Network structure of transdiagnostic dimensions of emotional disorders in adolescents with subthreshold anxiety and depression: Links with psychopathology and socio- emotional adjustment

Network structure of transdiagnostic dimensions of emotional disorders in adolescents with subthreshold anxiety and depression: Links with psychopathology and socio- emotional adjustment | 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 Network structure of transdiagnostic dimensions of emotional disorders in adolescents with subthreshold anxiety and depression: Links with psychopathology and socio- emotional adjustment Gabriel Ródenas Perea, Alfonso Pérez Esteban, Alicia Pérez Albéniz, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4650180/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: The transdiagnostic and network model approaches to psychopathology have emerged as an alternative to taxonomic systems. The main goal was to examine the network structure of the transdiagnostic dimensions of emotional disorders in adolescents with subclinical emotional symptoms. In addition, cross-sectional network analyses were conducted to investigate the relationships between transdiagnostic dimensions and socio-emotional adjustment. Method: The sample consisted of a total of 476 adolescents from 85 schools from different provinces in Spain with subthreshold anxiety and depression. The mean age was 13.77 years ( SD = 1.43, range 10 to 18 years), 73.9% were female. Several questionnaires assessing transdiagnostic dimensions of emotional disorders, mental health difficulties, self-esteem, perceived social support, feeling of belonging at school, and prosocial behavior were used. Results: A strong interrelation was found between the transdiagnostic variables, except with the positive temperament dimension. The dimensions with the greatest weight were those of traumatic reexperiencing, intrusive cognitions, avoidance, neuroticism, and depressed mood. Negative relationships were found between the transdiagnostic variables and the protective factors included in the study. Stability analyses indicated that the networks where accurately estimated. Conclusion: The present paper points to the value of conceptualizing emotional disorders from a transdiagnostic and network model perspective. In addition, the work provides new insights into the nature of the relationships between transdiagnostic dimensions of emotional disorders, and the role played by risk and protective factors. transdiagnostic adolescents network model subclinical symptoms emotional disorders Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The symptoms of anxiety and depression and their associated disorders are amongst the main causes of disability worldwide (Whiteford et al., 2013 ; WHO, 2021b), particularly in women (Patwardhan et al., 2024 ). The global prevalence of anxiety disorders in children and adolescents is 3.35%, while for depressive disorders it is 1.84% (Kieling et al., 2024 ). In the adolescent population, anxiety and depression are comorbid phenomena at both clinical and subclinical levels. In other words, young people who present problems of anxiety also have a higher risk of presenting mood-related problems and vice versa (Balázs et al., 2013 ; Canals et al., 2019 ; Melton et al., 2016 ). In addition, according to recent studies, the approximate age when mental disorders begin is around 14.5 years old, with a third appearing before the age of 14 and almost half (48.4%) before the age of 18 (Solmi et al., 2022 ). Experiencing mental health problems before the age of 14 is associated with an increased risk of being diagnosed with some kind of mental disorder during adulthood (Mulraney et al., 2021 ). This means that adolescence is a crucial stage for implementing both prevention strategies (Teesson et al., 2024 ) and early interventions, as well as for promoting psychological wellbeing (Uhlhaas et al., 2023 ). Advances in the study of psychopathology have indicated the need to look beyond the lens of the main diagnostic classification systems, such as the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, text revision (DSM-5-TR; American Psychiatric Association, 2022 ) and the International Classification of Diseases, 11th revision (ICD-11; WHO, 2019). This is why recent years have seen the emergence of a transdiagnostic perspective that avoids the main limitations of the “common latent cause” biomedical model (e.g., objectification, tautological reasoning, categorical nature) to develop better understanding and classification of mental health disorders (Brown & Barlow, 2009 ; Dalgleish et al., 2020 ; Fusar-Poli et al., 2019 ; Scheffer et al., 2024 ). As a result, alternative approaches have emerged (Bullis et al., 2019; Sauer-Zavala et al., 2017). These include the Research Domain Criteria (RDoC; Insel et al., 2010 ), the Hierarchical Taxonomy of Psychopathology (HiTOP; Kotov et al., 2017 ), the network model (Borsboom, 2017 ), the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP; Barlow et al., 2010 )) and the Modular Approach to Therapy for Anxiety, Depression, Trauma, or Conduct Problems in children (Chorpita et al., 2005 ) to name a few (Dalgleish et al., 2020 ). All of these transdiagnostic approaches to psychopathology provide unique insights into understanding mental health disorders beyond the limitations of established diagnostic taxonomy (e.g., DSM, ICD) (Brown & Barlow, 2009 ; Conway et al., 2019 ; Dalgleish et al., 2020 ; Fonseca-Pedrero, 2018 ; Manfro et al., 2023 ; Widiger, 2021 ). Transdiagnostic clinical approaches such as UP and the Modular Approach to Therapy for Anxiety, Depression, Trauma, or Conduct Problems, have also demonstrated their efficacy in treating depressive and anxiety disorders in adults (Carlucci et al., 2021 ; Cassiello-Robbins et al., 2020 ; Schaeuffele et al., 2024 ), as well as in children and adolescents (García-Escalera et al., 2019 ; Kennedy et al., 2019 ; Mohajerin et al., 2023 ; Zemestani et al., 2024 ). In this context, it is worth highlighting Brown and Barlow’s ( 2009 ) transdiagnostic classification system of emotional disorders as the theoretical base of UP. In this model, the authors propose ten dimensions based on theory and research into factors associated with the development, expression, maintenance, and treatment of these types of disorders (Brown & Barlow, 2009 ; Osma et al., 2022 ; Rosellini & Brown, 2018 ): (1) Neurotic temperament [NT]; (2) Positive temperament [PT]; (3) Depressed mood [DM]; (4) Mania [MAN]; (5) Autonomic arousal [AA]; (6) Somatic anxiety [SOM]; (7) Social anxiety [SOC]; (8) Intrusive cognitions [IC]; (9) Traumatic re-experiencing [TRM]; and (10) Avoidance [AVD]. The first two dimensions (NT and PT) are higher-level or temperamental dimensions that enable classification into psychopathological profiles. The remaining dimensions are lower-level dimensions that distinguish the phenotypic or symptomatic focus of the emotional disorder (Rosellini et al., 2015 ). This hybrid dimension-categorical approach allows classification of emotional disorders and establishment of distinct transdiagnostic profiles (Brown & Barlow, 2009 ; Rosellini & Brown, 2018 ). Another alternative to the international taxonomic systems that has begun to be used in recent years is the network model in psychopathology (Borsboom, 2017 ). Network models consist of groups of psychological variables (such as symptoms) that are interconnected in the form of nodes and edges. Nodes represent variables to be studied and the edges represent the connections between them. In psychopathology networks, the nodes represent symptoms (signs, traits, affects, etc.) and the edges represent associations between symptoms (inhibition or activation) (Blanchard & Heeren, 2022; Borsboom, 2017 ; Borsboom & Cramer, 2013 ; Fonseca-Pedrero, 2018 ; Fonseca-Pedrero et al., 2022 ). This approach allows dynamics between variables to be identified, along with different levels of analysis, while also respecting individual variability (Nelson et al., 2017 ; Pérez Álvarez, 2017 ). It is a novel way to understand and intervene in behavior, and has enormous potential because among other things, it allows alternative forms of data analysis and suggests different ways of modelling and analyzing relationships between variables (e.g. symptoms, signs, psychological processes, personality traits, environmental triggers, substance consumption, etc.), designing new forms of prevention and intervention, and even improving the search for etiological factors (Fonseca-Pedrero, 2018 ; Fonseca-Pedrero et al., 2022 b). Network models are being used to address the etiological and phenotypical heterogeneity of emotional disorders, understanding them as complex dynamic systems (Borsboom & Cramer, 2013 ; Cramer et al., 2016 ; Jones et al., 2018 ; Manfro et al., 2023 ). One example of that is the work by Malgaroli et al. ( 2021 ) involving a systematic review of 23 studies using network analysis to examine major depressive disorder. The results demonstrated significant variability between the different samples (e.g., different instruments for measuring depression, different age ranges, inclusion of accessory symptoms or not, etc.) and in the organization, structure, and arrangement of the symptoms in the networks. This is a clear example of the challenges we still face in conceptualizing psychopathology and the limitations of current models in research, although the accumulating evidence is ever more illuminating. This paradigm offers us the opportunity to be able to avoid the conceptual problems involved in studying emotional disorders and how they relate to protective and risk factors (Borsboom & Cramer, 2013 ; Boschloo et al., 2015 ; Cramer et al., 2016 ; Olthof et al., 2023 ). Current research in the field of mental health is at a turning point that is driving it towards models that champion the heterogeneity, interdependence, and dimensionality of these problems in order to achieve better understanding without discounting contextual factors. The search for validated dimensional models of psychopathology, centered on the internalizing spectrum, is fundamental for improving understanding mechanisms of risk, predicting results, and guiding interventions (Snyder et al., 2023 ; Watson et al., 2022 ). To the best of our knowledge, there have been no analyses of the transdiagnostic dimensions of emotional disorders proposed by Brown and Barlow ( 2009 ) via network models in adolescents with subclinical symptoms of anxiety and depression. In this context, the main objective of this study was to examine the network structure of those transdiagnostic dimensions in a sample of adolescents with subclinical emotional symptoms. The study analyzed the network structure of these dimensions and their relationship with potential risk (internalizing and externalizing problems) and protective factors (self-esteem, perceived social