Symptom network connectivity of PTSD comorbid with depression in bereaved Chinese parents

Symptom network connectivity of PTSD comorbid with depression in bereaved Chinese parents | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Symptom network connectivity of PTSD comorbid with depression in bereaved Chinese parents Buzohre Eli, Xuanang Liu, Fei Xiao, Zhengkui Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4567110/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Sep, 2025 Read the published version in BMC Psychiatry → Version 1 posted 4 You are reading this latest preprint version Abstract Background The death of a child is a highly traumatic event for parents and often leads to posttraumatic stress disorder (PTSD) and depression. PTSD and depression are frequent comorbid. However, the patterns of comorbidity at the symptom level among bereaved parents remain unclear. This study aims to identify symptom network connectivity of PTSD comorbid with depression in bereaved parents who have lost their only child, known as Shidu parents in Chinese society. Methods Data were obtained from 477 bereaved individuals who had lost an only child. A Gaussian graphical model (GGM) was used to construct two comorbidity networks of PTSD and depression with and without overlapping symptoms. A directed acyclic graph (DAG) was computed to determine potential directionality among symptoms in the network without overlapping symptoms. Results In the GGM, symptoms from alterations in arousal and reactivity (Cluster E) and negative alterations in cognition and mood (Cluster D) tended to be key bridging nodes in both networks with and without overlapping symptoms. The DAG results indicated the important triggering role of an exaggerated startle response and several negative alterations in cognition and mood symptoms. Conclusions The results suggest a crucial role of the exaggerated startle response in the comorbidity network between PTSD and depression among bereaved Chinese parents. This finding may serve as a significant target for psychological interventions in this population. Posttraumatic stress disorder Depression Bereaved parents (Shidu parents) Comorbidity Network analysis Figures Figure 1 Figure 2 Figure 3 Introduction The death of a child is one of the most traumatic events in bereavement and poses a significant risk to the mental health of parents [ 1 ]. There is a remarkable number of parents who have lost their only child, an unintentional consequence of the ‘one-child policy’ in China. Such bereaved parents who have passed their reproductive windows and cannot conceive another child (mothers over 49 years of age), and are unwilling to adopt a child are known as Shidu parents in Chinese society [ 2 ]. Children represent generational continuity and play an important economic and social support role for aging parents [ 3 ]. Losing their only child thus represents the termination of family lines and the loss of caregivers in old age in China. Moreover, Chinese culture regards the death of a child as a sign of bad luck [ 4 ], resulting in the stigmatization of Shidu parents. Therefore, these Shidu parents experience psychological trauma and cultural pressure that induce major mental health problems, including posttraumatic stress disorder (PTSD) and depression [ 2 , 5 ]. PTSD and depression are frequently comorbid after trauma. The comorbidity rate of PTSD and depression has been estimated to be 52.0% among populations with traumatic exposure [ 6 ]. A study reported that the comorbidity rate of PTSD and depression was 47.6% among Shidu parents [ 7 ]. The literature has indicated that the comorbidity of PTSD and depression may have more severe adverse effects on mental health and quality of life [ 8 ]. Identifying the comorbidity pattern of PTSD and depression might inform targets for proactive screening and treatment [ 9 ]. Recently, the network approach has provided an opportunity to examine the relationships between PTSD and depression [ 10 ]. According to network theory, mental disorders are viewed as systems of mutually interacting symptoms [ 11 ]. Symptoms correlate in a disorder because they directly activate and potentially exert causal effects upon each other [ 12 ]. In the network, a symptom triggers the activation of other symptoms, which are called central symptoms. Network analysis may contribute to an understanding of the development and maintenance of treatment for mental disorders by revealing the most central symptoms and how symptoms influence each other within disorders [ 13 ]. In addition, network analysis can elucidate the relationships between disorders at the symptom level by identifying the bridge symptoms that connect two disorders. A bridge symptom is a symptom that is central in connecting two disorders (PTSD and depression) and plays an essential role in maintaining and developing comorbidities in a network [ 14 ]. Thus, bridge symptoms can be regarded as target symptoms in clinical treatment [ 9 ]. Extant studies have explored the comorbidity of PTSD and depression in children and adolescents [ 15 , 16 ], as well as in adults [ 17 – 20 ] using the network approach. Although most studies have unanimously shown that some overlapping symptoms that are components of both PTSD and depression, such as sleep problems and concentration difficulties , play important bridging roles in the comorbidity network of PTSD and depression [ 9 , 15 , 17 ], there is still no consensus about the bridging roles of other nonoverlapping symptoms. Previous studies have indicated several bridging symptoms, including flashbacks , avoidance of thoughts , getting emotionally upset by trauma reminders , and anhedonia [ 17 , 18 , 20 ]. These mixed findings imply the need for more empirical research. Another limitation in existing network studies on the comorbidity between PTSD and depression is the lack of determination of a causal direction. Most cross-sectional studies have used the graphical Gaussian model (GGM) to identify the network structure of comorbid PTSD and depression [ 18 , 19 , 21 , 22 ]. The GGM calculates an undirected sparse network in which nodes represent symptoms and edges represent the partial correlation between two nodes after controlling for all other nodes [ 23 ]. The Bayesian network approach can help overcome the key limitations of a partial correlation network. Bayesian networks are probabilistic graphical models that represent conditional independence relationships among variables as directed acyclic graphs (DAGs). In this type of graph, edges are directed and noncircular and provide information about the directions of causal relationships between pairs of nodes from cross-sectional data [ 24 ]. A partial correlation network of the GGM could be utilized to explore potential two-way causality and causal loops, while a Bayesian network could be used to investigate the one-way causality among symptoms. These two approaches can be viewed as complementary. Combining these two approaches is more helpful for clarifying the causal system of PTSD and depression than using only one approach [ 25 ]. Finally, scholars have identified several bridging symptoms that connect PTSD and depression across adult populations with various traumatic experiences, including veterans, firefighters, and victims of interpersonal violence [ 17 , 18 , 20 ]. While these network outcomes have advanced our knowledge in the field, conclusions from previous studies are limited in their generalizability to Shidu parents because the loss of a child is different from other types of traumatic events. As scholars have suggested, the type of trauma as well as the network structure have an effect on the severity of PTSD [ 26 , 27 ]. The type of trauma may also potentially influence the comorbidity network of PTSD and depression. As such, more empirical studies are needed to explore the characteristics of comorbid PTSD and depression among bereaved parents. To address these gaps in the literature, the current cross-sectional study utilizes the network approach to identify the comorbidity network structure between PTSD and depression among Chinese Shidu parents who have lost their only child. The aims of this study were twofold. First, we utilized the GGM to investigate the comorbid network patterns of PTSD and depression and to identify bridge symptoms, the strongest edges, and potential causal loops. Second, we utilized the DAG to investigate the key driving symptoms in the comorbid network and the predominant pathways of activation between PTSD and depression. Methods Participants and procedure The participants were 447 bereaved individuals who had lost their only child. Of these participants, 188 (42.1%) were male, 224 (50.1%) were female, and 35 (7.8%) had missing information on sex. The age of the males ranged from 44 to 84 years ( M = 62.3, SD = 7.6), and the age of the females ranged from 55 to 88 years ( M = 61.6, SD = 7.2). The losses had occurred an average of 9.6 years ( SD = 7.3) prior to data collection. The sociodemographic characteristics of the participants are presented in Table 1 . Table 1 ༎ Sociodemographic characteristics of the participants ( N = 447). Sociodemographic characteristic Category Frequency % a Sex Male 188 42.1 Female 224 50.1 a Age ≤ 60 years 191 42.7 > 60 years 255 57.0 a Educational level Junior high school or below 286 64.0 High school or above 158 35.3 a Marital status Married (first/ remarriage) 339 75.8 Single/divorced/separated/widowed 83 18.6 a Religious belief None 354 79.2 Buddhism 15 3.4 Other 19 4.3 a Family income ≤ 3000 307 68.7 > 3000 40 8.9 a Subjective assessment of family economic status Poor and extremely poverty 129 28.9 Moderate 196 43.8 Wealthy and extremely wealthy 27 6.0 a Cause of death Unnatural cause 223 49.9 Natural cause 158 35.3 a Time since child’s death 1–3 years 93 20.8 4–8 years 105 27.5 9–15 years 113 30.4 ≥ 16 years 60 16.1 Note . a: There are missing values in sociodemographic characteristic, thus, the sum of the effective percentage is note equal to 100% in these cases; Unnatural cause: accident, homicide, suicide, natural disaster, or other; Natural cause: illness. Participants were recruited through community workers and local health and family planning departments. Those eligible for inclusion were bereaved parents who had lost their only child at least one year prior and currently had no living child and female participants who were older than 49 years, which means that they had passed their reproductive age. Participants who satisfied the inclusion criteria were invited to the community office. They were given a complete description of the survey and were assured that their responses would be kept completely confidential. It was also clearly indicated that they had the right to withdraw from the study at any time. Written informed consent was obtained from each participant, and all the research processes met ethical standards. After the investigation, each participant was given a gift of a daily necessity, such as a thermos bottle or hot pack. Professional psychological services were provided to the participants if psychological discomfort occurred during or after the investigation. This study was approved by the ethics review committee of the Institute of Psychology, Chinese Academy of Sciences (Ethics approval number: H21044). Measures PTSD symptoms were assessed using the Posttraumatic Stress Disorder Checklist for the DSM-5 (PCL-5) [ 28 ]. The PCL-5 is a 20-item self-report inventory with four subscales that correspond to the four DSM-5 symptom clusters: intrusion (Cluster B), avoidance (Cluster C), negative alterations in cognition and mood (Cluster D), and alterations in arousal and reactivity (Cluster E). Participants indicate the extent to which each symptom has bothered them over the past month using a 5-point Likert scale. The total score ranges from 0 to 80, and higher scores indicate greater PTSD symptom severity. A cut-off of 33 or above on the PCL-5 total score indicates the presence of significant PTSD symptoms [ 28 ]. In the present study, the Cronbach's alpha value for this measure was 0.96, and the KMO value of the confirmatory factor analysis was also 0.96. Depression symptoms were assessed using the shortened Center for Epidemiological Studies-Depression Scale (CES-D-10). The CES-D-10 