Coping and grief symptoms after parental loss in adolescence - a nationwide follow-up study.

Knowledge about effective coping strategies is important to support bereaved adolescents who have lost a parent. We used data on 104 bereaved adolescents (13-17 years) from the FALCON nationwide questionnaire study of parentally bereaved families. We examined associations between adolescents' control-oriented and escape-oriented coping strategies (KidCOPE scale) approximately two months after loss and grief symptoms (PG-13 scale) at six months follow-up. We also examined associations between the perceived efficacy of each coping strategy at baseline and grief symptoms at six months. The use of escape-oriented coping (e.g., distraction, self-criticism and social withdrawal) was associated with higher grief symptoms at six months follow-up (ß = 0.4, 95% CI 0.1-0.9, p = 0.02), but no associations were found for control-oriented coping or perceived coping efficacy. Finding alternatives to the use of escape-oriented coping strategies may be an important part of grief interventions for bereaved adolescents.

Client preferences, therapy activities and preference-activity match as predictors of therapy outcome.

OBJECTIVE: This study investigated whether distinct types of psychotherapy activities, the client's preference towards these activities prior to therapy, and the degree of match between client preferences and therapy activities, served as predictors of treatment outcomes. METHODS: A total of 621 clients (Mage = 42 years, 71.7% female) received individual psychotherapy by 54 psychologists. Associations between activity preferences, therapy activities, and preference-activity match as predictors, and symptom change and treatment dropout as outcomes were analyzed using multilevel longitudinal and logistic modelling and polynomial regression models with response surface analysis. RESULTS: No type of therapy activity or activity preference significantly predicted symptom change in therapy, while higher levels of inward orientation therapy activities predicted an increased risk of dropout. Moreover, matching and higher levels of inward orientation and affect expression activities predicted an increased risk of dropout, and matching and higher levels of outward orientation activities predicted a decreased risk of dropout. Finally, a preference-activity mismatch in affect suppression predicted an increased risk of dropout from therapy, both at higher and lower levels of affect suppression. CONCLUSION: Distinct types of therapy activity preferences may, especially when (mis)matched with similar levels of the same therapy activities, differentially predict particular dropout from therapy.Trial registration: ClinicalTrials.gov identifier: NCT05630560.

Prediction models of suicide and non-fatal suicide attempt after discharge from a psychiatric inpatient stay: A machine learning approach on nationwide Danish registers.

INTRODUCTION: To develop machine learning models capable of predicting suicide and non-fatal suicide attempt as separate outcomes in the first 30 days after discharge from a psychiatric inpatient stay. METHODS: Prospective cohort study using nationwide Danish registry data. We included individuals who were 18 years or older, and all discharges from psychiatric hospitalizations in Denmark from 1995 to 2018. We trained predictive models using 10-fold cross validation on 80% of the data and did testing on the remaining 20%. RESULTS: The best model for predicting non-fatal suicide attempt was an ensemble of predictions from gradient boosting (XGBoost) and categorical boosting (catBoost). The ROC-AUC for predicting suicide attempt was 0.85 (95% CI: 0.84-0.85). At a risk threshold of 4.36%, positive predictive value (PPV) was 11.0% and sensitivity was 47.2%. The best model for predicting suicide was an ensemble of predictions from random forest, XGBoost and catBoost. For suicide, the ROC-AUC was 0.71 (95% CI: 0.70-0.73). At a risk threshold of 0.15%, PPV was 0.34% and sensitivity was 56.0%. The most contributing predictors differed when predicting suicide and suicide attempt, indicating that separate models are needed. The ensemble model was fair across sex and age, and more so than the penalized logistic regression model. CONCLUSIONS: We achieved good performance for predicting suicide attempts and demonstrated a clinical application of ensemble models. Our results indicate a difference in predictive performance for models predicting suicide and suicide attempt, respectively. Thus, we recommend that suicide and suicide attempt are treated as two separate endpoints, in particular for clinical application. We demonstrated that the ensemble model is fairer across sex and age compared with a penalized logistic regression, and therefore we recommend the use of well-tested ensembles despite a more complex explainability.

