Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Symptoms of major depressive disorder and post-traumatic stress disorder in veterans with mild traumatic brain injury: A network analysis

  • Shuyuan Shi,

    Roles Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Psychology, University of British Columbia, Vancouver, BC, Canada

  • Erin Almklov,

    Roles Conceptualization, Supervision, Writing – review & editing

    Affiliations VA Center of Excellence for Stress and Mental Health, San Diego, CA, United States of America, VA San Diego Healthcare System, San Diego, CA, United States of America

  • Niloofar Afari,

    Roles Resources, Supervision, Writing – review & editing

    Affiliations VA Center of Excellence for Stress and Mental Health, San Diego, CA, United States of America, VA San Diego Healthcare System, San Diego, CA, United States of America, Department of Psychiatry, University of California San Diego, La Jolla, CA, United States of America

  • James O. E. Pittman

    Roles Resources, Supervision, Writing – review & editing

    Affiliations VA Center of Excellence for Stress and Mental Health, San Diego, CA, United States of America, VA San Diego Healthcare System, San Diego, CA, United States of America, Department of Psychiatry, University of California San Diego, La Jolla, CA, United States of America


Mild Traumatic Brain Injury (mTBI, or concussion) is a debilitating condition that often leads to persistent cognitive and mental health problems post-injury. Post-traumatic Stress Disorder (PTSD) and Major Depressive Disorder (MDD) are two most commonly occurring mental health problems following mTBI and are suggested to be strong contributors to the persistent post-concussion symptoms. Thus, it is important to understand the symptomatology of PTSD and MDD post-mTBI, to better inform targets for behavioral health interventions. Therefore, the current study examined the symptom structure of post-mTBI co-morbid PTSD and MDD through network approaches; we compared the network structure of participants with a positive mTBI screen (N = 753) to the network structure of participants with a negative mTBI screen (N = 2044); lastly, we examined a network of PTSD and MDD symptoms with clinical covariates in a positive mTBI sample. We found that feeling distant/cutoff (P10) and difficulty concentrating (P15) were the most central symptoms in the positive mTBI network and sleep problems were the most prominent bridge nodes across the disorders. No significant difference between the positive and negative mTBI network were found through network comparison tests. Moreover, anxiety and insomnia were strongly associated with sleep symptoms and irritability symptoms, and emotional support and resilience were potential buffers against most of the PTSD and MDD symptoms. The results of this study might be particularly useful for identifying targets (i.e., feeling distant, concentration and sleep problems) for screening, monitoring and treatment after concussion to better inform post-mTBI mental health care and to improve treatment outcomes.


Mild Traumatic Brain Injury (mTBI) is a common injury and a leading cause of disability in the United States [1]. Although most patients with mTBI recover within three months following injury, up to one third of patients develop persistent post-concussion symptoms [26]. Moreover, patients with mTBI are more likely to develop debilitating mental health complications, especially post-traumatic stress disorder (PTSD) and major depressive disorder (MDD), which in turn can lead to poorer recovery and persistent post-concussion symptoms [712]. The population-based prevalence rate of mTBI is estimated at 0.6% among general adult population [13]; while for military veterans, a highly combat-exposed population, prevalence rates are much higher (i.e., 12–23%) [1416]. Veterans also report higher rates of mental health complications following injury [15]. Of the approximately 25,000 post-deployment veterans returning from Afghanistan or Iraq between 2009 and 2014, a significant proportion (10%-30%) of those who received a positive mTBI diagnosis had symptoms that persisted for more than 3 months post-injury [17], and one of the most salient features of nonrecovery from mTBI are post-mTBI mental health problems [18]. Among patients with mTBI, up to 39% of veterans report PTSD [19], and 50% of veterans report MDD [20], compared to around 18% civilians report PTSD and/or depression [21]. Therefore, it is important to better understand the symptom structures of commonly occurring post-mTBI mental health problems and to develop tailored post-injury assessments and interventions for the veteran population.

PTSD and MDD are among the most common mental health disorders occurring post-injury among veterans [16, 22]. It is reported that among patients with PTSD, 55% have also been diagnosed with MDD at least once in their lifetime [23]. Both MDD and PTSD are associated with impaired physical functioning [24, 25], greater healthcare utilization and costs [26], and higher rates of disability [27]. Patients with co-morbid PTSD and MDD have also reported poorer treatment outcomes, increased health burden and increased suicidal behaviors compared to patients with only PTSD or MDD [28, 29]. Furthermore, one study found that among nearly 120,000 combat veterans, co-morbid PTSD and MDD post-mTBI is associated with elevated levels of chronic pain and pain-related disability [30]. Proactive management of mental health complications may improve overall recovery and return to productivity after mTBI [31]. However, to our knowledge, there lack systematic screening and proactive management for PTSD and MDD following mTBI in the veteran population. Understanding the symptom structure of comorbid PTSD and MDD in the context of mTBI might inform targets for proactive screening and treatment for the military population.

PTSD and MDD share a number of criterion symptoms, as listed in the most recent Diagnostic and Statistical Manual of Mental Disorders [32]. Multiple theories have been raised trying to explain the high prevalence of the comorbidity between PTSD and MDD. Factor analysis studies suggest that PTSD/MDD comorbidity is due to one or more shared underlying dimensions [33, 34], while other studies suggest that such comorbidity is caused by dimensional communality—items/dimensions are correlated but disorders are distinct [35]. However, in these above theories, symptoms are viewed as indicators of latent variable(s) that represent the disorders, and none have taken the symptom-level associations into account.

Recently, a network approach for understanding the symptomatology of comorbid PTSD and MDD has gained increasing attention. The network approaches holds that symptom level associations constitute the disorder and comorbidity [3638]. Unlike previous categorical or dimensional perspectives which view symptoms as equal contributors to one underlying variable, network perspective assumes interactive and causal relationships between symptoms that can trigger and/or reinforce each other, which constitute a dynamic network. According to the network theory, a symptom is central in the network when it triggers most activation of other symptoms [39], and a bridging symptom is a symptom that is central in connecting the two disorders, which might be driving comorbidity [40]. Network analysis is still a relatively new method in studying the complex symptomatology both between and within disorders.