consists of 10 items from the original 20-item questionnaire [ 29 ]. Participants indicate the extent to which each symptom has bothered them during the past week using a 4-point Likert scale. Two of the ten positively rated items (" I felt hopeful about the future " and " I was happy ") were reverse scored for the analysis. The total score ranges from 0 to 30, and higher scores indicate greater severity of depression. A cut-off of 10 or above on the CES-D-10 total score indicates the presence of significant depression [ 29 ]. In the present study, the Cronbach’s alpha value for this measure was 0.84, and the KMO value of the confirmatory factor analysis was 0.89. Statistical analyses Descriptive statistical analyses were conducted with SPSS (IBM SPSS, version 21.0). Given the difference in the scale range between the PCL-5 and CES-D-10, the scores for all items were converted to z scores. Network analysis was conducted using R Core Software, version 4.1.3 (R Team, Vienna, Austria). Networks are graphical models consisting of nodes and edges, and nodes represent the individual symptoms. In the current study, the nodes were the symptoms of PTSD and depression. The edges represented the relationships between two nodes after conditioning on all other nodes in the analysis. Undirected network estimation and visualization Two distinct comorbidity networks were constructed to explore the relationship between PTSD and depression. The first network included all symptoms of PTSD and depression (30 nodes). The second network excluded the two overlapping symptoms (i.e., sleep problems and concentration difficulties ; 26 nodes). The network structure of comorbidities was estimated using the R package qgraph [ 30 ]. The network was estimated using a GGM (EBICglasso) option [ 23 ] and visualized using the Fruchterman-Reingold algorithm [ 31 ] in the R package qgraph. The algorithm positions nodes with more or stronger connections at the center of the network. In the present study, green and red edges represent positive and negative associations, respectively. Line thickness reflects the strength of the association. Centrality estimation and bridge centrality estimation We utilized expected influence (EI) to evaluate node centrality using the R package qgraph, following recommendations from recent studies. The EI represents the summed weight of all its edges, including positive and negative associations with its neighboring nodes in the network [ 32 ]. Higher EI values reflect greater node centrality [ 33 ]. Moreover, it is essential to assess bridge centrality to assess comorbidities [ 14 ], which could indicate the bridge node as a link between PTSD and depression. Thus, the bridge EI was also computed to identify nodes that had symptom-level connections with nodes of the other disorder using the R package networktools [ 14 ]. There are two types of bridge expected influence (bEI): 1-step bEI and 2-step bEI. The 1-step bEI is the sum of all edges between a node and all other nodes in the second disorder, taking into account both positive and negative edges. The 2-step bEI is similar to the 1-step method but also considers the indirect influence of the node through other nodes [ 14 ]. We used 1-step bEI to calculate bridge centrality in this study. Based on Jones et al.’s suggestion, the nodes with the top 20% bEI values were considered bridge nodes. The network accuracy and stability were tested using the R package bootnet [ 23 ]. The more detailed content is displayed in the supplementary materials. Directed network estimation To identify the causality of the comorbidity network, a DAG analysis was performed using the R package bnlearn and employing the hill-climbing algorithm [ 34 ]. The DAG is a Bayesian network approach that models a network in which edges are directed and noncircular, representing the causal relationship directions between pairs of nodes from cross-sectional data [ 35 ]. The network excluded the two overlapping symptoms, which included 26 nodes. The estimation involved three steps, the more detailed content is displayed in the supplementary materials. Results Descriptive analyses The mean PCL-5 and CES-D-10 total scores for all participants were 38.2 ( SD = 20.9) and 14.1 ( SD = 6.6), respectively. Of the 447 participants, 55.0% (246) reported PTSD, 74.3% (332) reported depression, and 52.3% (234) reported comorbidity of PTSD and depression. Undirected network structure The network structures of PTSD and depression with and without overlapping symptoms are presented in Fig. 1 a and 1 b, respectively. In the comorbidity network with overlapping symptoms, 198 of the 435 edges were nonzero (density of 0.46). In the comorbidity network without overlapping symptoms, 167 of 325 edges were nonzero (density of 0.51). In networks with and without overlapping symptoms, the majority of symptoms were positively connected. PTSD and depression exhibited a strong internal connection. The strongest edge connections were the same in both networks, such as between symptoms E3 and E4 ( hypervigilance and exaggerated startle ), B1 and B2 ( recurrent thoughts and nightmares ), Dep5 and Dep8 ( hopelessness about the future and unhappiness ), and Dep1 and Dep10 ( bothered by things and could not get going ). Network centrality The standardized estimate of the expected influence (EI) for the PTSD and depression network with and without overlapping symptoms is depicted in online Supplementary Figure S1 a and S1b. The network centrality bridge expected influence (bEI) for PTSD and depression with and without overlapping symptoms is presented in Fig. 2 a and 2 b, respectively. In the comorbidity network with overlapping symptoms, D5 ( diminished interest ), E1 ( irritability/anger ), E2 ( reckless/self − destructive behavior ), D4 ( negative emotional state ), C2 ( avoidance of reminders ), and B3 ( flashbacks ) played crucial bridging roles in connecting to other symptoms of PTSD and depression. In the comorbidity network without overlapping symptoms, E1 ( irritability/anger ), D5 ( diminished interest ), E2 ( reckless/self − destructive behaviour ), E4 ( exaggerated startle ), and D4 ( negative emotional state ) played crucial bridging roles in connecting to other symptoms of PTSD and depression. The results showed moderate network accuracy and high stability. See online supplementary materials for more detailed contents. Directed network structure The DAG estimated from PTSD and depression is presented in Fig. 3 . From a holistic symptom perspective, depression symptoms, with the exception of Dep5 ( hopelessness about the future ) and Dep8 ( unhappiness ), are often triggered by PTSD symptoms. From an individual symptom perspective, E4 ( exaggerated startle ) again emerged as the most pivotal network node. It triggered seven other PTSD and depression symptoms, with the strongest connections with E3 ( hypervigilance ) and D6 ( detachment ). D5 ( diminished interest ), D4 ( negative emotional state ), Dep1 ( bothered by things ), Dep 10 ( could not get going ), and C1 ( avoidance of thoughts ) were also directly triggered by E4 ( exaggerated startle ). Moreover, Dep5 ( hopelessness about the future ) triggered E1 ( irritability/anger ) and Dep8 ( unhappiness ). Dep8 ( unhappiness ) further triggered E3 ( hypervigilance ) and Dep1 ( bothered by things ). The most downstream symptoms (i.e., triggered nodes that trigger no other nodes) were C1 ( avoidance of thoughts ), C2 ( avoidance of reminders ), D6 ( detachment ), and D1 ( amnesia ). Discussion The current study applied the GGM and DAG network analysis to map the relationship between PTSD and depression and to identify how symptoms interact with each other in comorbid conditions among Shidu parents. Generally, both the GGM and DAG results indicate that alterations in arousal and reactivity (Cluster E), especially exaggerated startle (E4), and negative alterations in cognition and mood (Cluster D) symptoms (except detachment and amnesia ) are of great importance in the comorbidity of PTSD and depression among Shidu parents. The GGM revealed several important results. First, PTSD and depression symptoms emerged as two discrete subnetworks, with the strongest connections appearing among almost all nodes within each disorder in both networks with and without overlapping symptoms. For example, the strongest connections were observed from recurrent thoughts to nightmares (B1-B2), hypervigilance to exaggerated startle (E3-E4), hopelessness about the future to unhappiness (Dep5-Dep8), and depressed mood to everything is an effort (Dep3-Dep4). These findings suggest that within-disorder symptom connectivity is greater than between-disorder connectivity, consistent with previous studies indicating that PTSD and depression are two separate disorders with mutually influential symptom structures [ 15 , 17 , 20 ]. Second, the same edges emerged as the strongest in the networks with and without overlapping symptoms, and the most important bridge symptoms in both comorbidity networks included nonoverlapping symptoms. We identified a significant bridging role of six symptoms in the comorbidity network with overlapping symptoms ( diminished interest , irritability/anger , reckless/self − destructive behavior , negative emotional state , avoidance of reminders , and flashbacks ) and five symptoms in the comorbidity network without overlapping symptoms ( irritability/anger , diminished interest , reckless/self − destructive behaviour , exaggerated startle , and negative emotional state ). This suggests a minimal influence of the overlapping symptoms on comorbidities. This finding is inconsistent with some studies showing that overlapping symptoms are the origin of high comorbidity [ 19 ] and consistent with other studies indicating that bridging symptoms are not limited to overlapping symptoms [ 9 , 12 ], suggesting that nonoverlapping symptoms also emerge as the strongest connection across PTSD and depression. Third, most bridging symptoms are similar in networks with and without overlapping symptoms (i.e., irritability/anger , diminished interest , reckless/self − destructive behaviour , and negative emotional state ), emphasizing their significance in the comorbidity network of PTSD and depression among Shidu parents. This finding echoes previous findings in nonbereaved trauma populations [ 9 , 36 ]. Avoidance of reminders and flashbacks also emerged as important bridging symptoms in the network with overlapping symptoms, reaffirming previous findings in various populations [ 9 , 20 ]. However, despite overlapping symptoms, we did not find evidence of the bridging role of sleep problems and concentration difficulties in the comorbidity network. This finding contrasts with numerous comorbidity network studies [ 9 , 17 , 19 – 21 ]. The differences between our study and previous studies might be caused by the variation in the trauma population. Previous studies have focused on PTSD comorbid with depression in veterans, refugees, and victims of interpersonal violence. In contrast, our study focused on Shidu parents. The type of trauma has been previously found to affect the presentation of PTSD symptoms [ 27 ] and network structure [ 26 ], which might also influence comorbidity patterns. Exaggerated startle emerged as a key bridging node in the network without overlapping symptoms, suggesting that exaggerated startle may be a leading cause of comorbidity between PTSD and depression among Shidu parents. This bridging symptom has not been found in existing network studies about comorbid PTSD and depression, even though the importance of an exaggerated startle response in PTSD has been clarified in PTSD network studies [ 5 , 37 ]. The PTSD sensitization model suggests that traumatic experiences make individuals extremely sensitive to threats; over time, this leads to a stronger startle response [ 38 ]. The death of a child means the loss of active parenthood and emotional ties for bereaved parents. The sudden loss of an only child is devastating for Shidu parents and can make them prone to being easily startled [ 39 ]. Moreover, strong associations were observed between exaggerated startle and hypervigilance , replicating previous findings [ 5 , 37 , 40 ]. This association is a self-reinforcing feedback loop that might lead to the development of