Transdiagnostic symptom dynamics during psychotherapy.

Psychotherapy is an effective treatment for many common mental health problems, but the mechanisms of action and processes of change are unclear, perhaps driven by the focus on a single diagnosis which does not reflect the heterogeneous symptom experiences of many patients. The objective of this study was to better understand therapeutic change, by illustrating how symptoms evolve and interact during psychotherapy. Data from 113,608 patients from psychological therapy services who completed depression and anxiety symptom measures across three to six therapy sessions were analysed. A panel graphical vector-autoregression model was estimated in a model development sample (N = 68,165) and generalizability was tested in a confirmatory model, fitted to a separate (hold-out) sample of patients (N = 45,443). The model displayed an excellent fit and replicated in the confirmatory holdout sample. First, we found that nearly all symptoms were statistically related to each other (i.e. dense connectivity), indicating that no one symptom or association drives change. Second, the structure of symptom interrelations which emerged did not change across sessions. These findings provide a dynamic view of the process of symptom change during psychotherapy and give rise to several causal hypotheses relating to structure, mechanism, and process.

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.

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.

Lysophosphatidic acid- and Gbeta-dependent activation of Dictyostelium MAP kinase ERK2.

Exogenous lysophosphatidic acid (LPA) has been shown to evoke a chemotactic response in aggregative cells of the social amoeba Dictyostelium discoideum. In this paper, we demonstrate that extracellular LPA is also able to induce activation of mitogen-activated protein (MAP) kinase DdERK2 (extracellular signal regulated kinase 2) in these cells. This activation is independent of cyclic AMP receptors, yet fully dependent on the single Gbeta subunit, hinting to the presence of functional heptahelical LPA receptors in a primitive eukaryote. We did not observe LPA-dependent cyclic GMP accumulation, which suggests that the pathways for LPA-induced and "classical" chemotaxis of D. discoideum cells are substantially different.

Isolation of a Dictyostelium discoideum 14-3-3 homologue.

A 1.0 kb cDNA clone (Dd14-3-3) encoding a 14-3-3 homologue was isolated from a Dictyostelium discoideum cDNA library. The putative Dd14-3-3 protein has highest sequence identity to a barley 14-3-3 isoform (74%). Southern blot analysis suggests that only one 14-3-3 gene is present in the Dictyostelium genome. Highest Dd14-3-3 expression is observed in vegetatively growing cells, and expression decreases during multicellular development. In contrast, Dd14-3-3 protein levels detected immunochemically remained constant during Dictyostelium development. Expression of the Dd14-3-3 cDNA in Saccharomyces cerevisiae complemented the lethal disruption of the two yeast genes encoding 14-3-3 proteins (BMH1 and BMH2). This shows that Dd14-3-3 can fulfil the same function(s) as the yeast 14-3-3 proteins.

Dual role of cAMP and involvement of both G-proteins and ras in regulation of ERK2 in Dictyostelium discoideum.

Dictyostelium discoideum expresses two Extracellular signal Regulated Kinases, ERK1 and ERK2, which are involved in growth, multicellular development and regulation of adenylyl cyclase. Binding of extracellular cAMP to cAMP receptor 1, a G-protein coupled cell surface receptor, transiently stimulates phosphorylation, activation and nuclear translocation of ERK2. Activation of ERK2 by cAMP is dependent on heterotrimeric G-proteins, since activation of ERK2 is absent in cells lacking the Galpha4 subunit. The small G-protein rasD also activates ERK2. In cells overexpressing a mutated, constitutively active rasD, ERK2 activity is elevated prior to cAMP stimulation. Intracellular cAMP and cAMP-dependent protein kinase (PKA) are essential for adaptation of the ERK2 response. This report shows that multiple signalling pathways are involved in regulation of ERK2 activity in D.discoideum.