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Eur J Psychotraumatol
Eur J Psychotraumatol
European Journal of Psychotraumatology
2000-8066
Taylor & Francis

39286882
2391656
10.1080/20008066.2024.2391656
Version of Record
Basic Research Article
Research Article
Dynamic networks of complex posttraumatic stress disorder and depression among college students with childhood trauma: insights from cross-sectional and cross-lagged panel network analysis
Redes dinámicas de trastorno de estrés postraumático complejo y depresión en estudiantes universitarios con trauma infantil: perspectivas a partir del análisis de redes transversales y de panel cruzadoEUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY
A. LIU ET AL.
Liu Aiyi a
Liu Mingxiao a
Ren Yizhen a
Zhang Lake Mozi a
Peng Yu bc
a Faculty of Psychology, Beijing Normal University, Beijing, People’s Republic of China
b Students Mental Health Education & Counseling Center, Kunming University of Science and Technology, Kunming, People’s Republic of China
c Faculty of Social Sciences & Liberal Arts, UCSI University, Kuala Lumpur, Malaysia
CONTACT Yu Peng sunshinepy123@hotmail.com Students Mental Health Education & Counseling Center, Kunming University of Science and Technology, Kunming 650500, People’s Republic of China
Supplemental data for this article can be accessed online at https://doi.org/10.1080/20008066.2024.2391656.

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https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

ABSTRACT

Background and Objective: There is a current research gap regarding the symptom structure and underlying causal relationships between complex posttraumatic stress disorder (CPTSD) and depressive symptoms. This longitudinal study used a cross-sectional network and cross-lag panel network (CLPN) to examine how CPTSD and depression symptoms interact over time in Chinese college students with childhood trauma.

Methods: From 18,933 college students who took part in 2 surveys 12 months apart, 4006 participants (mean age: 20.07 ± 2.04) who reported childhood trauma were screened. Within this sample, there were 2354 (58.8%) males and 1652 (41.2%) females.

Results: In the one-year interval CLPN model, it was found that depressive symptoms may precede other symptoms. Specifically, negative emotions and negative self-evaluations are more likely to predict subsequent symptoms. Conversely, in CPTSD, symptoms related to fear and anxiety, such as avoidance, intrusion, and hyperarousal, are more frequently activated by other symptoms, including negative emotions

Conclusions: This finding offers a novel perspective on the interplay between CPTSD and depression, extending the existing theory. From a clinical standpoint, the points of intervention for comorbidity between depression and CPTSD who have experienced childhood trauma differ across different stages.

HIGHLIGHTS

This study used network analysis to examine the evolving symptom structure of complex posttraumatic stress disorder (CPTSD) and depression, as well as the relationships between symptoms, in a large-scale longitudinal study among college students who have experienced childhood trauma.

Symptoms such as emotional dysregulation and negative self-concept serve as stable central symptoms of both CPTSD and depression.

Depression, tends to activate other symptoms, whereas CPTSD, is more frequently predicted by other symptoms.

Antecedentes y objetivo: Existe una brecha en la investigación actual sobre la estructura de los síntomas y las relaciones causales subyacentes entre el trastorno de estrés postraumático complejo (TEPTC) y los síntomas depresivos. Este estudio longitudinal utilizó una red transversal y una red de panel cruzado (CLPN por su sigla en inglés) para examinar cómo interactúan el TEPTC y los síntomas de depresión a lo largo del tiempo en estudiantes universitarios chinos con trauma infantil.

Métodos: De 18.933 estudiantes universitarios que participaron en dos encuestas con 12 meses de diferencia, se examinó a 4,006 participantes (edad media: 20.07 ± 2.04) que informaron haber sufrido un trauma infantil. Dentro de esta muestra, hubo 2,354 (58.8%) varones y 1.652 (41.2%) mujeres.

Resultados: En el modelo CLPN con un intervalo de un año, se encontró que los síntomas depresivos pueden preceder a otros síntomas. Específicamente, las emociones negativas y las autoevaluaciones negativas tienen más probabilidades de predecir los síntomas posteriores. En cambio, en el TEPTC, los síntomas relacionados con el miedo y la ansiedad, como la evitación, la intrusión y la hiperactivación, se activan con mayor frecuencia por otros síntomas, incluidas las emociones negativas.

Conclusiones: Estos hallazgos ofrecen una perspectiva novedosa sobre la interacción entre el TEPTC y la depresión, ampliando la teoría existente. Desde un punto de vista clínico, los puntos de intervención para la comorbilidad entre la depresión y el TEPTC en personas que han experimentado un trauma infantil difieren en las diferentes etapas.