PTSD and promote comorbidity between PTSD and depression. The DAG network also revealed the important driving roles of exaggerated startle in comorbidity. Exaggerated startle is at the top of the network and drives other symptoms, which is consistent with the findings of An et al. (2021). Exaggerated startle triggers hypervigilance , diminished interest , and detachment , which further activate other symptoms of PTSD, such as negative emotional states , restricted affect , flashbacks , reckless/self − destructive behaviour and depression ( depressed mood and fear ). In addition, exaggerated startle directly triggers symptoms of depression, such as bothered by things and could not get going . We conclude that most of the symptoms in clusters E and D are located in the upper part of the network, indicating a significant influence on the overall structure of the comorbidity network. According to the PTSD sensitization model, alterations in arousal and reactivity ( exaggerated startle , irritability , and hypervigilance ) are common responses after trauma and have been empirically validated in Shidu parents [ 5 ]. Prolonged hyperarousal causes emotional exhaustion and affects executive resources, leading to the depletion of cognitive and emotional resources [ 41 ]. This can easily generate negative alterations in cognition and mood symptoms, including negative emotional state , negative beliefs , distorted blame , diminished interest , and detachment . Impaired regulation of emotion and cognition reduces the capacity to cope well with traumatic events [ 42 , 43 ], which may lead to comorbid PTSD and depression. Notably, two symptoms of depression ( hopelessness about the future and unhappiness ) trigger some PTSD and depression symptoms, while the remaining depression symptoms, mostly located in the lower part, are triggered by PTSD symptoms among Shidu parents. Hopelessness about the future activates unhappiness and irritability/anger , while unhappiness further triggers hypervigilance and bothered by things . A child is the major source of hope and meaning in life for parents [ 44 ]. The death of an only child can cause parents to lose their sense of meaning, often leading them to feel as though life has become meaningless and hopeless [ 39 , 45 ]. This can result in negative emotions, such as unhappiness and irritability/anger . Negative emotions, such as unhappiness , may prompt parents to recall memories related to their child. Previous studies have indicated that PTSD patients remember traumatic events well [ 46 ], and Shidu parents are highly alert to environmental stimuli and easily bothered by things. These findings indicate that the bidirectional relationship between PTSD and depression is complex. Additional research is needed to investigate the associations between PTSD and depression in greater depth. Finally, avoidance of thoughts and avoidance of reminders were the most common downstream symptoms triggered only by other PTSD and depression symptoms, indicating that these are the outcome variables. Avoidance is prominent in traumatized groups and is considered a culturally acceptable coping mechanism [ 47 ]. Empirical evidence has also indicated that Shidu parents exhibit attentional avoidance from trauma-related stimuli [ 48 ]. Scholars suggest that attentional avoidance is a maladaptive coping strategy [ 49 ], which may be attributed to the significant increase in negative affect that occurs when individuals have difficulty disengaging from trauma-related cues [ 50 ]. In addition, detachment emerged as a downstream symptom triggered by other symptoms, including diminished interest , flashbacks , reckless/self − destructive behavior , depressed mood , loneliness , and bothered by things . These traumatic responses may cause interpersonal problems such as detachment from others. Bereaved parents in China are reluctant to associate with others, especially with families that have children, as Chinese culture regards the death of a child as a sign of bad luck [ 4 ]. Cultural context may exacerbate the relationship trajectory. Amnesia was found to be a downstream symptom, consistent with PTSD network [ 5 , 19 , 20 ] and factor analysis studies [ 51 , 52 ]. This suggests that amnesia might not serve as a core symptom in PTSD [ 19 ]. This study should be interpreted in light of several limitations. First, we utilized cross-sectional data, it is not possible to infer direct causation between symptoms. Although DAG network analysis can represent the causal relationship directions between pairs of nodes from cross-sectional data, the analysis is constrained by some strict assumptions that limit potential inferences. Future longitudinal studies are needed to evaluate the comorbidity patterns of PTSD and depression. Second, PTSD and depression symptoms were assessed using a self-report questionnaire instead of a clinical assessment. Future studies should assess comorbidity network structures based on clinical observations to replicate and validate our findings. Finally, the participants in the current study were limited to Chinese Shidu parents, and the sample size was small due to difficulties accessing this group. These considerations may limit the generalizability of our conclusions. Therefore, the findings of this study must be interpreted with caution, and the conclusions should be generalized carefully with awareness of the potential stability issues in the data. Conclusions Network analysis offers powerful empirical tools to visualize interactions among symptoms of mental disorders. The results indicate a pivotal role of exaggerated startle in the comorbidity network between PTSD and depression among bereaved Chinese parents who have lost their only child. This finding may highlight important targets for psychological treatment in this population. PTSD and depression are independent but mutually influential among bereaved Chinese parents. Declarations Author contributions Buzohre Eli: Data curation, Formal analysis, Methodology, Writing–original draft, Writing–review & editing. Xuanang Liu: Methodology, Writing–review & editing. Fei Xiao: Writing–review & editing. Zhengkui Liu: Conceptualization, Investigation, Project administration, Resources, Supervision. Funding This work was supported by the National Key R&D Program of China (2023YFC3605304); the Tianchi Talent Program of Xinjiang Uygur Autonomous Region (2024, Buzohre Eli); and the Shihezi University self-funded support for university-level research projects (philosophy and social sciences; ZZZC2023076). Data availability statement Due to considerations of participants’ privacy, the data that support the findings of this study are available from the corresponding author, upon reasonable request. Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki guidelines. This study was approved by the ethics review committee of the Institute of Psychology, Chinese Academy of Sciences (Ethics approval number: H21044). All surveys were conducted after obtaining informed consent from participants. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Youngblut JM, Brooten D, Cantwell GP, del Moral T, Totapally B. Parent health and functioning 13 months after infant or child NICU/PICU death. Pediatrics. 2013;132:e1295–301. Eli B, Zhou Y, Liang Y, Fu L, Zheng H. Liu Z A profile analysis of post-traumatic stress disorder and depressive symptoms among Chinese shidu parents. Eur J Psychotraumatol. 2020;11:1766770. Wei Y, Jiang Q, Gietel-Basten S. The well-being of bereaved parents in an onlychild society. Death Stud. 2016;40:22–31. Zheng Y, Lawson TR. Identity reconstruction as Shiduers: narratives from Chinese older adults who lost their only child. Int J Soc Welf. 2015;24:399–406. Eli B, Liang Y, Chen Y, Huang X, Liu Z. Symptom structure of posttraumatic stress disorder after parental bereavement – a network analysis of Chinese parents who have lost their only child. J Affect Disord. 2021;295:673–80. Rytwinski NK, Scur MD, Feeny NC. Youngstrom EA The co-occurrence of major depressive disorder among individuals with posttraumatic stress disorder: a meta-analysis. J Trauma Stress. 2013;26:299–309. Zhang Y, Jia X. Mental health status of the Shiduers: based on latent profile analysis. Chin J Clin Psychol. 2019;27:362–6. Flory JD, Yehuda R. Comorbidity between post-traumatic stress disorder and major depressive disorder: alternative explanations and treatment considerations. Dialogues Clin Neurosci. 2015;17:141–50. Afzali MH, Sunderland M, Teesson M, Mills NC. Slade T A network approach to the comorbidity between posttraumatic stress disorder and major depressive disorder: the role of overlapping symptoms. J Affect Disord. 2017;208:490–6. Borsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013;9:91–121. Fried EI, van Borkulo CD, Cramer AO, Boschloo L, Schoevers RA. Borsboom D Mental disorders as networks of problems: a review of recent insights. Soc Psychiatry Psychiatr Epidemiol. 2017;52:1–10. Boschloo L, van Borkulo CD, Rhemtulla M, Keyes KM, Borsboom D. Schoevers RA The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS ONE. 2015;10:e0137621. Jones PJ, Heeren A, McNally RJ. Commentary: a network theory of mental disorders. Front Psychol. 2017;8:1305. Jones PJ, Ma R, McNally RJ. Bridge centrality: a network approach to understanding comorbidity. Multivar Behav Res. 2021;56:353–67. Qi J, Ye Y, Sun R, Zhen R, Zhou X. Comorbidity of posttraumatic stress disorder and depression among adolescents following an earthquake: a longitudinal study based on network analysis. J Affect Disord. 2023;324:354–63. Xu B, Yuan H, Wu X, Wang W. Comorbidity patterns of posttraumatic stress disorder and depression symptoms: cross-validation in two postearthquake child and adolescent samples. Depress Anxiety. 2023;2023:1–12. An Y, Shi J, Chuan-Peng H, Wu X. The symptom structure of posttraumatic stress disorder and co-morbid depression among college students with childhood abuse experience: a network analysis. J Affect Disord. 2021;293:466–75. Cheng P, Wang L, Zhou Y, Ma W, Zhao G, Zhang L, Li W. Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis. Front Public Health. 2023;11:1096771. Duek O, Spiller TR, Pietrzak RH, Fried EI, Harpaz-Rotem I. Network analysis of PTSD and depressive symptoms in 158,139 treatment-seeking veterans with PTSD. Depress Anxiety. 2021;38:554–62. Lazarov A, Suarez-Jimenez B, Levy O, Coppersmith DDL, Neria Y. Symptom structure of PTSD and co-morbid depressive symptoms – a network analysis of combat veteran patients. Psychol Med. 2020;50:2154–70. Garabiles MR, Lao CK, Siyuan W, Hall BJ. The network structure of posttraumatic stress disorder among filipina migrant domestic workers: comorbidity with depression. Eur J Psychotraumatol. 2020;11:1765544. Gilbar O. Examining the boundaries between ICD-11 PTSD/CPTSD and depression and anxiety symptoms: a network analysis perspective. J Affect Disord. 2020;262:429–39. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods. 2018;50:195–212. Briganti G, Scutari M, McNally. RJ A tutorial on bayesian networks for psychopathology researchers. Psychol Methods. 2023;28:947–61. McNally RJ, Mair P, Mugno BL, Riemann BC. Co-morbid obsessive-compulsive disorder and depression: a Bayesian network approach. Psychol Med. 2017b;47:1204–14. Benfer N, Bardeen JR, Cero I, Kramer LB, Whiteman SE, Rogers TA, Silverstein MW. Weathers FW Network models of posttraumatic stress symptoms across trauma types. J Anxiety Disord. 2018;58:70–7. Smith HL, Summers BJ, Dillon KH, Cougle JR. Is worst-event trauma type related to PTSD symptom presentation and associated features? J Anxiety Disord. 2016;38:55–61. The PTSD. checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD. Andresen E, Malmgren JA, Carter WB. Patrick DL Screening for depression in well older adults: Evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med. 1994;10:77–84. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D. Qgraph: network visualizations of relationships in psychometric data. J Stat Softw. 2012;48:1–18. Fruchterman TMJ, Reingold M. Graph drawing by force-directed placement. Softw Pract Experience. 1991;21:1129–64. Heeren A, Jones PJ. McNally RJ Mapping network connectivity among symptoms of social anxiety and comorbid depression in people with social anxiety disorder. J Affect Disord. 