Relating ASD symptoms to well-being: moving across different construct levels.

BACKGROUND: Little is known about the specific factors that contribute to the well-being (WB) of individuals with autism spectrum disorder (ASD). A plausible hypothesis is that ASD symptomatology has a direct negative effect on WB. In the current study, the emerging tools of network analysis allow to explore the functional interdependencies between specific symptoms of ASD and domains of WB in a multivariate framework. We illustrate how studying both higher-order (total score) and lower-order (subscale) representations of ASD symptomatology can clarify the interrelations of factors relevant for domains of WB. METHODS: We estimated network structures on three different construct levels for ASD symptomatology, as assessed with the Adult Social Behavior Questionnaire (item, subscale, total score), relating them to daily functioning (DF) and subjective WB in 323 adult individuals with clinically identified ASD (aged 17-70 years). For these networks, we assessed the importance of specific factors in the network structure. RESULTS: When focusing on the highest representation level of ASD symptomatology (i.e. a total score), we found a negative connection between ASD symptom severity and domains of WB. However, zooming in on lower representation levels of ASD symptomatology revealed that this connection was mainly funnelled by ASD symptoms related to insistence on sameness and experiencing reduced contact and that those symptom scales, in turn, impact different domains of WB. CONCLUSIONS: Zooming in across construct levels of ASD symptom severity into subscales of ASD symptoms can provide us with important insights into how specific domains of ASD symptoms relate to specific domains of DF and WB.

An Introduction to Network Psychometrics: Relating Ising Network Models to Item Response Theory Models.

In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models-the Ising model from physics-and one of the most important latent variable models-the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection.

Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk.

BACKGROUND: Genetic risk and environmental adversity-both important risk factors for major depression (MD)-are thought to differentially impact on depressive symptom types and associations. Does heterogeneity in these risk factors result in different depressive symptom networks in patients with MD? METHODS: A clinical sample of 5784 Han Chinese women with recurrent MD were interviewed about their depressive symptoms during their lifetime worst episode of MD. The cases were classified into subgroups based on their genetic risk for MD (family history, polygenic risk score, early age at onset) and severe adversity (childhood sexual abuse, stressful life events). Differences in MD symptom network structure were statistically examined for these subgroups using permutation-based network comparison tests. RESULTS: Although significant differences in symptom endorsement rates were seen in 18.8% of group comparisons, associations between depressive symptoms were similar across the different subgroups of genetic and environmental risk. Network comparison tests showed no significant differences in network strength, structure, or specific edges (P-value > 0.05) and correlations between edges were strong (0.60-0.71). LIMITATIONS: This study analyzed depressive symptoms retrospectively reported by severely depressed women using novel statistical methods. Future studies are warranted to investigate whether similar findings hold in prospective longitudinal data, less severely depressed patients, and men. CONCLUSIONS: Similar depressive symptom networks for MD patients with a higher or lower genetic or environmental risk suggest that differences in these etiological influences may produce similar symptom networks downstream for severely depressed women.

Kinds versus continua: a review of psychometric approaches to uncover the structure of psychiatric constructs.

The question of whether psychopathology constructs are discrete kinds or continuous dimensions represents an important issue in clinical psychology and psychiatry. The present paper reviews psychometric modelling approaches that can be used to investigate this question through the application of statistical models. The relation between constructs and indicator variables in models with categorical and continuous latent variables is discussed, as are techniques specifically designed to address the distinction between latent categories as opposed to continua (taxometrics). In addition, we examine latent variable models that allow latent structures to have both continuous and categorical characteristics, such as factor mixture models and grade-of-membership models. Finally, we discuss recent alternative approaches based on network analysis and dynamical systems theory, which entail that the structure of constructs may be continuous for some individuals but categorical for others. Our evaluation of the psychometric literature shows that the kinds-continua distinction is considerably more subtle than is often presupposed in research; in particular, the hypotheses of kinds and continua are not mutually exclusive or exhaustive. We discuss opportunities to go beyond current research on the issue by using dynamical systems models, intra-individual time series and experimental manipulations.

Exploring the underlying structure of mental disorders: cross-diagnostic differences and similarities from a network perspective using both a top-down and a bottom-up approach.

BACKGROUND: It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques. METHOD: A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states ('cheerful', 'content', 'down', 'insecure' and 'suspicious') were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters. RESULTS: Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics. CONCLUSIONS: Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.