KEYWORDS

CPTSD symptoms
depression
network analysis
college students
childhood trauma
longitudinal study
PALABRAS CLAVE

Síntomas de TEPTC
depresión
análisis de redes
estudiantes universitarios
trauma infantil
estudio longitudinal
Scientific Research Foundation of the Yunnan Provincial Education Department, China 10.13039/501100013097 2023J0097 This work was supported by Scientific Research Foundation of the Yunnan Provincial Education Department, China [grant number 2023J0097].
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pmcChildhood adversities, which include experiences such as sexual abuse, emotional abuse, and neglect, are a prevalent and widely shared phenomenon, impacting 10–30% of children and adolescents globally with at least one form reported (Stoltenborgh et al., 2015). The repercussions of childhood trauma on mental health have been well-documented (Cook et al., 2010), with the cumulative number of trauma types experienced during childhood emerging as a significant predictor of the severity and diversity of mental health issues in adulthood (Briere et al., 2008; Cloitre et al., 2009). Emerging into adulthood, College students traverse a pivotal developmental juncture replete with manifold academic and personal transitions, presenting them with diverse challenges. Consequently, this juncture makes them vulnerable to psychological health issues (Chi et al., 2020). In the survey of the mental health of Chinese college students, a notable observation has been made: a substantial correlation exists between many of their psychological problems and experiences of childhood trauma (Li et al., 2022).

The 11th revision to the World Health Organization’s International Classification of Diseases (ICD-11) (World Health Organization [WHO], 2018) includes two typical psychological responses after trauma, posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD), under a category of ‘disorders specifically associated with stress’. PTSD is comprised of three symptom clusters including (1) re-experiencing of the trauma in the here and now, (2) avoidance of traumatic reminders and (3) a persistent sense of current threat that is manifested by exaggerated startle and hypervigilance. CPTSD of ICD-11 includes the three PTSD clusters and three additional clusters that reflect ‘disturbances in self-organization’ (DSO); (1) affect dysregulation, (2) negative self-concept and (3) disturbances in relationships (Maercker et al., 2013). These disturbances are proposed to be typically associated with sustained, repeated or multiple forms of traumatic exposure (Karatzias et al., 2019), reflecting loss of emotional, psychological and social resources under conditions of prolonged adversity (Cloitre et al., 2013). Previous research suggests that while individuals who have experienced various trauma types can develop CPTSD (Hyland et al., 2017), enduring adversities like childhood abuse, domestic violence may represent a more common trigger for CPTSD symptoms (Ho et al., 2019; Karatzias et al., 2022; Tian et al., 2020).

In addition to CPTSD, depression is a prevalent mental disorder among individuals who have experienced childhood trauma. Numerous cross-sectional (Molnar et al., 2001) and longitudinal (Widom et al., 2007) studies have substantiated the link between childhood trauma and an elevated risk of depression in adulthood. Moreover, research has shown that CPTSD and depression may co-occur in people with a history of trauma. For example, Hyland et al. (2018) found that among people who experienced traumatic life events, the prevalence of CPTSD was 65.5%, and 69.2% of them met the criteria for depression. In comparison to individuals grappling with a single disorder, those contending with both conditions often grapple with more severe mental health challenges (Najavits & Capezza, 2014). These encompass a less favourable prognosis (Campbell et al., 2007; Stander et al., 2014) and an augmented propensity for engaging in suicidal behaviour (Campbell et al., 2007; Ramsawh et al., 2014). Such comorbidity can significantly complicate the trajectory of the disorders and their responsiveness to treatment approaches (Mueser & Drake, 2007).

A comprehensive theoretical framework elucidating the comorbidity between CPTSD and depression is notably absent. However, various perspectives have been proposed in the realm of the theory of comorbidity between PTSD and depression. For instance, the synchronous change model postulates that PTSD and depression manifest simultaneously and are influenced by one or more additional factors (Breslau et al., 2000). Consequently, the presence of shared risk factors emerges as a pivotal catalyst in the synchronous development of comorbidity over time (Schindel-Allon et al., 2010). In stark contrast, the depressogenic model contends that depression symptoms presage subsequent PTSD symptoms (Schindel-Allon et al., 2010). Empirical support for this proposition stems from cross-lagged studies, which underscore the significant role of depression in driving the progression of PTSD (Cheng et al., 2018; Ying et al., 2012). Conversely, the demoralization model advances an alternative hypothesis, suggesting that PTSD symptoms may precipitate the onset of depression (Wittchen et al., 2003). This potential pathway could be mediated through a mechanism involving inadequate emotional processing and the cultivation of negative cognitive patterns (Schindel-Allon et al., 2010). These theories and hypotheses have propelled prior investigations into the interplay between PTSD and depression, spanning from cross-sectional assessments to longitudinal analyses, and extending from a macroscopic disease level to a more granular focus on symptomatology (An et al., 2021; Cheng et al., 2018; Qi et al., 2023; Ying et al., 2012). However, the exploration of the interrelationship between CPTSD and depression has predominantly remained confined to cross-sectional investigations and an examination at the level of disorders. As an emerging independent diagnostic entity, especially following the delineation of DSO as a distinct symptom cluster of chronic stress, what is the nature of the relationship between CPTSD and depression? Can this relationship provide novel insights into previous theories regarding the association between PTSD and depression? These questions await validation through longitudinal research.