2018;228:75–82. Bringmann LF, Elmer T, Epskamp S, Krause RW, Schoch D, Wichers M, Wigman JTW. Snippe E What do centrality measures measure in psychological networks? J Abnorm Psychol. 2019;128:892–903. Scutari M. Learning Bayesian networks with the bnlearn R package. J Stat Softw. 2010;35:1–22. Jones PJ, Mair P, Riemann BC, Mugno BL. McNally RJ A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder. J Anxiety Disord. 2018;53:1–8. Yang F, Huang N, Zhang B, Fu M, Guo J. Network analysis of COVID-19-related PTSD and depressive symptoms among Chinese general population and comorbidity subgroup. Psychol Trauma: Theory Res Pract Policy. 2023;15:431–42. Bryant RA, Creamer M, O’Donnell M, Forbes D, Mcfarlane AC, Silove D. Hadzi-Pavlovic D Acute and chronic posttraumatic stress symptoms in the emergence of posttraumatic stress disorder: a network analysis. JAMA Psychiatry. 2017;74:1–8. Stam RPTSD. stress sensitisation: a tale of brain and body part 1: human studies. Neurosci Biobehav Rev. 2007;31:530–57. Wang N, Hu Q. It is not simply the loss of a child: the challenges facing parents who have lost their only child in post-reproductive age in China. Death Stud. 2019;45:209–18. Armour C, Fried EI, Deserno MK, Tsai J. Pietrzak RH A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in U.S. military veterans. J Anxiety Disord. 2017;45:49–59. Frewen PA, Lanius RA. Toward a psychobiology of posttraumatic self-dysregulation: reexperiencing, hyperarousal, dissociation, and emotional numbing. Ann N Y Acad Sci. 2006;1071:110–24. Hayes JP, Vanelzakker MB, Shin LM. Emotion and cognition interactions in PTSD: a review of neurocognitive and neuroimaging studies. Front Integr Nuerosci. 2012;6:89. New AS, Fan J, Murrough JW, Liu X, Liebman RE, Guise KG, Tang CY. Charney DS A functional magnetic resonance imaging study of deliberate emotion regulation in resilience and posttraumatic stress disorder. Biol Psychiatry. 2009;66:656–64. Zimmer Z, Kwong J. Family size and support of older adults in urban and rural China: current effects and future implications. Demography. 2003;40:23–44. Eli B. Latent profiles and stage characteristics of posttraumatic stress disorder symptoms among parents who lost their only child. University of Chinese Academy of Sciences; 2019. Ross J, Murphy D, Armour C. A network analysis of DSM-5 posttraumatic stress disorder and functional impairment in UK treatmentseeking veterans. J Anxiety Disord. 2018;57:7–15. Hinton DE, Lewis-Fernández. R The cross-cultural validity of posttraumatic stress disorder: Implications for DSM-5. Depress Anxiety. 2011;28:783–801. Eli B, Chen Y, Zhang J, Huang X, Wang Q, Ma Z, Yv Y. Liu Zk Time course of attentional bias and its relationship with PTSD symptoms in bereaved Chinese parents who have lost their only child. Eur J Psychotraumatol. 2023;14:2235980. Mekawi Y, Murphy L, Munoz A, Briscione M, Tone EB, Norrholm SD, Jovanovic T, Bradley B. Powers A The role of negative affect in the association between attention bias to threat and posttraumatic stress: an eye-tracking study. Psychiatry Res. 2020;284:112674. Bar-Haim Y, Holoshitz Y, Eldar S, Frenkel TI, Muller D, Charney DS, Pine DS, Fox NA. Wald I Life-threatening danger and suppression of attention bias to threat. Am J Psychiatry. 2010;167:694–8. Armour C, Műllerová J. Elhai JD A systematic literature review of PTSD’s latent structure in the Diagnostic and Statistical Manual of Mental Disorders: DSM-IV to DSM‐5. Clin Psychol Rev. 2016;44:60–74. Birkeland MS, Greene T, Spiller TR. The network approach to posttraumatic stress disorder: A systematic review. Eur J Psychotraumatol. 2020;11:1700614. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.doc Cite Share Download PDF Status: Published Journal Publication published 26 Sep, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 25 Jun, 2024 Editor assigned by journal 20 Jun, 2024 Submission checks completed at journal 20 Jun, 2024 First submitted to journal 11 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. 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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-4567110","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318674988,"identity":"fe8ff023-4a6e-480f-8a85-3fd5cf63df1c","order_by":0,"name":"Buzohre Eli","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Buzohre","middleName":"","lastName":"Eli","suffix":""},{"id":318674989,"identity":"443c52e0-2dca-41ad-9302-a8a946be4ffe","order_by":1,"name":"Xuanang Liu","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xuanang","middleName":"","lastName":"Liu","suffix":""},{"id":318674990,"identity":"8466c0f5-6d24-4a43-84b1-375a1253a5b6","order_by":2,"name":"Fei Xiao","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Xiao","suffix":""},{"id":318674991,"identity":"90dea069-774a-4c0f-9161-51e73518526b","order_by":3,"name":"Zhengkui Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACPmYGBsaGChCTh0gtbGAtZ0jSAsSMjW0kaWHnMf44c94de4PjZw8w/KhhkDcn7DAeA8ON254lbjiTl8DYc4zBcGcDEVoSH247nGBwg8eAgbeBIcHgABFaDj6cc9gepIXxL5FaDBs3Nhxm3ADUwkykLWzFjDOOPUuceSbH4LDMMQnDDYS08PMf3vyxp+aOPd/xM4YP39TYyBO0BQoOMCgcAJEMEsSpB2uRbyBa8SgYBaNgFIw0AAD7uD5/gK4woAAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Zhengkui","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-06-12 03:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4567110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4567110/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-025-07283-4","type":"published","date":"2025-09-26T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60599534,"identity":"1a6f049d-624d-4181-b892-756257b39c78","added_by":"auto","created_at":"2024-07-18 15:58:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":420441,"visible":true,"origin":"","legend":"\u003cp\u003eThe GGM network of PTSD and depression, with (a) and without (b) overlapping symptoms.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4567110/v1/1d7cffb56c585e7e7472ef85.jpeg"},{"id":60599535,"identity":"65161a06-89c3-4253-970d-c0dac655f3a3","added_by":"auto","created_at":"2024-07-18 15:58:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":355486,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork centrality bridge expected influence (bEI) for PTSD and depression, with (a) and without overlapping symptoms (b).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Higher values of bridge expected influence reflect greater bridging node centrality. B1 = Recurrent thoughts; B2 = Nightmares; B3 = Flashbacks; B4 = Psychological cue reactivity; B5 = Physiological cue reactivity; C1 = Avoidance of thoughts; C2 = Avoidance of reminders; D1 = Amnesia; D2 = Negative beliefs; D3 = Distorted blame; D4 = Negative emotional state; D5 = Diminished interest; D6 = Detachment; D7 = Restricted affect; E1 = Irritability or anger; E2 = Reckless or self-destructive behaviour; E3 = Hypervigilance; E4 = Exaggerated startle; E5 = Difficulty concentrating; E6 = Sleeping difficulties. Dep1 = Bothered by things; Dep2 = Difficulty keeping mind; Dep3 = Depressed mood; Dep4 = Everything is an effort; Dep5 = Hopelessness about the future; Dep6 = Fear; Dep7 = Poor sleep; Dep8 = Unhappiness; Dep9 = Loneliness; Dep10 = Could not get going.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4567110/v1/bd6366989f4be87f79080519.jpeg"},{"id":60599533,"identity":"946660dd-f0a7-4596-a7eb-11a6ffd4a78d","added_by":"auto","created_at":"2024-07-18 15:58:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1117793,"visible":true,"origin":"","legend":"\u003cp\u003eThe Directed acyclic graph (DAG) of PTSD and depression\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Arrows indicate significant predictive relationships. Thickness represents greater importance in the network structure. B1 = Recurrent thoughts; B2 = Nightmares; B3 = Flashbacks; B4 = Psychological cue reactivity; B5 = Physiological cue reactivity; C1 = Avoidance of thoughts; C2 = Avoidance of reminders; D1 = Amnesia; D2 = Negative beliefs; D3 = Distorted blame; D4 = Negative emotional state; D5 = Diminished interest; D6 = Detachment; D7 = Restricted affect; E1 = Irritability or anger; E2 = Reckless or self-destructive behaviour; E3 = Hypervigilance; E4 = Exaggerated startle; E5 = Difficulty concentrating; E6 = Sleeping difficulties. Dep1 = Bothered by things; Dep2 = Difficulty keeping mind; Dep3 = Depressed mood; Dep4 = Everything is an effort; Dep5 = Hopelessness about the future; Dep6 =Fear; Dep7 = Poor sleep; Dep8 = Unhappiness; Dep9 = Loneliness; Dep10 = Could not get going.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4567110/v1/50941a9cc799392cd4aeb5bd.png"},{"id":92430781,"identity":"8120c44c-b82c-45dc-b990-72b7e95450e8","added_by":"auto","created_at":"2025-09-29 16:07:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1699068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4567110/v1/7076c951-871f-4135-8f5d-a23407b82804.pdf"},{"id":60601733,"identity":"4c202444-f280-427b-8ec1-db2ace9e83c3","added_by":"auto","created_at":"2024-07-18 16:06:36","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5554176,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-4567110/v1/9f5cb8d2e1ffa06a845c1ea7.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Symptom network connectivity of PTSD comorbid with depression in bereaved Chinese parents","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe death of a child is one of the most traumatic events in bereavement and poses a significant risk to the mental health of parents [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. There is a remarkable number of parents who have lost their only child, an unintentional consequence of the \u0026lsquo;one-child policy\u0026rsquo; in China. Such bereaved parents who have passed their reproductive windows and cannot conceive another child (mothers over 49 years of age), and are unwilling to adopt a child are known as Shidu parents in Chinese society [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Children represent generational continuity and play an important economic and social support role for aging parents [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Losing their only child thus represents the termination of family lines and the loss of caregivers in old age in China. Moreover, Chinese culture regards the death of a child as a sign of bad luck [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], resulting in the stigmatization of Shidu parents. Therefore, these Shidu parents experience psychological trauma and cultural pressure that induce major mental health problems, including posttraumatic stress disorder (PTSD) and depression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePTSD and depression are frequently comorbid after trauma. The comorbidity rate of PTSD and depression has been estimated to be 52.0% among populations with traumatic exposure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A study reported that the comorbidity rate of PTSD and depression was 47.6% among Shidu parents [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The literature has indicated that the comorbidity of PTSD and depression may have more severe adverse effects on mental health and quality of life [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Identifying the comorbidity pattern of PTSD and depression might inform targets for proactive screening and treatment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, the network approach has provided an opportunity to examine the relationships between PTSD and depression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to network theory, mental disorders are viewed as systems of mutually interacting symptoms [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Symptoms correlate in a disorder because they directly activate and potentially exert causal effects upon each other [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the network, a symptom triggers the activation of other symptoms, which are called central symptoms. Network analysis may contribute to an understanding of the development and maintenance of treatment for mental disorders by revealing the most central symptoms and how symptoms influence each other within disorders [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, network analysis can elucidate the relationships between disorders at the symptom level by identifying the bridge symptoms that connect two disorders. A bridge symptom is a symptom that is central in connecting two disorders (PTSD and depression) and plays an essential role in maintaining and developing comorbidities in a network [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thus, bridge symptoms can be regarded as target symptoms in clinical treatment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtant studies have explored the comorbidity of PTSD and depression in children and adolescents [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], as well as in adults [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] using the network approach. Although most studies have unanimously shown that some overlapping symptoms that are components of both PTSD and depression, such as \u003cem\u003esleep problems\u003c/em\u003e and \u003cem\u003econcentration difficulties\u003c/em\u003e, play important bridging roles in the comorbidity network of PTSD and depression [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], there is still no consensus about the bridging roles of other nonoverlapping symptoms. Previous studies have indicated several bridging symptoms, including \u003cem\u003eflashbacks\u003c/em\u003e, \u003cem\u003eavoidance of thoughts\u003c/em\u003e, \u003cem\u003egetting emotionally upset by trauma reminders\u003c/em\u003e, and \u003cem\u003eanhedonia\u003c/em\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These mixed findings imply the need for more empirical research.