support, subjective wellbeing, sense of belonging at school, and prosocial behavior). Our starting hypothesis was that the transdiagnostic dimensions would be positively related to each other (except for the positive temperament variable, which would have the opposite relation), and at the same time would be negatively related to the protective factors and positively related to the risk factors being assessed. Method Participants The sample for the screening phase comprised 8,746 students belonging to 85 schools and 532 classrooms from different provinces of Spain (La Rioja, Andalucía, Madrid, Galicia, Murcia, Asturias, Valencia, and Castilla-La Mancha) that participated in a multi-centre project (Fonseca-Pedrero et al., 2023 ). The students attended various public and independent centres for compulsory secondary education and vocational training, representing diverse socioeconomic backgrounds. Out of the initial 8,746 adolescents who participated in the screening phase, 475 selected at-risk adolescents with a moderate score in both the PHQ-9 and GAD-7 (between 10 and 15 points) agreed to participate in a trial examining the effectiveness of UP-A transdiagnostic intervention in educational settings. The exclusion criteria were a) having been diagnosed with a mental disorder or alcohol and/or substance dependence disorder, b) the presence of high risk for suicidal behaviour, c) having a medical disease or condition that prevents the participant from taking part in the psychological treatment, d) receiving another psychological treatment while the study is ongoing, e) increases and/or changes in participants’ medication if they are receiving pharmacological treatment during the study, and f) not completing or improperly completing (e.g., acquiescence, random response) baseline or post assessments. The final sample, comprising 376 participants (73.9% female), had a mean age of 13.77 years ( SD = 1.432), ranging from 10 to 18 years. The age distribution was as follows: 10 years, n = 1; 11 years, n = 2; 12 years, n = 99; 13 years, n = 124; 14 years, n = 115; 15 years, n = 66; 16 years, n = 50; 17 years, n = 18; 18 years, n = 1. Instruments The Multidimensional Emotional Disorder Inventory (MEDI; Rosellini & Brown, 2019 ). The MEDI is a self-report questionnaire used to assess the transdiagnostic dimensions of emotional disorders. This transdiagnostic assessment tool consists of 49 Likert-type items that measure the nine dimensions of emotional disorders. It was developed to use a 9-point Likert response scale (0 = not at all characteristic of me ; 8 = totally characteristic of me ). Low rates and severity of manic symptoms precluded the development of a mania dimension, which was therefore not included in the final version of the instrument (Rosellini & Brown, 2018 ). The present study used the validated Spanish version of the MEDI for adults (Osma et al., 2021). MEDI scores showed adequate psychometric properties in Spanish adolescents with subclinical emotional symptoms (Pérez-Esteban et al., 2024 ). The Strengths and Difficulties Questionnaire (SDQ) (Goodman, 1997 ). The SDQ is a self-report questionnaire that is widely used to assess different emotional and behavioural problems related to mental health in adolescents. The SDQ is made up of 25 statements using a Likert-type response format with three options (0 = not true , 1 = somewhat true , 2 = certainly true ) spread over five subscales: Emotional symptoms, Conduct problems, Hyperactivity, Peer problems, and Prosocial behaviour. The first four subscales yield a Total difficulties score. In this study we used the validated Spanish version of the SDQ (Ortuño-Sierra et al., 2022). We used the three-factor structure: Internalizing problems (Emotional and Peer Problems), Externalizing problems (Conduct problems and Hyperactivity), and Prosocial behaviour, which presented good psychometric properties in Spanish adolescents (Ortuño-Sierra et al., 2015). The Rosenberg Self-Esteem Scale (Rosenberg, 1965 ). This tool assesses respondents’ self-esteem. It is a unidimensional scale made up of 10 items (for example “In general I am happy with myself”), with responses given on a 4-point Likert-type scale (1 = completely disagree and 4 = completely agree ). A higher score indicates greater self-esteem or positive self-esteem. The present study used the Spanish version whose psychometric properties have been examined in previous studies (Oliva et al., 2011 ). The Maryland Safe and Supportive Schools Initiative Questionnaire (MDS3). This was used to assess the feeling of belonging at school. Participants were administered 14 of the items making up the MDS3 developed by Johns Hopkins Center for Youth Violence Prevention (Bradshaw et al., 2014 ). These items are valid indicators related to feelings of safety, engagement, and the atmosphere at the school. Responses are made using a Likert-type scale (1 = completely disagree and 4 = completely agree ). It evaluates three subscales: student harmony, relations with teachers, and overall sense of belonging at school. The present study used the Spanish adaptation (Lucas-Molina et al., 2018 ). The Multidimensional Scale of Perceived Social Support (MSPSS) (Zimet et al., 1990 ). This is a self-reported measure with 12 items assessing perceptions of social support from three sources: (1) Family, (2) Friends, and (3) Significant Others. Responses are made using a Likert-type scale from 1 ( strongly disagree ) to 7 ( strongly agree ). The validated Spanish version of the MPSS demonstrates suitable psychometric properties in the adolescent Spanish population (Trejos-Herrera et al., 2018 ). The KIDSCREEN-10 Index (KS-10 Index; Ravens-Sieberer et al., 2010 ). The KS-10 Index is the shortest version of the KIDSCREEN, a generic questionnaire that was transculturally developed in 13 European countries for children and adolescents aged 8 to 18 years. The KS-10 Index has 10 items that score as an index of well-being and health-related quality of life (HRQoL). The items present a recall period of one week and use a 5-point Likert response scale (0 = not at all ; 4 = extremely ). The validity and reliability of the KS-10 Index have been proven in the European population (Erhart et al., 2009 ; Ravens-Sieberer et al., 2010 ). The Oviedo Infrequency Scale-revisited (INF-OV-R). The INF-OV-R (Fonseca-Pedrero, Pérez-Albéniz, et al., 2019 ) was administered to participants in order to detect those who responded in a random, pseudorandom or dishonest manner. The INF-OV-R is a self-report instrument consisting of 10 items in a dichotomous scale format ( yes / no ). Students with more than three incorrect responses on the INF-OV-R scale were excluded from the sample. Procedure The study was approved by the La Rioja Clinical Research Ethics Committee (CEICLAR number for the project PI 552). With the exception of the MEDI, the tests were administered in the screening phase collectively, using personal computers, to groups of 10 to 30 students during school hours in a specially-prepared classroom. The MEDI was administered to the 476 subclinical participants selected one week after the screening phase through personal electronic devices. This was done in smaller groups of 10 to 15 students, outside school hours in a specially-prepared classroom. No incentive was provided for participation. For participants under 16, parents were asked to provide their written informed consent for the child to participate in the study. Participants were informed of the confidentiality of their responses and the voluntary nature of the study. This study is part of the PSICE project (Evidence-based Psychology in Educational Contexts) in order to examine the UP-A’s effectiveness (Fonseca-Pedrero et al., 2023 ). Registration number in ClinicalTrials.gov NCT05322642. Data analysis First, descriptive statistics were calculated for all of the measuring instruments. Then, a network was estimated for the transdiagnostic domains measured with the MEDI. A second network was estimated, incorporating the protection and risk factors addressed in the study to analyse their relationship with the MEDI domains. The details of network analysis have been documented in-depth elsewhere (Epskamp et al., 2012 , 2016 ). Network estimation. For the domains, which were constructed by summing items per domain and then standardizing the resulting variable, we specified a Gaussian Graphical Model (GGM) (Epskamp & Fried, 2018 ). This model resulted in conditional dependence relations which are akin to partial correlations: if two nodes are connected in the resulting graph via an edge, they are statistically related after controlling for all other variables in the network; if they are unconnected, they are conditionally independent. The Fruchterman-Reingold algorithm was used for the layout, with strongly connected nodes placed closer to each other and the least connected nodes far apart (Epskamp et al., 2012 ). Network inference. In line with previous studies examining networks (Fonseca-Pedrero et al., 2018 ), we estimated two measures: Expected Influence (EI) and predictability. EI is the sum of all edges of a node (Robinaugh et al., 2016 ). We used EI instead of strength centrality (Opsahl et al., 2010 )—which has been used in previous studies—because strength centrality uses the sum of absolute weights (i.e., negative edges are turned into positive edges before summing), which distorts the interpretation if negative edges are present. Predictability is an absolute measure of interconnectedness: it provides us with the variance of each node that is explained by all its neighbors (Haslbeck & Fried, 2017 ). By predictability of node A we mean how well node A can be predicted by all its neighboring nodes in the network (Haslbeck & Waldorp, 2018 ). In the figures, dark areas in the circles around nodes can be interpreted akin to R 2 (% of explained variance) (Haslbeck & Fried, 2017 ). Network stability. To test network stability and accuracy, we used bootstrapping routines implemented in the R-package bootnet (Epskamp et al., 2017 ). Results Estimated Network of the MEDI Dimensions. Table 1 shows the descriptive statistics for the measures used in the study. Figure 1 shows the specified network of the transdiagnostic variables evaluated in the study sample. There was a strong positive relation between the TRM and IC dimensions. NT also appeared to be positively related to the other dimensions evaluated with MEDI and PT was negatively related with the variables DM and SOC. Figure 2 shows the inference measures for the network. The most central nodes in terms of expected standardized influence were mainly AVD and IC, followed by TRM and NT. The node with the least expected influence was PT. The most central notes in terms of strength were AVD and IC, followed by DM. The node with the least strength was SOM. The average predictability was 46.86%. Estimated Network of the MEDI Dimensions