As the examination of psychopathological symptoms becomes more profound, an increasing number of studies are employing network analysis to conduct finer-grained analyses at the level of symptoms. This approach contrasts with the latent variable model, which posits that concealed latent variables trigger the emergence of observable symptoms (Borsboom & Cramer, 2013). In contrast, the network theory of psychopathologies operates on the assumption of interactions and connections between symptoms (van Borkulo et al., 2015). This intricate interplay gives rise to a constellation of symptoms forming a symptom network, ultimately culminating in the manifestation of mental disorders (Borsboom, 2017; Sullivan et al., 2018). Building upon this foundation, network analysis has been devised to assess the interrelationships among symptoms within mental disorders. Within a network, nodes represent specific symptoms of disorders, while edges signify the connections between these nodes (Jones et al., 2017). The centrality measures denote the significance of a node, reflecting its connectivity within the network and its role in sustaining the disorder (Hofmann et al., 2016). The activation of a central symptom within this network could potentially trigger the development of other symptoms (Borsboom & Cramer, 2013).

While several studies, such as those by Gilbar (2020) and Haselgruber et al. (2021), have employed network analysis methods to investigate the network structure between CPTSD and depression symptoms, these analyses are still in early stages and they relied exclusively on cross-sectional data (Gilbar, 2020; Haselgruber et al., 2021). In the meta-analysis of network models for the relatively well-established concept of PTSD, it was found that while several clear symptom-links, interpretable clusters, and significant differences in the strength of edges and centrality of nodes can be identified within the network, no single or small set of nodes could be pinpointed as playing a more central role than others (Isvoranu et al., 2021). Consequently, the ability to ascertain direct influences between nodes across various disorders remains challenging. Additionally, although there is a current lack of longitudinal studies examining the relationship between CPTSD and depression in the context of childhood trauma, previous longitudinal research on the relationship and influencing factors between PTSD and depression has consistently indicated that these dynamics evolve over time (Armour et al., 2012; Horesh et al., 2017; Maslow et al., 2015). In a recent cross-lagged panel network (CLPN) analysis of PTSD and depression comorbidities in post-earthquake adolescents, it was found that the connectivity between PTSD and depression symptoms decreased over time, presenting a gradually differentiated network structure (Qi et al., 2023). In another study employing CLPN analysis on adolescents who had experienced a tornado, it was discovered that, over time, avoidance and intrusive symptoms have distinctly different impacts on an individual’s mental health (Xie et al., 2024). Therefore, the utilization of a longitudinal network analysis approach at the symptom level proves invaluable in elucidating the causal interplay between symptoms of CPTSD and depression. This methodology also facilitates a more precise identification of intervention targets, thereby enhancing the efficacy of treatment strategies.

1. The current study

We embarked on a large sample longitudinal study and comparison of disparities within contemporaneous networks that characterize the relationship of CPTSD and depression among early adults who have experienced childhood trauma.

In this study, we first examined and compared the differences between contemporaneous networks of CPTSD and depression at two time points. The aim was to elucidate the longitudinal development of CPTSD and depression network structure and deepen understanding of the relationship between CPTSD and depression. Second, we introduced a CLPN as a valuable approach to assessing the relationships in symptom-symptom interaction in the various symptoms of CPTSD and depression. Using this approach, we can identify which symptoms have the most significant impact on others and determine the activation pathways for each symptom.

2. Method

2.1. Participants and procedure

This study is a longitudinal research with a one-year interval between two data collection waves. The data was collected at a university in Yunnan Province, China. The first measurement was conducted from late September to early October 2021 (Time [T] 1), and the second follow-up survey took place one year later in late September 2022 (T2). The study was carried out in a classroom during a mental health class at the beginning of the university year. Researchers explained the study’s aims, participants’ rights, and risks before collecting informed consent. Students then completed an online questionnaire. The study was approved by the Institutional Review Board (Approval No: KMUST-MEC-149). Participants could leave the study anytime, and psychological support was available from school counsellors if required.

In the first survey, 28,202 young adults aged 18–30 years provided valid socio-demographic information, with a mean age of 22.07 ± 2.04 years. Because some students had dropped out of school or graduated from their original school after the first wave, it was difficult to include all students in the longitudinal investigation. Thus, of the participants in the original sample, 18,933 (67.13%) were invited to participate in the survey at T2. To ensure the accuracy of the results, students were included in the sample only if they had participated in both measurement waves. The analysis of participant attrition is detailed in Table S1 of the Supplementary Material. After excluding university students who did not report any adverse childhood experiences, the final sample consisted of 4006 university students who reported at least one adverse childhood experience (mean age: 22.07 ± 2.04 years). Within this sample, there were 2354 (58.8%) males and 1652 (41.2%) females. Detailed demographic statistics are presented in Table 1. Table 1. Descriptive statistics.