What we talk about when we talk about trauma: Content overlap and heterogeneity in the assessment of trauma exposure.

The accurate definition and assessment of trauma exposure is the foundation for replicable studies of mental health problems following trauma exposure. However, scales developed to assess trauma exposure might vary widely in terms of item content; overlap; and specifications of trauma intensity, frequency, duration, and timing. We compared eight frequently used self-report measures of trauma exposure to address content overlap and measurement heterogeneity. Combined, these measures assess 44 disparate exposures. Mean overlap across scales was moderate (M = 0.41, range: 0.25-0.48 across scales). Pairwise overlap between scales ranged from .19 to .59. We found 18 exposures (40.9%) that were included in one scale and three exposures (6.8%) that were included in all eight scales. Four of the included scales assess trauma frequency, five assess intensity or perceived danger, two assess duration, and four assess timing. The implications of measurement heterogeneity for clinical research as well as for comparability and replication of trauma-related research are discussed.

Changes in perceived social support and PTSD symptomatology among Danish army military personnel.

PURPOSE: Previous research has identified social support to be associated with risk of posttraumatic stress disorder (PTSD) symptoms among military personnel. While the lack of social support influences PTSD symptomatology, it is unknown how changes in perceived social support affect the PTSD symptom level in the aftermath of deployment. Furthermore, the influence of specific sources of social support from pre- to post-deployment on level of PTSD symptoms is unknown. We aim to examine how changes in perceived social support (overall and from specific sources) from pre- to 2.5 year post-deployment are associated with the level of post-deployment PTSD symptoms. METHODS: Danish army military personnel deployed to Afghanistan in 2009 and 2013 completed questionnaires at pre-deployment and at 2.5 year post-deployment measuring perceived social support and PTSD symptomatology and sample characteristics of the two cohorts. Data were analyzed using univariate and multivariate nominal logistic regression. RESULTS: Negative changes in perceived social support from pre- to post-deployment were associated with both moderate (OR 1.99, CI 1.51-2.57) and high levels (OR 2.71, CI 1.94-3.78) of PTSD symptoms 2.5 year post-deployment (adjusted analysis). Broadly, the same direction was found for specific sources of social support and level of PTSD symptoms. In the adjusted analyses, pre-deployment perceived social support and military rank moderated the associations. CONCLUSIONS: Deterioration in perceived social support (overall and specific sources) from pre- to 2.5 year post-deployment increases the risk of an elevated level of PTSD symptoms 2.5 year post-deployment.

Measuring Social Support among Soldiers with the Experienced Post-Deployment Social Support Scale (EPSSS): A Rasch-Based Construct Validity Study.

Perceived social support following deployment is a known buffer against post-deployment adverse mental health outcomes. Given contextual sensitivity of social support measures, scales that address specific social support needs of soldiers in the first months after home coming should be developed and validated. In a sample of 553 soldiers deployed to Afghanistan at two different time points (2009 and 2013, respectively), we selected items for and tested the construct validity of an 8-item measure of experienced post-deployment social support (experienced post-deployment social support scale; EPSSS). Within the item response theory framework, we used Rasch models (RM) to conduct item analysis with an emphasis on testing for differential item functioning (DIF) across background variables such as previous deployments and cohort. In short, we found that the scale did not fit the Rasch model, but with exclusion of two items, a 6-item version of the scale did fit an extended graphical loglinear Rasch model (GLLRM) with only one instance of DIF, for which the score can be adjusted. We also demonstrated that when applied as a scale, the DIF will not affect the results substantially. Hence, we conclude that the constructed 6-item EPSSS can be validly applied without score correction to assess the level of social support in Danish soldiers after home coming.

Low-level cognitive ability in young adulthood and other risk factors of depression in an observational cohort study among deployed Danish soldiers.