and the Risk and Protective Factors. Figure 3 shows the specified network of relationships between the MEDI dimensions and the psychometric indicators of psychopathology and socio-emotional adjustment. There was a positive interdependence between all of the transdiagnostic dimensions, with the exception of the PT variable, which was basically negatively related to DM and SOC. The MEDI dimensions were weakly related to SDQ internalizing and externalizing problems. In addition, there was a strong negative relationship between DM and the PT and self-esteem variables. Self-esteem, in turn, was negatively related to internalizing problems, which demonstrated a strong negative relationship with the perceived social support variable. The PT dimension was positively associated with the perceived quality of life, which was also strongly related to the perceived social support and sense of belonging at school variables. Lastly, there was a strong negative relationship between prosocial behavior and externalizing problems. Figure 4 shows the inference measures for the second network specified. The most central nodes in terms of standardized expected influence were AVD and IC, followed by TRM and NT. The nodes with the least expected influence were internalizing problems, externalizing problems, and self-esteem. The most central nodes in terms of strength were IC, DM, and AVD. The node with the least strength was externalizing problems. The average predictability was 36.07%. Network stability of the estimated networks The results of the stability and accuracy analysis indicated that all networks were accurately estimated, with moderate confidence intervals around the edge weights (See Figs. 1S to 4S in supplementary material). Discussion The transdiagnostic approach and the network model approach to psychopathology have emerged as alternatives to traditional diagnostic systems. This is the first study to date that has examined the network structure of the transdiagnostic dimensions of emotional disorders in a large sample of adolescents with subclinical emotional symptoms and the relationship with risk factors and protective factors. New approaches such as transdiagnostic and network models may provide new perspectives on the origin, maintenance, clinical management, and recovery from experiences of disabling mental distress (Conway et al., 2019 ; Dalgleish et al., 2020 ; Fonseca-Pedrero, 2018 ; Widiger, 2021 ). The first objective of this study was to analyze the network structure of the transdiagnostic dimensions of emotional disorders put forward by Brown and Barlow ( 2009 ) and their applicability in a representative sample of adolescents with subclinical emotional problems. The results demonstrate a strong interaction between the transdiagnostic dimensions, with the exception of PT. This is consistent as positive temperament is understood as the tendency to experience a positive affect in response to social tasks and goal-directed tasks (e.g. extraversion, behavioral activation, positive affectivity) (Rosellini & Brown, 2019 ). Previous studies have shown that PT is significantly and inversely correlated with the other transdiagnostic dimensions (Brown & Barlow, 2009 ; Rosellini & Brown, 2019 ; Osma et al., 2022 ; Pérez-Esteban et al., 2024 ). In addition, the scientific literature has demonstrated that low positive affect is related to developing greater depressive disorder and anxiety disorders (Barlow & Kennedy, 2016 ). Our results add to the previous evidence as PT was the dimension with the least expected influence within the network. In terms of strength and expected influence, the nodes in the network with the greatest weight were AVD, IC, TRM, NT and DM. The strong association and the weight of TRM in the network, along with the IC dimension, is particularly interesting. It is well known in the literature that adverse experiences in childhood and adolescence mean a greater risk of experiencing generalized anxiety and/or symptoms of depression during or after subsequent stressful life events (Ródenas-Perea et al., 2022 ; Li et al., 2022 ; Zhou et al., 2022 ; Reis et al., 2024 ). One of the explanations for this is that traumatic childhood experiences result in the development of ineffective coping strategies (Barlow, 2004 ; Snyder et al., 2019 ; Cludius et al., 2020 ; Sætren et al., 2023 ) such as excessive focusing on events themselves, the emotions they provoke, and analyzing their causes and effects (Aldao et al., 2010 ; Nolen-Hoeksema et al., 2008 ; Snyder et al., 2019 ). This is known as rumination and arises as an attempt to understand and resolve the situation, although rumination has been shown to be negatively related to problem solving and is maladaptive for coping with stressful life events (Battista et al., 2023 ; Michael et al., 2007 ; Papageorgiou & Wells, 2003 ; Snyder et al., 2019 ). Another response to traumatic experiences is the appearance of intrusive thoughts. Research has found a strong relationship between the presence of intrusive thoughts and the experience of traumatic events in early ages (Barzilay et al., 2019 ; Ródenas‐Perea et al., 2022). These people also present higher levels of avoidance behaviors and symptoms of anxiety or depression than those who have not had such experiences (Jhang, 2020 ; Pérez et al., 2017 ). This is consistent with our results. Lastly, it is worth noting that in addition to the strong relationship between these variables, there were also mediating relationships. For example, maladaptive strategies such as rumination and the appearance of phenomena such as intrusive thoughts are mediating variables between traumatic experiences in childhood and the subsequent appearance of symptoms of stress, anxiety, and depression in later life (Kim et al., 2021 ; Ródenas‐Perea et al., 2022; Sætren et al., 2023 ). The second objective of our study was to examine the network structure of the transdiagnostic dimensions in relation to other psychometric indicators of psychopathology and socio-emotional adjustment. In the first place, the results corroborate the importance of the AVD, IC, and TRM dimensions. In addition, it seems logical to have found a negative relationship between the transdiagnostic dimensions (except PT) and the other protective variables (perceived social support, sense of belonging at school, prosocial behavior, quality of life, and self-esteem). These factors were closely interrelated with each other, and seem to act as an important source of protection against different types of psychopathological domains in the adolescent population (Fonseca-Pedrero et al., 2021 ; Fonseca-Pedrero, Ortuño-Sierra, et al., 2019 ). Our results also indicate the importance of perceived social support, a sense of belonging at school, and adolescents’ reported quality of life (Kumcağız & Şahin, 2017 ; Nowicki, 2008 ; Singstad et al., 2021 ). Having a positive, cohesive, social support network seems to be a fundamental protective factor for mental health in adolescence. In fact, research has shown that there is a significant positive relationship between social support and psychological wellbeing (Agbaria & Bdier, 2020 ; Singstad et al., 2021 ; Tian et al., 2013 ) and that such support allows adolescents to better cope with stressful life events (Camara et al., 2017 ; Reife et al., 2020 ). In addition, satisfaction with teachers and feeling a sense of belonging at school may protect young people from the effects of stressful life events, promoting resilience and reducing the likelihood of developing depression or behavioral problems (Höltge et al., 2021 ; Wang et al., 2013 ). In contrast, dissatisfaction with teachers is a potential risk factor for developing anxiety, emotional, and behavioral problems (Arslan, 2021 ; Mameli et al., 2018 ). Lastly, in our study, prosocial behavior was positively related to a feeling of belonging at school and perceived social support, and negatively related to externalizing symptomatology and poor behavioral adjustment (Inglés et al., 2015 ). Our results have notable clinical implications. On the one hand, studying subclinical states may help us understand the dynamics between different variables that may develop into different mental health problems. Network analysis has confirmed that problems that manifest sub-clinically in specific dimensions in early stages of development are consistent over subsequent years in more serious psychopathological processes (Groen et al., 2019 ). These aspects are related to the clinical staging model that seeks to facilitate treatment selection based on the progression and severity of the problem (Hartmann et al., 2020 ). On the other hand, implementing network analysis is useful for uncovering specific treatment pathways, which may encourage the development of more effective interventions for addressing symptoms of emotional problems based on where they are in their progression and the severity. Previous studies have shown that intervention in specific symptoms indirectly triggers a wave of change in other symptoms, altering the connections between elements in the network (Jurado-González et al., 2024 ). Incorporating the network model together with the transdiagnostic approach applied to adolescents’ emotional problems opens the door to better understanding of the psychopathology and possible focuses of individualized treatment (Borsboom et al., 2011 ; Manfro et al., 2023 ). In addition, the results of our study may feed into novel theoretical dimensional and transdiagnostic models applied to psychopathology, such as the internalizing spectrum within the HiTOP model (Snyder et al., 2023 ; Watson et al., 2022 ). Similarly, the relationship with school-related risk and protective factors allows us to consider the importance of relationships in adolescents’ developmental surroundings (social support, sense of belonging), encouraging development of structural initiatives or universal prevention programs in schools that promote protective factors and reduce risk factors (Fonseca-Pedrero et al., 2023 ). The present study is not without limitations. Firstly, the use of solely self-reported data limits the conclusions that can be drawn. Secondly, the study was transversal, which means it does not allow us to examine the dynamic individual interactions between the variables nor to do so longitudinally. A time-series analysis would avoid this issue, but we must be cautious, as the field of network analysis is in its infancy, which means there still fundamental questions open about the best way to estimate a network over time. Thirdly, only some risk and protective factors were selected, although they were consistent and based on the literature. It would be useful to consider others that are involved in various problems that are particularly prevalent in adolescence (González-Roz et al., 2023 ). Another limitation was the structure of the networks being restricted by the tool used in the