 	Full follow-up data (N = 18,933)	Participants with a history of childhood trauma (N = 4006)	χ2/t	p	
Variables	N/M	%/SD	N/M	%/SD	 	 	
Age	22.43	2.08	22.05	1.92	10.53	<.001	
Gender	 	 	 	 	41.20	<.001	
 Male	9594	64.27%	2354	58.76%	 	 	
 Female	5333	35.73%	1652	41.24%	 	 	
Education level	 	 	 	 	12.37	<.001	
 Studying for a bachelor degree	9790	65.59%	3035	75.76%	 	 	
 Studying for a master degree	5137	34.41%	971	32.26%	 	 	
Subjective socio-economic status	 	 	 	 	132.45	<.001	
 Terrible	3322	24.82%	1240	30.95%	 	 	
 General	10,950	70.91%	2591	64.68%	 	 	
 Good	655	4.26%	175	4.37%	 	 	
Negative life events	 	 	 	 	 	 	
 Left-behind experience	1188	7.96%	2045	10.80%	596.83	<.001	
 Physical illness	359	2.41%	253	6.32%	154.41	<.001	
 Diagnosed with a mental illness	84	0.57%	140	3.49%	247.19	<.001	
 Disharmony in family relationships	250	1.68%	432	10.78%	754.70	<.001	
 Serious accident	20	0.13%	34	0.85%	56.73	<.001	
 Conflict with teachers	14	0.09%	22	0.55%	222.85	<.001	
 Conflicts in relationships with peers or friends	2215	14.84%	1049	26.19%	285.01	.624	
 A close person attempted or committed suicide	144	0.97%	221	5.51%	347.09	<.001	
 Difficulty with school or job	3159	21.16%	1223	30.53%	209.56	<.001	
 Feeling severe stress	316	2.12%	256	6.39%	196.86	<.001	
Note: *p < .05, **p < .01, ***p < .001.

2.2. Measures

2.2.1. Demographic variables

Participants were asked to provide demographic information including their age and gender (1 = male, 2 = female). They also reported their subjective socio-economic status and their educational level. In addition to demographic details, participants were assessed for additional adverse life events through 10-items. See Table 1 for details.

2.2.2. Adverse childhood experiences

Adverse childhood experiences (ACEs) before the age of 18 were measured using a revised version of the Adverse Childhood Experiences Inventory (Finkelhor et al., 2015; Murphy et al., 2014). This inventory comprises 10 items covering physical abuse, emotional abuse, sexual abuse, emotional neglect, physical neglect, parental separation or divorce, domestic violence, familial substance abuse, familial mental illness, and familial incarceration (Wiss et al., 2022). These adverse experiences were scored as either absent (no) or present (yes), corresponding to scores of 0 or 1, respectively, and were cumulatively added to obtain a total ACEs score. The ACE inventory has been revised and validated among Chinese university students (Wang et al., 2019).

2.2.3. International Trauma Questionnaire

The International Trauma Questionnaire (ITQ) (Cloitre et al., 2018) is a self-report tool designed to assess the diagnostic criteria for CPTSD as outlined in the ICD-11. It comprises 12 items, with 2 items for each of the 3 symptom clusters of PTSD (re-experiencing, avoidance, and sense of current threat). In addition, the ITQ includes six items that specifically measure DSO symptoms (affective dysregulation, negative self-concept, and disturbed relationships), with two items for each cluster. All items in the ITQ are rated on a 5-point Likert scale ranging from 0 (Not at all) to 4 (Extremely). The ITQ have been extensively validated in various trauma-exposed populations in China (Ho et al., 2019; Tian et al., 2020). In the present study, the internal consistency coefficients, measured by Cronbach’s α, for PTSD at T1 and T2 were 0.91 and 0.91, respectively.

2.2.4. Beck Depression Inventory

The Beck Depression Inventory was designed to evaluate both the nature and extent of depression by examining its associated symptoms (Beck et al., 1996). This inventory comprises 21 questions that delve into various aspects of emotional, cognitive, motivational, and physiological experiences linked to depression. Each question comprises four statements that progressively portray heightened levels of depressive symptoms. Participants assign ratings on a scale from 0 to 3, reflecting their feelings over the preceding week. Cronbach’s α, for CPTSD at T1 and T2 were 0.90 and 0.91, respectively.

2.3. Data analysis

SPSS 23.0 and R 3.6.3 were used for data analysis. The former was used for descriptive analysis, and the latter was used for network analysis. The option setting of the e-questionnaire did not allow participants to skip any items. Thus, no item-level data were missing in the current study.

2.4. Estimation method

2.4.1. Cross-sectional networks and comparison

We used the qgraph package in R to estimate network structures. This involved a Gaussian graphical model computed with a graphical lasso (glasso) and an extended Bayesian information criterion model. Each variable was a node, connected by edges, with thickness indicating connection strength (Borsboom & Cramer, 2013). These edges could be positive (blue lines) or negative (red lines).

We used NetworkComparisonTest (NCT) function to estimate the differences between networks, indicated by global and local differences. We applied the network invariance and global strength invariance tests to quantify global differences. The edge and node invariance tests were applied to quantify local differences (van Borkulo et al., 2015).

2.4.2. Cross-lagged panel network

We employed the glmnet package (Friedman et al., 2010) to compute a directed CLPN from baseline to follow-up (baseline→follow-up). Previous studies have utilized time-series data with more than three time points to construct temporal networks (Epskamp et al., 2018). In contrast, CLPN models enable the construction of symptom networks between two time points, offering an advantage over other panel data methods that typically necessitate at least three time points (Epskamp, 2020). To estimate autoregressive and cross-lagged coefficients, we initially calculated regression models. Autoregressive pathways involved predicting a symptom at baseline and how it relates to itself at follow-up, considering all other symptoms at the initial time point. Cross-lagged pathways explored how a symptom at baseline predicts a different symptom at follow-up while accounting for all other symptoms at baseline. For this analysis, we again used a LASSO penalized maximum likelihood procedure with a 10-fold cross-validation tuning parameter. This procedure effectively highlighted only the most relevant cross-lagged effects for our study aims by setting small regression coefficients to zero. The visual representation of the directed CLPN displayed symptoms as nodes and cross-lagged effects as arrows. Blue arrows indicated positive effects, while red arrows represented negative effects, with the thickness of the lines indicating the strength of associations.

