This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.

Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1–3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3–4 months.

Models 1–7 all outperformed the null model and model 8. Model performance was very similar across models 1–6, meaning that differential weights applied to the baseline sum scores had little impact.

Any of the modelling techniques (models 1–7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

It remains unknown whether peritraumatic reactions predict PTSD symptoms in younger populations. To prospectively investigated the power of self-reported peritraumatic distress and dissociation to predict the development of PTSD symptoms at 1-month in school-aged children.

A sample of 103 school-aged children (8-15 years old) admitted to an Emergency Department after a road traffic accident were consecutively enrolled. Peritraumatic distress was assessed using the Peritraumatic Distress Inventory (range 0-52) and peritraumatic dissociation was assessed using the Peritraumatic Dissociative Experiences Questionnaire (PDEQ) (range 10-50). PTSD symptoms were measured at 1-month by both the child version of the clinician-administered PTSD Scale (CAPS-CA) (range: 0-136) and the Child Post-traumatic Stress Reaction Index (CPTS-RI) (range 0-80).

Mean(SD) participants’ age was 11.7(2.2) and 53.4% (n=55) of them were of male gender. At baseline, mean PDI and PDEQ scores were 21.4 (SD=7.8) and 19.2 (SD=10.2), respectively. At 1-month, mean self-reported (CPTS-RI) and interviewer-based (CAPS-CA) PTSD symptom scores were 23.2 (SD=12.1) and 19 (SD=16.9), respectively. According to the CAPS-CA, 5 children (4.9%) suffered from full PTSD. Bivariate analyses demonstrated a significant association between peritraumatic variables (PDI and PDEQ) and both CAPS-CA and CPTS-RI (r=0.22-0.57; all p< 0.05). However, in a multivariate analysis, PDI was the only significant predictor of acute PTSD symptoms (Beta=0.33, p< 0.05).

As has been found in adults, peritraumatic distress is a robust predictor of who will develop PTSD symptoms among school-aged children.

Despite therapeutic advances, we still have difficulties in predicting response to treatment in bipolar disorder (BD). Brain-Derived Neurotrophic Factor (BDNF) has been put forward as a potential peripheral marker of treatment response.

To prospectively study the relation between clinical response to treatment and serum BDNF levels.

To investigate a) a possible association between serum BDNF levels and clinical response along 16 week follow-up and b) the role of val66met polymorphism in clinical response in a sample of drug-free patients with BD going through a mood episode.

This is a naturalistic, open-label prospective nested case control study matched for age, gender and ethnicity. Patients were 18 years or older, required BD diagnosis, undergoing a current manic, mixed or depressive episode and be off-medication for at least 2 weeks. Clinical assessment and blood withdrawn were conducted along follow-up. At the end of the study, patients were classified according to clinical response.

25 of 36 (69.4%) of the patients were female and the mean age was 37.8 (SD 11.8) years old. Baseline serum BDNF levels did not show any difference between patients and controls (p = 0.075).There was a significant negative correlation between differences in serum BDNF levels and in CGI score along follow-up (r = −.372, p = 0.028). Serum BDNF levels were significant higher in responders compared to non-responders at week 4, 8 and 16 (p = 0.026, p = 0.009, p = 0.001 respectively). Val66met polymorphism did not seem to interfere in clinical response.

Changes in serum BDNF levels may help in monitoring treatment response.

Studies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the specific inflammatory markers studied and covariates accounted for. Second, specific depressive symptoms may be differentially related to inflammation. We address both challenges using network psychometrics.

We estimated seven regularized Mixed Graphical Models in the Netherlands Study of Depression and Anxiety (NESDA) data (N = 2321) to explore shared variances among (1) depression severity, modeled via depression sum-score, nine DSM-5 symptoms, or 28 individual depressive symptoms; (2) inflammatory markers C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α); (3) before and after adjusting for sex, age, body mass index (BMI), exercise, smoking, alcohol, and chronic diseases.

The depression sum-score was related to both IL-6 and CRP before, and only to IL-6 after covariate adjustment. When modeling the DSM-5 symptoms and CRP in a conceptual replication of Jokela et al., CRP was associated with ‘sleep problems’, ‘energy level’, and ‘weight/appetite changes’; only the first two links survived covariate adjustment. In a conservative model with all 38 variables, symptoms and markers were unrelated. Following recent psychometric work, we re-estimated the full model without regularization: the depressive symptoms ‘insomnia’, ‘hypersomnia’, and ‘aches and pain’ showed unique positive relations to all inflammatory markers.

We found evidence for differential relations between markers, depressive symptoms, and covariates. Associations between symptoms and markers were attenuated after covariate adjustment; BMI and sex consistently showed strong relations with inflammatory markers.

Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node – its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality.

We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes.

Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis.

Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.

Researchers have studied psychological disorders extensively from a common cause perspective, in which symptoms are treated as independent indicators of an underlying disease. In contrast, the causal systems perspective seeks to understand the importance of individual symptoms and symptom-to-symptom relationships. In the current study, we used network analysis to examine the relationships between and among depression and anxiety symptoms from the causal systems perspective.

We utilized data from a large psychiatric sample at admission and discharge from a partial hospital program (N = 1029, mean treatment duration = 8 days). We investigated features of the depression/anxiety network including topology, network centrality, stability of the network at admission and discharge, as well as change in the network over the course of treatment.

Individual symptoms of depression and anxiety were more related to other symptoms within each disorder than to symptoms between disorders. Sad mood and worry were among the most central symptoms in the network. The network structure was stable both at admission and between admission and discharge, although the overall strength of symptom relationships increased as symptom severity decreased over the course of treatment.

Examining depression and anxiety symptoms as dynamic systems may provide novel insights into the maintenance of these mental health problems.

For diagnostic purposes, the nine symptoms that compose the DSM-5 criteria for major depressive disorder (MDD) are assumed to be interchangeable indicators of one underlying disorder, implying that they should all have similar risk factors. The present study investigates this hypothesis, using a population cohort that shifts from low to elevated depression levels.

We assessed the nine DSM-5 MDD criterion symptoms (using the Patient Health Questionnaire; PHQ-9) and seven depression risk factors (personal and family MDD history, sex, childhood stress, neuroticism, work hours, and stressful life events) in a longitudinal study of medical interns prior to and throughout internship (n = 1289). We tested whether risk factors varied across symptoms, and whether a latent disease model could account for heterogeneity between symptoms.

All MDD symptoms increased significantly during residency training. Four risk factors predicted increases in unique subsets of PHQ-9 symptoms over time (depression history, childhood stress, sex, and stressful life events), whereas neuroticism and work hours predicted increases in all symptoms, albeit to varying magnitudes. MDD family history did not predict increases in any symptom. The strong heterogeneity of associations persisted after controlling for a latent depression factor.

The influence of risk factors varies substantially across DSM depression criterion symptoms. As symptoms are etiologically heterogeneous, considering individual symptoms in addition to depression diagnosis might offer important insights obfuscated by symptom sum scores.