PURPOSE: Evidence exists of an association between pre-morbid lower cognitive ability and higher risk of hospitalization for depressive disorder in civilian cohorts. The purpose of this study was to examine the relationship of cognitive ability at conscription with post-deployment depression and the influence of (1) baseline factors: age, gender, and pre-deployment educational level, (2) deployment-related factors: e.g., war-zone stress and social support, and (3) co-morbid PTSD. METHODS: An observational cohort study linking conscription board registry data with post-deployment self-report data. The study population consisted of Danish Army military personnel deployed to different war zones from 1997 to 2015. The association between cognitive ability at conscription and post-deployment depression was analyzed using repeated-measure logistic regression models. RESULTS: Study population totaled 9716 with a total of 13,371 deployments. Low-level cognitive ability at conscription was found to be weakly associated with post-deployment probable depression after adjustment for more important risk factors like gender, education, and deployment-related factors [odds ratio (OR) 0.93, 95% confidence interval (CI) 0.88-0.99]. The co-occurrence rate with PTSD was nearly 60%. When adding co-morbid PTSD as an independent variable, the association between cognitive ability and probable depression became insignificant, OR 0.95, CI 0.89-1.02. CONCLUSIONS: Low cognitive ability at conscription is a risk factor for depression among returning military personnel, but unimportant compared to gender, education, and deployment-related factors. Part of this effect may be related to co-morbid PTSD. Use of cognitive ability score as an isolated selection tool cannot be recommended because of low predictive performance.

Predictors of PTSD 40 years after combat: Findings from the National Vietnam Veterans longitudinal study.

BACKGROUND: Few studies have longitudinally examined predictors of posttraumatic stress disorder (PTSD) in a nationally representative sample of US veterans. We examined predictors of warzone-related PTSD over a 25-year span using data from the National Vietnam Veterans Longitudinal Study (NVVLS). METHODS: The NVVLS is a follow-up study of Vietnam theater veterans (N = 699) previously assessed in the National Vietnam Veterans Readjustment Study (NVVRS), a large national-probability study conducted in the late 1980s. We examined the ability of 22 premilitary, warzone, and postmilitary variables to predict current warzone-related PTSD symptom severity and PTSD symptom change in male theater veterans participating in the NVVLS. Data included a self-report Health Questionnaire survey and a computer-assisted telephone Health Interview Survey. Primary outcomes were self-reported PTSD symptoms assessed by the PTSD Checklist for DSM-5 (PCL 5) and Mississippi PTSD Scale (M-PTSD). RESULTS: Predictors of current PTSD symptoms most robust in hierarchical multivariable models were African-American race, lower education level, negative homecoming reception, lower current social support, and greater past-year stress. PTSD symptoms remained largely stable over time, and symptom exacerbation was predicted by African-American race, lower education level, younger age at entry into Vietnam, greater combat exposure, lower current social support, and greater past-year stressors. CONCLUSIONS: Findings confirm the robustness of a select set of risk factors for warzone-related PTSD, establishing that these factors can predict PTSD symptom severity and symptom change up to 40 years postdeployment.

Assessing PTSD in the military: Validation of a scale distributed to Danish soldiers after deployment since 1998.

Since 1998, soldiers deployed to war zones with the Danish Defense (≈31,000) have been invited to fill out a questionnaire on post-mission reactions. This provides a unique data source for studying the psychological toll of war. Here, we validate a measure of PTSD-symptoms from the questionnaire. Soldiers from two cohorts deployed to Afghanistan with the International Security Assistance Force (ISAF) in 2009 (ISAF7, N = 334) and 2013 (ISAF15, N = 278) filled out a standard questionnaire (Psychological Reactions following International Missions, PRIM) concerning a range of post-deployment reactions including symptoms of PTSD (PRIM-PTSD). They also filled out a validated measure of PTSD-symptoms in DSM-IV, the PTSD-checklist (PCL). We tested reliability of PRIM-PTSD by estimating Cronbach's alpha, and tested validity by correlating items, clusters, and overall scale with corresponding items in the PCL. Furthermore, we conducted two confirmatory factor analytic models to test the factor structure of PRIM-PTSD, and tested measurement invariance of the selected model. Finally, we established a screening and a clinical cutoff score by application of ROC analysis. We found high internal consistency of the PRIM-PTSD (Cronbach's alpha = 0.88; both cohorts), strong item-item (0.48-0.83), item-cluster (0.43-0.72), cluster-cluster (0.71-0.82) and full-scale (0.86-0.88) correlations between PRIM-PTSD and PCL. The factor analyses showed adequate fit of a one-factor model, which was also found to display strong measurement invariance across cohorts. ROC curve analysis established cutoff scores for screening (sensitivity = 1, specificity = 0.93) and clinical use (sensitivity = 0.71, specificity = 0.98). In conclusion, we find that PRIM-PTSD is a valid measure for assessing PTSD-symptoms in Danish soldiers following deployment.