Over the past few years, many studies have utilized this novel approach to investigate the symptom structure of PTSD or MDD [4143], while few have examined the symptom structure of co-occurring PTSD/MDD, despite the high comorbidity rate [33]. Findings from the limited studies that have investigated the comorbid PTSD/MDD symptom structure network among various populations have identified sleep problems, concentration difficulties, irritability and anhedonia to be central symptoms that trigger most activation of other symptoms across the network [4447]. Afzali et al. (2017) found that bridging symptoms are not limited to the overlapping criterion symptoms outlined in DSM-V; they identified five non-overlapping symptoms (i.e., sense of foreshortened future, feelings of guilt, feeling sad, psychomotor retardation and flashbacks) that are central in the network and can spread activation to other symptoms which result in an expression of the disorder [47]. However, studies that have investigated comorbidity are scarce and are often limited in sample size or target population, and no extant literature has examined the comorbid symptom structure of PTSD and MDD within a mTBI population.

To bridge the knowledge gap, the current study aimed to: 1) establish a network of comorbid PTSD and MDD symptoms, and identify the central symptoms that might be contributing to persistent post-concussive symptoms in the mTBI sample; 2) compare the network of co-morbid PTSD and MDD symptoms among veterans with and without mTBI; and 3) include clinical covariates (i.e., anxiety, insomnia, resilience and emotional support) into the comorbidity network to examine whether they display particular relationships with certain symptoms. The findings of this investigation may have important clinical implications, such as better informing symptom targets for assessment, treatment, and monitoring of post-MTBI patients to prevent the development of chronic health conditions.

Materials & method

Study sample

This study was a retrospective cross-sectional evaluation of 2,797 veterans registering for care at the VA San Diego Healthcare System (VASDHS) between July 1, 2014, and November 22, 2017. Data were collected as part of standard clinical screening processes in Transition Care Management (TCM) clinics with the use of eScreening, an electronic mobile self-report screening tool [48]. Participants were primarily male (84.7%) with a mean age of 36.3 (SD = 9.0).


Sociodemographic and service history.

A researcher designed self-report questionnaire was used to record age, gender, race/ethnicity, relationship status, and work status. Service history related to branch of service, number of deployments, and combat exposure were also captured with the questionnaire.

MDD symptoms.

Patient Health Questionnaire 9-Item Depression Module (PHQ-9) [49] measured depression symptoms occurring within the past two week period. Items are rated on a 4-point scale with a maximum score of 27. Higher scores indicate greater severity, with the clinically cutoff score of 10 and above indicating moderate to severe depressive symptoms. The PHQ-9 is generally a reliable and valid measure of depression, although evidence for inter-rater reliability is lacking [49, 50]. The questionnaire items are presented in Table 1.

PTSD symptoms.

The seventeen item PTSD Checklist—Civilian Version (PCL-C) [51] was used to assess PTSD symptoms over the past month. The items were scored on a 5-point scale with scores range from 17–85, such that higher scores indicating greater severity. The clinically cutoff score of 30 and above indicating moderate to severe PTSD symptoms The PCL-C was chosen for its high internal consistency across military and nonclinical populations [51]. The questionnaire items are presented in Table 1.

MTBI screening.

The Brief Traumatic Brain Injury Screen (BTBIS) [52] is a three item questionnaire used to detect mTBI following combat deployment. It has been routinely administered within military samples to screen for potential mTBIs. A participant is considered to have a positive screen if he or she selects items on the first two questions, which asks for mTBI exposures during their deployment and altered mental status associated with the injury.

Clinical covariates.

Anxiety was assessed with the seven item Generalized Anxiety Disorder 7 scale (GAD-7) [53]. Sleep symptoms present within the past two week period were measured with the seven item Insomnia Severity Index (ISI) [54]. Resilience was assessed with the 10-item Connor-Davidson Resilience Scale (CD-RISC 10) [55]. Emotional support was measured by self-reported scores on the Patient-Reported Outcomes Measurement Information System (PROMIS) Emotional Support questionnaire [56]. The validity and reliability of above measures were proved by previous literature.

Analysis plan

Assessment of psychiatric symptoms.

Descriptive statistics were used to characterize the study sample. Chi-square tests were used to compare the rates of PTSD, MDD, and comorbidity across mTBI and non-mTBI samples. Furthermore, independent t-tests were conducted to compare the PTSD total score and MDD total score between mTBI and non-mTBI samples, in order to assess if one group report significantly worse symptoms than the other group.

Network analysis.

Network visualization. Network analyses of co-morbid PTSD/MDD symptom structures across mTBI and non-mTBI sample were conducted in R using the qgraph package [57]. PTSD symptoms, MDD symptoms were included in the network, with each symptom represented by a node and relationship between symptoms represented by edges. The strength of the relationship was represented by line (edge) thickness, with blue lines indicating positive correlations and orange lines indicating negative correlations. To control for false positive rates, we applied the Least Absolute Shrinkage and Selector Operator (LASSO) regularization procedure which sets all weak partial correlations (determined by a set parameter) to exact zero. The parameter is chosen using the Extended Bayesian Information Criterion (EBIC), and is set as 0.5 as recommended by previous research [58].

Node centrality measures. The relative importance of the symptoms within the networks were assessed through the centrality function in the package, and node strength, betweenness, closeness and expected influence (EI) was calculated for each symptom [59] to indicate node centrality. Specifically, we chose EI to indicate node (symptom) centrality, as previous research (which the current study came to the same conclusion through centrality stability tests, see Fig 3) consistently considered EI to be a more reliable and stable measure of centrality than measures like closeness and betweenness [39, 60, 61]. EI is calculated by summing the weight of all positive and negative edges of a node, and higher EI values indicate greater centrality/importance of the node/symptom to the network/disorder. Moreover, EI was computed for bridge nodes–nodes that have symptoms level connections with nodes of the other disorder—for all networks [40]. Tests of differences were also conducted to better distinguish significant differences in edge weights and node EI.

Network robustness (accuracy and stability). Network accuracy was assessed using the bootnet package [59]. Through a non-parametric approach, we bootstrapped 95% confidence intervals of the edge weights 1000 times to test the interrelations’ accuracy. Network stability was scrutinized through re-calculating the correlation stability coefficients (CS-coefficient) using subsetting bootstrap. The network is identified as stable if the interrelatedness coefficients are similar across the subsets. A CS-coefficient that is greater 0.70 indicates good network stability [59].

Network comparison. To compare the two networks, correlation test and network structure invariance tests were ran [62]. Moreover, the global network strength (i.e., the overall network connectivity calculated by the sum of absolute edge weight values) and global network expected influence (i.e., the degree to which the symptoms are assumed to enhance each other by the sum of all positive and negative edge weight values) of the two networks was compared using the R package NCT.

Clinical covariates. A new network model with comorbid PTSD and MDD symptoms and clinical covariates was established for the positive mTBI group. The node centrality test, network accuracy test, as well as network stability test was conducted to see if clinical covariates were important influential factors for the development/progression of the post-mTBI comorbid symptoms.


