Lynn Boschloo

A new method for constructing networks from binary data

Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.

Mental disorders as networks of problems: a review of recent insights

PurposeThe network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years.MethodsThis paper provides a review of all empirical network studies published between 2010 and 2016 and discusses them according to three main themes: comorbidity, prediction, and clinical intervention.ResultsPertaining to comorbidity, the network approach provides a powerful new framework to explain why certain disorders may co-occur more often than others. For prediction, studies have consistently found that symptom networks of people with mental disorders show different characteristics than that of healthy individuals, and preliminary evidence suggests that networks of healthy people show early warning signals before shifting into disordered states. For intervention, centrality—a metric that measures how connected and clinically relevant a symptom is in a network—is the most commonly studied topic, and numerous studies have suggested that targeting the most central symptoms may offer novel therapeutic strategies.ConclusionsWe sketch future directions for the network approach pertaining to both clinical and methodological research, and conclude that network analysis has yielded important insights and may provide an important inroad towards personalized medicine by investigating the network structures of individual patients.

Alcohol use disorders and the course of depressive and anxiety disorders

BACKGROUND Inconsistent findings have been reported on the role of comorbid alcohol use disorders as risk factors for a persistent course of depressive and anxiety disorders. AIMS To determine whether the course of depressive and/or anxiety disorders is conditional on the type (abuse or dependence) or severity of comorbid alcohol use disorders. METHOD In a large sample of participants with current depression and/or anxiety (n = 1369) we examined whether the presence and severity of DSM-IV alcohol abuse or alcohol dependence predicted the 2-year course of depressive and/or anxiety disorders. RESULTS The persistence of depressive and/or anxiety disorders at the 2-year follow-up was significantly higher in those with remitted or current alcohol dependence (persistence 62% and 67% respectively), but not in those with remitted or current alcohol abuse (persistence 51% and 46% respectively), compared with no lifetime alcohol use disorder (persistence 53%). Severe (meeting six or seven diagnostic criteria) but not moderate (meeting three to five criteria) current dependence was a significant predictor as 95% of those in the former group still had a depressive and/or anxiety disorder at follow-up. This association remained significant after adjustment for severity of depression and anxiety, psychosocial factors and treatment factors. CONCLUSIONS Alcohol dependence, especially severe current dependence, is a risk factor for an unfavourable course of depressive and/or anxiety disorders, whereas alcohol abuse is not.

The Network Structure of Symptoms of the Diagnostic and Statistical Manual of Mental Disorders

Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially important information is lost. The network approach, in contrast, assumes that psychopathology results from the causal interplay between psychiatric symptoms and focuses specifically on these symptoms and their complex associations. By using a sophisticated network analysis technique, this study constructed an empirically based network structure of 120 psychiatric symptoms of twelve major DSM-IV diagnoses using cross-sectional data of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, second wave; N = 34,653). The resulting network demonstrated that symptoms within the same diagnosis showed differential associations and indicated that the strategy of summing symptoms, as in current classification systems, leads to loss of information. In addition, some symptoms showed strong connections with symptoms of other diagnoses, and these specific symptom pairs, which both concerned overlapping and non-overlapping symptoms, may help to explain the comorbidity across diagnoses. Taken together, our findings indicated that psychopathology is very complex and can be more adequately captured by sophisticated network models than current classification systems. The network approach is, therefore, promising in improving our understanding of psychopathology and moving our field forward.

A Prospective Study on How Symptoms in a Network Predict the Onset of Depression

To explain the overt heterogeneous nature of major depressive disorder (MDD), it could be valuable to focus on individual symptoms [1]. Recent research, for example, showed that MDD symptoms differ in their underlying biology, risk factors and psychosocial impairments [for a review, see [2]]. In addition, the presence of specific symptoms (e.g. psychomotor agitation) may have important clinical implications, such as expectations regarding the response to antidepressants [3].

The association between immune activation and manic symptoms in patients with a depressive disorder

Although recent studies have shown that immunological processes play an important role in the pathophysiology of mood disorders, immune activation may only be present in specific subgroups of patients. Our study aimed to examine whether immune activation was associated with (a) the presence of manic symptoms and (b) the onset of manic symptoms during 2 years of follow-up in depressed patients. Patients with a depressive disorder at baseline (N=957) and healthy controls (N=430) were selected from the Netherlands Study of Depression and Anxiety. Assessments included lifetime manic symptoms at baseline and two-year follow up, as well as C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) at baseline. Within depressed patients, immune activation was not related to the presence or absence of lifetime manic symptoms at baseline. However, CRP levels were strongly elevated in depressed men who developed manic symptoms compared with those who did not develop manic symptoms over 2 years (P<0.001, Cohen’s d=0.89). IL-6 and TNF-α were also higher in depressed men with an onset of manic symptoms, but this association was not significant. However, we found that the onset of manic symptoms was particularly high in men with multiple elevated levels of inflammatory markers. Depressed men who developed manic symptoms during follow-up had increased immunological activity (especially CRP) compared with depressed men who did not develop manic symptoms. Further research should explore whether a treatment approach focusing on inflammatory processes may be more effective in this specific subgroup of depressed patients.

Depressive and anxiety disorders predicting first incidence of alcohol use disorders: results of the Netherlands Study of Depression and Anxiety (NESDA).