To focus on the cross-lagged effects, which were particularly relevant to our analysis, we set the autoregressive paths to 0. This suppression allowed us to emphasize the cross-lagged pathways, as it is of particular interest to our present study.

2.5. Accuracy and stability of edge-estimates

We used the bootstrap technique in the bootnet package to resample the data 1000 times and estimate the 95% confidence intervals (Cls) of the edge weights, to determine the accuracy of the network. In additional, we used case-dropping subset bootstrap to examine the consistency of centrality order in different data subsets, to evaluate the stability of node centrality. The stability was measured by the correlation stability coefficient (CS-coefficient), with a more stable network indicated by a CS-coefficient close to 0.5 or higher, and an unstable network indicated by a CS-coefficient below 0.25 (Epskamp & Fried, 2018).

2.6. Centrality indices

When exploring cross-sectional networks, the three centrality indicators are strength, closeness, and betweenness (Epskamp et al., 2012). However, recent research suggests that closeness and betweenness may be less stable (Armour et al., 2017). Due to potential biases in calculating strength centrality (Robinaugh et al., 2016), we used the Expected Influence Index (EI) to measure node influence. Furthermore, we calculated two centrality indices for the directed CLPN: ‘in’ expected influence (IEI) and ‘out’ expected influence (OEI). The IEI quantifies the degree to which each symptom is predicted by other symptoms in the network, whereas the OEI describes the degree to which each symptom predicts other symptoms in the network. These centrality indices provide valuable insights into the predictive roles of symptoms within the network.

3. Results

3.1. Descriptive statistics

To evaluate potential differences between the complete original dataset and the subset selected for this study, which included participants with a history of childhood trauma, we conducted t-tests and chi-square tests. These tests were aimed at analysing differences in demographic characteristics and the incidence of negative life events between the two groups, as detailed in Table 1. The analyses revealed significant disparities between the two datasets in several areas, including age, gender, perceived socio-economic status, level of education, and the frequency of all negative life events examined. Additionally, we conducted a comparative analysis between the baseline dataset and the dataset from participants who completed both surveys, with results presented in Table S1. Table 2 showcases the prevalence of adverse childhood experiences and negative life events among college students who have reported a history of childhood trauma. Table 2. Prevalence of adverse childhood experiences and negative life events among college students with a history of childhood trauma (N = 4006).

Adverse experiences	N	%	
ACE	 	 	
 Physical abuse	543	13.55%	
 Emotional abuse	198	4.94%	
 Sexual abuse	274	6.84%	
 Emotional neglect	308	7.69%	
 Physical neglect	818	20.42%	
 Parental marital discord	1555	38.82%	
 Domestic violence	668	16.67%	
 Substance abuse	429	10.71%	
 Mental illness	616	15.38%	
Negative life events	 	 	
 Left-behind experience	857	21.39%	
 Physical illness	253	6.32%	
 Diagnosed with a mental illness	140	3.49%	
 Disharmony in family relationships	432	10.78%	
 Serious accident	34	0.85%	
 Conflict with teachers	22	0.55%	
 Conflicts in relationships with peers or friends	1049	26.19%	
 A close person attempted or committed suicide	31	0.77%	
 Difficulty with school or job	942	23.5%	
 Feeling severe stress	256	6.39%	

3.2. Results of accuracy and stability checks

In the cross-sectional networks, edge weight bootstrapping (Figure S1) demonstrated moderate accuracy in estimating the two networks. The CS coefficients (Figure S2) revealed high overall network stability, marked by a consistent value of 0.75 for the EI in both T1 and T2 networks, indicating strong stability.

The results of the edge weight bootstrapping program (Figure S4) demonstrate that both cross-lagged network estimations are moderately accurate. There is considerable overlap in the 95% confidence interval of the edge weights, while some of the strongest edges do not overlap with the confidence intervals. The results of the bootstrapping program (Figure S5) indicate that the estimations of OEI, and IEI in both networks are stable and generalizable. The centrality stability coefficient for IEI was 0.75, and OEI was 0.44. Refer to Figure S6 for the centrality difference tests of the CLPN.

3.3. Network visualization

3.3.1. Cross-sectional networks

Only edges with significant associations between nodes were presented in the network. Both networks had many similar edge connections at the two measured time points. The proportion of edges that were estimated to be above zero in the network at the two time points were 288/528 and 296/528, respectively. The global network strength at the two time points was 0.028, and 0.028 (Figure 1). Figure 1. Symptoms network of CPTSD and depression at two time points.