study. Network analysis is still in the early stages and is not without its critics. This means we should be careful when it comes to extrapolating from these results to other populations in which a wider range of scores may lead to networks behaving differently. Nonetheless, it is a promising methodology for obtaining important information in a variety of research fields (Bringmann et al., 2021; Bringmann et al., 2019 ; Guloksuz et al., 2017 ). Future lines of research should include network models with variables from different levels of analysis, especially those providing information about other transdiagnostic variables (such as rumination), clinical variables (such as frequency and type of traumatic experiences, suicidal behavior, etc.), and contextual variables (such as bullying). In addition, it would be extremely interesting to collect longitudinal information via new methodologies such as ambulatory assessment in order to move closer towards dynamic, contextual, tailored models. Finally, it would also be interesting to be able to incorporate new analytical methodologies such as Directed Acyclic Graphs (DAG) (Briganti et al., 2021 ; Moffa et al., 2017 ). Conclusions New psychological approaches, such as network models and transdiagnostic approaches to emotional problems in adolescence, may offer new understanding in the study of mental health in this developmental stage. Only by embracing the complexity will we be able to improve prevention, detection and intervention for these problems. Declarations Funding This research was funded by a national project awarded by the Ministry of Science and Innovation of the Government of Spain and the Agency and the European Regional Development Fund (Project “PID2021-127301OB-I00” funded by MCIN / AEI / 10.13039/501100011033 FEDER, UE) and Psicofundación. Author Contribution G.R. is the main author of the article based on the mentorship of E.F and S.A. Both helped G.R with the main theme of the article and the pursued objectives. E.F also helped with Network analysis and S.A. with the introduction and discussion. 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Clin Psychol science: J Association Psychol Sci 11(6):1044–1063. https://doi.org/10.1177/21677026221119483 Solmi M, Radua J, Olivola M, Croce E, Soardo L, Salazar de Pablo G, Il Shin J, Kirkbride JB, Jones P, Kim JH, Kim JY, Carvalho AF, Seeman MV, Correll CU, Fusar-Poli P (2022) Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry 27(1):281–295. https://doi.org/10.1038/s41380-021-01161-7 Teesson M, Birrell L, Slade T, Mewton LR, Olsen N, Hides L, McBride N, Chatterton ML, Allsop S, Furneaux-Bate A, Bryant Z, Ellem R, Baker MJ, Healy A, Debenham J, Boyle J, Mather M, Mihalopoulos C, Chapman C, Newton NC (2024) Effectiveness of a universal, school-based, online programme for the prevention of anxiety, depression, and substance misuse among adolescents in Australia: 72-month outcomes from a cluster-randomised controlled trial. Lancet Digit Health 6(5):e334–e344. https://doi.org/10.1016/S2589-7500(24)00046-3 Tian L, Liu B, Huang S, Huebner ES (2013) Perceived social support and school well-being among Chinese early and middle adolescents: The mediational role of self-esteem. Soc Indic Res 113(3):991–1008. https://doi.org/10.1007/s11205-012-0123-8 Trejos-Herrera AM, Bahamón MJ, Alarcón-Vásquez Y, Vélez JI, Vinaccia S (2018) Validity and Reliability of the Multidimensional Scale of Perceived Social Support in Colombian Adolescents. Psychosocial Intervention 27(1):56–63 Uhlhaas PJ, Davey CG, Mehta UM, Shah J, Torous J, Allen NB, Avenevoli S, Bella-Awusah T, Chanen A, Chen EYH, Correll CU, Do KQ, Fisher HL, Frangou S, Hickie IB, Keshavan MS, Konrad K, Lee FS, Liu CH, Wood SJ (2023) Towards a youth mental health paradigm: A perspective and roadmap. Mol Psychiatry 28(8):3171–3181. https://doi.org/10.1038/s41380-023-02202-z Wang M-T, Brinkworth M, Eccles J (2013) Moderating effects of teacher-student relationship in adolescent trajectories of emotional and behavioral adjustment. Dev Psychol 49(4):690–705. https://doi.org/10.1037/a0027916 Watson D, Levin-Aspenson HF, Waszczuk MA, Conway CC, Dalgleish T, Dretsch MN, Eaton NR, Forbes MK, Forbush KT, Hobbs KA, Michelini G, Nelson BD, Sellbom M, Slade T, South SC, Sunderland M, Waldman I, Witthöft M, Wright AGC, Workgroup HU (2022) Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): III. Emotional dysfunction superspectrum. World Psychiatry 21(1):26–54. https://doi.org/10.1002/wps.20943 Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, Charlson FJ, Norman RE, Flaxman AD, Johns N, Burstein R, Murray CJL, Vos T (2013) Global burden of disease attributable to mental and substance use disorders: Findings from the Global Burden of Disease Study 2010. Lancet (London England) 382(9904):1575–1586. https://doi.org/10.1016/S0140-6736(13)61611-6 Widiger TA (2021) Alternative Models of Psychopathology: The Diagnostic and Statistical Manual of Mental Disorders, the Hierarchical Taxonomy of Psychopathology, Research Domain Criteria, Network Analysis, the Cambridge Model, and the Five-Factor Model. Clin Psychol Sci 9(3):340–342. https://doi.org/10.1177/2167702621989351 World Health Organisation (WHO) (2019) International statistical classification of diseases and related health problems (11th ed.) . https://icd.who.int/ World Health Organisation (WHO) (2021) WHO guideline on school health services . https://www.who.int/publications-detail-redirect/9789240029392 Zemestani M, Ezzati S, Nasiri F, Gallagher MW, Barlow DH, Kendall PC (2024) A culturally adapted unified protocol for transdiagnostic treatment of anxiety disorders in adolescents (UP-A): A randomized waitlist-controlled trial. Psychol Med 54(2):385–398. https://doi.org/10.1017/S0033291723001903 Zhou J, Fan A, Zhou X, Pao C, Xiao L, Feng Y, Xi R, Chen Y, Huang Q, Dong B, Zhou J (2022) Interrelationships between childhood maltreatment, depressive symptoms, functional impairment, and quality of life in patients with major depressive disorder: A network analysis approach. Child Abuse Negl 132:105787. https://doi.org/10.1016/j.chiabu.2022.105787 Zimet GD, Powell SS, Farley GK, Werkman S, Berkoff KA (1990) Psychometric characteristics of the Multidimensional Scale of Perceived Social Support. J Pers Assess 55(3–4):610–617. https://doi.org/10.1080/00223891.1990.9674095 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.jpg Figure1S.jpg Figure2S.jpg Figure3S.jpg Figure4S.jpg Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 07 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviews received at journal 17 Jul, 2024 Reviewers agreed at journal 17 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers agreed at journal 14 Jul, 2024 Reviewers invited by journal 14 Jul, 2024 Editor assigned by journal 28 Jun, 2024 Submission checks completed at journal 28 Jun, 2024 First submitted to journal 27 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4650180","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329597575,"identity":"b367e744-d5df-477d-a20a-9bacaedede5b","order_by":0,"name":"Gabriel Ródenas Perea","email":"","orcid":"","institution":"University Hospital Virgen Macarena I Andalusian Health Service","correspondingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"Ródenas","lastName":"Perea","suffix":""},{"id":329597576,"identity":"323b1d88-0b23-4315-9c03-26469a654304","order_by":1,"name":"Alfonso Pérez Esteban","email":"","orcid":"","institution":"University of La Rioja","correspondingAuthor":false,"prefix":"","firstName":"Alfonso","middleName":"Pérez","lastName":"Esteban","suffix":""},{"id":329597577,"identity":"3ea30bef-b3ba-4234-9d31-8df2550903a5","order_by":2,"name":"Alicia Pérez Albéniz","email":"","orcid":"","institution":"University of La Rioja","correspondingAuthor":false,"prefix":"","firstName":"Alicia","middleName":"Pérez","lastName":"Albéniz","suffix":""},{"id":329597578,"identity":"9b7a2a80-19cd-4abf-9e5d-595a8765c229","order_by":3,"name":"Susana Al-Halabí","email":"","orcid":"","institution":"University of Oviedo","correspondingAuthor":false,"prefix":"","firstName":"Susana","middleName":"","lastName":"Al-Halabí","suffix":""},{"id":329597579,"identity":"9acbfb1b-4816-4340-9bab-8986e78b2eba","order_by":4,"name":"Eduardo Fonseca 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15:34:38","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":21645,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4S.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4650180/v1/d08870f5bdefe9ab3e6d5508.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network structure of transdiagnostic dimensions of emotional disorders in adolescents with subthreshold anxiety and depression: Links with psychopathology and socio- emotional adjustment","fulltext":[{"header":"Background","content":"\u003cp\u003eThe symptoms of anxiety and depression and their associated disorders are amongst the main causes of disability worldwide (Whiteford et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; WHO, 2021b), particularly in women (Patwardhan et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The global prevalence of anxiety disorders in children and adolescents is 3.35%, while for depressive disorders it is 1.84% (Kieling et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the adolescent population, anxiety and depression are comorbid phenomena at both clinical and subclinical levels. In other words, young people who present problems of anxiety also have a higher risk of presenting mood-related problems and vice versa (Bal\u0026aacute;zs et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Canals et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Melton et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, according to recent studies, the approximate age when mental disorders begin is around 14.5 years old, with a third appearing before the age of 14 and almost half (48.4%) before the age of 18 (Solmi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Experiencing mental health problems before the age of 14 is associated with an increased risk of being diagnosed with some kind of mental disorder during adulthood (Mulraney et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This means that adolescence is a crucial stage for implementing both prevention strategies (Teesson et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and early interventions, as well as for promoting psychological wellbeing (Uhlhaas et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAdvances in the study of psychopathology have indicated the need to look beyond the lens of the main diagnostic classification systems, such as the \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders, fifth edition, text revision\u003c/em\u003e (DSM-5-TR; American Psychiatric Association, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u003cem\u003ethe International Classification of Diseases, 11th