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.

IMPORTANCE Major depressive disorder (MDD) is a heterogeneous condition in terms of symptoms, course, and underlying disease mechanisms. Current classifications do not adequately address this complexity. In novel network approaches to psychopathology, psychiatric disorders are conceptualized as complex dynamic systems of mutually interacting symptoms. This perspective implies that a more densely connected network of symptoms is indicative of a poorer prognosis, but, to date, no previous study has examined whether network structure is indeed associated with the longitudinal course of MDD. OBJECTIVE To examine whether the baseline network structure of MDD symptoms is associated with the longitudinal course of MDD. DESIGN, SETTING, AND PARTICIPANTS In this prospective study, in which remittent and persistent MDD was defined on the basis of a follow-up assessment after 2 years, 515 patients from the Netherlands Study of Depression and Anxiety with past-year MDD (established with the Composite International Diagnostic Interview) and at least moderate depressive symptoms (assessed with the Inventory of Depressive Symptomatology [IDS]) at baseline were studied. Baseline starting and ending dates were September 1, 2004, through February 28, 2007. Follow-up starting and ending dates were September 1, 2006, through February 28, 2009. Analysis was conducted August 2015. The MDD was considered persistent if patients had at least moderate depressive symptoms (IDS) at 2-year follow-up; otherwise, the MDD was considered remitted. MAIN OUTCOMES AND MEASURES Sparse network structures of baseline MDD symptoms assessed via IDS were computed. Global and local connectivity of network structures were compared across persisters and remitters using a permutation test. RESULTS Among the 515 patients, 335 (65.1%) were female, mead (SD) age was 40.9 (12.1) years, and 253 (49.1%) had persistent MDD at 2-year follow-up. Persisters (n = 253) had a higher baseline IDS sum score than remitters (n = 262) (mean [SD] score, 40.2 [8.9] vs 35.1 [7.1]; the test statistic for the difference in IDS sum score was 22 027; P < .001). The test statistic for the difference in network connectivity was 1.79 (P = .01) for the original data, 1.55 for data matched on IDS sum score (P = .04), and 1.65 for partialed out data (P = .02). At the symptom level, fatigue or loss of energy and feeling guilty had the largest difference in importance in persisters' network compared with that of remitters (Cohen d = 1.13 and 1.18, respectively). CONCLUSIONS AND RELEVANCE This study reports that symptom networks of patients with MDD are related to longitudinal course: persisters exhibited a more densely connected network at baseline than remitters. More pronounced associations between symptoms may be an important determinant of persistence in MDD.

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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00127-016-1319-z) contains supplementary material, which is available to authorized users.

Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed.

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.

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Alcohol dependence, but not abuse, is more prevalent in anxious and/or depressed persons. Persons with comorbid alcohol dependence constitute a distinct subgroup of anxious and/or depressed persons, characterized by addiction-related habits and vulnerability. However, considerable variation in characteristics exists depending on temporal sequencing of disorders. This knowledge may improve identification and treatment of those anxious and/or depressed patients who are additionally suffering from alcohol dependence.

Background Perinatal depression (PND) is a common complication of pregnancy and postpartum associated with significant morbidity. Objectives We had three goals: 1) to explore the performance of a new lifetime version of the Edinburgh Postnatal Depression Scale (EPDS-Lifetime) to assess lifetime prevalence of PND; 2) to assess prevalence of lifetime PND in women with prior histories of MDD; and 3) to evaluate risk factors for PND. Methods Subjects were from the Netherlands Study of Depression and Anxiety (NESDA). The EPDS was modified by adding lifetime PND screening questions, assessing worst episode and symptom timing of onset. Results Of 682 women with lifetime MDD and a live birth, 276 (40.4%) had a positive EPDS score of ≥12 consistent with PND. Women with PND more often sought professional help (p <0.001), and received treatment (p =0.001). Independent risk indicators for PND included younger age, higher education, high neuroticism, childhood trauma, and sexual abuse. Conclusions We found that two in five parous women with a history of MDD had lifetime PND and that the PND episodes were more severe than MDD occurring outside of the perinatal period. The EPDS-Lifetime shows promise as a tool for assessing lifetime histories of PND in clinical and research settings.