Descriptive results

Veterans’ self-reported sociodemographic and service history characteristics are provided in Table 2. This sample of veterans consist of mostly male (84.7%) with a mean age of 36.3 (SD = 9.0). Around twenty-six percent of the sample identified as Hispanic/Latino. Majority of the sample were Caucasian (59.8%) followed by African American (15.6%) and Asian (13.9%). Over eighty percent of the sample had completed some college or beyond, and approximately 58% reported being unemployed while 31% had full-time employment with varied levels of income. Consistent with local demographics, 93.8% had served active duty mostly in the Navy (48.8%) or Marines (32.1%).

Chi-square tests revealed that veterans with positive mTBI screening are significantly more likely to develop PTSD, MDD and comorbid MDD/PTSD (as shown in S1 Table in S1 File). Among those who met clinical cutoffs for PTSD and MDD, veterans with positive mTBI report greater MDD and PTSD symptoms severity (t(986) = 8.62, P < 0.001; t(986) = 3.40, P < 0.001) than veterans without mTBI.

Network results

mTBI network & non-mTBI network.

The network of PTSD and MDD symptoms of the positive mTBI sample is depicted in Fig 1.

Fig 1. Network of PTSD and MDD symptoms structure for veterans with mTBI (N = 753).

Positive relations are represented by blue edges and negative relations are represented by orange edges. The thicker and more saturated edges indicated stronger partial correlations between the nodes/symptoms.

Overall, the strongest edges were within the PTSD symptoms cluster, and they were trouble falling/staying asleep (P13) -to- trouble falling/staying asleep or sleep too much (D3), being super alert (P16) -to- feeling jumpy (P17), avoid thinking about the event (P6) -to- avoid activities that remind one of stressful event (P7), repeated disturbing memories (P1) -to- repeated disturbing dreams (P2) and feeling distant/cutoff (P10) -to- feeling emotionally numb (P11). The strongest edges within MDD symptoms cluster were feeling down/hopeless (D2) -to- feeling bad for oneself (D6), and little interest to doing things (D1) -to- feeling down/hopeless (D2). The strongest edges across disorders were mainly overlapping symptoms: trouble falling/staying asleep (P13) -to- trouble falling/staying asleep or sleep too much (D3), difficulty concentrating (P15) -to- trouble concentrating (D7), feeling future will be cut short (P12) -to- thought that one would be better off dead (D9) and loss of interest in things that used to enjoy (P9) -to- little interest in doing things (D1). The tests of significance for edge weight difference are presented in S1 Fig in S1 File.

The network of PTSD and MDD symptoms of the negative mTBI sample is depicted in Fig 2.

Fig 2. Network of PTSD and MDD symptoms structure for veterans without mTBI (N = 2044).

Positive relations are represented by blue edges and negative relations are represented by orange edges. The thicker and more saturated edges indicated stronger partial correlations between the nodes/symptoms.

Similar to the positive mTBI network, the overall strongest edges were within the PTSD symptoms cluster, and they were trouble falling/staying asleep (P13) -to- trouble falling/staying asleep or sleep too much (D3), being super alert (P16) -to- feeling jumpy (P17), repeated disturbing memories (P1) -to- repeated disturbing dreams (P2), and feeling distant/cutoff (P10) -to- feeling emotionally numb (P11). The strongest edges within MDD symptoms cluster were feeling down/hopeless (D2) -to- feeling bad for oneself (D6) and trouble falling/staying asleep or sleep too much (D3) -to- feeling tired/having little energy (D4). The strongest edges across disorders were again mainly overlapping symptoms: trouble falling/staying asleep (P13) -to- trouble falling/staying asleep or sleep too much (D3), difficulty concentrating (P15) -to- trouble concentrating (D7), feeling future will be cut short (P12) -to- thought that one would be better off dead (D9) and loss of interest in things that used to enjoy (P9) -to- little interest in doing things (D1). The tests of significance for edge weight differences are presented in S2 Fig in S1 File

The stability values of the estimated networks (CS-coefficients) were 0.75 for EI and network strength for both samples, indicating highly stable networks. EI appears to be a slightly more stable centrality measure than network strength, which justifies our choice of using EI to infer node/symptoms centrality/importance (See S3C & S3D Fig in S1 File). The non-parametric bootstrapped tests suggest moderately accurate estimations of edge weights (See S3A & S3B Fig in S1 File).

The standardized EIs for the two networks are depicted in Fig 3.

Fig 3. Network centrality–Expected influence.

A. The EI measure for PTSD and MDD symptoms network among positive mTBI veterans. B. The EI measure for PTSD and MDD symptoms among negative mTBI veterans.

The most influential symptoms were feeling distant/cutoff (P10) and difficulty concentrating (P15) for veterans with mTBI and feeling/distant/cutoff (P10) and feeling down/hopeless (D2) for veterans without mTBI. For both groups, the least influential symptoms were thought that one would be better off dead (D9), trouble remembering parts of the stressful event (P8) and moving/speaking noticeably slow (D8). The tests of significance for node EI differences are presented in S4 and S5 Figs in S1 File. Centrality measures of node strength, closeness and betweenness are presented in S6 and S7 Figs in S1 File.

When considering only the bridge nodes, results were similar across the two networks. Results showed that the most influential bridge nodes were mostly the overlapping symptoms across the two disorders, they are trouble falling/staying asleep (P13), difficulty concentrating (P15) and feeling future will be cut short (P12) from PTSD symptoms cluster and trouble falling/staying asleep or sleep too much (D3), trouble concentrating (D7) and little interest in doing things (D1) from MDD symptoms cluster. The least influential bridge symptoms were different across the disorders, with avoid thinking/having feelings related to the event (P6) having the lowest EI for positive mTBI network and being super alert (P16) having the lowest EI for negative mTBI network (more details on S8 & S9 Figs in S1 File).

Network comparison tests.

The positive mTBI and negative mTBI networks were similar in structure, with high correlation between the regularized symptoms interrelations of each group (r = 0.93). Moreover, the network structure invariance test was not significant (M = 0.14, permutations = 2000, p = 0.17), and the two networks did not differ significantly in regard to global network strength (S = 0.31, Spos = 12.66, Sneg = 12.96, permutations = 2000, p = 0.43) and the network global EI (C = -0.03, permutations = 2000, p = 0.096).

MTBI network with clinical covariates.

The network of PTSD and MDD symptoms with clinical covariates of a subset of the positive mTBI sample is depicted in Fig 4. The stability values of the estimated network (CS-coefficient) were 0.75 for EI and 0.67 for network strength, indicating highly stable networks. For edge weight accuracy, see S10 Fig in S1 File.