The NCT showed that the network invariance test indicated significant differences between the structures in the two networks (M = 0.090, p < .001). The global strength invariance test showed that the global strength did not change significantly from T1 to T2 (global connectionT1 = 14.82, global connectionT2 = 15.05, S = 0.23, p = .142). The permutation test concerning the maximum difference in edge weights is not significant (diff = 0.091, p = .142).

3.3.2. Cross-lagged panel networks

The CLPN is plotted as a directed network (Figure 4). Because the plotting algorithm determines path thickness relative to the strongest path, autoregressive edges were excluded from Figure 4 to make the cross-lagged edges more visually interpretable. As plotting all cross-lagged edges would reduce interpretability, weaker edges (threshold = 0.05) were excluded from Figure 4. A plot that includes weaker edges is provided in Figure S3.

3.4. Centrality indices

Figure 2 shows, that in the T1 symptom network, the symptoms with the highest EI values included node feel like a failure from the DSO symptom cluster and nodes feel worthless, irritability from the depression symptom cluster (Figure 3). In the T2 symptom network, the symptoms with the highest EI values included nodes feel like a failure and avoid activities reminiscent of the trauma from the DSO symptom cluster and node self-dislike, from the depression symptom cluster. Figure 4 illustrates the centrality estimates in the CLPN model. Irritability, Self-dislike, and Past failure had the highest OEI; Avoid activities reminiscent of the trauma, Feel like a failure, and Intrusive memories had the highest IEI. Figure 2. Standardized estimates of centrality in CPTSD and depression network at two time points.

Note: Nightmares, Traumatic dreams; Intrusion, Intrusive memories; Avoidth, Avoid activities reminiscent of the trauma; Avoidclu, Avoid thoughts and feelings about the trauma; Hyper, Exaggerated startle; Startle, Hypervigilance; HardCalm, Takes long time to calm down; Numb, Emotional numbness; Failure, Feel like a failure; Worthless, Feel worthless; Distant, Feel distant or disconnected from others; Notclosed, Difficult to stay emotionally close to other; Sad, Sadness; Pess, Pessimism; Fail, Past failure; Pleas, Loss of pleasure; Guil, Guilty feelings; Punis, Punishment feelings; SelfDis, Self-dislike; SelfCri, Self-criticalness; Suicide, Suicidal thoughts; Crying, Crying; Agitation, Agitation; Interest, Loss of interest; Indec, Indecisiveness; Worth, Worthlessness; Energy, Loss of energy; Sleep, Changes in sleeping; Irrit, Irritability; Appetite, Changes in appetite; Concen, Concentration difficulty; Tired, Tiredness; Sex, Loss of interest in sex.

Figure 3. Cross-lagged panel network from T1 to T2.

Note: For visualization, a beta threshold of 0.05 for the regression weights was chosen. Abbreviations in the figure are the same as those in Figure 1.

Figure 4. Out-EI and in-EI of CPTSD and depression in the cross-lagged panel network.

Note: Abbreviations in the figure are the same as those in Figure 2.

4. Discussion

This is the first study to examine the network of CPTSD and depression in longitudinal data among early adults who experienced childhood trauma. By comparing CPTSD and depression contemporaneous networks at two time points, the structure and development of the networks between individuals in early adulthood were explored. Additionally, the CLPN model delved deeper into the directed relationships between CPTSD and depressive symptoms. By identifying the symptoms with the most significant impact on others, this model enhances the development of targeted interventions for individual psychological issues stemming in the context of childhood trauma.

