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

Applying a Dynamical Systems Model and Network Theory to Major Depressive Disorder

Mental disorders like major depressive disorder can be seen as complex dynamical systems. In this study we investigate the dynamic behaviour of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After validating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients.Comment: arXiv admin note: text overlap with arXiv:1610.0504

Network Structure Explains the Impact of Attitudes on Voting Decisions

Attitudes can have a profound impact on socially relevant behaviours, such as voting. However, this effect is not uniform across situations or individuals, and it is at present difficult to predict whether attitudes will predict behaviour in any given circumstance. Using a network model, we demonstrate that (a) more strongly connected attitude networks have a stronger impact on behaviour, and (b) within any given attitude network, the most central attitude elements have the strongest impact. We test these hypotheses using data on voting and attitudes toward presidential candidates in the US presidential elections from 1980 to 2012. These analyses confirm that the predictive value of attitude networks depends almost entirely on their level of connectivity, with more central attitude elements having stronger impact. The impact of attitudes on voting behaviour can thus be reliably determined before elections take place by using network analyses.Comment: Final version published in Scientific Report

Age Differentiation within Gray Matter, White Matter, and between Memory and White Matter in an Adult Life Span Cohort.

It is well established that brain structures and cognitive functions change across the life span. A long-standing hypothesis called "age differentiation" additionally posits that the relations between cognitive functions also change with age. To date, however, evidence for age-related differentiation is mixed, and no study has examined differentiation of the relationship between brain and cognition. Here we use multigroup structural equation models (SEMs) and SEM trees to study differences within and between brain and cognition across the adult life span (18-88 years) in a large (N > 646, closely matched across sexes), population-derived sample of healthy human adults from the Cambridge Centre for Ageing and Neuroscience (www.cam-can.org). After factor analyses of gray matter volume (from T1- and T2-weighted MRI) and white matter organization (fractional anisotropy from diffusion-weighted MRI), we found evidence for the differentiation of gray and white matter, such that the covariance between brain factors decreased with age. However, we found no evidence for age differentiation among fluid intelligence, language, and memory, suggesting a relatively stable covariance pattern among cognitive factors. Finally, we observed a specific pattern of age differentiation between brain and cognitive factors, such that a white matter factor, which loaded most strongly on the hippocampal cingulum, became less correlated with memory performance in later life. These patterns are compatible with the reorganization of cognitive functions in the face of neural decline, and/or with the emergence of specific subpopulations in old age.SIGNIFICANCE STATEMENT The theory of age differentiation posits age-related changes in the relationships among cognitive domains, either weakening (differentiation) or strengthening (dedifferentiation), but evidence for this hypothesis is mixed. Using age-varying covariance models in a large cross-sectional adult life span sample, we found age-related reductions in the covariance among both brain measures (neural differentiation), but no covariance change among cognitive factors of fluid intelligence, language, and memory. We also observed evidence of uncoupling (differentiation) between a white matter factor and cognitive factors in older age, most strongly for memory. Together, our findings support age-related differentiation as a complex, multifaceted pattern that differs for brain and cognition, and discuss several mechanisms that might explain the changing relationship between brain and cognition

arf3DS4: An Integrated Framework for Localization and Connectivity Analysis of fMRI Data

In standard fMRI analysis all voxels are tested in a massive univariate approach, that is, each voxel is tested independently. This requires stringent corrections for multiple comparisons to control the number of false positive tests (i.e., marking voxels as active while they are actually not). As a result, fMRI analyses may suffer from low power to detect activation, especially in studies with high levels of noise in the data, for example developmental or single-subject studies. Activated region fitting (ARF) yields a solution by modeling fMRI data by multiple Gaussian shaped regions. ARF only requires a small number of parameters and therefore has increased power to detect activation. If required, the estimated regions can be directly used as regions of interest in a functional connectivity analysis. ARF is implemented in the R package arf3DS4. In this paper ARF and its implementation are described and illustrated with an example

The Small World of Psychopathology

Background: Mental disorders are highly comorbid: people having one disorder are likely to have another as well. We explain empirical comorbidity patterns based on a network model of psychiatric symptoms, derived from an analysis of symptom overlap in the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV). Principal Findings: We show that a) half of the symptoms in the DSM-IV network are connected, b) the architecture of these connections conforms to a small world structure, featuring a high degree of clustering but a short average path length, and c) distances between disorders in this structure predict empirical comorbidity rates. Network simulations of Major Depressive Episode and Generalized Anxiety Disorder show that the model faithfully reproduces empirical population statistics for these disorders. Conclusions: In the network model, mental disorders are inherently complex. This explains the limited successes of genetic, neuroscientific, and etiological approaches to unravel their causes. We outline a psychosystems approach to investigate the structure and dynamics of mental disorders

A network analysis of female sexual function: comparing symptom networks in women with decreased, increased, and stable sexual desire

Problems related to low sexual desire in women are common clinical complaints, and the aetiology is poorly understood. We investigated predictors of change in levels of sexual desire using a novel network approach, which assumes that mental disorders arise from direct interactions between symptoms. Using population-based data from 1,449 Finnish women, we compared between-subject networks of women whose sexual desire decreased, increased, or remained stable over time. Networks were estimated and analyzed at T1 (2006) and replicated at T2 (2013) using R. Domains included were, among others, sexual functions, sexual distress, anxiety, depression, body dissatisfaction, and relationship status. Overall, networks were fairly similar across groups. Sexual arousal, satisfaction, and relationship status were the most central variables, implying that they might play prominent roles in female sexual function; sexual distress mediated between general distress and sexual function; and sexual desire and arousal showed different patterns of relationships, suggesting that they represent unique sexual function aspects. Potential group-differences suggested that sex-related pain and body dissatisfaction might play roles in precipitating decreases of sexual desire. The general network structure and similarities between groups replicated well; however, the potential group-differences did not replicate. Our study sets the stage for future clinical and longitudinal network modelling of female sexual function

Recovering history: Philip Morton Shand and the mission of modernism

Within the context of Modern architectural history the position of Philip Morton Shand (1888 - 1960) as a key figure in its dissemination has been historically understated. Although not a designer, his role as architectural critic and writer in conjunction with the breadth of his international contacts enabled him to bridge a gap between continental Europe and England. His contributions to the major English architectural journals (i.e. Architect's Journal, Architectural Review and the Architectural Association Journal) between the late 1920s and early 1950s, in addition to his travels, language skills and his involvement in the CIAM and the MARS Group, facilitated the dissemination of ideas to the English-speaking population. Beyond his architectural writings, Shand was also a connoisseur of wine and food and published seminal texts on the topics. However, despite his significant literary contributions, a biography of Shand has not yet been written. An investigation into Shand's life and activities, particularly during the interwar years, will hopefully illuminate the magnitude of his involvement in the architectural scene and its effects on the dissemination of Modern architectural history