revision\u003c/em\u003e (ICD-11; WHO, 2019). This is why recent years have seen the emergence of a transdiagnostic perspective that avoids the main limitations of the \u0026ldquo;common latent cause\u0026rdquo; biomedical model (e.g., objectification, tautological reasoning, categorical nature) to develop better understanding and classification of mental health disorders (Brown \u0026amp; Barlow, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dalgleish et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fusar-Poli et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Scheffer et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a result, alternative approaches have emerged (Bullis et al., 2019; Sauer-Zavala et al., 2017). These include the Research Domain Criteria (RDoC; Insel et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), the Hierarchical Taxonomy of Psychopathology (HiTOP; Kotov et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), the network model (Borsboom, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP; Barlow et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e)) and the Modular Approach to Therapy for Anxiety, Depression, Trauma, or Conduct Problems in children (Chorpita et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) to name a few (Dalgleish et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). All of these transdiagnostic approaches to psychopathology provide unique insights into understanding mental health disorders beyond the limitations of established diagnostic taxonomy (e.g., DSM, ICD) (Brown \u0026amp; Barlow, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Conway et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dalgleish et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fonseca-Pedrero, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Manfro et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Widiger, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Transdiagnostic clinical approaches such as UP and the Modular Approach to Therapy for Anxiety, Depression, Trauma, or Conduct Problems, have also demonstrated their efficacy in treating depressive and anxiety disorders in adults (Carlucci et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cassiello-Robbins et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schaeuffele et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), as well as in children and adolescents (Garc\u0026iacute;a-Escalera et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kennedy et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mohajerin et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zemestani et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn this context, it is worth highlighting Brown and Barlow\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) transdiagnostic classification system of emotional disorders as the theoretical base of UP. In this model, the authors propose ten dimensions based on theory and research into factors associated with the development, expression, maintenance, and treatment of these types of disorders (Brown \u0026amp; Barlow, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Osma et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rosellini \u0026amp; Brown, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e): (1) Neurotic temperament [NT]; (2) Positive temperament [PT]; (3) Depressed mood [DM]; (4) Mania [MAN]; (5) Autonomic arousal [AA]; (6) Somatic anxiety [SOM]; (7) Social anxiety [SOC]; (8) Intrusive cognitions [IC]; (9) Traumatic re-experiencing [TRM]; and (10) Avoidance [AVD]. The first two dimensions (NT and PT) are higher-level or temperamental dimensions that enable classification into psychopathological profiles. The remaining dimensions are lower-level dimensions that distinguish the phenotypic or symptomatic focus of the emotional disorder (Rosellini et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). This hybrid dimension-categorical approach allows classification of emotional disorders and establishment of distinct transdiagnostic profiles (Brown \u0026amp; Barlow, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rosellini \u0026amp; Brown, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAnother alternative to the international taxonomic systems that has begun to be used in recent years is the network model in psychopathology (Borsboom, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Network models consist of groups of psychological variables (such as symptoms) that are interconnected in the form of nodes and edges. Nodes represent variables to be studied and the edges represent the connections between them. In psychopathology networks, the nodes represent symptoms (signs, traits, affects, etc.) and the edges represent associations between symptoms (inhibition or activation) (Blanchard \u0026amp; Heeren, 2022; Borsboom, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Borsboom \u0026amp; Cramer, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fonseca-Pedrero, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fonseca-Pedrero et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This approach allows dynamics between variables to be identified, along with different levels of analysis, while also respecting individual variability (Nelson et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; P\u0026eacute;rez \u0026Aacute;lvarez, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is a novel way to understand and intervene in behavior, and has enormous potential because among other things, it allows alternative forms of data analysis and suggests different ways of modelling and analyzing relationships between variables (e.g. symptoms, signs, psychological processes, personality traits, environmental triggers, substance consumption, etc.), designing new forms of prevention and intervention, and even improving the search for etiological factors (Fonseca-Pedrero, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fonseca-Pedrero et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eNetwork models are being used to address the etiological and phenotypical heterogeneity of emotional disorders, understanding them as complex dynamic systems (Borsboom \u0026amp; Cramer, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cramer et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jones et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Manfro et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). One example of that is the work by Malgaroli et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) involving a systematic review of 23 studies using network analysis to examine major depressive disorder. The results demonstrated significant variability between the different samples (e.g., different instruments for measuring depression, different age ranges, inclusion of accessory symptoms or not, etc.) and in the organization, structure, and arrangement of the symptoms in the networks. This is a clear example of the challenges we still face in conceptualizing psychopathology and the limitations of current models in research, although the accumulating evidence is ever more illuminating. This paradigm offers us the opportunity to be able to avoid the conceptual problems involved in studying emotional disorders and how they relate to protective and risk factors (Borsboom \u0026amp; Cramer, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Boschloo et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cramer et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Olthof et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCurrent research in the field of mental health is at a turning point that is driving it towards models that champion the heterogeneity, interdependence, and dimensionality of these problems in order to achieve better understanding without discounting contextual factors. The search for validated dimensional models of psychopathology, centered on the internalizing spectrum, is fundamental for improving understanding mechanisms of risk, predicting results, and guiding interventions (Snyder et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Watson et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). To the best of our knowledge, there have been no analyses of the transdiagnostic dimensions of emotional disorders proposed by Brown and Barlow (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) via network models in adolescents with subclinical symptoms of anxiety and depression. In this context, the main objective of this study was to examine the network structure of those transdiagnostic dimensions in a sample of adolescents with subclinical emotional symptoms. The study analyzed the network structure of these dimensions and their relationship with potential risk (internalizing and externalizing problems) and protective factors (self-esteem, perceived social support, subjective wellbeing, sense of belonging at school, and prosocial behavior). Our starting hypothesis was that the transdiagnostic dimensions would be positively related to each other (except for the positive temperament variable, which would have the opposite relation), and at the same time would be negatively related to the protective factors and positively related to the risk factors being assessed.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe sample for the screening phase comprised 8,746 students belonging to 85 schools and 532 classrooms from different provinces of Spain (La Rioja, Andaluc\u0026iacute;a, Madrid, Galicia, Murcia, Asturias, Valencia, and Castilla-La Mancha) that participated in a multi-centre project (Fonseca-Pedrero et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The students attended various public and independent centres for compulsory secondary education and vocational training, representing diverse socioeconomic backgrounds.\u003c/p\u003e \u003cp\u003eOut of the initial 8,746 adolescents who participated in the screening phase, 475 selected at-risk adolescents with a moderate score in both the PHQ-9 and GAD-7 (between 10 and 15 points) agreed to participate in a trial examining the effectiveness of UP-A transdiagnostic intervention in educational settings. The exclusion criteria were a) having been diagnosed with a mental disorder or alcohol and/or substance dependence disorder, b) the presence of high risk for suicidal behaviour, c) having a medical disease or condition that prevents the participant from taking part in the psychological treatment, d) receiving another psychological treatment while the study is ongoing, e) increases and/or changes in participants\u0026rsquo; medication if they are receiving pharmacological treatment during the study, and f) not completing or improperly completing (e.g., acquiescence, random response) baseline or post assessments.