\u003c/p\u003e \u003cp\u003eAnother limitation in existing network studies on the comorbidity between PTSD and depression is the lack of determination of a causal direction. Most cross-sectional studies have used the graphical Gaussian model (GGM) to identify the network structure of comorbid PTSD and depression [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The GGM calculates an undirected sparse network in which nodes represent symptoms and edges represent the partial correlation between two nodes after controlling for all other nodes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The Bayesian network approach can help overcome the key limitations of a partial correlation network. Bayesian networks are probabilistic graphical models that represent conditional independence relationships among variables as directed acyclic graphs (DAGs). In this type of graph, edges are directed and noncircular and provide information about the directions of causal relationships between pairs of nodes from cross-sectional data [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A partial correlation network of the GGM could be utilized to explore potential two-way causality and causal loops, while a Bayesian network could be used to investigate the one-way causality among symptoms. These two approaches can be viewed as complementary. Combining these two approaches is more helpful for clarifying the causal system of PTSD and depression than using only one approach [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, scholars have identified several bridging symptoms that connect PTSD and depression across adult populations with various traumatic experiences, including veterans, firefighters, and victims of interpersonal violence [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While these network outcomes have advanced our knowledge in the field, conclusions from previous studies are limited in their generalizability to Shidu parents because the loss of a child is different from other types of traumatic events. As scholars have suggested, the type of trauma as well as the network structure have an effect on the severity of PTSD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The type of trauma may also potentially influence the comorbidity network of PTSD and depression. As such, more empirical studies are needed to explore the characteristics of comorbid PTSD and depression among bereaved parents.\u003c/p\u003e \u003cp\u003eTo address these gaps in the literature, the current cross-sectional study utilizes the network approach to identify the comorbidity network structure between PTSD and depression among Chinese Shidu parents who have lost their only child. The aims of this study were twofold. First, we utilized the GGM to investigate the comorbid network patterns of PTSD and depression and to identify bridge symptoms, the strongest edges, and potential causal loops. Second, we utilized the DAG to investigate the key driving symptoms in the comorbid network and the predominant pathways of activation between PTSD and depression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and procedure\u003c/h2\u003e \u003cp\u003eThe participants were 447 bereaved individuals who had lost their only child. Of these participants, 188 (42.1%) were male, 224 (50.1%) were female, and 35 (7.8%) had missing information on sex. The age of the males ranged from 44 to 84 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;62.3, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.6), and the age of the females ranged from 55 to 88 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61.6, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.2). The losses had occurred an average of 9.6 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.3) prior to data collection. The sociodemographic characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003e༎\u003c/b\u003eSociodemographic characteristics of the participants (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;447).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Educational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried (first/ remarriage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle/divorced/separated/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Religious belief\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuddhism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Family income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Subjective assessment of family economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor and extremely poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWealthy and extremely wealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Cause of death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnnatural cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Time since child\u0026rsquo;s death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;8 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e. a: There are missing values in sociodemographic characteristic, thus, the sum of the effective percentage is note equal to 100% in these cases; Unnatural cause: accident, homicide, suicide, natural disaster, or other; Natural cause: illness.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eParticipants were recruited through community workers and local health and family planning departments. Those eligible for inclusion were bereaved parents who had lost their only child at least one year prior and currently had no living child and female participants who were older than 49 years, which means that they had passed their reproductive age. Participants who satisfied the inclusion criteria were invited to the community office. They were given a complete description of the survey and were assured that their responses would be kept completely confidential. It was also clearly indicated that they had the right to withdraw from the study at any time. Written informed consent was obtained from each participant, and all the research processes met ethical standards. After the investigation, each participant was given a gift of a daily necessity, such as a thermos bottle or hot pack. Professional psychological services were provided to the participants if psychological discomfort occurred during or after the investigation. This study was approved by the ethics review committee of the Institute of Psychology, Chinese Academy of Sciences (Ethics approval number: H21044).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003ePTSD symptoms were assessed using the Posttraumatic Stress Disorder Checklist for the DSM-5 (PCL-5) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The PCL-5 is a 20-item self-report inventory with four subscales that correspond to the four DSM-5 symptom clusters: intrusion (Cluster B), avoidance (Cluster C), negative alterations in cognition and mood (Cluster D), and alterations in arousal and reactivity (Cluster E). Participants indicate the extent to which each symptom has bothered them over the past month using a 5-point Likert scale. The total score ranges from 0 to 80, and higher scores indicate greater PTSD symptom severity. A cut-off of 33 or above on the PCL-5 total score indicates the presence of significant PTSD symptoms [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In the present study, the Cronbach's alpha value for this measure was 0.96, and the KMO value of the confirmatory factor analysis was also 0.96.\u003c/p\u003e \u003cp\u003eDepression symptoms were assessed using the shortened Center for Epidemiological Studies-Depression Scale (CES-D-10). The CES-D-10 consists of 10 items from the original 20-item questionnaire [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Participants indicate the extent to which each symptom has bothered them during the past week using a 4-point Likert scale. Two of the ten positively rated items (\"\u003cem\u003eI felt hopeful about the future\u003c/em\u003e\" and \"\u003cem\u003eI was happy\u003c/em\u003e\") were reverse scored for the analysis. The total score ranges from 0 to 30, and higher scores indicate greater severity of depression. A cut-off of 10 or above on the CES-D-10 total score indicates the presence of significant depression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the present study, the Cronbach\u0026rsquo;s alpha value for this measure was 0.84, and the KMO value of the confirmatory factor analysis was 0.89.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eDescriptive statistical analyses were conducted with SPSS (IBM SPSS, version 21.0). Given the difference in the scale range between the PCL-5 and CES-D-10, the scores for all items were converted to z scores. Network analysis was conducted using R Core Software, version 4.1.3 (R Team, Vienna, Austria). Networks are graphical models consisting of nodes and edges, and nodes represent the individual symptoms. In the current study, the nodes were the symptoms of PTSD and depression. The edges represented the relationships between two nodes after conditioning on all other nodes in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eUndirected network estimation and visualization\u003c/h2\u003e \u003cp\u003eTwo distinct comorbidity networks were constructed to explore the relationship between PTSD and depression. The first network included all symptoms of PTSD and depression (30 nodes). The second network excluded the two overlapping symptoms (i.e., \u003cem\u003esleep problems\u003c/em\u003e and \u003cem\u003econcentration difficulties\u003c/em\u003e; 26 nodes). The network structure of comorbidities was estimated using the R package qgraph [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The network was estimated using a GGM (EBICglasso) option [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and visualized using the Fruchterman-Reingold algorithm [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] in the R package qgraph. The algorithm positions nodes with more or stronger connections at the center of the network. In the present study, green and red edges represent positive and negative associations, respectively. Line thickness reflects the strength of the association.