Trajectories of Suicidal Ideation in People Seeking Web-Based Help for Suicidality: Secondary Analysis of a Dutch Randomized Controlled Trial.

BACKGROUND: Suicidal ideation (SI) is a common mental health problem. Variability in intensity of SI over time has been linked to suicidal behavior, yet little is known about the temporal course of SI. OBJECTIVE: The primary aim was to identify prototypical trajectories of SI in the general population and, secondarily, to examine whether receiving Web-based self-help for SI, psychiatric symptoms, or sociodemographics predicted membership in the identified SI trajectories. METHODS: We enrolled 236 people, from the general Dutch population seeking Web-based help for SI, in a randomized controlled trial comparing a Web-based self-help for SI group with a control group. We assessed participants at inclusion and at 2, 4, and 6 weeks. The Beck Scale for Suicide Ideation was applied at all assessments and was included in latent growth mixture modeling analysis to empirically identify trajectories. RESULTS: We identified 4 SI trajectories. The high stable trajectory represented 51.7% (122/236) of participants and was characterized by constant high level of SI. The high decreasing trajectory (50/236, 21.2%) consisted of people with a high baseline SI score followed by a gradual decrease to a very low score. The third trajectory, high increasing (12/236, 5.1%), also had high initial SI score, followed by an increase to the highest level of SI at 6 weeks. The fourth trajectory, low stable (52/236, 22.0%) had a constant low level of SI. Previous attempted suicide and having received Web-based self-help for SI predicted membership in the high decreasing trajectory. CONCLUSIONS: Many adults experience high persisting levels of SI, though results encouragingly indicate that receiving Web-based self-help for SI increased membership in a decreasing trajectory of SI.

Bridging a translational gap: using machine learning to improve the prediction of PTSD.

BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivors. Identifying interchangeable sets of risk indicators may increase the efficiency of early risk assessment. The study goal is to use supervised machine learning (ML) to uncover interchangeable, maximally predictive combinations of early risk indicators. METHODS: Data variables (features) reflecting event characteristics, emergency department (ED) records and early symptoms were collected in 957 trauma survivors within ten days of ED admission, and used to predict PTSD symptom trajectories during the following fifteen months. A Target Information Equivalence Algorithm (TIE*) identified all minimal sets of features (Markov Boundaries; MBs) that maximized the prediction of a non-remitting PTSD symptom trajectory when integrated in a support vector machine (SVM). The predictive accuracy of each set of predictors was evaluated in a repeated 10-fold cross-validation and expressed as average area under the Receiver Operating Characteristics curve (AUC) for all validation trials. RESULTS: The average number of MBs per cross validation was 800. MBs' mean AUC was 0.75 (95% range: 0.67-0.80). The average number of features per MB was 18 (range: 12-32) with 13 features present in over 75% of the sets. CONCLUSIONS: Our findings support the hypothesized existence of multiple and interchangeable sets of risk indicators that equally and exhaustively predict non-remitting PTSD. ML's ability to increase prediction versatility is a promising step towards developing algorithmic, knowledge-based, personalized prediction of post-traumatic psychopathology.

Community integration after deployment to Afghanistan: a longitudinal investigation of Danish soldiers.