INTRODUCTION Depressive and anxiety disorders may predict first incidence of alcohol abuse and alcohol dependence. This study aims to identify those persons who are at an increased risk of developing alcohols abuse or alcohol dependence by considering the heterogeneity of depressive and anxiety disorders and exploring the role of other risk factors. METHOD In a large sample of persons with and without baseline DSM-IV depressive or anxiety disorders (n = 2,676; 18-65 years; assessed in 2004-2007), the first incidences of DSM-IV alcohol abuse and alcohol dependence during a 4-year follow-up were considered as primary outcomes. Status (remitted or current disorder), severity, and type (specific disorders) of depressive and anxiety disorders were assessed, as well as other risk factors, such as sociodemographic, vulnerability, and addiction-related factors. RESULTS Cumulative first-incidence rates of alcohol abuse and alcohol dependence were 2.0% and 3.0%, respectively. Persons with current, but not remitted, depressive or anxiety disorders were at an increased risk of a first incidence of alcohol dependence (hazard ratio [HR] = 2.69; 95% CI, 1.37-5.29), but not first incidence of alcohol abuse (HR = 0.55; 95% CI, 0.28-1.09). Although this association was not conditional on the type of disorder, first-incidence rates of alcohol dependence gradually increased with the number of depressive and anxiety disorders (HR per SD increase = 1.65; 95% CI, 1.37-2.00). Subthreshold alcohol problems especially (P < .001), but also recent negative life events (P = .06), were additional independent predictors of first incidence of alcohol dependence. CONCLUSION Current depressive disorder, anxiety disorder, or both significantly predicted first incidence of alcohol dependence, which stresses the importance of addiction prevention strategies for depressed and anxious patients in mental health settings. Subthreshold alcohol problems and recent negative life events may help to identify persons at an increased risk for developing alcohol dependence.

Differential associations of specific depressive and anxiety disorders with somatic symptoms.

OBJECTIVE Previous studies have shown that depressive and anxiety disorders are strongly related to somatic symptoms, but much is unclear about the specificity of this association. This study examines the associations of specific depressive and anxiety disorders with somatic symptoms, and whether these associations are independent of comorbid depressive and anxiety disorders. METHODS Cross-sectional data were derived from The Netherlands Study of Depression and Anxiety (NESDA). A total of 2008 persons (mean age: 41.6 years, 64.9% women) were included, consisting of 1367 patients with a past-month DSM-diagnosis (established with the Composite International Diagnostic Interview [CIDI]) of depressive disorder (major depressive disorder, dysthymic disorder) and/or anxiety disorder (generalized anxiety disorder, social phobia, panic disorder, agoraphobia), and 641 controls. Somatic symptoms were assessed with the somatization scale of the Four-Dimensional Symptom Questionnaire (4DSQ), and included cardiopulmonary, musculoskeletal, gastrointestinal, and general symptoms. Analyses were adjusted for covariates such as chronic somatic diseases, sociodemographics, and lifestyle factors. RESULTS All clusters of somatic symptoms were more prevalent in patients with depressive and/or anxiety disorders than in controls (all p<.001). Multivariable logistic regression analyses showed that all types of depressive and anxiety disorders were independently related to somatic symptoms, except for dysthymic disorder. Major depressive disorder showed the strongest associations. Associations remained similar after adjustment for covariates. CONCLUSION This study demonstrated that depressive and anxiety disorders show strong and partly differential associations with somatic symptoms. Future research should investigate whether an adequate consideration and treatment of somatic symptoms in depressed and/or anxious patients improve treatment outcomes.

The Network Structure of Psychopathology in a Community Sample of Preadolescents

Psychopathology is often classified according to diagnostic categories or scale scores. These ignore potentially important information about associations between specific symptoms and, consequently, lead to heterogeneous constructs that may mask relevant individual differences. Network analyses focus on these specific symptom associations, providing the opportunity to explore the complex structure of psychopathology in more detail. We examined the empirical network structure of 95 emotional and behavioral problems of the Youth Self-Report (YSR) to explore how well this structure reflected the predefined YSR domains. The study was conducted in a large community sample (N = 2,175) of preadolescents (mean age = 11.1, SD = 0.6 years), and the network structure was determined by means of the recently developed network analysis technique, eLasso. Although problems within the same domain, in general, showed more and stronger connections than problems belonging to different domains, some problems showed substantially more or stronger associations than others; consequently, problems cannot be considered interchangeable indicators of their domain. Furthermore, no sharp boundaries were found between the domains as specific symptom pairs of different domains showed strong connections. Taken together, our findings indicate that network models provide a promising addition to the more traditional way of distinguishing diagnoses or scale scores. (PsycINFO Database Record

The Mood Disorder Questionnaire (MDQ) for detecting (hypo)manic episodes: its validity and impact of recall bias.

BACKGROUND Bipolar disorders often remain unrecognized in clinical practice, which may be a consequence of imprecise recall of manic symptoms earlier in life. This study will therefore examine the validity of the widely-used Mood Disorder Questionnaire (MDQ) in detecting a (hypo)manic episode and explore the impact of recall bias. METHODS As an indication of impairments in recalling manic symptoms, we examined the long-term reliability of the MDQ after two years of follow-up in a sample of 2087 persons. Then, the validity of the MDQ was tested against the gold standard of a CIDI-based DSM-IV (hypo)manic episode. Its performance was compared for detecting a lifetime episode (at T1) versus a recent episode in the past two years (at T2). RESULTS The long-term reliability of the MDQ was limited as the correct recall of individual items ranged from 44.6% to 68.8% after two years. The overall validity of the MDQ in detecting a lifetime (hypo)manic episode was limited and no adequate cut-off point with acceptable sensitivity and specificity could be identified. However, the MDQ accurately detected a recent episode with a sensitivity of 0.83 and a specificity of 0.82 for the standard and optimal cut-off point of ≥ 7. Taking into account two additional MDQ questions on clustering in time and severity of problems decreased its validity. LIMITATIONS Patients with a primary, clinical diagnosis of bipolar disorder were excluded. CONCLUSIONS The MDQ accurately detected recent (hypo)manic episodes, but imprecise recall may result in a limited performance for episodes earlier in life.