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCentrality estimation and bridge centrality estimation\u003c/h2\u003e \u003cp\u003eWe utilized expected influence (EI) to evaluate node centrality using the R package qgraph, following recommendations from recent studies. The EI represents the summed weight of all its edges, including positive and negative associations with its neighboring nodes in the network [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Higher EI values reflect greater node centrality [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, it is essential to assess bridge centrality to assess comorbidities [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which could indicate the bridge node as a link between PTSD and depression. Thus, the bridge EI was also computed to identify nodes that had symptom-level connections with nodes of the other disorder using the R package networktools [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. There are two types of bridge expected influence (bEI): 1-step bEI and 2-step bEI. The 1-step bEI is the sum of all edges between a node and all other nodes in the second disorder, taking into account both positive and negative edges. The 2-step bEI is similar to the 1-step method but also considers the indirect influence of the node through other nodes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We used 1-step bEI to calculate bridge centrality in this study. Based on Jones et al.\u0026rsquo;s suggestion, the nodes with the top 20% bEI values were considered bridge nodes.\u003c/p\u003e \u003cp\u003eThe network accuracy and stability were tested using the R package bootnet [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The more detailed content is displayed in the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDirected network estimation\u003c/h2\u003e \u003cp\u003eTo identify the causality of the comorbidity network, a DAG analysis was performed using the R package bnlearn and employing the hill-climbing algorithm [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The DAG is a Bayesian network approach that models a network in which edges are directed and noncircular, representing the causal relationship directions between pairs of nodes from cross-sectional data [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The network excluded the two overlapping symptoms, which included 26 nodes. The estimation involved three steps, the more detailed content is displayed in the supplementary materials.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eDescriptive analyses\u003c/h2\u003e\n \u003cp\u003eThe mean PCL-5 and CES-D-10 total scores for all participants were 38.2 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20.9) and 14.1 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.6), respectively. Of the 447 participants, 55.0% (246) reported PTSD, 74.3% (332) reported depression, and 52.3% (234) reported comorbidity of PTSD and depression.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eUndirected network structure\u003c/h2\u003e\n \u003cp\u003eThe network structures of PTSD and depression with and without overlapping symptoms are presented in Fig. \u003cspan\u003e1\u003c/span\u003ea and \u003cspan\u003e1\u003c/span\u003eb, respectively. In the comorbidity network with overlapping symptoms, 198 of the 435 edges were nonzero (density of 0.46). In the comorbidity network without overlapping symptoms, 167 of 325 edges were nonzero (density of 0.51). In networks with and without overlapping symptoms, the majority of symptoms were positively connected. PTSD and depression exhibited a strong internal connection. The strongest edge connections were the same in both networks, such as between symptoms E3 and E4 (\u003cem\u003ehypervigilance\u003c/em\u003e and \u003cem\u003eexaggerated startle\u003c/em\u003e), B1 and B2 (\u003cem\u003erecurrent thoughts\u003c/em\u003e and \u003cem\u003enightmares\u003c/em\u003e), Dep5 and Dep8 (\u003cem\u003ehopelessness about the future\u003c/em\u003e and \u003cem\u003eunhappiness\u003c/em\u003e), and Dep1 and Dep10 (\u003cem\u003ebothered by things\u003c/em\u003e and \u003cem\u003ecould not get going\u003c/em\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eNetwork centrality\u003c/h2\u003e\n \u003cp\u003eThe standardized estimate of the expected influence (EI) for the PTSD and depression network with and without overlapping symptoms is depicted in online Supplementary Figure \u003cspan\u003eS1\u003c/span\u003ea and S1b. The network centrality bridge expected influence (bEI) for PTSD and depression with and without overlapping symptoms is presented in Fig. \u003cspan\u003e2\u003c/span\u003ea and \u003cspan\u003e2\u003c/span\u003eb, respectively. In the comorbidity network with overlapping symptoms, D5 (\u003cem\u003ediminished interest\u003c/em\u003e), E1 (\u003cem\u003eirritability/anger\u003c/em\u003e), E2 (\u003cem\u003ereckless/self\u0026thinsp;\u0026minus;\u0026thinsp;destructive behavior\u003c/em\u003e), D4 (\u003cem\u003enegative emotional state\u003c/em\u003e), C2 (\u003cem\u003eavoidance of reminders\u003c/em\u003e), and B3 (\u003cem\u003eflashbacks\u003c/em\u003e) played crucial bridging roles in connecting to other symptoms of PTSD and depression. In the comorbidity network without overlapping symptoms, E1 (\u003cem\u003eirritability/anger\u003c/em\u003e), D5 (\u003cem\u003ediminished interest\u003c/em\u003e), E2 (\u003cem\u003ereckless/self\u0026thinsp;\u0026minus;\u0026thinsp;destructive behaviour\u003c/em\u003e), E4 (\u003cem\u003eexaggerated startle\u003c/em\u003e), and D4 (\u003cem\u003enegative emotional state\u003c/em\u003e) played crucial bridging roles in connecting to other symptoms of PTSD and depression.\u003c/p\u003e\n \u003cp\u003eThe results showed moderate network accuracy and high stability. See online supplementary materials for more detailed contents.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eDirected network structure\u003c/h2\u003e\n \u003cp\u003eThe DAG estimated from PTSD and depression is presented in Fig. \u003cspan\u003e3\u003c/span\u003e. From a holistic symptom perspective, depression symptoms, with the exception of Dep5 (\u003cem\u003ehopelessness about the future\u003c/em\u003e) and Dep8 (\u003cem\u003eunhappiness\u003c/em\u003e), are often triggered by PTSD symptoms. From an individual symptom perspective, E4 (\u003cem\u003eexaggerated startle\u003c/em\u003e) again emerged as the most pivotal network node. It triggered seven other PTSD and depression symptoms, with the strongest connections with E3 (\u003cem\u003ehypervigilance\u003c/em\u003e) and D6 (\u003cem\u003edetachment\u003c/em\u003e). D5 (\u003cem\u003ediminished interest\u003c/em\u003e), D4 (\u003cem\u003enegative emotional state\u003c/em\u003e), Dep1 (\u003cem\u003ebothered by things\u003c/em\u003e), Dep 10 (\u003cem\u003ecould not get going\u003c/em\u003e), and C1 (\u003cem\u003eavoidance of thoughts\u003c/em\u003e) were also directly triggered by E4 (\u003cem\u003eexaggerated startle\u003c/em\u003e). Moreover, Dep5 (\u003cem\u003ehopelessness about the future\u003c/em\u003e) triggered E1 (\u003cem\u003eirritability/anger\u003c/em\u003e) and Dep8 (\u003cem\u003eunhappiness\u003c/em\u003e). Dep8 (\u003cem\u003eunhappiness\u003c/em\u003e) further triggered E3 (\u003cem\u003ehypervigilance\u003c/em\u003e) and Dep1 (\u003cem\u003ebothered by things\u003c/em\u003e). The most downstream symptoms (i.e., triggered nodes that trigger no other nodes) were C1 (\u003cem\u003eavoidance of thoughts\u003c/em\u003e), C2 (\u003cem\u003eavoidance of reminders\u003c/em\u003e), D6 (\u003cem\u003edetachment\u003c/em\u003e), and D1 (\u003cem\u003eamnesia\u003c/em\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study applied the GGM and DAG network analysis to map the relationship between PTSD and depression and to identify how symptoms interact with each other in comorbid conditions among Shidu parents. Generally, both the GGM and DAG results indicate that alterations in arousal and reactivity (Cluster E), especially \u003cem\u003eexaggerated startle\u003c/em\u003e (E4), and negative alterations in cognition and mood (Cluster D) symptoms (except \u003cem\u003edetachment\u003c/em\u003e and \u003cem\u003eamnesia\u003c/em\u003e) are of great importance in the comorbidity of PTSD and depression among Shidu parents.\u003c/p\u003e \u003cp\u003eThe GGM revealed several important results. First, PTSD and depression symptoms emerged as two discrete subnetworks, with the strongest connections appearing among almost all nodes within each disorder in both networks with and without overlapping symptoms. For example, the strongest connections were observed from \u003cem\u003erecurrent thoughts\u003c/em\u003e to \u003cem\u003enightmares\u003c/em\u003e (B1-B2), \u003cem\u003ehypervigilance\u003c/em\u003e to \u003cem\u003eexaggerated startle\u003c/em\u003e (E3-E4), \u003cem\u003ehopelessness about the future\u003c/em\u003e to \u003cem\u003eunhappiness\u003c/em\u003e (Dep5-Dep8), and \u003cem\u003edepressed mood\u003c/em\u003e to \u003cem\u003eeverything is an effort\u003c/em\u003e (Dep3-Dep4). These findings suggest that within-disorder symptom connectivity is greater than between-disorder connectivity, consistent with previous studies indicating that PTSD and depression are two separate disorders with mutually influential symptom structures [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, the same edges emerged as the strongest in the networks with and without overlapping symptoms, and the most important bridge symptoms in both comorbidity networks included nonoverlapping symptoms. We identified a significant bridging role of six symptoms in the comorbidity network with overlapping symptoms (\u003cem\u003ediminished interest\u003c/em\u003e, \u003cem\u003eirritability/anger\u003c/em\u003e, \u003cem\u003ereckless/self\u0026thinsp;\u0026minus;\u0026thinsp;destructive behavior\u003c/em\u003e, \u003cem\u003enegative emotional state\u003c/em\u003e, \u003cem\u003eavoidance of reminders\u003c/em\u003e, and \u003cem\u003eflashbacks\u003c/em\u003e) and five symptoms in the comorbidity network without overlapping symptoms (\u003cem\u003eirritability/anger\u003c/em\u003e, \u003cem\u003ediminished interest\u003c/em\u003e, \u003cem\u003ereckless/self\u0026thinsp;\u0026minus;\u0026thinsp;destructive behaviour\u003c/em\u003e, \u003cem\u003eexaggerated startle\u003c/em\u003e, and \u003cem\u003enegative emotional state\u003c/em\u003e). This suggests a minimal influence of the overlapping symptoms on comorbidities. This finding is inconsistent with some studies showing that overlapping symptoms are the origin of high comorbidity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and consistent with other studies indicating that bridging symptoms are not limited to overlapping symptoms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], suggesting that nonoverlapping symptoms also emerge as the strongest connection across PTSD and depression.\u003c/p\u003e \u003cp\u003eThird, most bridging symptoms are similar in networks with and without overlapping symptoms (i.e., \u003cem\u003eirritability/anger\u003c/em\u003e, \u003cem\u003ediminished interest\u003c/em\u003e, \u003cem\u003ereckless/self\u0026thinsp;\u0026minus;\u0026thinsp;destructive behaviour\u003c/em\u003e, and \u003cem\u003enegative emotional state\u003c/em\u003e), emphasizing their significance in the comorbidity network of PTSD and depression among Shidu parents. This finding echoes previous findings in nonbereaved trauma populations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. \u003cem\u003eAvoidance of reminders\u003c/em\u003e and \u003cem\u003eflashbacks\u003c/em\u003e also emerged as important bridging symptoms in the network with overlapping symptoms, reaffirming previous findings in various populations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, despite overlapping symptoms, we did not find evidence of the bridging role of \u003cem\u003esleep problems\u003c/em\u003e and \u003cem\u003econcentration difficulties\u003c/em\u003e in the comorbidity network. This finding contrasts with numerous comorbidity network studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The differences between our study and previous studies might be caused by the variation in the trauma population. Previous studies have focused on PTSD comorbid with depression in veterans, refugees, and victims of interpersonal violence. In contrast, our study focused on Shidu parents. The type of trauma has been previously found to affect the presentation of PTSD symptoms [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and network structure [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which might also influence comorbidity patterns.