OBJECTIVE: In the years following military deployment, soldiers may experience problems integrating into the community. However, little is known about the nature and prevalence of these problems and if they relate to posttraumatic symptomatology. METHODS: In a prospective, longitudinal study of Danish soldiers deployed to Afghanistan in 2009 (N = 743), we assessed community reintegration difficulties 2.5 years after home coming (study sample: N = 454). Furthermore, symptoms of posttraumatic stress disorder (PTSD) were assessed before, during, and after deployment. Trajectories of PTSD symptoms from a previously published latent growth mixture modeling analysis were used to address whether community reintegration difficulties differ as a result of course and level of PTSD symptoms. RESULTS: Between 3.6 and 18.0% reported to have some, a lot, or extreme difficulties in reintegration domains such as interpersonal functioning, productivity, community involvement, and self-care. Mean level of reintegration difficulties differed significantly across six PTSD symptom trajectories (range 6.35-36.00); with more symptomatic trajectories experiencing greater community reintegration difficulties. CONCLUSIONS: Reintegration difficulties after deployment are present in less than 20% of Danish soldiers who return from Afghanistan. Difficulties are greater in individuals who follow symptomatic PTSD trajectories in the first years following deployment than in those who follow a low-stable trajectory with no or few symptoms.

The co-occurrence of PTSD and dissociation: differentiating severe PTSD from dissociative-PTSD.

PURPOSE: A dissociative-posttraumatic stress disorder (PTSD) subtype has been included in the DSM-5. However, it is not yet clear whether certain socio-demographic characteristics or psychological/clinical constructs such as comorbid psychopathology differentiate between severe PTSD and dissociative-PTSD. The current study investigated the existence of a dissociative-PTSD subtype and explored whether a number of trauma and clinical covariates could differentiate between severe PTSD alone and dissociative-PTSD. METHODS: The current study utilized a sample of 432 treatment seeking Canadian military veterans. Participants were assessed with the Clinician Administered PTSD Scale (CAPS) and self-report measures of traumatic life events, depression, and anxiety. CAPS severity scores were created reflecting the sum of the frequency and intensity items from each of the 17 PTSD and 3 dissociation items. The CAPS severity scores were used as indicators in a latent profile analysis (LPA) to investigate the existence of a dissociative-PTSD subtype. Subsequently, several covariates were added to the model to explore differences between severe PTSD alone and dissociative-PTSD. RESULTS: The LPA identified five classes: one of which constituted a severe PTSD group (30.5 %), and one of which constituted a dissociative-PTSD group (13.7 %). None of the included, demographic, trauma, or clinical covariates were significantly predictive of membership in the dissociative-PTSD group compared to the severe PTSD group. CONCLUSIONS: In conclusion, a significant proportion of individuals report high levels of dissociation alongside their PTSD, which constitutes a dissociative-PTSD subtype. Further investigation is needed to identify which factors may increase or decrease the likelihood of membership in a dissociative-PTSD subtype group compared to a severe PTSD only group.

Stressful life events exhibit complex patterns of associations with depressive symptoms in two population-based samples using network analysis.

BACKGROUND: Stressful life events (SLEs) constitute key risk factors for depression. However, previous studies examining associations between SLEs and depression have been limited by focusing on single events, combining events into broad categories, and/or ignoring interrelationships between events in statistical analyses. Network analysis comprises a set of statistical methods well-suited for assessing relationships between multiple variables and can help surpass several limitations of previous studies. METHODS: We applied network analysis using mixed graphical models combining two large-scale population-based samples and >34,600 randomly sampled adults to investigate the associations between SLEs and current depressive symptoms in the general population. RESULTS: Numerous SLEs were uniquely associated with specific symptoms. Strong pairwise links were observed between SLEs during the past year and individual symptoms, e.g., between having experienced illness or injury and sleeping problems, having been degraded or humiliated and feeling blue, and between financial problems and hopelessness and being worried and anxious. Several SLEs, such as financial problems, sexual abuse, and having been degraded or humiliated, were associated with symptoms across more than one timepoint. More recent SLEs were generally more strongly associated with depressive symptoms. Several life events were strongly interrelated, such as multiple forms of abuse, and financial problems, unemployment, divorce, and serious illness or injury. LIMITATIONS: Limitations include a retrospective SLE measure, cross-sectional data, a brief self-report measure of depressive symptoms, and possible attrition bias in the sample. CONCLUSIONS: Our findings may have implications for public health efforts seeking to improve population mental health.