\u003c/p\u003e \u003cp\u003eThe final sample, comprising 376 participants (73.9% female), had a mean age of 13.77 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.432), ranging from 10 to 18 years. The age distribution was as follows: 10 years, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1; 11 years, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2; 12 years, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;99; 13 years, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;124; 14 years, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;115; 15 years, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66; 16 years, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50; 17 years, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18; 18 years, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInstruments\u003c/h2\u003e \u003cp\u003e\u003cem\u003eThe Multidimensional Emotional Disorder Inventory\u003c/em\u003e (MEDI; Rosellini \u0026amp; Brown, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The MEDI is a self-report questionnaire used to assess the transdiagnostic dimensions of emotional disorders. This transdiagnostic assessment tool consists of 49 Likert-type items that measure the nine dimensions of emotional disorders. It was developed to use a 9-point Likert response scale (0\u0026thinsp;=\u0026thinsp;\u003cem\u003enot at all characteristic of me\u003c/em\u003e; 8\u0026thinsp;=\u0026thinsp;\u003cem\u003etotally characteristic of me\u003c/em\u003e). Low rates and severity of manic symptoms precluded the development of a mania dimension, which was therefore not included in the final version of the instrument (Rosellini \u0026amp; Brown, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The present study used the validated Spanish version of the MEDI for adults (Osma et al., 2021). MEDI scores showed adequate psychometric properties in Spanish adolescents with subclinical emotional symptoms (P\u0026eacute;rez-Esteban et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cem\u003eThe Strengths and Difficulties Questionnaire\u003c/em\u003e (SDQ) (Goodman, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The SDQ is a self-report questionnaire that is widely used to assess different emotional and behavioural problems related to mental health in adolescents. The SDQ is made up of 25 statements using a Likert-type response format with three options (0\u0026thinsp;=\u0026thinsp;\u003cem\u003enot true\u003c/em\u003e, 1\u0026thinsp;=\u0026thinsp;\u003cem\u003esomewhat true\u003c/em\u003e, 2\u0026thinsp;=\u0026thinsp;\u003cem\u003ecertainly true\u003c/em\u003e) spread over five subscales: Emotional symptoms, Conduct problems, Hyperactivity, Peer problems, and Prosocial behaviour. The first four subscales yield a Total difficulties score. In this study we used the validated Spanish version of the SDQ (Ortu\u0026ntilde;o-Sierra et al., 2022). We used the three-factor structure: Internalizing problems (Emotional and Peer Problems), Externalizing problems (Conduct problems and Hyperactivity), and Prosocial behaviour, which presented good psychometric properties in Spanish adolescents (Ortu\u0026ntilde;o-Sierra et al., 2015).\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Rosenberg Self-Esteem Scale\u003c/em\u003e (Rosenberg, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1965\u003c/span\u003e). This tool assesses respondents\u0026rsquo; self-esteem. It is a unidimensional scale made up of 10 items (for example \u0026ldquo;In general I am happy with myself\u0026rdquo;), with responses given on a 4-point Likert-type scale (1\u0026thinsp;=\u0026thinsp;\u003cem\u003ecompletely disagree\u003c/em\u003e and 4\u0026thinsp;=\u0026thinsp;\u003cem\u003ecompletely agree\u003c/em\u003e). A higher score indicates greater self-esteem or positive self-esteem. The present study used the Spanish version whose psychometric properties have been examined in previous studies (Oliva et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Maryland Safe and Supportive Schools Initiative Questionnaire (MDS3).\u003c/em\u003e This was used to assess the feeling of belonging at school. Participants were administered 14 of the items making up the MDS3 developed by \u003cem\u003eJohns Hopkins Center for Youth Violence Prevention\u003c/em\u003e (Bradshaw et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These items are valid indicators related to feelings of safety, engagement, and the atmosphere at the school. Responses are made using a Likert-type scale (1\u0026thinsp;=\u0026thinsp;\u003cem\u003ecompletely disagree\u003c/em\u003e and 4\u0026thinsp;=\u0026thinsp;\u003cem\u003ecompletely agree\u003c/em\u003e). It evaluates three subscales: student harmony, relations with teachers, and overall sense of belonging at school. The present study used the Spanish adaptation (Lucas-Molina et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Multidimensional Scale of Perceived Social Support (MSPSS)\u003c/em\u003e (Zimet et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). This is a self-reported measure with 12 items assessing perceptions of social support from three sources: (1) Family, (2) Friends, and (3) Significant Others. Responses are made using a Likert-type scale from 1 (\u003cem\u003estrongly disagree\u003c/em\u003e) to 7 (\u003cem\u003estrongly agree\u003c/em\u003e). The validated Spanish version of the MPSS demonstrates suitable psychometric properties in the adolescent Spanish population (Trejos-Herrera et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe KIDSCREEN-10 Index\u003c/em\u003e (KS-10 Index; Ravens-Sieberer et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The KS-10 Index is the shortest version of the KIDSCREEN, a generic questionnaire that was transculturally developed in 13 European countries for children and adolescents aged 8 to 18 years. The KS-10 Index has 10 items that score as an index of well-being and health-related quality of life (HRQoL). The items present a recall period of one week and use a 5-point Likert response scale (0\u0026thinsp;=\u0026thinsp;\u003cem\u003enot at all\u003c/em\u003e; 4\u0026thinsp;=\u0026thinsp;\u003cem\u003eextremely\u003c/em\u003e). The validity and reliability of the KS-10 Index have been proven in the European population (Erhart et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ravens-Sieberer et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Oviedo Infrequency Scale-revisited\u003c/em\u003e (INF-OV-R). The INF-OV-R (Fonseca-Pedrero, P\u0026eacute;rez-Alb\u0026eacute;niz, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was administered to participants in order to detect those who responded in a random, pseudorandom or dishonest manner. The INF-OV-R is a self-report instrument consisting of 10 items in a dichotomous scale format (\u003cem\u003eyes\u003c/em\u003e/\u003cem\u003eno\u003c/em\u003e). Students with more than three incorrect responses on the INF-OV-R scale were excluded from the sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003e The study was approved by the La Rioja Clinical Research Ethics Committee (CEICLAR number for the project PI 552). With the exception of the MEDI, the tests were administered in the screening phase collectively, using personal computers, to groups of 10 to 30 students during school hours in a specially-prepared classroom. The MEDI was administered to the 476 subclinical participants selected one week after the screening phase through personal electronic devices. This was done in smaller groups of 10 to 15 students, outside school hours in a specially-prepared classroom. No incentive was provided for participation. For participants under 16, parents were asked to provide their written informed consent for the child to participate in the study. Participants were informed of the confidentiality of their responses and the voluntary nature of the study. This study is part of the PSICE project (Evidence-based Psychology in Educational Contexts) in order to examine the UP-A\u0026rsquo;s effectiveness (Fonseca-Pedrero et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Registration number in ClinicalTrials.gov NCT05322642.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFirst, descriptive statistics were calculated for all of the measuring instruments. Then, a network was estimated for the transdiagnostic domains measured with the MEDI. A second network was estimated, incorporating the protection and risk factors addressed in the study to analyse their relationship with the MEDI domains. The details of network analysis have been documented in-depth elsewhere (Epskamp et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eNetwork estimation.\u003c/em\u003e For the domains, which were constructed by summing items per domain and then standardizing the resulting variable, we specified a Gaussian Graphical Model (GGM) (Epskamp \u0026amp; Fried, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This model resulted in conditional dependence relations which are akin to partial correlations: if two nodes are connected in the resulting graph via an edge, they are statistically related after controlling for all other variables in the network; if they are unconnected, they are conditionally independent. The Fruchterman-Reingold algorithm was used for the layout, with strongly connected nodes placed closer to each other and the least connected nodes far apart (Epskamp et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNetwork inference.\u003c/em\u003e In line with previous studies examining networks (Fonseca-Pedrero et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we estimated two measures: Expected Influence (EI) and predictability. EI is the sum of all edges of a node (Robinaugh et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We used EI instead of strength centrality (Opsahl et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u0026mdash;which has been used in previous studies\u0026mdash;because strength centrality uses the sum of absolute weights (i.e., negative edges are turned into positive edges before summing), which distorts the interpretation if negative edges are present. Predictability is an absolute measure of interconnectedness: it provides us with the variance of each node that is explained by all its neighbors (Haslbeck \u0026amp; Fried, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By predictability of node A we mean how well node A can be predicted by all its neighboring nodes in the network (Haslbeck \u0026amp; Waldorp, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the figures, dark areas in the circles around nodes can be interpreted akin to R\u003csup\u003e2\u003c/sup\u003e (% of explained variance) (Haslbeck \u0026amp; Fried, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eNetwork stability.