types of dimensions. In terms of overall severity, the \u003cem\u003eDSG \u003c/em\u003eshowed minimal psychopathology severity, while the \u003cem\u003eSSG\u003c/em\u003e exhibited maximal severity. Consistent with traditional nosology, the \u003cem\u003eDSG\u003c/em\u003e had the highest proportion (92%) without a diagnosed mental disorder. Within the \u003cem\u003eSSG\u003c/em\u003e, despite the majority having a diagnosed mental disorder (42.2%), a significant portion of individuals did not report such a diagnosis. Our findings may indicate the limitations of traditional diagnostic frameworks, particularly the rigid diagnostic thresholds that may fail to adequately capture psychopathological issues within the general population. A study using data from the China National Health and Wellness Survey found that the prevalence of generalized anxiety disorder in urban China was 5.3%, with only 0.5% of those reporting a formal diagnosis. This suggests, as our study also found, that traditional psychiatric nosology may overlook and underestimate mental health issues in the general population.\u003c/p\u003e\n\u003cp\u003eIn terms of specific dimensions, this study categorizes the \u003cem\u003eSUG\u003c/em\u003e as exhibiting elevated levels exclusively in \u003cem\u003eSubstance-Related\u003c/em\u003e dimension and being predominantly composed of males (72.8%). Alcohol and tobacco consumption are significant public health concerns in China, increasing multiple disease risks, such as mental disorders and cardiovascular conditions [65]. Our findings highlight the necessity for healthcare professionals to collaborate in bolstering screening and treatment of \u003cem\u003eSubstance-Related\u003c/em\u003e dimension, particularly among Chinese men. Unlike the \u003cem\u003eSUG\u003c/em\u003e, the \u003cem\u003eMSG\u003c/em\u003e showed below-average intensity solely on \u003cem\u003eSubstance-Related\u003c/em\u003e dimensions, while demonstrating average to above-average intensity across all other dimensions. The MSG may represent a population experiencing psychological stress and heightened vulnerability to mental disorders, suggesting a high-risk subclinical group. Current diagnostic frameworks have predominantly concentrated on individuals with severe mental disorders [66]. Early monitoring and intervention for this high-risk group are crucial aspects of future mental health initiatives [67], helping prevent the deterioration toward severe disorders.\u003c/p\u003e\n\u003cp\u003eThis study highlights that deconstructing psychopathological heterogeneity requires consideration of both dimensional and categorical solutions. This step in the phased framework identified four psychopathology profiles in a non-selective population, reflecting differences in both overall severity and specific dimensions. These profiles span clinical populations above the diagnostic threshold, subclinical populations, and individuals with optimal mental health, collectively providing a comprehensive representation of the general population along the psychopathology continuum. The differentiation of psychopathological subtypes provides a foundation for conceptualizing symptomatological characteristics, ultimately guiding the development of targeted intervention strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.3 Conceptualizing symptomatological characteristics through network approach\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstead of focusing solely on the average intensity of symptoms, we explored the interaction patterns among underlying symptoms through network perspective. It is essential for evidence-based personalized medicine, which advocates for tailored interventions based on person-specific symptom networks to improve treatment outcomes and healthcare efficiency [26].\u003c/p\u003e\n\u003cp\u003eThis study found substantial differences in network characteristics among the four profiles, with the \u003cem\u003eSSG\u003c/em\u003e notably diverging from other profiles by displaying the highest overall network connectivity. Consistent with dynamic models of mental disorder [68], the densely interconnected symptom network observed in the \u003cem\u003eSSG\u003c/em\u003e, characterized by heightened mutual reinforcement and feedback loops among symptoms, signals that the psychopathological system is in a fragile state with low resilience.\u003c/p\u003e\n\u003cp\u003eAdditionally, core symptom discrepancies emerged between the \u003cem\u003eSSG\u003c/em\u003e and the other three profiles. In the \u003cem\u003eSSG\u003c/em\u003e, hostility in the \u003cem\u003eAggressive\u003c/em\u003e dimension and delusions in the \u003cem\u003ePsychotic\u003c/em\u003e dimension played pivotal roles in their associations with other symptoms. However, the \u003cem\u003eEmotional Distress\u003c/em\u003e and \u003cem\u003eObsessive-Compulsive\u003c/em\u003e dimension exerted central influences for the other psychopathological profiles. According to the centrality hypothesis, improvements in core symptom dimensions specific to each profile will foster overall mental health improvements [69]. This study suggests that interventions tailored for individuals with severe mental illness should emphasize \u003cem\u003ePsychotic\u003c/em\u003e and \u003cem\u003eAggressive\u003c/em\u003e dimensions, while those with mild to moderate symptoms might respond better to interventions targeting \u003cem\u003eEmotional Distress\u003c/em\u003e and \u003cem\u003eObsessive-Compulsive\u003c/em\u003e dimensions.