Exploring the benefit of synaesthetic colours: testing for "pop-out" in individuals with grapheme-colour synaesthesia.

In grapheme-colour synaesthesia, letters, numbers, and words elicit involuntary colour experiences. Recently, there has been much emphasis on individual differences and possible subcategories of synaesthetes with different underlying mechanisms. In particular, there are claims that for some, synaesthesia occurs prior to attention and awareness of the inducing stimulus. We first characterized our sample using two versions of the "Synaesthetic Congruency Task" to distinguish "projector" and "associator" synaesthetes who may differ in the extent to which their synaesthesia depends on attention and awareness. We then used a novel modification of the "Embedded Figures Task" that included a set-size manipulation to look for evidence of preattentive "pop-out" from synaesthetic colours, at both a group and an individual level. We replicate an advantage for synaesthetes over nonsynaesthetic controls on the Embedded Figures Task in accuracy, but find no support for pop-out of synaesthetic colours. We conclude that grapheme-colour synaesthetes are fundamentally similar in their visual processing to the general population, with the source of their unusual conscious colour experiences occurring late in the cognitive hierarchy.

Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models.

Military personnel deployed to war zones are at increased risk of mental health problems such as posttraumatic stress disorder (PTSD) or depression. Early pre- or post-deployment identification of those at highest risk of such problems is crucial to target intervention to those in need. However, sufficiently accurate models predicting objectively assessed mental health outcomes have not been put forward. In a sample consisting of all Danish military personnel who deployed to war zones for the first (N = 27,594), second (N = 11,083) and third (N = 5,161) time between 1992 and 2013, we apply neural networks to predict psychiatric diagnoses or use of psychotropic medicine in the years following deployment. Models are based on pre-deployment registry data alone or on pre-deployment registry data in combination with post-deployment questionnaire data on deployment experiences or early post-deployment reactions. Further, we identified the most central predictors of importance for the first, second, and third deployment. Models based on pre-deployment registry data alone had lower accuracy (AUCs ranging from 0.61 (third deployment) to 0.67 (first deployment)) than models including pre- and post-deployment data (AUCs ranging from 0.70 (third deployment) to 0.74 (first deployment)). Age at deployment, deployment year and previous physical trauma were important across deployments. Post-deployment predictors varied across deployments but included deployment exposures as well as early post-deployment symptoms. The results suggest that neural network models combining pre- and early post-deployment data can be utilized for screening tools that identify individuals at risk of severe mental health problems in the years following military deployment.

Risk factors, comorbidity and social impairment of ICD-11 PTSD and complex PTSD in Danish treatment-seeking military veterans.

PURPOSE: While a number of studies have investigated risk factors and comorbidities of ICD-11 post-traumatic stress disorder (PTSD) and complex PTSD (CPTSD) in various trauma exposed samples, few studies have been conducted in military samples. Existing studies with military samples have included rather small samples. The aim of the present study was to identify risk factors and comorbidities of ICD-11 PTSD and CPTSD in a large sample of previously deployed, treatment-seeking soldiers and veterans. METHODS: Previously deployed, treatment-seeking Danish soldiers and veterans (N = 599), recruited from the Military Psychology Department of the Danish Defense, completed the International Trauma Questionnaire (ITQ), as well as questionnaires of common mental health difficulties, trauma exposure, functioning and demographics. Multivariate multinomial logistic regression analysis explored differences in self-reported exposure to adversity and health outcomes between those meeting ICD-11 criteria for probable PTSD, CPTSD and no trauma disorder. RESULTS: A total of 13.0% met probable ICD-11 criteria for PTSD and 31.4% for CPTSD. Risk factors for CPTSD (compared to those with no trauma disorder) included exposure to warfare or combat, longer duration since the traumatic event and being single. Those with CPTSD were more likely than those with PTSD or no trauma disorder to endorse symptoms of depression, anxiety, stress, use of psychotropic medication, and suicide attempts. CONCLUSION: CPTSD is a more common and debilitating condition compared to PTSD in treatment-seeking soldiers and veterans. Further research should focus on testing existing and novel interventions for CPTSD in the military.