Fig 4. Network of PTSD and MDD symptoms with clinical covariates for veterans with mTBI (N = 234).

Positive relations are represented by blue edges and negative relations are represented by orange edges. The thicker and more saturated edges indicated stronger partial correlations between the nodes/symptoms.

The strongest positive edges between the clinical covariates and the symptoms were trouble falling/staying asleep (P13) and trouble falling/staying asleep or sleep too much (D3) -to- Insomnia score, followed by feeling irritable/having outbursts (P14) -to- Anxiety score. The strongest negative edges between the clinical covariates were feeling bad for oneself (D6) -to- Resilience score and feeling down/hopeless (D2) -to- Emotional Support score. Specifically, most of MDD symptoms were negatively partially correlated with both the Resilience score and the Emotional Support score, while only feeling emotionally numb (P11) and trouble remembering parts of the stressful event (P8) of the PTSD symptoms were negatively correlated with Resilience score and Emotional Support score. The tests of significance for edge weight differences are presented in S11 Fig in S1 File.

Both Anxiety and Insomnia appeared to have high positive EI in the network, indicating their strong positive influence over PTSD and MDD symptoms. Moreover, both Resilience and Emotional Support appeared to have high negative EI in the network, indicating their strong negative influence over PTSD and MDD symptoms (as shown in Fig 5). The tests of significance for node EI differences are presented in S12 Fig in S1 File. Centrality measures of node strength, closeness and betweenness are presented in S13 Fig in S1 File.

Fig 5. EI for positive mTBI PTSD & MDD symptoms with clinical covariates network.


This study examined the symptoms and comorbidity of PTSD and MDD across a sample of veterans with and without mTBI using the novel network approach. Past research suggests MDD and PTSD are among the two most common mental health sequalae occurring in mTBI populations and are strong predictors for persistent post-concussion symptoms and disability [8, 12, 30]. Results from our study supported this: PTSD and MDD are more prevalent and severe among veterans with mTBI than veterans without. Thus, it is important to proactively screen, monitor and provide treatment for PTSD and MDD symptoms post-mTBI. Unfortunately, one study found that less than half of patients with mental health complication(s) following injury were referred to appropriate care and received timely treatment [63]. Therefore, important gaps between knowledge and practice remain. One solution is to implement routine mental health screening, such as eScreening, in primary care settings. EScreening provides customized and automated self-report mental and physical health screening and has been proven to be an effective tool for initial and ongoing symptoms monitoring with high efficiency and patient satisfaction [48, 64]. Successful implementation of highly effective screening methods like eSreening in primary care settings could potentially improve the detection and monitoring of mental health symptoms post-MTBI and expedite the referring processes of veterans to adequate care.

To further assess the PTSD and MDD symptomology after mTBI, we examined the positive mTBI network structure of the PTSD/MDD symptoms. Feeling distant/cutoff (P10) and difficulty concentrating (P15) emerged as the two most central nodes in the positive mTBI network, echoing the findings of previous studies [41, 44, 65]. The centrality of these two symptoms suggested their importance within the PTSD and MDD network and their strong influence over other symptoms. Although further evaluation of the symptoms and its effect on the development of disorders is required, our results provide preliminary targets for screening, monitoring and treatment for post-mTBI PTSD and MDD. For example, it might be useful to develop brief screening measures that assess for emotional numbness and concentration problems post-mTBI to intervene early and prevent the development of other PTSD and MDD symptoms. The implementation of post-mTBI rehabilitation interventions that address concentration, emotional numbness and sleep problems etc., such as CogSMART, can also be an effective way to improve post-mTBI treatment outcomes [66, 67].

Moreover, when considering only the bridging nodes, overlapping symptoms between the disorders—sleep and concentration problems—came up as the most central nodes, which is consistent with literature showing overlapping symptoms are an origin for high comorbidity [44, 47, 68]. However, there is ongoing debate as to whether PTSD and MDD comorbidity is driven by overlapping symptoms or whether such symptoms constitute a subtype of PTSD [69]; thus further clarifying research is needed. Similar to previous research, trouble remembering parts of the stressful event (P8) were found to be among the least central nodes in the network [44, 46], suggesting that it might not be as important in PTSD and MDD detection and symptoms development.

We also examined the PTSD and MDD symptom structure network in veterans with and without mTBI. The central nodes and bridge nodes were similar across the networks, and there were no significant differences in network structure, global strength and global expected influence. The results suggested that although mTBI is indicative of higher likelihood to develop more severe PTSD and/or MDD, mTBI does not seem to significantly influence the symptoms structure of co-morbid PTSD and MDD. Since no studies have investigated the PTSD and MDD symptom structure post-mTBI, our findings provide preliminary knowledge on which future studies should build.

Sleep symptoms across PTSD and MDD consistently appeared as the strongest bridge nodes, and the edge connecting two sleep symptoms/nodes were the strongest across the two networks. Our results are consistent with previous network studies that found sleep difficulty to be the major bridging nodes across various populations [46, 47]. This is not surprising as insomnia/sleep problems are a risk factor for many mental health problems, such as depression and PTSD [70, 71]. Insomnia is also related to worse self-rated health among older veterans [72]. Thus, symptoms of sleep difficulty within PTSD and MDD appear to be an important treatment target for post-mTBI care; and providing sleep-related interventions like Cognitive Behavioral Therapy-Insomnia may be highly beneficial for this population (i.e., veterans with mTBI) [7375].

Last but not the least, we examined a network of PTSD and MDD symptoms with several clinical covariates to understand how these added variables influence the network structure. We found that both anxiety and insomnia have high positive EI within the network, that is, anxiety and insomnia are highly influential over PTSD and MDD symptoms. Additionally, the edges between PTSD/MDD symptoms such as sleep problems to insomnia, and irritability to anxiety were among the strongest. Previous research has suggested that insomnia is a strong predictor rather than outcome for PTSD and MDD symptom development among veterans [71], which is consistent with our findings. Anxiety has also been shown to predict the deterioration of PTSD symptoms [76], which is similar to our results that showed a strong link between anxiety and PTSD symptoms. Thus, in addition to MDD and PTSD, it is important to routinely screen for and monitor anxiety and insomnia to improve the quality and effectiveness of post-mTBI care.