\u003c/p\u003e\n\u003cp\u003eIn summary, this study reveals the heterogeneity of symptom network characteristics across psychopathology profiles. The network approach provides a more precise characterization of the complexity of psychopathology, providing unique insights into profile heterogeneity from a systemic and holistic perspective. The findings in this step also validate the effectiveness of the first two steps for deconstructing heterogeneity and providing supportive evidence for the incremental validity in the final step of the phased framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.4 Assessing incremental validity through neurobehavioral function association\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo deepen the understanding of the neurobiological basis of psychopathological profiles and examine their incremental validity, this study established links between the identified profiles and neurobehavioral functioning within the RDoC domain [70,71]. Our findings revealed differences in neurobehavioral functioning among psychopathological profiles. For instance, the\u003cem\u003e SUG\u003c/em\u003e exhibited diminished levels of behavioral inhibition, whereas the\u003cem\u003e MSG \u003c/em\u003emanifested elevated levels of behavioral inhibition compared to the \u003cem\u003eDSG\u003c/em\u003e. Therefore, intervention strategies for different profiles may be markedly divergent. Most importantly, profile memberships add unique information to the prediction of neurobehavioural functioning, even under overly rigorous testing conditions. This suggests that the profiles offer unique theoretical value beyond descriptive and dimensional approaches, while also demonstrating the preliminary feasibility of the phased framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.5 Limitations and future research directions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough we have ensured the robustness of our results through empirical conceptualization and advanced statistical models, this study has some limitations. Firstly, this study utilized snap-shot cross-sectional designs. The temporal dynamics represent important aspects of psychopathology [68]. The next phase of research could adopt ecological momentary assessment to extract time-series features at the intra-individual level, thus validating the phased framework from dynamic perspectives [72]. Secondly, this study validated psychopathological profiles using self-reported RDoC constructs. Future studies could take multimodal perspectives to enhance validation by incorporating neurological unit of analysis such as genes, molecules, or brain circuits. Exploring computationally well-defined neurocognitive processes could also offer deeper insights into the neurophysiological mechanisms underpinning these transdiagnostic phenotypes [23]. Furthermore, future research should validate the replicability and generalizability of the phased framework using independent samples. Additionally, exploring its manifestation across diverse cultural contexts represents an important cross-cultural research avenue.\u003c/p\u003e"},{"header":"5.\tConclusion","content":"\u003cp\u003eBuilding on novel conceptualizations of psychopathology, this study proposes a phased framework for deconstructing psychopathological heterogeneity. Using a large-scale unselected sample, we applied a dimensional approach to deconstruct symptom heterogeneity into seven dimensions, a person-centered approach to identify four psychopathological profiles, and a network approach to characterize the symptom patterns of these profiles. Finally, we verified their incremental validity within the RDoC neurobehavioral framework. By using this framework, this study established a comprehensive psychopathological map of the Chinese population. The proposed phased framework for deconstructing psychopathological heterogeneity requires further validation with independent samples from diverse cultural and regional contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was ethically cleared by the Shanghai Mental Health Center\u0026rsquo;s Ethics Committee (Ref. No. 2023-54). The study design was preregistered on the Protocol Registration and Results System (NCT06105970) on September 26, 2023, prior to data collection. This study was conducted in accordance with the ethical standards of the Declaration of Helsinki. The questionnaire was administered online after informed consent was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset and analysis code are publicly available on OSF (DOI: 10.17605/OSF.IO/AWV6F, https://osf.io/awv6f/files/osfstorage).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the grants from Shanghai Municipal Health Commission (2019ZB0201).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCYW, CYY, YQF, and ZW designed the study and conducted data collection. CYW, CYY, YQF, and AH handled data preprocessing. CYW developed the research ideas, led data analysis, and wrote the manuscript. ZW provided critical feedback on the research design, analysis methods and the manuscript. All authors participated in the revision of the manuscript and approved the manuscript\u0026rsquo;s content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants for their valuable contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHuang Y, Wang YU, Wang H, Liu Z, Yu X, Yan J, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry 2019;6:211\u0026ndash;24. https://doi.org/10.1016/S2215-0366(18)30511-X.\u003c/li\u003e\n\u003cli\u003eClark DM. Realizing the mass public benefit of evidence-based psychological therapies: the IAPT program. 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J Med Internet Res 2022;24:e30907. https://doi.org/10.2196/30907.\u003c/li\u003e\n\u003cli\u003eNelson B, McGorry PD, Wichers M, Wigman JTW, Hartmann JA. Moving From Static to Dynamic Models of the Onset of Mental Disorder: A Review. JAMA Psychiatry 2017;74:528. https://doi.org/10.1001/jamapsychiatry.2017.0001.\u003c/li\u003e\n\u003cli\u003eSpiller TR, Levi O, Neria Y, Suarez-Jimenez B, Bar-Haim Y, Lazarov A. On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology. BMC Med 2020;18:1\u0026ndash;14. https://doi.org/10.1186/s12916-020-01740-5.\u003c/li\u003e\n\u003cli\u003eMichelini G, Palumbo IM, DeYoung CG, Latzman RD, Kotov R. Linking RDoC and HiTOP: A new interface for advancing psychiatric nosology and neuroscience. Clin Psychol Rev 2021;86:102025. https://doi.org/10.1016/j.cpr.2021.102025.