Trajectories of disability in low back pain.

INTRODUCTION: Low back pain (LBP) is the leading course of years lived with disability. Unfortunately, not much knowledge exists about distinct trajectories of recovery from disability after LBP and their potential psychological predictors. OBJECTIVES: Hence, the aim of the present study was to identify trajectories of functional disability in LBP and their potential baseline psychological predictors. METHODS: A 1-year consecutive cohort (N = 1048) of patients with LBP referred to the Spine Centre if they have not improved satisfactorily from a course of treatment in primary care after 1 to 2 months were assessed by self-report questionnaires at their first visit and at 6- and 12-month follow-up. Data from patients who responded to the Roland Morris Disability Questionnaire at least twice (N = 747) were used to assess trajectories of functional disability by Latent Growth Mixture Modeling. The following measures were used as baseline predictors of the trajectories: Pain Intensity Numerical Rating Scales, Pain Catastrophizing Scale, Tampa Scale for Kinesiophobia, and Hospital Anxiety and Depression Scale. RESULTS: Four distinct trajectories were identified: high-stable (22.0%), high-decreasing (20.4%), medium-stable (29.7%), and low-decreasing (27.9%). Using the low-decreasing trajectory as reference, baseline pain intensity, depressive symptoms, and pain-catastrophizing predicted membership of all 3 symptomatic trajectories. However, using the high-decreasing trajectory as reference, age, baseline pain intensity, and depression were predictors of the high-stable trajectory. CONCLUSION: In particular, the finding of a high-stable trajectory characterized by high levels of baseline psychological distress is of potential clinical importance because psychological distress may be targeted by cognitive behavioral therapeutic approaches.

Resting-state EEG functional connectivity predicts post-traumatic stress disorder subtypes in veterans.

Objective. Post-traumatic stress disorder (PTSD) is highly heterogeneous, and identification of quantifiable biomarkers that could pave the way for targeted treatment remains a challenge. Most previous electroencephalography (EEG) studies on PTSD have been limited to specific handpicked features, and their findings have been highly variable and inconsistent. Therefore, to disentangle the role of promising EEG biomarkers, we developed a machine learning framework to investigate a wide range of commonly used EEG biomarkers in order to identify which features or combinations of features are capable of characterizing PTSD and potential subtypes.Approach. We recorded 5 min of eyes-closed and 5 min of eyes-open resting-state EEG from 202 combat-exposed veterans (53% with probable PTSD and 47% combat-exposed controls). Multiple spectral, temporal, and connectivity features were computed and logistic regression, random forest, and support vector machines with feature selection methods were employed to classify PTSD. To obtain robust results, we performed repeated two-layer cross-validation to test on an entirely unseen test set.Main results. Our classifiers obtained a balanced test accuracy of up to 62.9% for predicting PTSD patients. In addition, we identified two subtypes within PTSD: one where EEG patterns were similar to those of the combat-exposed controls, and another that were characterized by increased global functional connectivity. Our classifier obtained a balanced test accuracy of 79.4% when classifying this PTSD subtype from controls, a clear improvement compared to predicting the whole PTSD group. Interestingly, alpha connectivity in the dorsal and ventral attention network was particularly important for the prediction, and these connections were positively correlated with arousal symptom scores, a central symptom cluster of PTSD.Significance. Taken together, the novel framework presented here demonstrates how unsupervised subtyping can delineate heterogeneity and improve machine learning prediction of PTSD, and may pave the way for better identification of quantifiable biomarkers.