Emotional support and resilience also came up as influential factors in the positive mTBI with clinical covariates network, such that they demonstrated high negative EIs in the network and were negatively linked to most of the MDD symptoms and some of the PTSD symptoms. The results suggest that emotional support (or perceived social support) and resilience both appear to be strong protective factors for PTSD and MDD. Previous research has shown that perceived social support is associated with better mental health outcomes and reduced symptoms severity [77, 78]. Moreover, resilience has been found to be a promising buffer for mental health, physical health, and well-being [7981]. Interventions promoting resilience have been associated with reductions in stress and depression [82], but such resilience building practices have not been widely adopted in large healthcare organizations [83]. VA is working to improve quality of care by shifting from a reactive, disease-focused care model to a proactive, patient-centered approach, which aims to develop a personalized health plan based on the unique values, needs, and goals (e.g., building resilience, improve emotional support) of every individual [84, 85]. Therefore, systematically develop and evaluate interventions that incorporate techniques which enhance patients’ perception of emotional support, or interventions that promotes resilience in the context of post-mTBI care align with VA’s focus on this personalized health approach.

The study has a number of limitations. As eScreening was originally developed for Transition Care Management programs, which coordinate health care for post-9/11 veterans at the point of enrollment, the data available for this study were limited to post-9/11 era veterans. The current sample consist of mostly male (84.7%), caucasian (59.8%), Navy (48.8%) or Marines (32.1%) veterans. Thus, findings may not generalize to the larger veteran population. Second, the data for this study is cross-sectional, which limits our ability to make temporal inferences and to draw directions between the symptoms [86]. Third, the network analysis approach assumes that all relative and influential variables are incorporated in the network, but we may have missed some important symptoms and/or non-symptoms that play an important role in the comorbidity network. Although we tried to address this problem by including clinical covariates, there still exist many other clinical symptoms/comorbidities (e.g., substance use, physical disability) [25, 87, 88] and/or factors (e.g., coping styles, employment status, stressful life events and combat exposure) [42, 8991] that might be vital in the PTSD and MDD network. Fourth, the study used data from service-seeking veterans registering for care through the VASDHS, of which the veterans reported various degrees of symptoms that may affect the network accuracy. The veterans reported symptoms severity ranged from sub-threshold PTSD/MDD to severe PTSD/MDD in our samples, while previous study suggested that sub-threshold PTSD symptoms structure might be different from moderate to severe PTSD symptoms structure [46]. Fifth, although the positive mTBI with clinical covariates network showed promising stability and accuracy, the sample for this network is still relatively small. The use of a larger sample would significantly improve accuracy and stability of the network, and thus allow us to make stronger inferences. Finally, the BTBIS used in the current study is a short screening tool for probable mTBI. We might overestimate the actual prevalence rate of mTBI among veterans using BTBIS. Future study should use a more valid measure for mTBI (e.g., clinician diagnoses based on standard criteria) to improve the validity of the results.

Despite these limitations, the current study has many strengths and can contribute to extant knowledge in the field. The study provides quantitative evidence for the greater prevalence and severity of PTSD and MDD symptoms and comorbidity after mTBI. The study also is the first and largest to examine the comorbid PTSD and MDD symptom structure in a sample of veterans with mTBI; and identified Feeling distant/cutoff (P10) and difficulty concentrating (P15) as the most central symptoms in the network and sleep problems as the most prominent bridge nodes across the disorders. These symptoms might be particular useful targets for screening, monitoring and treatment planning for post-MTBI mental health care and for improving treatment outcomes. Further, the use of effective screening tools, like eScreening, could potentially improve the detection of mental health symptoms post-mTBI and expedite the referral to appropriate care [48, 64]. Future research should use longitudinal data to better inform the development of post-mTBI comorbid PTSD and MDD symptoms; future research should also incorporate more potential influential factors for the progression or prevention of PTSD and MDD symptoms and examine the network structure in a larger veteran sample.