\u003c/li\u003e\n\u003cli\u003eHagerty SL. Toward Precision Characterization and Treatment of Psychopathology: A Path Forward and Integrative Framework of the Hierarchical Taxonomy of Psychopathology and the Research Domain Criteria. Perspect Psychol Sci 2023;18:91\u0026ndash;109. https://doi.org/10.1177/17456916221079597.\u003c/li\u003e\n\u003cli\u003eGillan CM, Rutledge RB. Smartphones and the Neuroscience of Mental Health. Annu Rev Neurosci 2021;44:129\u0026ndash;51. https://doi.org/10.1146/annurev-neuro-101220-014053.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"psychopathology, heterogeneity, dimensional approach, network approach, person-centered approach","lastPublishedDoi":"10.21203/rs.3.rs-5694361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5694361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eTraditional descriptive nosology arbitrarily distinguishes between mental illness and health, hindering the progress of scientific research and clinical practice. Building on recent advancements in psychiatric conceptualization, this study proposes an innovative phased framework for deconstructing psychopathological heterogeneity. The framework involves four key steps: extraction of symptom dimensions, identification of psychopathological subtypes, characterization of symptom interaction patterns using a network approach, and validation of their incremental validity through links to neurobehavioral functions. This framework is preliminarily applied to a large, non-selective community sample (\u003cem\u003eN \u003c/em\u003e= 4102) to explore its utility and potential for deconstructing psychopathological heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData on comprehensive psychopathology and RDoC negative valence constructs were collected from the sample. Factor analysis and exploratory graph analysis were used to extract symptom dimensions. Latent profile analysis based on these dimensions was applied to identify psychopathological profiles. Partial correlation networks were estimated for each profile, and symptom network characteristics were compared across profiles. Finally, hierarchical multiple regression was applied to assess incremental validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe first step of the phased framework involves extracting homogeneous dimensions based on symptom co-occurrence patterns, yielding seven distinct dimensions:\u003cem\u003eObsessive-Compulsive\u003c/em\u003e, \u003cem\u003eEmotional Distress\u003c/em\u003e, \u003cem\u003eEating-Related\u003c/em\u003e, \u003cem\u003eSubstance-Related\u003c/em\u003e, \u003cem\u003eAggressive\u003c/em\u003e, \u003cem\u003ePsychotic\u003c/em\u003e, and \u003cem\u003eSomatoform\u003c/em\u003e dimensions. The second step involves applying a person-centered approach to identify latent subgroups based on these symptom dimensions. Four profiles were identified, namely\u003cem\u003e Substance Use Group\u003c/em\u003e, \u003cem\u003eModerate Symptomatology Group\u003c/em\u003e, \u003cem\u003eDisengaged from Symptomatology Group\u003c/em\u003e, and \u003cem\u003eSevere Symptomatology Group\u003c/em\u003e. The third step involves characterizing symptom interaction patterns across subgroups. Using a network approach, the \u003cem\u003eSevere Symptomatology Group\u003c/em\u003e exhibited the densest interconnections and the highest global network strength, with \u003cem\u003eAggressive\u003c/em\u003e and \u003cem\u003ePsychotic \u003c/em\u003edimensions serving as core issuescompared to other profiles. Finally, incremental validity was assessed through associations with neurobehavioral functions. Results showed that these profiles provided unique predictive value for RDoC negative valence constructs beyond both dichotomousdiagnostic status and purely dimensional approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study introduces a fine-grained framework for deconstructing psychopathological heterogeneity, providing a comprehensive approach to parsing psychopathology. While the framework is preliminarily applied to a large sample from the Chinese population, further validation is needed across diverse cultural and regional contexts.\u003c/p\u003e","manuscriptTitle":"A Framework for Parsing Psychopathological Heterogeneity: Initial Application in a Large-Scale Unselected Community Sample","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-01 15:25:33","doi":"10.21203/rs.3.rs-5694361/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-21T07:24:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T08:30:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69377713215614613057074974880924654765","date":"2025-03-25T08:14:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292283465086178931271931601665386465517","date":"2025-02-19T21:31:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-03T11:27:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223856702023994931146873959836510025592","date":"2025-01-23T08:47:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-21T07:16:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-01T08:58:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-30T17:14:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-30T17:12:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2024-12-22T14:44:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"761cc1f8-403d-455d-aca4-e384f827380e","owner":[],"postedDate":"January 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:07:39+00:00","versionOfRecord":{"articleIdentity":"rs-5694361","link":"https://doi.org/10.1186/s12888-025-06960-8","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-07-01 15:58:27","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-01-01 15:25:33","video":"","vorDoi":"10.1186/s12888-025-06960-8","vorDoiUrl":"https://doi.org/10.1186/s12888-025-06960-8","workflowStages":[]},"version":"v1","identity":"rs-5694361","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5694361","identity":"rs-5694361","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}