BackgroundMajor depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom... Show moreBackgroundMajor depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis.MethodsThe 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2weeks for a median of 11weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated "DTW distances"). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level.ResultsThe sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories.ConclusionsSymptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care. Show less
Background In recent years, a new framework for analyzing and understanding posttraumatic stress disorder (PTSD) was introduced; the network approach. Up until now, network analysis studies of PTSD... Show moreBackground In recent years, a new framework for analyzing and understanding posttraumatic stress disorder (PTSD) was introduced; the network approach. Up until now, network analysis studies of PTSD were largely conducted on small to medium sample sizes (N < 1,000), which might be a possible cause of variability in main findings. Moreover, only a limited number of network studies investigated comorbidity.Methods In this study, we utilized a large sample to conduct a network analysis of 17 symptoms of PTSD (DSM-IV), and compared it to the result of a second network consisting of symptoms of PTSD and depression (based on Patient Health Questionnaire-9 [PHQ-9]). Our sample consisted of 502,036 treatment-seeking veterans, out of which 158,139 had fully completed the assessment of symptoms of PTSD and a subsample of 32,841 with valid PCL and PHQ-9 that was administered within 14 days or less.Results Analyses found that in the PTSD network, the most central symptoms were feeling distant or cut off from others, followed by feeling very upset when reminded of the event, and repeated disturbing memories or thoughts of the event. In the combined network, we found that concentration difficulties and anhedonia are two of the five most central symptoms.Conclusion Our findings replicate the centrality of intrusion symptoms in PTSD symptoms' network. Taking into account the large sample and high stability of the network structure, we believe our study can answer some of the criticism regarding stability of cross-sectional network structures. Show less
Multiple studies show an association between inflammatory markers and major depressive disorder (MDD). People with chronic low-grade inflammation may be at an increased risk of MDD, often in the... Show moreMultiple studies show an association between inflammatory markers and major depressive disorder (MDD). People with chronic low-grade inflammation may be at an increased risk of MDD, often in the form of sickness behaviors. We hypothesized that inflammation is predictive of the severity and the course of a subset of MDD symptoms, especially symptoms that overlap with sickness behavior, such as anhedonia, anorexia, low concentration, low energy, loss of libido, psychomotor slowness, irritability, and malaise. We tested the association between basal and lipopolysaccharide (LPS)-induced inflammatory markers with individual MDD symptoms (measured using the Inventory of Depressive Symptomatology Self-Report) over a period of up to 9 years using multivariate-adjusted mixed models in 1147-2872 Netherlands Study of Depression and Anxiety (NESDA) participants. At baseline, participants were on average 42.2 years old, 66.5% were women and 53.9% had a current mood or anxiety disorder. We found that basal and LPS-stimulated inflammatory markers were more strongly associated with sickness behavior symptoms at up to 9-year follow-up compared with non-sickness behavior symptoms of depression. However, we also found significant associations with some symptoms that are not typical of sickness behavior (e.g., sympathetic arousal among others). Inflammation was not related to depression as a unified syndrome but rather to the presence and the course of specific MDD symptoms, of which the majority were related to sickness behavior. Anti-inflammatory strategies should be tested in the subgroup of MDD patients who report depressive symptoms related to sickness behavior. Show less
In their recent paper, Forbes et al. (2019; FWMK) evaluate the replicability of network models in two studies. They identify considerable replicability issues, concluding that "current 'state-of... Show moreIn their recent paper, Forbes et al. (2019; FWMK) evaluate the replicability of network models in two studies. They identify considerable replicability issues, concluding that "current 'state-of-the-art' methods in the psychopathology network literature [ horizontal ellipsis ] are not well-suited to analyzing the structure of the relationships between individual symptoms". Such strong claims require strong evidence, which the authors do not provide. FWMK identify low replicability by analyzing point estimates of networks; contrast low replicability with results of two statistical tests that indicate higher replicability, and conclude that these tests are problematic. We make four points. First, statistical tests are superior to the visual inspection of point estimates, because tests take into account sampling variability. Second, FWMK misinterpret the statistical tests in several important ways. Third, FWMK did not follow established recommendations when estimating networks in their first study, underestimating replicability. Fourth, FWMK draw conclusions about methodology, which does not follow from investigations of data, and requires investigations of methodology. Overall, we show that the "poor replicability "observed by FWMK occurs due to sampling variability and use of suboptimal methods. We conclude by discussing important recent simulation work that guides researchers to use models appropriate for their data, such as nonregularized estimation routines. Show less