  1. 1. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Report to Congress on mild traumatic brain injury in the United States: Steps to prevent a serious public health problem. Atlanta (GA); 2003.
  2. 2. Cassidy JD, Boyle E, Carroll LJ. Population-Based, Inception Cohort Study of the Incidence, Course, and Prognosis of Mild Traumatic Brain Injury After Motor Vehicle Collisions. Arch Phys Med Rehabil. 2014 Mar 1;95(3):S278–85. pmid:24581913
  3. 3. McMahon P, Hricik A, Yue JK, Puccio AM, Inoue T, Lingsma HF, et al. Symptomatology and functional outcome in mild traumatic brain injury: Results from the prospective TRACK-TBI study. J Neurotrauma. 2014;31(1):26–33. pmid:23952719
  4. 4. Voormolen DC, Cnossen MC, Polinder S, Gravesteijn BY, Von Steinbuechel N, Real RGL, et al. Prevalence of post-concussion-like symptoms in the general population in Italy, The Netherlands and the United Kingdom. Brain Inj. 2019;33(8):1078–86. pmid:31032649
  5. 5. Voormolen DC, Cnossen MC, Polinder S, Von Steinbuechel N, Vos PE, Haagsma JA. Divergent classification methods of post-concussion syndrome after mild traumatic brain injury: Prevalence rates, risk factors, and functional outcome. J Neurotrauma. 2018;35(11):1233–41. pmid:29350085
  6. 6. Dean PJA O’Neill D, Sterr A. Post-concussion syndrome: Prevalence after mild traumatic brain injury in comparison with a sample without head injury. Brain Inj. 2012;26(1):14–26. pmid:22107176
  7. 7. Carroll LJ, Cassidy JD, Cancelliere C, Côté P, Hincapié CA, Kristman VL, et al. Systematic Review of the Prognosis After Mild Traumatic Brain Injury in Adults: Cognitive, Psychiatric, and Mortality Outcomes: Results of the International Collaboration on Mild Traumatic Brain Injury Prognosis. Arch Phys Med Rehabil. 2014 Mar 1;95(3):S152–73. pmid:24581903
  8. 8. Porter KE, Stein MB, Martis B, Avallone KM, McSweeney LB, Smith ER, et al. Postconcussive symptoms (PCS) following combat-related traumatic brain injury (TBI) in Veterans with posttraumatic stress disorder (PTSD): Influence of TBI, PTSD, and depression on symptoms measured by the Neurobehavioral Symptom Inventory (NSI). J Psychiatr Res. 2018;102:8–13. pmid:29554536
  9. 9. Roy D, Ghosh A, Yan H, Leoutsakos J-M, Rao V, Peters ME, et al. Prevalence and Correlates of Depressive Symptoms Within 6 Months After First-Time Mild Traumatic Brain Injury. J Neuropsychiatry Clin Neurosci. 2022 Mar. pmid:35306831
  10. 10. Seal KH, Bertenthal D, Samuelson K, Maguen S, Kumar S, Vasterling JJ. Association between mild traumatic brain injury and mental health problems and self-reported cognitive dysfunction in Iraq and Afghanistan Veterans. J Rehabil Res Dev. 2016;53(2):185–98. pmid:27148692
  11. 11. Stein MB, Jain S, Giacino JT, Levin H, Dikmen S, Nelson LD, et al. Risk of Posttraumatic Stress Disorder and Major Depression in Civilian Patients after Mild Traumatic Brain Injury: A TRACK-TBI Study. JAMA Psychiatry. 2019;76(3):249–58. pmid:30698636
  12. 12. Zahniser E, Nelson LD, Dikmen SS, Machamer JE, Stein MB, Yuh E, et al. The Temporal Relationship of Mental Health Problems and Functional Limitations following mTBI: A TRACK-TBI and TED Study. J Neurotrauma. 2019;36(11):1786–93. pmid:30543138
  13. 13. Cassidy JD, Carroll LJ, Peloso PM, Borg J, von Holst H, Holm L, et al. Incidence, risk factors and prevention of mild traumatic brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J Rehabil Med. 2004 Feb;(43 Suppl):28–60. pmid:15083870
  14. 14. Terrio H, Brenner LA, Ivins BJ, Cho JM, Helmick K, Schwab K, et al. Traumatic brain injury screening: Preliminary findings in a US army brigade combat team. J Head Trauma Rehabil. 2009;24(1):14–23. pmid:19158592
  15. 15. Stein MB, Kessler RC, Heeringa SG, Jain S, Campbell-Sills L, Colpe LJ, et al. Prospective longitudinal evaluation of the effect of deployment-acquired traumatic brain injury on posttraumatic stress and related disorders: Results from the army study to assess risk and resilience in servicemembers (army STARRS). Am J Psychiatry. 2015;172(1101–11). pmid:26337036
  16. 16. O’Neil M, Carlson K, Storzbach D, Brenner L, Freeman M, Quinones A, et al. Complications of Mild Traumatic Brain Injury in Veterans and Military Personnel: A Systematic Review. Dep Veterans Aff. 2014 Mar 7;1–162.
  17. 17. Schwab K, Terrio HP, Brenner LA, Pazdan RM, McMillan HP, MacDonald M, et al. Epidemiology and prognosis of mild traumatic brain injury in returning soldiers. Neurology. 2017;88(16):1571–9.
  18. 18. Radhakrishnan R, Garakani A, Gross LS, Goin MK, Pine J, Slaby AE, et al. Neuropsychiatric aspects of concussion. The Lancet Psychiatry. 2016;3(12):1166–75. pmid:27889010
  19. 19. Carlson KF, Kehle SM, Meis LA, Greer N, MacDonald R, Rutks I, et al. Prevalence, assessment, and treatment of mild traumatic brain injury and posttraumatic stress disorder: A systematic review of the evidence. J Head Trauma Rehabil. 2011;26(2):103–15. pmid:20631631
  20. 20. Greer N, Sayer NA, Spoont M, Taylor BC, Ackland PE, Macdonald R, et al. Prevalence and Severity of Psychiatric Disorders and Suicidal Behavior in Service Members and Veterans with and Without Traumatic Brain Injury: Systematic Review. J Head Trauma Rehabil. 2020 Jan 1;35(1):1–13. pmid:31033741
  21. 21. Delmonico RL, Theodore BR, Sandel ME, Armstrong MA, Camicia M. Prevalence of depression and anxiety disorders following mild traumatic brain injury. PM&R. 2022;14(7):753–63. pmid:34156769
  22. 22. Vasterling JJ, Verfaellie M, Sullivan KD. Mild traumatic brain injury and posttraumatic stress disorder in returning veterans: Perspectives from cognitive neuroscience. Clin Psychol Rev. 2009 Dec 1;29(8):674–84. pmid:19744760
  23. 23. Elhai JD, Grubaugh AL, Kashdan TB, Frueh BC. Empirical examination of a proposed refinement to DSM-IV posttraumatic stress disorder symptom criteria using the national comorbidity survey replication data. J Clin Psychiatry. 2008;69(4):597–602. pmid:18294026
  24. 24. Pietrzak RH, Goldstein RB, Southwick SM, Grant BF. Medical comorbidity of full and partial posttraumatic stress disorder in us adults: Results from wave 2 of the national epidemiologic survey on alcohol and related conditions. Psychosom Med. 2011;73(8):697–707. pmid:21949429
  25. 25. Nichter B, Norman S, Haller M, Pietrzak RH. Physical health burden of PTSD, depression, and their comorbidity in the U.S. veteran population: Morbidity, functioning, and disability. J Psychosom Res. 2019;124:109744. pmid:31443821
  26. 26. Deykin EY, Keane TM, Kaloupek D, Fincke G, Rothendler J, Siegfried M, et al. Posttraumatic stress disorder and the use of health services. Psychosom Med. 2001;63(5):835–41. pmid:11573033