\u003c/p\u003e \u003cp\u003e \u003cem\u003eExaggerated startle\u003c/em\u003e emerged as a key bridging node in the network without overlapping symptoms, suggesting that \u003cem\u003eexaggerated startle\u003c/em\u003e may be a leading cause of comorbidity between PTSD and depression among Shidu parents. This bridging symptom has not been found in existing network studies about comorbid PTSD and depression, even though the importance of an exaggerated startle response in PTSD has been clarified in PTSD network studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The PTSD sensitization model suggests that traumatic experiences make individuals extremely sensitive to threats; over time, this leads to a stronger startle response [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The death of a child means the loss of active parenthood and emotional ties for bereaved parents. The sudden loss of an only child is devastating for Shidu parents and can make them prone to being easily startled [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, strong associations were observed between \u003cem\u003eexaggerated startle\u003c/em\u003e and \u003cem\u003ehypervigilance\u003c/em\u003e, replicating previous findings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This association is a self-reinforcing feedback loop that might lead to the development of PTSD and promote comorbidity between PTSD and depression.\u003c/p\u003e \u003cp\u003eThe DAG network also revealed the important driving roles of \u003cem\u003eexaggerated startle\u003c/em\u003e in comorbidity. \u003cem\u003eExaggerated startle\u003c/em\u003e is at the top of the network and drives other symptoms, which is consistent with the findings of An et al. (2021). \u003cem\u003eExaggerated startle\u003c/em\u003e triggers \u003cem\u003ehypervigilance\u003c/em\u003e, \u003cem\u003ediminished interest\u003c/em\u003e, and \u003cem\u003edetachment\u003c/em\u003e, which further activate other symptoms of PTSD, such as \u003cem\u003enegative emotional states\u003c/em\u003e, \u003cem\u003erestricted affect\u003c/em\u003e, \u003cem\u003eflashbacks\u003c/em\u003e, \u003cem\u003ereckless/self\u0026thinsp;\u0026minus;\u0026thinsp;destructive behaviour\u003c/em\u003e and depression (\u003cem\u003edepressed mood\u003c/em\u003e and \u003cem\u003efear\u003c/em\u003e). In addition, \u003cem\u003eexaggerated startle\u003c/em\u003e directly triggers symptoms of depression, such as \u003cem\u003ebothered by things\u003c/em\u003e and \u003cem\u003ecould not get going\u003c/em\u003e. We conclude that most of the symptoms in clusters E and D are located in the upper part of the network, indicating a significant influence on the overall structure of the comorbidity network. According to the PTSD sensitization model, alterations in arousal and reactivity (\u003cem\u003eexaggerated startle\u003c/em\u003e, \u003cem\u003eirritability\u003c/em\u003e, and \u003cem\u003ehypervigilance\u003c/em\u003e) are common responses after trauma and have been empirically validated in Shidu parents [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prolonged hyperarousal causes emotional exhaustion and affects executive resources, leading to the depletion of cognitive and emotional resources [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This can easily generate negative alterations in cognition and mood symptoms, including \u003cem\u003enegative emotional state\u003c/em\u003e, \u003cem\u003enegative beliefs\u003c/em\u003e, \u003cem\u003edistorted blame\u003c/em\u003e, \u003cem\u003ediminished interest\u003c/em\u003e, and \u003cem\u003edetachment\u003c/em\u003e. Impaired regulation of emotion and cognition reduces the capacity to cope well with traumatic events [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which may lead to comorbid PTSD and depression.\u003c/p\u003e \u003cp\u003eNotably, two symptoms of depression (\u003cem\u003ehopelessness about the future\u003c/em\u003e and \u003cem\u003eunhappiness\u003c/em\u003e) trigger some PTSD and depression symptoms, while the remaining depression symptoms, mostly located in the lower part, are triggered by PTSD symptoms among Shidu parents. \u003cem\u003eHopelessness about the future\u003c/em\u003e activates \u003cem\u003eunhappiness\u003c/em\u003e and \u003cem\u003eirritability/anger\u003c/em\u003e, while \u003cem\u003eunhappiness\u003c/em\u003e further triggers \u003cem\u003ehypervigilance\u003c/em\u003e and \u003cem\u003ebothered by things\u003c/em\u003e. A child is the major source of hope and meaning in life for parents [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The death of an only child can cause parents to lose their sense of meaning, often leading them to feel as though life has become meaningless and hopeless [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This can result in negative emotions, such as \u003cem\u003eunhappiness\u003c/em\u003e and \u003cem\u003eirritability/anger\u003c/em\u003e. Negative emotions, such as \u003cem\u003eunhappiness\u003c/em\u003e, may prompt parents to recall memories related to their child. Previous studies have indicated that PTSD patients remember traumatic events well [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and Shidu parents are highly alert to environmental stimuli and easily bothered by things. These findings indicate that the bidirectional relationship between PTSD and depression is complex. Additional research is needed to investigate the associations between PTSD and depression in greater depth.\u003c/p\u003e \u003cp\u003eFinally, \u003cem\u003eavoidance of thoughts\u003c/em\u003e and \u003cem\u003eavoidance of reminders\u003c/em\u003e were the most common downstream symptoms triggered only by other PTSD and depression symptoms, indicating that these are the outcome variables. Avoidance is prominent in traumatized groups and is considered a culturally acceptable coping mechanism [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Empirical evidence has also indicated that Shidu parents exhibit attentional avoidance from trauma-related stimuli [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Scholars suggest that attentional avoidance is a maladaptive coping strategy [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], which may be attributed to the significant increase in negative affect that occurs when individuals have difficulty disengaging from trauma-related cues [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In addition, \u003cem\u003edetachment\u003c/em\u003e emerged as a downstream symptom triggered by other symptoms, including \u003cem\u003ediminished interest\u003c/em\u003e, \u003cem\u003eflashbacks\u003c/em\u003e, \u003cem\u003ereckless/self\u0026thinsp;\u0026minus;\u0026thinsp;destructive behavior\u003c/em\u003e, \u003cem\u003edepressed mood\u003c/em\u003e, \u003cem\u003eloneliness\u003c/em\u003e, and \u003cem\u003ebothered by things\u003c/em\u003e. These traumatic responses may cause interpersonal problems such as detachment from others. Bereaved parents in China are reluctant to associate with others, especially with families that have children, as Chinese culture regards the death of a child as a sign of bad luck [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Cultural context may exacerbate the relationship trajectory. \u003cem\u003eAmnesia\u003c/em\u003e was found to be a downstream symptom, consistent with PTSD network [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and factor analysis studies [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This suggests that \u003cem\u003eamnesia\u003c/em\u003e might not serve as a core symptom in PTSD [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study should be interpreted in light of several limitations. First, we utilized cross-sectional data, it is not possible to infer direct causation between symptoms. Although DAG network analysis can represent the causal relationship directions between pairs of nodes from cross-sectional data, the analysis is constrained by some strict assumptions that limit potential inferences. Future longitudinal studies are needed to evaluate the comorbidity patterns of PTSD and depression. Second, PTSD and depression symptoms were assessed using a self-report questionnaire instead of a clinical assessment. Future studies should assess comorbidity network structures based on clinical observations to replicate and validate our findings. Finally, the participants in the current study were limited to Chinese Shidu parents, and the sample size was small due to difficulties accessing this group. These considerations may limit the generalizability of our conclusions. Therefore, the findings of this study must be interpreted with caution, and the conclusions should be generalized carefully with awareness of the potential stability issues in the data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eNetwork analysis offers powerful empirical tools to visualize interactions among symptoms of mental disorders. The results indicate a pivotal role of \u003cem\u003eexaggerated startle\u003c/em\u003e in the comorbidity network between PTSD and depression among bereaved Chinese parents who have lost their only child. This finding may highlight important targets for psychological treatment in this population. PTSD and depression are independent but mutually influential among bereaved Chinese parents.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuzohre Eli: Data curation, Formal analysis, Methodology, Writing\u0026ndash;original draft, Writing\u0026ndash;review \u0026amp; editing. Xuanang Liu: Methodology, Writing\u0026ndash;review \u0026amp; editing. Fei Xiao: Writing\u0026ndash;review \u0026amp; editing. Zhengkui Liu: Conceptualization, Investigation, Project administration, Resources, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2023YFC3605304); the Tianchi Talent Program of Xinjiang Uygur Autonomous Region (2024, Buzohre Eli); and the Shihezi University self-funded support for university-level research projects (philosophy and social sciences; ZZZC2023076).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to considerations of participants\u0026rsquo; privacy, the data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki guidelines. This study was approved by the ethics review committee of the Institute of Psychology, Chinese Academy of Sciences (Ethics approval number: H21044). All surveys were conducted after obtaining informed consent from participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYoungblut JM, Brooten D, Cantwell GP, del Moral T, Totapally B. Parent health and functioning 13 months after infant or child NICU/PICU death. Pediatrics. 2013;132:e1295\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEli B, Zhou Y, Liang Y, Fu L, Zheng H. Liu Z A profile analysis of post-traumatic stress disorder and depressive symptoms among Chinese shidu parents. Eur J Psychotraumatol. 2020;11:1766770.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei Y, Jiang Q, Gietel-Basten S. The well-being of bereaved parents in an onlychild society. Death Stud. 2016;40:22\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, Lawson TR. Identity reconstruction as Shiduers: narratives from Chinese older adults who lost their only child. Int J Soc Welf. 2015;24:399\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEli B, Liang Y, Chen Y, Huang X, Liu Z. Symptom structure of posttraumatic stress disorder after parental bereavement \u0026ndash; a network analysis of Chinese parents who have lost their only child. J Affect Disord. 2021;295:673\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRytwinski NK, Scur MD, Feeny NC. Youngstrom EA The co-occurrence of major depressive disorder among individuals with posttraumatic stress disorder: a meta-analysis. J Trauma Stress. 2013;26:299\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Jia X. Mental health status of the Shiduers: based on latent profile analysis. Chin J Clin Psychol. 2019;27:362\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlory JD, Yehuda R. Comorbidity between post-traumatic stress disorder and major depressive disorder: alternative explanations and treatment considerations. Dialogues Clin Neurosci. 2015;17:141\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfzali MH, Sunderland M, Teesson M, Mills NC. Slade T A network approach to the comorbidity between posttraumatic stress disorder and major depressive disorder: the role of overlapping symptoms. J Affect Disord. 