Although the two contemporaneous networks show a slight difference in the structural invariance test, there is no significant difference in centrality, network density and overall connectivity. This finding contrasts with prior longitudinal studies investigating the networks of depression and PTSD in contexts of earthquakes (Qi et al., 2023). The underlying reasons for this disparity may be attributed to variations in the types of traumas and the composition of the participant samples. Previous research suggests that the psychological responses and symptom development following type I trauma events (single event; sudden and unexpected, high levels of acute threat) may differ from those associated with type II trauma (repeated and/or protracted; anticipated) (Birkeland et al., 2022; Stefanovic et al., 2022). In the context of abrupt traumatic events such as earthquakes or warfare, survivors typically exhibit a decline in psychological symptoms over time since the occurrence of the trauma (Schlechter et al., 2022). Consequently, the connectivity and central symptoms within the symptom network may also undergo alterations (Qi et al., 2023). However, the participants in this study experienced complex childhood traumas, characterized by enduring, chronic adversities. These prolonged adversities may give rise to a series of profound psychological and physiological changes, including the shaping of enduring cognitive, emotional, and physiological response patterns to stressors (Wilson et al., 2011). These patterns may crystallize into stable cognitive structures and emotional response patterns in adulthood, thereby rendering symptom expression relatively stable and less susceptible to significant shifts over time (Billen et al., 2023). In terms of network structure, the relationship between DSO symptoms and depression is stronger compared to PTSD, a finding consistent with prior research (Gilbar, 2020; Haselgruber et al., 2021). The DSO symptoms encompass negative self-concept, and emotional dysregulation, among others, which bear resemblance to the negative self-evaluation and negative emotions present in depressive symptoms. This may imply that in CPTSD, issues pertaining to self-identity and emotional regulation are more prone to mutual influence with depression, thus forging a closer relationship between them. We also found that some strong edges in both two contemporaneous networks; for example, between Feel like a failure and Feel worthless, Avoid activities reminiscent of the trauma and Avoiding thoughts, Feel distant or disconnected from others, and Difficult to stay emotionally close to other, Exaggerated startle and Hypervigilance. As posited by network analysis theory, the tight connections between internal nodes of symptoms indicate a higher risk of recurrence and a less favourable prognosis (Borsboom & Cramer, 2013; Smith et al., 2019). Although these strong edges exist between conceptually similar symptoms, they all belong to the internal symptoms of CPTSD. This suggests that for young adults who have experienced childhood trauma, the symptoms of CPTSD may more prone to mutual activation and recurrence, and this finding exhibits stability across different time points. In our analysis of contemporaneous networks, we identified several central symptoms prevalent among individuals with a history of childhood trauma: Irritability, Self-dislike, Feel like a failure, and Feel worthless. These symptoms are indicative of DSO and mood disorders, often resulting from negative self-perceptions and chronic mood alterations. Our findings align with the predictive processing model, which suggests that prolonged exposure to type II trauma such as childhood adversity leads to a cognitive bias towards broad, threat-based hypotheses over more optimistic ones (Wilkinson et al., 2017). This bias may significantly impact the victim’s ability to process information, consistently skewing it towards negative interpretations of self, others, and the surrounding environment. For instance, the perception of self as ‘worthless’ or the belief that ‘others cannot be trusted’ are reflective of an ingrained negative self-concept, a common outcome of type II trauma. Such cognitive biases can exacerbate the difficulties in emotional regulation, where affected individuals might experience emotional outbursts or exhibit agitated responses more frequently (Bilgi et al., 2017; Cloitre et al., 2014). Moreover, the enduring impact of childhood trauma is evident in the development of negative self-assessments regarding one’s worth, capabilities, and self-efficacy, further perpetuating a negative self-concept (Evans et al., 2015; Shahar et al., 2015). These complex interrelations highlight the severe consequences that type II trauma can have on an individual’s general information processing and overall mental health.

The results of the CLPN, help to clarify the potential associations among high-impact symptoms in both depression and CPTSD. This interpretation is suggestive rather than definitive, given the study’s design and the longitudinal data spanning only two time points. Specifically, the CLPN showed that Irritability, Sadness, Past failure, and Self-dislike, which exhibited the high OEI, frequently served as predictors for other symptoms. At the same time, Avoid activities reminiscent of the trauma, Intrusive memories, Feel like a failure, and Exaggerated startle have the highest IEI, as a symptom predicted by other symptoms. It is notable that among the predictive factors, the highest prognostic capabilities pertain to symptoms within the domain of depressive symptoms, specifically encompassing negative emotions and symptoms associated with negative self-appraisal. Conversely, those symptoms most susceptible to activation by other manifestations are within the purview of CPTSD, notably characterized by intrusive, avoidance, hyperarousal symptoms within PTSD, and negative self-concept features within the domain of DSO. Firstly, individuals who have experienced childhood maltreatment often encounter difficulties in regulating their emotions. This may stem from the fact that the maltreatment experienced during childhood disrupts the normal development and establishment of emotional regulatory mechanisms (Kim & Cicchetti, 2010). The frequent occurrence and fluctuations of negative emotions, such as irritability and sadness, can lead individuals to persistently reside in a state of negative affect (An et al., 2021). Furthermore, enduring and recurrent childhood maltreatment may induce a sense of self-doubt and negative self-evaluation in individuals regarding their own worth, capabilities, and value. Consequently, this culminates in the formation of a negative self-concept (Evans et al., 2015; Shahar et al., 2015). The unstable negative emotions and negative self-concept frequently interact, engendering a deleterious cycle that may further triggers other trauma-related symptoms. We also found that nodes exhibiting high IEI predominantly fell within the domain of PTSD symptoms, with avoidance symptoms displaying the highest IEI, followed by hyperarousal and intrusive symptoms. These symptoms are consistent with previous cross-sectional network analysis findings in PTSD (Brewin, 2014; Ehlers & Clark, 2000; Schnyder et al., 2015) and are further supported by the results of this longitudinal network analysis. The centrality of avoidance symptoms possibly initiated by the negative emotions and cognitions associated with traumatic events (Schlechter et al., 2022). Arousal, on the other hand, may signify a state of heightened sensitivity to threat, which could, in turn, exacerbate intrusive symptoms. For instance, there may be in-strength connections to experiences like ‘I found myself acting like I was back at that time’ (Greene et al., 2020). These sensations, coupled with a reduced capacity for contextual memory integration, may lead to a perception of immediate threat, prompting subsequent avoidance responses aimed at mitigating overwhelming trauma-related sensations (Ehlers & Clark, 2000). Furthermore, research has identified variations in the manifestation of core PTSD symptoms across distinct phases following traumatic incidents. Specifically, hyperarousal and intrusive symptoms assume paramount significance in the early aftermath of trauma events, while avoidance symptoms emerge as primary features in the later stages posttrauma (McMillen et al., 2000). Given that the occurrence of childhood traumas may have transpired a considerable time in the past, it follows that among these early adulthood university students, within the comorbid network of depression and CPTSD, the avoidance symptom shows a susceptibility to activation and perpetuation by a diverse array of symptoms.