\u003c/em\u003e To test network stability and accuracy, we used bootstrapping routines implemented in the R-package \u003cem\u003ebootnet\u003c/em\u003e (Epskamp et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eEstimated Network of the MEDI Dimensions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1 shows the descriptive statistics for the measures used in the study. Figure\u0026nbsp;1 shows the specified network of the transdiagnostic variables evaluated in the study sample. There was a strong positive relation between the TRM and IC dimensions. NT also appeared to be positively related to the other dimensions evaluated with MEDI and PT was negatively related with the variables DM and SOC. Figure\u0026nbsp;2 shows the inference measures for the network. The most central nodes in terms of expected standardized influence were mainly AVD and IC, followed by TRM and NT. The node with the least expected influence was PT. The most central notes in terms of strength were AVD and IC, followed by DM. The node with the least strength was SOM. The average predictability was 46.86%.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEstimated Network of the MEDI Dimensions and the Risk and Protective Factors.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the specified network of relationships between the MEDI dimensions and the psychometric indicators of psychopathology and socio-emotional adjustment. There was a positive interdependence between all of the transdiagnostic dimensions, with the exception of the PT variable, which was basically negatively related to DM and SOC. The MEDI dimensions were weakly related to SDQ internalizing and externalizing problems. In addition, there was a strong negative relationship between DM and the PT and self-esteem variables. Self-esteem, in turn, was negatively related to internalizing problems, which demonstrated a strong negative relationship with the perceived social support variable. The PT dimension was positively associated with the perceived quality of life, which was also strongly related to the perceived social support and sense of belonging at school variables. Lastly, there was a strong negative relationship between prosocial behavior and externalizing problems.\u003c/p\u003e\n\u003cp\u003eFigure 4 shows the inference measures for the second network specified. The most central nodes in terms of standardized expected influence were AVD and IC, followed by TRM and NT. The nodes with the least expected influence were internalizing problems, externalizing problems, and self-esteem. The most central nodes in terms of strength were IC, DM, and AVD. The node with the least strength was externalizing problems. The average predictability was 36.07%.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eNetwork stability of the estimated networks\u003c/h2\u003e\n\u003cp\u003eThe results of the stability and accuracy analysis indicated that all networks were accurately estimated, with moderate confidence intervals around the edge weights (See Figs.\u0026nbsp;1S to 4S in supplementary material).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe transdiagnostic approach and the network model approach to psychopathology have emerged as alternatives to traditional diagnostic systems. This is the first study to date that has examined the network structure of the transdiagnostic dimensions of emotional disorders in a large sample of adolescents with subclinical emotional symptoms and the relationship with risk factors and protective factors. New approaches such as transdiagnostic and network models may provide new perspectives on the origin, maintenance, clinical management, and recovery from experiences of disabling mental distress (Conway et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dalgleish et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fonseca-Pedrero, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Widiger, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe first objective of this study was to analyze the network structure of the transdiagnostic dimensions of emotional disorders put forward by Brown and Barlow (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and their applicability in a representative sample of adolescents with subclinical emotional problems. The results demonstrate a strong interaction between the transdiagnostic dimensions, with the exception of PT. This is consistent as positive temperament is understood as the tendency to experience a positive affect in response to social tasks and goal-directed tasks (e.g. extraversion, behavioral activation, positive affectivity) (Rosellini \u0026amp; Brown, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Previous studies have shown that PT is significantly and inversely correlated with the other transdiagnostic dimensions (Brown \u0026amp; Barlow, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rosellini \u0026amp; Brown, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Osma et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; P\u0026eacute;rez-Esteban et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, the scientific literature has demonstrated that low positive affect is related to developing greater depressive disorder and anxiety disorders (Barlow \u0026amp; Kennedy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our results add to the previous evidence as PT was the dimension with the least expected influence within the network.\u003c/p\u003e \u003cp\u003eIn terms of strength and expected influence, the nodes in the network with the greatest weight were AVD, IC, TRM, NT and DM. The strong association and the weight of TRM in the network, along with the IC dimension, is particularly interesting. It is well known in the literature that adverse experiences in childhood and adolescence mean a greater risk of experiencing generalized anxiety and/or symptoms of depression during or after subsequent stressful life events (R\u0026oacute;denas-Perea et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Reis et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One of the explanations for this is that traumatic childhood experiences result in the development of ineffective coping strategies (Barlow, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Snyder et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cludius et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S\u0026aelig;tren et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) such as excessive focusing on events themselves, the emotions they provoke, and analyzing their causes and effects (Aldao et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nolen-Hoeksema et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Snyder et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is known as rumination and arises as an attempt to understand and resolve the situation, although rumination has been shown to be negatively related to problem solving and is maladaptive for coping with stressful life events (Battista et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Michael et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Papageorgiou \u0026amp; Wells, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Snyder et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Another response to traumatic experiences is the appearance of intrusive thoughts. Research has found a strong relationship between the presence of intrusive thoughts and the experience of traumatic events in early ages (Barzilay et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; R\u0026oacute;denas‐Perea et al., 2022). These people also present higher levels of avoidance behaviors and symptoms of anxiety or depression than those who have not had such experiences (Jhang, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; P\u0026eacute;rez et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This is consistent with our results. Lastly, it is worth noting that in addition to the strong relationship between these variables, there were also mediating relationships. For example, maladaptive strategies such as rumination and the appearance of phenomena such as intrusive thoughts are mediating variables between traumatic experiences in childhood and the subsequent appearance of symptoms of stress, anxiety, and depression in later life (Kim et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; R\u0026oacute;denas‐Perea et al., 2022; S\u0026aelig;tren et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second objective of our study was to examine the network structure of the transdiagnostic dimensions in relation to other psychometric indicators of psychopathology and socio-emotional adjustment. In the first place, the results corroborate the importance of the AVD, IC, and TRM dimensions. In addition, it seems logical to have found a negative relationship between the transdiagnostic dimensions (except PT) and the other protective variables (perceived social support, sense of belonging at school, prosocial behavior, quality of life, and self-esteem). These factors were closely interrelated with each other, and seem to act as an important source of protection against different types of psychopathological domains in the adolescent population (Fonseca-Pedrero et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fonseca-Pedrero, Ortu\u0026ntilde;o-Sierra, et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our results also indicate the importance of perceived social support, a sense of belonging at school, and adolescents\u0026rsquo; reported quality of life (Kumcağız \u0026amp; Şahin, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nowicki, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Singstad et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Having a positive, cohesive, social support network seems to be a fundamental protective factor for mental health in adolescence. In fact, research has shown that there is a significant positive relationship between social support and psychological wellbeing (Agbaria \u0026amp; Bdier, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singstad et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and that such support allows adolescents to better cope with stressful life events (Camara et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Reife et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, satisfaction with teachers and feeling a sense of belonging at school may protect young people from the effects of stressful life events, promoting resilience and reducing the likelihood of developing depression or behavioral problems (H\u0026ouml;ltge et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, dissatisfaction with teachers is a potential risk factor for developing anxiety, emotional, and behavioral problems (Arslan, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mameli et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Lastly, in our study, prosocial behavior was positively related to a feeling of belonging at school and perceived social support, and negatively related to externalizing symptomatology