Revealing the dynamic network structure of the Beck Depression Inventory-II.

BACKGROUND: Structured interviews and questionnaires are important tools to screen for major depressive disorder. Recent research suggests that, in addition to studying the mean level of total scores, researchers should focus on the dynamic relations among depressive symptoms as they unfold over time. Using network analysis, this paper is the first to investigate these patterns of short-term (i.e. session to session) dynamics for a widely used psychological questionnaire for depression - the Beck Depression Inventory (BDI-II). METHOD: With the newly developed vector autoregressive (VAR) multilevel method we estimated the network of symptom dynamics that characterizes the BDI-II, based on repeated administrations of the questionnaire to a group of depressed individuals who participated in a treatment study of an average of 14 weekly assessments. Also the centrality of symptoms and the community structure of the network were examined. RESULTS: The analysis showed that all BDI-II symptoms are directly or indirectly connected through patterns of temporal influence. In addition, these influences are mutually reinforcing, 'loss of pleasure' being the most central item in the network. Community analyses indicated that the dynamic structure of the BDI-II involves two clusters, which is consistent with earlier psychometric analyses. CONCLUSION: The network approach expands the range of depression research, making it possible to investigate the dynamic architecture of depression and opening up a whole new range of questions and analyses. Regarding clinical practice, network analyses may be used to indicate which symptoms should be targeted, and in this sense may help in setting up treatment strategies.

The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the pattern of depressive symptom inter-correlations.

BACKGROUND: Previous research has shown that stressful life events (SLEs) influence the pattern of individual depressive symptoms. However, we do not know how these differences arise. Two theories about the nature of psychiatric disorders have different predictions about the source of these differences: (1) SLEs influence depressive symptoms and correlations between them indirectly, via an underlying acute liability to develop a dysphoric episode (DE; common cause hypothesis); and (2) SLEs influence depressive symptoms and correlations between them directly (network hypothesis). The present study investigates the predictions of these two theories. METHOD: We divided a population-based sample of 2096 Caucasian twins (49.9% female) who reported at least two aggregated depressive symptoms in the last year into four groups, based on the SLE they reported causing their symptoms. For these groups, we calculated tetrachoric correlations between the 14 disaggregated depressive symptoms and, subsequently, tested whether the resulting correlation patterns were significantly different and if those differences could be explained by underlying differences in a single acute liability to develop a DE. RESULTS: The four SLE groups had markedly different correlation patterns between the depressive symptoms. These differences were significant and could not be explained by underlying differences in the acute liability to develop a DE. CONCLUSIONS: Our results are not compatible with the common cause perspective but are consistent with the predictions of the network hypothesis. We elaborate on the implications of a conceptual shift to the network perspective for our diagnostic and philosophical approach to the concept of what constitutes a psychiatric disorder.

Production, characterization and control of a Neisseria meningitidis hexavalent class 1 outer membrane protein containing vesicle vaccine.

An experimental serogroup B meningococcal vaccine was prepared from two genetically engineered strains; each expressing three different class 1 outer membrane proteins (OMPs) (PorA). The two strains expressed the subtypes P1.7,16;P1.5,2;P1.19,15 and P1.5c,10;P1.12,13;P1.7h,4, respectively. Outer membrane vesicles (OMV) were prepared from these strains by deoxycholate extraction, mixed with aluminiumphosphate as adjuvant and formulated to final vaccines. The class 1 OMPs represent ca 90% of the protein in the vaccine. The vaccine was found safe for human use and induced a bactericidal immune response in mice against five of the six wild type strains, which served as donors for the various por A genes.

The modified leukocytosis promoting factor (LPF)-test: a valuable supplement to the mouse weight gain (MWG)-test in toxicity control of whole cell pertussis vaccine.

For the safety testing of pertussis vaccine, many in vivo assays have been developed, but none of these assays, except the Mouse Weight Gain (MWG)-test, are obligatory. Leukocytosis Promoting Factor (LPF) test, performed in mice, is one of the tests to examine the toxicity. However, due to lack of standardization, this test has not been implemented in the regular safety testing of the vaccine. Our investigations demonstrate that the LPF-test becomes more reproducible and sensitive if preparations are administered subcutaneously on day 0 and and counting of the leukocytes are done on day 6. Therefore, it is suggested to include the revised LPF-test in the quality control panel for the assessment of the toxicity of whole-cell pertussis vaccine.