mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data

We present the R-package mgm for the estimation of k-order Mixed Graphical Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data sets consisting of mixed types of variables (continuous, count, categorical) are ubiquitous. In addition, we allow to relax the stationarity assumption of both models by introducing time-varying versions MGMs and mVAR models based on a kernel weighting approach. Time-varying models offer a rich description of temporally evolving systems and allow to identify external influences on the model structure such as the impact of interventions. We provide the background of all implemented methods and provide fully reproducible examples that illustrate how to use the package

A Tutorial on Estimating Time-Varying Vector Autoregressive Models

Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted as a linear function of all variables at previous time points. A key assumption of this model is that its parameters are constant (or stationary) across time. However, in many areas of psychological research time-varying parameters are plausible or even the subject of study. In this tutorial paper, we introduce methods to estimate time-varying VAR models based on splines and kernel-smoothing with/without regularization. We use simulations to evaluate the relative performance of all methods in scenarios typical in applied research, and discuss their strengths and weaknesses. Finally, we provide a step-by-step tutorial showing how to apply the discussed methods to an openly available time series of mood-related measurements

Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates

Background   Studies 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.   Methods   We 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.   Results   The 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.   Conclusions   We 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

The permanently chaperone-active small heat shock protein Hsp17 from Caenorhabditis elegans exhibits topological separation of its N-terminal regions

Small Heat shock proteins (sHsps) are a family of molecular chaperones that bind nonnative proteins in an ATP-independent manner. Caenorhabditis elegans encodes 16 different sHsps, among them Hsp17, which is evolutionarily distinct from other sHsps in the nematode. The structure and mechanism of Hsp17 and how these may differ from other sHsps remain unclear. Here, we find that Hsp17 has a distinct expression pattern, structural organization, and chaperone function. Consistent with its presence under nonstress conditions, and in contrast to many other sHsps, we determined that Hsp17 is a mono-disperse, permanently active chaperone in vitro, which interacts with hundreds of different C. elegans proteins under physiological conditions. Additionally, our cryo-EM structure of Hsp17 reveals that in the 24-mer complex, 12 N-terminal regions are involved in its chaperone function. These flexible regions are located on the outside of the spherical oligomer, whereas the other 12 N-terminal regions are engaged in stabilizing interactions in its interior. This allows the same region in Hsp17 to perform different functions depending on the topological context. Taken together, our results reveal structural and functional features that further define the structural basis of permanently active sHsps

Moderated Network Models

Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research this is often implausible. In this paper, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct the MNM and propose an L1-regularized nodewise regression approach to estimate it. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification

How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets

Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node – its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality. We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes. Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis. Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed

The Gaussian graphical model in cross-sectional and time-series data

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in 3 kinds of psychological datasets: datasets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered datasets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means---the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.Comment: Accepted pending revision in Multivariate Behavioral Researc

Small but crucial : the novel small heat shock protein Hsp21 mediates stress adaptation and virulence in Candida albicans

Peer reviewedPublisher PD

Small Heat Shock Proteins Potentiate Amyloid Dissolution by Protein Disaggregases from Yeast and Humans

The authors define how small heat-shock proteins synergize to regulate the assembly and disassembly of a beneficial prion, and then they exploit this knowledge to identify the human amyloid depolymerase

Polymorphisms in the Hsp70 gene locus are genetically associated with systemic lupus erythematosus

Background Heat shock proteins (Hsps) play a role in the delivery and presentation of antigenic peptides and are thought to be involved in the pathogenesis of multifactorial diseases. Objective To investigate genes encoding cytosolic Hsp70 proteins for associations of allelic variants with systemic lupus erythematosus (SLE). Methods Case-control studies of two independent Caucasian SLE cohorts were performed. In a haplotype-tagging single-nucleotide polymorphism approach, common variants of HspA1L, HspA1A and HspA1B were genotyped and principal component analyses were performed for the cohort from the Oklahoma Medical Research Foundation (OMRF). Relative quantification of mRNA was carried out for each Hsp70 gene in healthy controls. Conditional regression analysis was performed to determine if allelic variants in Hsp70 act independently of HLA-DR3. Results On analysis of common genetic variants of HspA1L, HspA1A and HspA1B, a haplotype significantly associated with SLE in the Erlangen-SLE cohort was identified, which was confirmed in the OMRF cohort. Depending on the cohorts, OR ranging from 1.43 to 1.88 and 2.64 to 3.16 was observed for individuals heterozygous and homozygous for the associated haplotype, respectively. Patients carrying the risk haplotype or the risk allele more often displayed autoantibodies to Ro and La in both cohorts. In healthy controls bearing this haplotype, the amount of HspA1A mRNA was significantly increased, whereas total Hsp70 protein concentration was not altered. Conclusions Allelic variants of the Hsp70 genes are significantly associated with SLE in Caucasians, independently of HLA-DR3, and correlate with the presence of autoantibodies to Ro and La. Hence, the Hsp70 gene locus appears to be involved in SLE pathogenesis