From the directional relationship of these core symptoms, from Irritability to Traumatic dreams and Intrusive memories, form Sadness to Avoid activities reminiscent of the trauma, from Past failure and Self-dislike to Feel like a failure are the stronger directed edges in the longitudinal network. These results are consistent with prior research suggesting that depression may act as a precursor to subsequent PTSD symptoms (Cheng et al., 2018; Ying et al., 2012), thereby expanding upon the depressogenic model posited by Schindel-Allon et al. (2010). The depressogenic model posits that negative affect, anhedonia, and a negative self-concept, serving as distinctive indicators of depressive symptoms, are involved in the development of posttraumatic distress (Bryant & Guthrie, 2007). This study further advances our understanding at the symptom level, uncovering two pathways through which depressive symptoms activate CPTSD symptoms in early adulthood individuals who have experienced childhood trauma. These pathways involve trauma-related stress disorder symptoms activated by negative emotions, as well as the pathway characterized by the negative self-concept within DSO symptoms, which is activated by self-deprecating evaluations typical of depression.

The present study carries certain limitations that require acknowledgment. Firstly, the sample primarily consisted of college students with a history of childhood trauma. Thus, caution should be exercised when generalizing the research findings to other populations. Secondly, the predominant measurement relied on self-report measures without the inclusion of clinical interviews. This approach may introduce biases in estimating trauma history CPTSD, and depressive symptoms in college students. Future research could consider incorporating assessments by clinicians and collecting objective indicators to broaden the scope of measurement. Despite the longitudinal design, which tracked changes in CPTSD symptoms over a year, it is crucial to recognize that the progression of these symptoms may require a longer timeline for comprehensive understanding. Extending the study duration could be advantageous in gaining a more thorough grasp of the potential development and fluctuations of these symptoms. Finally, it is important to acknowledge that current methods of CLPN analysis are limited to examining two time points, which limits our understanding of the potential causal relationship between symptoms. Future research endeavours could enhance this methodology by incorporating more time intervals and encompassing a wider range of participant groups. This expansion would contribute to a deeper understanding of depression and CPTSD network characteristics and the evolution of the relationship between symptoms.

Despite these limitations, the present study represents a substantial theoretical contribution to the field. We systematically combined the contemporaneous network and CLPN network analysis method and investigated the cross confirmation of methods. We found that for early adulthood individuals with a history of childhood trauma, the structure and manifestation of both depression and CPTSD symptoms tend to be relatively stable. The core symptoms revolve around unstable emotional states and negative self-concept. Additionally, our results suggest a potential predictive relationship where depression symptoms may precede CPTSD symptoms. The potential pathways of symptom activation are suggested to involve primarily two scenarios: one where somatic PTSD symptoms are triggered by negative emotions, and another where enduring negative self-concept follows negative self-evaluations. This insight provides a fresh perspective on the interaction between CPTSD and depression in early adulthood, enriching existing theories. However, it is important to interpret these relationships with caution given the yearly intervals of our study, recognizing that the dynamics might differ over shorter or longer timescales. From a clinical standpoint, the points of intervention for comorbidity between depression and CPTSD who have experienced childhood trauma differ across different stages. In the early stages, interventions should concentrate on stabilizing emotions and training emotion regulation abilities. This period often involves significant emotional volatility, which can exacerbate symptoms of both depression and CPTSD. Techniques such as cognitive–behavioural therapy (CBT) may be effective during this stage for teaching individuals how to identify and manage their emotional responses to triggers (Lonergan, 2014). Additionally, mindfulness-based interventions may also play a role at this stage (Dumarkaite et al., 2021), helping patients to achieve a state of awareness of the present moment while calmly acknowledging and accepting one’s feelings, thoughts, and bodily sensations. This practice can help mitigate the automatic negative responses to stress that are common in these individuals. As individual progress and gain better control over their emotional responses, the focus of interventions can shift towards addressing cognitive aspects, particularly irrational or negative self-perceptions. According to the multimodal phase-based approach for CPTSD (Lonergan, 2014), this stage should also involve efforts to build resilience and enhance coping strategies, which are crucial for the long-term management of symptoms and prevention of relapse. However, it is important to note that most of the clinical implications discussed above are associated with the central symptoms identified in this study. While the centrality hypothesis posits that core symptoms are particularly crucial for the course of the disease, some studies have found that these central symptoms do not predict subsequent disease progression (Spiller et al., 2020). Given that evidence regarding the causal role of central nodes remains mixed (Dablander & Hinne, 2019), caution should be exercised in interpretation and application, and further validation in clinical practice is warranted.

Supplementary Material

Rcode.txt

Supplementary materialsR1.docx

CPTSDDEPT2.csv

CPTSDDEPT1.csv

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets and code generated and/or analysed during the current study are available in the supplementary materials accompanying this paper. These resources are provided to ensure transparency and reproducibility of the results reported herein. For further inquiries, please contact the corresponding author.
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