2017;208:490\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013;9:91\u0026ndash;121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFried EI, van Borkulo CD, Cramer AO, Boschloo L, Schoevers RA. Borsboom D Mental disorders as networks of problems: a review of recent insights. Soc Psychiatry Psychiatr Epidemiol. 2017;52:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoschloo L, van Borkulo CD, Rhemtulla M, Keyes KM, Borsboom D. Schoevers RA The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS ONE. 2015;10:e0137621.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones PJ, Heeren A, McNally RJ. Commentary: a network theory of mental disorders. Front Psychol. 2017;8:1305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones PJ, Ma R, McNally RJ. Bridge centrality: a network approach to understanding comorbidity. Multivar Behav Res. 2021;56:353\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi J, Ye Y, Sun R, Zhen R, Zhou X. Comorbidity of posttraumatic stress disorder and depression among adolescents following an earthquake: a longitudinal study based on network analysis. J Affect Disord. 2023;324:354\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu B, Yuan H, Wu X, Wang W. Comorbidity patterns of posttraumatic stress disorder and depression symptoms: cross-validation in two postearthquake child and adolescent samples. Depress Anxiety. 2023;2023:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn Y, Shi J, Chuan-Peng H, Wu X. The symptom structure of posttraumatic stress disorder and co-morbid depression among college students with childhood abuse experience: a network analysis. J Affect Disord. 2021;293:466\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng P, Wang L, Zhou Y, Ma W, Zhao G, Zhang L, Li W. Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis. Front Public Health. 2023;11:1096771.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuek O, Spiller TR, Pietrzak RH, Fried EI, Harpaz-Rotem I. Network analysis of PTSD and depressive symptoms in 158,139 treatment-seeking veterans with PTSD. Depress Anxiety. 2021;38:554\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazarov A, Suarez-Jimenez B, Levy O, Coppersmith DDL, Neria Y. Symptom structure of PTSD and co-morbid depressive symptoms \u0026ndash; a network analysis of combat veteran patients. Psychol Med. 2020;50:2154\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarabiles MR, Lao CK, Siyuan W, Hall BJ. The network structure of posttraumatic stress disorder among filipina migrant domestic workers: comorbidity with depression. Eur J Psychotraumatol. 2020;11:1765544.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilbar O. Examining the boundaries between ICD-11 PTSD/CPTSD and depression and anxiety symptoms: a network analysis perspective. J Affect Disord. 2020;262:429\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods. 2018;50:195\u0026ndash;212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBriganti G, Scutari M, McNally. RJ A tutorial on bayesian networks for psychopathology researchers. Psychol Methods. 2023;28:947\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNally RJ, Mair P, Mugno BL, Riemann BC. Co-morbid obsessive-compulsive disorder and depression: a Bayesian network approach. Psychol Med. 2017b;47:1204\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenfer N, Bardeen JR, Cero I, Kramer LB, Whiteman SE, Rogers TA, Silverstein MW. Weathers FW Network models of posttraumatic stress symptoms across trauma types. J Anxiety Disord. 2018;58:70\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith HL, Summers BJ, Dillon KH, Cougle JR. Is worst-event trauma type related to PTSD symptom presentation and associated features? J Anxiety Disord. 2016;38:55\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe PTSD. checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndresen E, Malmgren JA, Carter WB. Patrick DL Screening for depression in well older adults: Evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med. 1994;10:77\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D. Qgraph: network visualizations of relationships in psychometric data. J Stat Softw. 2012;48:1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFruchterman TMJ, Reingold M. Graph drawing by force-directed placement. Softw Pract Experience. 1991;21:1129\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeeren A, Jones PJ. McNally RJ Mapping network connectivity among symptoms of social anxiety and comorbid depression in people with social anxiety disorder. J Affect Disord. 2018;228:75\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBringmann LF, Elmer T, Epskamp S, Krause RW, Schoch D, Wichers M, Wigman JTW. Snippe E What do centrality measures measure in psychological networks? J Abnorm Psychol. 2019;128:892\u0026ndash;903.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScutari M. Learning Bayesian networks with the bnlearn R package. J Stat Softw. 2010;35:1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones PJ, Mair P, Riemann BC, Mugno BL. McNally RJ A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder. J Anxiety Disord. 2018;53:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang F, Huang N, Zhang B, Fu M, Guo J. Network analysis of COVID-19-related PTSD and depressive symptoms among Chinese general population and comorbidity subgroup. Psychol Trauma: Theory Res Pract Policy. 2023;15:431\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryant RA, Creamer M, O\u0026rsquo;Donnell M, Forbes D, Mcfarlane AC, Silove D. Hadzi-Pavlovic D Acute and chronic posttraumatic stress symptoms in the emergence of posttraumatic stress disorder: a network analysis. JAMA Psychiatry. 2017;74:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStam RPTSD. stress sensitisation: a tale of brain and body part 1: human studies. Neurosci Biobehav Rev. 2007;31:530\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang N, Hu Q. It is not simply the loss of a child: the challenges facing parents who have lost their only child in post-reproductive age in China. Death Stud. 2019;45:209\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmour C, Fried EI, Deserno MK, Tsai J. Pietrzak RH A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in U.S. military veterans. J Anxiety Disord. 2017;45:49\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrewen PA, Lanius RA. Toward a psychobiology of posttraumatic self-dysregulation: reexperiencing, hyperarousal, dissociation, and emotional numbing. Ann N Y Acad Sci. 2006;1071:110\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayes JP, Vanelzakker MB, Shin LM. Emotion and cognition interactions in PTSD: a review of neurocognitive and neuroimaging studies. Front Integr Nuerosci. 2012;6:89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNew AS, Fan J, Murrough JW, Liu X, Liebman RE, Guise KG, Tang CY. Charney DS A functional magnetic resonance imaging study of deliberate emotion regulation in resilience and posttraumatic stress disorder. Biol Psychiatry. 2009;66:656\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmer Z, Kwong J. Family size and support of older adults in urban and rural China: current effects and future implications. Demography. 2003;40:23\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEli B. Latent profiles and stage characteristics of posttraumatic stress disorder symptoms among parents who lost their only child. University of Chinese Academy of Sciences; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss J, Murphy D, Armour C. A network analysis of DSM-5 posttraumatic stress disorder and functional impairment in UK treatmentseeking veterans. J Anxiety Disord. 2018;57:7\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinton DE, Lewis-Fern\u0026aacute;ndez. R The cross-cultural validity of posttraumatic stress disorder: Implications for DSM-5. Depress Anxiety. 2011;28:783\u0026ndash;801.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEli B, Chen Y, Zhang J, Huang X, Wang Q, Ma Z, Yv Y. Liu Zk Time course of attentional bias and its relationship with PTSD symptoms in bereaved Chinese parents who have lost their only child. Eur J Psychotraumatol. 2023;14:2235980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMekawi Y, Murphy L, Munoz A, Briscione M, Tone EB, Norrholm SD, Jovanovic T, Bradley B. Powers A The role of negative affect in the association between attention bias to threat and posttraumatic stress: an eye-tracking study. Psychiatry Res. 2020;284:112674.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBar-Haim Y, Holoshitz Y, Eldar S, Frenkel TI, Muller D, Charney DS, Pine DS, Fox NA. Wald I Life-threatening danger and suppression of attention bias to threat. Am J Psychiatry. 2010;167:694\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmour C, Műllerov\u0026aacute; J. Elhai JD A systematic literature review of PTSD\u0026rsquo;s latent structure in the Diagnostic and Statistical Manual of Mental Disorders: DSM-IV to DSM‐5. Clin Psychol Rev. 2016;44:60\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirkeland MS, Greene T, Spiller TR. The network approach to posttraumatic stress disorder: A systematic review. Eur J Psychotraumatol. 2020;11:1700614.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Posttraumatic stress disorder, Depression, Bereaved parents (Shidu parents), Comorbidity, Network analysis","lastPublishedDoi":"10.21203/rs.3.rs-4567110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4567110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe death of a child is a highly traumatic event for parents and often leads to posttraumatic stress disorder (PTSD) and depression. PTSD and depression are frequent comorbid. However, the patterns of comorbidity at the symptom level among bereaved parents remain unclear. This study aims to identify symptom network connectivity of PTSD comorbid with depression in bereaved parents who have lost their only child, known as Shidu parents in Chinese society.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were obtained from 477 bereaved individuals who had lost an only child. A Gaussian graphical model (GGM) was used to construct two comorbidity networks of PTSD and depression with and without overlapping symptoms. A directed acyclic graph (DAG) was computed to determine potential directionality among symptoms in the network without overlapping symptoms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the GGM, symptoms from alterations in arousal and reactivity (Cluster E) and negative alterations in cognition and mood (Cluster D) tended to be key bridging nodes in both networks with and without overlapping symptoms. The DAG results indicated the important triggering role of an exaggerated startle response and several negative alterations in cognition and mood symptoms.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe results suggest a crucial role of the exaggerated startle response in the comorbidity network between PTSD and depression among bereaved Chinese parents. This finding may serve as a significant target for psychological interventions in this population.\u003c/p\u003e","manuscriptTitle":"Symptom network connectivity of PTSD comorbid with depression in bereaved Chinese parents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 15:58:31","doi":"10.21203/rs.3.rs-4567110/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-25T07:24:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-20T12:26:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-20T12:25:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2024-06-12T03:03:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e77a5e5-c0d2-46c9-bbb1-ad0e4fad66f7","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:06:15+00:00","versionOfRecord":{"articleIdentity":"rs-4567110","link":"https://doi.org/10.1186/s12888-025-07283-4","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-09-26 15:57:20","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2024-07-18 15:58:31","video":"","vorDoi":"10.1186/s12888-025-07283-4","vorDoiUrl":"https://doi.org/10.1186/s12888-025-07283-4","workflowStages":[]},"version":"v1","identity":"rs-4567110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4567110","identity":"rs-4567110","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}