  27. 27. Hall KS, Beckham JC, Bosworth HB, Sloane R, Pieper CF, Morey MC. PTSD is negatively associated with physical performance and physical function in older overweight military veterans. J Rehabil Res Dev. 2014;51(2):285–95. pmid:24933726
  28. 28. Green BL, Krupnick JL, Chung J, Siddique J, Krause ED, Revicki D, et al. Impact of PTSD comorbidity on one-year outcomes in a depression trial. J Clin Psychol. 2006;62(7):815–35. pmid:16703602
  29. 29. Kimbrel NA, Meyer EC, DeBeer BB, Gulliver SB, Morissette SB. A 12-Month prospective study of the effects of PTSD-depression comorbidity on suicidal behavior in Iraq/Afghanistan-era veterans. Psychiatry Res. 2016;243:97–9. pmid:27376669
  30. 30. Seal KH, Bertenthal D, Barnes DE, Byers AL, Strigo I, Yaffe K. Association of Traumatic Brain Injury With Chronic Pain in Iraq and Afghanistan Veterans: Effect of Comorbid Mental Health Conditions. Arch Phys Med Rehabil. 2017;98(8):1636–45. pmid:28455190
  31. 31. Silverberg ND, Panenka WJ, Lizotte PP, Bayley MT, Dance D, Li LC. Promoting early treatment for mild traumatic brain injury in primary care with a guideline implementation tool: a pilot cluster randomised trial. BMJ Open. 2020 Oct 20;10(10).
  32. 32. American Psychiatric Association. DSM-5 Diagnostic Classification. In: Diagnostic and Statistical Manual of Mental Disorders. 2013.
  33. 33. Gros DF, Simms LJ, Acierno R. Specificity of posttraumatic stress disorder symptoms: An investigation of comorbidity between posttraumatic stress disorder symptoms and depression in treatment-seeking veterans. J Nerv Ment Dis. 2010;198(12):885–90. pmid:21135640
  34. 34. Elhai JD, de Francisco Carvalho L, Miguel FK, Palmieri PA, Primi R, Christopher Frueh B. Testing whether posttraumatic stress disorder and major depressive disorder are similar or unique constructs. J Anxiety Disord. 2011;25(3):404–10. pmid:21129914
  35. 35. Post LM, Zoellner LA, Youngstrom E, Feeny NC. Understanding the relationship between co-occurring PTSD and MDD: Symptom severity and affect. J Anxiety Disord. 2011;25(8):1123–30. pmid:21899984
  36. 36. Borsboom D, Cramer AOJ. Network analysis: An integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013;9:91–121. pmid:23537483
  37. 37. Borsboom D. A network theory of mental disorders. World Psychiatry. 2017;16(1):5–13. pmid:28127906
  38. 38. Borsboom D, Epskamp S, Kievit RA, Cramer AOJ, Schmittmann VD. Transdiagnostic networks: Commentary on Nolen-Hoeksema and Watkins. Perspect Psychol Sci. 2011;6(6).
  39. 39. Bringmann LF, Elmer T, Epskamp S, Krause RW, Schoch D, Wichers M, et al. What Do Centrality Measures Measure in Psychological Networks? J Abnorm Psychol. 2019;128(8):892–903. pmid:31318245
  40. 40. Jones PJ, Ma R, McNally RJ. Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivariate Behav Res. 2019;353–67. pmid:31179765
  41. 41. 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. pmid:27936411
  42. 42. Phillips RD, Wilson SM, Sun D, Morey R. Posttraumatic Stress Disorder Symptom Network Analysis in U.S. Military Veterans: Examining the Impact of Combat Exposure. Front Psychiatry. 2018;9:608. pmid:30519198
  43. 43. Fried EI, Epskamp S, Nesse RM, Tuerlinckx F, Borsboom D. What are “good” depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. J Affect Disord. 2016;189:314–20. pmid:26458184
  44. 44. 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. 2020;38(5):554–62. pmid:33190348
  45. 45. Choi KW, Batchelder AW, Ehlinger PP, Safren SA, O’Cleirigh C. Applying Network Analysis to Psychological Comorbidity and Health Behavior: Depression, PTSD, and Sexual Risk in Sexual Minority Men with Trauma Histories. J Consult Clin Psychol. 2017;85(12):1157–70.
  46. 46. Lazarov A, Suarez-Jimenez B, Levy O, Coppersmith DDL, Lubin G, Pine DS, et al. Symptom structure of PTSD and co-morbid depressive symptoms—A network analysis of combat veteran patients. Psychol Med. 2019;50(13):2154–70. pmid:31451119
  47. 47. Afzali MH, Sunderland M, Teesson M, Carragher N, Mills K, 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. pmid:27810269
  48. 48. Pittman JOE, Floto E, Lindamer L, Baker DG, Lohr JB, Afari N. VA escreening program: Technology to improve care for post-9/11 veterans. Psychol Serv. 2017;14(1):23–33. pmid:28134554
  49. 49. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16:606–13.
  50. 50. Carroll HA, Hook K, Perez OFR, Denckla C, Vince CC, Ghebrehiwet S, et al. Establishing reliability and validity for mental health screening instruments in resource-constrained settings: Systematic review of the PHQ-9 and key recommendations. Psychiatry Res. 2020 Sep 1;291:113236. pmid:32593853
  51. 51. Conybeare D, Behar E, Solomon A, Newman MG, Borkovec TD. The PTSD Checklist-Civilian Version: Reliability, Validity, and Factor Structure in a Nonclinical Sample. J Clin Psychol. 2012;68(6):699–713. pmid:22517497
  52. 52. Schwab KA, Ivins B, Cramer G, Johnson W, Sluss-Tiller M, Kiley K, et al. Screening for traumatic brain injury in troops returning from deployment in Afghanistan and Iraq: Initial investigation of the usefulness of a short screening tool for traumatic brain injury. J Head Trauma Rehabil. 2007;22(6):377–89. pmid:18025970
  53. 53. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med. 2006;166(10):1092–7. pmid:16717171
  54. 54. Morin CM, Belleville G, Bélanger L, Ivers H. The insomnia severity index: Psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34(5):601–8. pmid:21532953
  55. 55. Connor KM, Davidson JRT. Development of a new Resilience scale: The Connor-Davidson Resilience scale (CD-RISC). Depress Anxiety. 2003;18(2):76–82. pmid:12964174
  56. 56. Reeve BB, Hays RD, Bjorner JB, Cook KF, Crane PK, Teresi JA, et al. Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Med Care. 2007;45(5 Suppl 1):S22–31. pmid:17443115
  57. 57. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D. Qgraph: Network visualizations of relationships in psychometric data. J Stat Softw. 2012;48(4):1–18.
  58. 58. Foygel R, Drton M. Extended Bayesian Information Criteria for Gaussian Graphical Models. In: Advances in Neural Information Processing Systems 23. 2010.
  59. 59. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50:195–212. pmid:28342071
  60. 60. Fried EI, Eidhof MB, Palic S, Costantini G, Huisman-van Dijk HM, Bockting CLH, et al. Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples. Clin Psychol Sci. 2018;6(3):335–51. pmid:29881651
  61. 61. Robinaugh DJ, Millner AJ, McNally RJ. Identifying highly influential nodes in the complicated grief network. J Abnorm Psychol. 2016;125(6):747–57. pmid:27505622