and poor behavioral adjustment (Ingl\u0026eacute;s et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results have notable clinical implications. On the one hand, studying subclinical states may help us understand the dynamics between different variables that may develop into different mental health problems. Network analysis has confirmed that problems that manifest sub-clinically in specific dimensions in early stages of development are consistent over subsequent years in more serious psychopathological processes (Groen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These aspects are related to the clinical staging model that seeks to facilitate treatment selection based on the progression and severity of the problem (Hartmann et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, implementing network analysis is useful for uncovering specific treatment pathways, which may encourage the development of more effective interventions for addressing symptoms of emotional problems based on where they are in their progression and the severity. Previous studies have shown that intervention in specific symptoms indirectly triggers a wave of change in other symptoms, altering the connections between elements in the network (Jurado-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Incorporating the network model together with the transdiagnostic approach applied to adolescents\u0026rsquo; emotional problems opens the door to better understanding of the psychopathology and possible focuses of individualized treatment (Borsboom et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Manfro et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, the results of our study may feed into novel theoretical dimensional and transdiagnostic models applied to psychopathology, such as the internalizing spectrum within the HiTOP model (Snyder et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Watson et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, the relationship with school-related risk and protective factors allows us to consider the importance of relationships in adolescents\u0026rsquo; developmental surroundings (social support, sense of belonging), encouraging development of structural initiatives or universal prevention programs in schools that promote protective factors and reduce risk factors (Fonseca-Pedrero et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study is not without limitations. Firstly, the use of solely self-reported data limits the conclusions that can be drawn. Secondly, the study was transversal, which means it does not allow us to examine the dynamic individual interactions between the variables nor to do so longitudinally. A time-series analysis would avoid this issue, but we must be cautious, as the field of network analysis is in its infancy, which means there still fundamental questions open about the best way to estimate a network over time. Thirdly, only some risk and protective factors were selected, although they were consistent and based on the literature. It would be useful to consider others that are involved in various problems that are particularly prevalent in adolescence (Gonz\u0026aacute;lez-Roz et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another limitation was the structure of the networks being restricted by the tool used in the study. Network analysis is still in the early stages and is not without its critics. This means we should be careful when it comes to extrapolating from these results to other populations in which a wider range of scores may lead to networks behaving differently. Nonetheless, it is a promising methodology for obtaining important information in a variety of research fields (Bringmann et al., 2021; Bringmann et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Guloksuz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFuture lines of research should include network models with variables from different levels of analysis, especially those providing information about other transdiagnostic variables (such as rumination), clinical variables (such as frequency and type of traumatic experiences, suicidal behavior, etc.), and contextual variables (such as bullying). In addition, it would be extremely interesting to collect longitudinal information via new methodologies such as ambulatory assessment in order to move closer towards dynamic, contextual, tailored models. Finally, it would also be interesting to be able to incorporate new analytical methodologies such as \u003cem\u003eDirected Acyclic Graphs\u003c/em\u003e (DAG) (Briganti et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Moffa et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eNew psychological approaches, such as network models and transdiagnostic approaches to emotional problems in adolescence, may offer new understanding in the study of mental health in this developmental stage. Only by embracing the complexity will we be able to improve prevention, detection and intervention for these problems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by a national project awarded by the Ministry of Science and Innovation of the Government of Spain and the Agency and the European Regional Development Fund (Project \u0026ldquo;PID2021-127301OB-I00\u0026rdquo; funded by MCIN / AEI /\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13039/501100011033\u003c/span\u003e\u003cspan address=\"10.13039/501100011033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e FEDER, UE) and Psicofundaci\u0026oacute;n.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.R. is the main author of the article based on the mentorship of E.F and S.A. Both helped G.R with the main theme of the article and the pursued objectives. E.F also helped with Network analysis and S.A. with the introduction and discussion. A.P.E and A.P.A provided a deep feeback of the final manuscript and helped with formal aspects. G.R. integrated all corrections and sent the manuscript to the journal\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgbaria Q, Bdier D (2020) The Role of Self-Control, Social Support and (Positive and Negative Affects) in Reducing Test Anxiety among Arab Teenagers in Israel. Child Indic Res 13(3):1023\u0026ndash;1041. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12187-019-09669-9\u003c/span\u003e\u003cspan address=\"10.1007/s12187-019-09669-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAldao A, Nolen-Hoeksema S, Schweizer S (2010) Emotion-regulation strategies across psychopathology: A meta-analytic review. 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Child Abuse Negl 132:105787. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chiabu.2022.105787\u003c/span\u003e\u003cspan address=\"10.1016/j.chiabu.2022.105787\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimet GD, Powell SS, Farley GK, Werkman S, Berkoff KA (1990) Psychometric characteristics of the Multidimensional Scale of Perceived Social Support. J Pers Assess 55(3\u0026ndash;4):610\u0026ndash;617. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00223891.1990.9674095\u003c/span\u003e\u003cspan address=\"10.1080/00223891.1990.9674095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"transdiagnostic, adolescents, network model, subclinical symptoms, emotional disorders","lastPublishedDoi":"10.21203/rs.3.rs-4650180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4650180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The transdiagnostic and network model approaches to psychopathology have emerged as an alternative to taxonomic systems. The main goal was to examine the network structure of the transdiagnostic dimensions of emotional disorders in adolescents with subclinical emotional symptoms. In addition, cross-sectional network analyses were conducted to investigate the relationships between transdiagnostic dimensions and socio-emotional adjustment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e The sample consisted of a total of 476 adolescents from 85 schools from different provinces in Spain with subthreshold anxiety and depression. The mean age was 13.77 years (\u003cem\u003eSD\u003c/em\u003e = 1.43, range 10 to 18 years), 73.9% were female. Several questionnaires assessing transdiagnostic dimensions of emotional disorders, mental health difficulties, self-esteem, perceived social support, feeling of belonging at school, and prosocial behavior were used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A strong interrelation was found between the transdiagnostic variables, except with the positive temperament dimension. The dimensions with the greatest weight were those of traumatic reexperiencing, intrusive cognitions, avoidance, neuroticism, and depressed mood. Negative relationships were found between the transdiagnostic variables and the protective factors included in the study. Stability analyses indicated that the networks where accurately estimated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The present paper points to the value of conceptualizing emotional disorders from a transdiagnostic and network model perspective. In addition, the work provides new insights into the nature of the relationships between transdiagnostic dimensions of emotional disorders, and the role played by risk and protective factors.\u003c/p\u003e","manuscriptTitle":"Network structure of transdiagnostic dimensions of emotional disorders in adolescents with subthreshold anxiety and depression: Links with psychopathology and socio- emotional adjustment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 15:34:33","doi":"10.21203/rs.3.rs-4650180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-08-07T09:54:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181734601254388882433439821895117829473","date":"2024-08-07T09:04:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-17T11:39:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230636990388445145119593280928532818343","date":"2024-07-17T07:59:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23423043324543257680045285279585295100","date":"2024-07-15T10:03:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146456217955137231622435969267748660783","date":"2024-07-14T22:03:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-14T22:01:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-28T12:38:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-28T12:38:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Child \u0026 Adolescent Psychiatry","date":"2024-06-27T16:27:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5172de4d-1cc0-44f2-94bf-3ade737946c9","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-22T15:34:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 15:34:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4650180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4650180","identity":"rs-4650180","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}