BackgroundChildhood adversity (CA) is strongly associated with mental health problems. Resilience factors (RFs) reduce mental health problems following CA. Yet, knowledge on the nature of RFs is... Show moreBackgroundChildhood adversity (CA) is strongly associated with mental health problems. Resilience factors (RFs) reduce mental health problems following CA. Yet, knowledge on the nature of RFs is scarce. Therefore, we examined RF mean levels, RF interrelations, RF-distress pathways, and their changes between early (age 14) and later adolescence (age 17).MethodsWe studied 10 empirically supported RFs in adolescents with (CA+; n = 631) and without CA (CA−; n = 499), using network psychometrics.ResultsAll inter-personal RFs (e.g. friendships) showed stable mean levels between age 14 and 17, and three of seven intra-personal RFs (e.g. distress tolerance) changed in a similar manner in the two groups. The CA+ group had lower RFs and higher distress at both ages. Thus, CA does not seem to inhibit RF changes, but to increase the risk of persistently lower RFs. At age 14, but not 17, the RF network of the CA+ group was less positively connected, suggesting that RFs are less likely to enhance each other than in the CA− group. Those findings underpin the notion that CA has a predominantly strong proximal effect. RF-distress pathways did not differ in strength between the CA+ and the CA− group, which suggests that RFs have a similarly protective strength in the two groups. Yet, as RFs are lower and distress is higher, RF-distress pathways may overall be less advantageous in the CA+ group. Most RF interrelations and RF-distress pathways were stable between age 14 and 17, which may help explain why exposure to CA is frequently found to have a lasting impact on mental health.ConclusionsOur findings not only shed light on the nature and changes of RFs between early and later adolescence, but also offer some accounts for why exposure to CA has stronger proximal effects and is often found to have a lasting impact on mental health. Show less
BackgroundStudies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the... Show moreBackgroundStudies 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.MethodsWe 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.ResultsThe 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.ConclusionsWe 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. Show less
De Beurs, D.; Fried, E.I.; Wetherall, K.; Cleare, S.; O' Connor, D.B.; Ferguson, E.; ... ; O' Connor, R.C. 2019
Two leading theories within the field of suicide prevention are the interpersonal psychological theory of suicidal behaviour (IPT) and the integrated motivational-volitional (IMV) model. The IPT... Show moreTwo leading theories within the field of suicide prevention are the interpersonal psychological theory of suicidal behaviour (IPT) and the integrated motivational-volitional (IMV) model. The IPT posits that suicidal thoughts emerge from high levels of perceived burdensomeness and thwarted belongingness. The IMV model is a multivariate framework that conceptualizes defeat and entrapment as key drivers of suicide ideation. We applied network analysis to cross-sectional data collected as part of the Scottish Wellbeing Study, in which a nationally representative sample of 3508 young adults (18–34 years) completed a battery of psychological measures. Network analysis can help us to understand how the different theoretical components interact and how they relate to suicide ideation. Within a network that included only the core factors from both models, internal entrapment and perceived burdensomeness were most strongly related to suicide ideation. The core constructs defeat, external entrapment and thwarted belonginess were mainly related to other factors than suicide ideation. Within the network of all available psychological factors, 12 of the 20 factors were uniquely related to suicide ideation, with perceived burdensomeness, internal entrapment, depressive symptoms and history of suicide ideation explaining the most variance. None of the factors was isolated, and we identified four larger clusters: mental wellbeing, interpersonal needs, personality, and suicide-related factors. Overall, the results suggest that relationships between suicide ideation and psychological risk factors are complex, with some factors contributing direct risk, and others having indirect impact. Show less
For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a... Show moreFor more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system—the Hierarchical Taxonomy of Psychopathology (HiTOP)—that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness. Show less
This article explores attachment relationships from a network theory perspective: Correlations among behaviors, beliefs, and feelings related to attachment are hypothesized to stem from causal... Show moreThis article explores attachment relationships from a network theory perspective: Correlations among behaviors, beliefs, and feelings related to attachment are hypothesized to stem from causal relations. The authors used two data sets that assessed relationships with four attachment figures (mother, father, romantic partner, and best friend) using the Relationship Structures Questionnaire. Separate networks (Gaussian Graphical Models) were estimated based on 10 items for each attachment figure. Across networks in Study 1 (N = 310), items related to anxiety, seeking support, and discomfort disclosing feelings clustered with other items from their respective domains; a trust‐related item bridged the clusters. Study 2 replicated these findings in a larger and more diverse sample (N = 3,710). The potential of network analysis for advancing the study of attachment is discussed. Show less
A growing body of evidence highlights the role of life stress as a risk factor for the development and relapse of substance use disorders (SUDs), but the relationship of life stress with the... Show moreA growing body of evidence highlights the role of life stress as a risk factor for the development and relapse of substance use disorders (SUDs), but the relationship of life stress with the interactions among SUD symptoms is overlooked. The current study investigated the role of life stress in symptom networks of 3 different SUDs—alcohol, tobacco, and drug use—using the U.S. representative data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) I and II (N = 34,653). The symptom networks were estimated using the Ising model and l1-regularziation with model selection based on the Extended Bayesian Information Criterion. We examined the association of stress with 2 network characteristics: the network connectivity and the network structure. In addition, we applied bootstrap routines to examine the stability of our results and tested whether our findings of Wave 1 replicated in Wave II of the NESARC. For alcohol and drug use symptoms, but not for tobacco use symptoms, greater network connectivity (which is related to psychiatric severity and prognosis) was associated with the number of stressors. In contrast, the structure of SUD symptom networks remained stable regardless of the level of stress, which indicated that the order of central nodes in the symptom networks was not significantly associated with stress. Altogether, our findings suggest that there is a quantitative (i.e., greater connectivity), but not qualitative (i.e., structure), difference in alcohol and drug use symptom relationships associated with life stress. Show less