  62. 62. van Borkulo CD, Boschloo L, Kossakowski JJ, Tio P, Schoevers RA, Borsboom D, et al. Comparing network structures on three aspects: A permutation test. Psychol Methods. 2022;Advance on. pmid:35404628
  63. 63. Bombardier CH, Fann JR, Temkin NR, Esselman PC, Barber J, Dikmen SS. Rates of major depressive disorder and clinical outcomes following traumatic brain injury. JAMA. 2010;303(19):1938–45. pmid:20483970
  64. 64. Pittman JOE, Afari N, Floto E, Almklov E, Conner S, Rabin B, et al. Implementing eScreening technology in four VA clinics: A mixed-method study. BMC Health Serv Res. 2019;19(1):604. pmid:31462280
  65. 65. Birkeland MS, Greene T, Spiller TR. The network approach to posttraumatic stress disorder: a systematic review. Eur J Psychotraumatol. 2020;11(1):1700614. pmid:32002135
  66. 66. Twamley EW, Thomas KR, Gregory AM, Jak AJ, Bondi MW, Delis DC, et al. CogSMART compensatory cognitive training for traumatic brain injury: Effects over 1 year. J Head Trauma Rehabil. 2015;30(6):391–401. pmid:25033034
  67. 67. Twamley EW, Jak AJ, Delis DC, Bondi MW, Lohr JB. Cognitive symptom management and rehabilitation therapy (CogSMART) for veterans with traumatic brain injury: Pilot randomized controlled trial. J Rehabil Res Dev. 2014;51(1):59–70. pmid:24805894
  68. 68. 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.
  69. 69. Flory JD, Yehuda R. Comorbidity between post-traumatic stress disorder and major depressive disorder: Alternative explanations and treatment considerations. Dialogues Clin Neurosci. 2015;17(2):141–150. pmid:26246789
  70. 70. Gehrman P, Seelig AD, Jacobson IG, Boyko EJ, Hooper TI, Gackstetter GD, et al. Predeployment sleep duration and insomnia symptoms as risk factors for new-onset mental health disorders following military deployment. Sleep. 2013;36(7):1009–18. pmid:23814337
  71. 71. Wright KM, Britt TW, Bliese PD, Adler AB, Picchioni D, Moore D. Insomnia as predictor versus outcome of PTSD and depression among Iraq combat veterans. J Clin Psychol. 2011;67(12):1240–58. pmid:22065464
  72. 72. Ryden AM, Martin JL, Matsuwaka S, Fung CH, Dzierzewski JM, Song Y, et al. Insomnia disorder among older veterans: Results of a postal survey. J Clin Sleep Med. 2019;15(4):543–51. pmid:30952212
  73. 73. Schutte-Rodin SL, Broch L, Buysee D, Dorsey C, Sateia M. Clinical guideline for the evaluation and management of chronic insomnia in adults. J Clin Sleep Med. 2008;4(5):487–504. pmid:18853708
  74. 74. Trockel M, Karlin BE, Taylor CB, Manber R. Cognitive Behavioral Therapy for insomnia with veterans: Evaluation of effectiveness and correlates of treatment outcomes. Behav Res Ther. 2014;53:41–6. pmid:24412462
  75. 75. Karlin BE, Trockel M, Taylor CB, Gimeno J, Manber R. National dissemination of cognitive behavioral therapy for insomnia in veterans: Therapist-and patient-level outcomes. J Consult Clin Psychol. 2013;81(5):912–7. pmid:23586730
  76. 76. Smid GE, Lensvelt-Mulders GJLM, Knipscheer JW, Gersons BPR, Kleber RJ. Late-onset PTSD in unaccompanied refugee minors: Exploring the predictive utility of depression and anxiety symptoms. J Clin Child Adolesc Psychol. 2011;40(5):742–55. pmid:21916692
  77. 77. Ciarleglio MM, Aslan M, Proctor SP, Concato J, Ko J, Kaiser AP, et al. Associations of Stress Exposures and Social Support With Long-Term Mental Health Outcomes Among U.S. Iraq War Veterans. Behav Ther. 2018;49(5):653–67. pmid:30146134
  78. 78. Graziano R, Elbogen EB. Improving mental health treatment utilization in military veterans: Examining the effects of perceived need for care and social support. Mil Psychol. 2017;29(5):359–69. pmid:29335663
  79. 79. Laird KT, Lavretsky H, Paholpak P, Vlasova RM, Roman M, St Cyr N, et al. Clinical correlates of resilience factors in geriatric depression. Int Psychogeriatrics. 2019;31(2):193–202. pmid:29335041
  80. 80. Caroli ME De, Sagone E. Generalized Self-efficacy and Well-being in Adolescents with High vs. Low Scholastic Self-efficacy. Procedia—Soc Behav Sci. 2014;141:867–74.
  81. 81. Van Schrojenstein Lantman M, Mackus M, Otten LS, De Kruijff D, van de Loo AJAE, Kraneveld AD, et al. Mental resilience, perceived immune functioning, and health. J Multidiscip Healthc. 2017;10:107–112. pmid:28356753
  82. 82. Southwick SM, Charney DS. The science of resilience: Implications for the prevention and treatment of depression. Science (80-). 2012;338(6103):79–82. pmid:23042887
  83. 83. Southwick SM, Litz BT, Charney D, Friedman MJ. Resilience and mental health: Challenges across the lifespan. Resilience and Mental Health: Challenges Across the Lifespan. Cambridge University Press; 2011.
  84. 84. Krejci L. P., Carter K., Gaudet T. Whole health: the vision and implementation of personalized, proactive, patient-driven health care for veterans. Med Care. 2014;52(12 Suppl 5):S5–8. pmid:25397823
  85. 85. Gaudet T. Transforming the veterans health administration system: Personalized, proactive, and patient-centered care. Altern Complement Ther. 2014;20(1):11–5.
  86. 86. Fried EI, Cramer AOJ. Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology. Perspect Psychol Sci. 2017;12(6):999–1020. pmid:28873325
  87. 87. Vazan P, Golub A, Bennett AS. Substance Use and Other Mental Health Disorders Among Veterans Returning to the Inner City: Prevalence, Correlates, and Rates of Unmet Treatment Need. Subst Use Misuse. 2013 Jul;48(10):880–93. pmid:23869460
  88. 88. Bohnert KM, Ilgen MA, Rosen CS, Desai RA, Austin K, Blow FC. The association between substance use disorders and mortality among a cohort of Veterans with posttraumatic stress disorder: Variation by age cohort and mortality type. Drug Alcohol Depend. 2013 Feb 1;128(1–2):98–103. pmid:22974491
  89. 89. Badour CL, Blonigen DM, Boden MT, Feldner MT, Bonn-Miller MO. A longitudinal test of the bi-directional relations between avoidance coping and PTSD severity during and after PTSD treatment. Behav Res Ther. 2012;50(10):610–6. pmid:22835842
  90. 90. Possemato K, McKenzie S, McDevitt-Murphy ME, Williams JL, Ouimette P. The relationship between postdeployment factors and PTSD severity in recent combat veterans. Mil Psychol. 2014;26(1):15–22. pmid:25892847
  91. 91. Zhou J, Feng L, Hu C, Pao C, Xiao L, Wang G. Associations among depressive symptoms, childhood abuse, neuroticism, social support, and coping style in the population covering general adults, depressed patients, bipolar disorder patients, and high risk population for depression. Front Psychol. 2019;10:1321. pmid:31231288