Growing Up Together in Society (GUTS): A team science effort to predict societal trajectories in adolescence and young adulthood.

Our society faces a great diversity of opportunities for youth. The 10-year Growing Up Together in Society (GUTS) program has the long-term goal to understand which combination of measures best predict societal trajectories, such as school success, mental health, well-being, and developing a sense of belonging in society. Our leading hypothesis is that self-regulation is key to how adolescents successfully navigate the demands of contemporary society.

The Bradley-Terry Regression Trunk approach for Modeling Preference Data with Small Trees.

This paper introduces the Bradley-Terry regression trunk model, a novel probabilistic approach for the analysis of preference data expressed through paired comparison rankings. In some cases, it may be reasonable to assume that the preferences expressed by individuals depend on their characteristics. Within the framework of tree-based partitioning, we specify a tree-based model estimating the joint effects of subject-specific covariates over and above their main effects.

Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification.

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure.

Neurobiological correlates of antisociality across adolescence and young adulthood: a multi-sample, multi-method study.

Background: Antisociality across adolescence and young adulthood puts individuals at high risk of developing a variety of problems. Prior research has linked antisociality to autonomic nervous system and endocrinological functioning. However, there is large heterogeneity in antisocial behaviors, and these neurobiological measures are rarely studied conjointly, limited to small specific studies with narrow age ranges, and yield mixed findings due to the type of behavior examined.

Pre-trained MRI-based Alzheimer's disease classification models to classify memory clinic patients.

Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics. To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set.

Bias of Two-Level Scalability Coefficients and Their Standard Errors.

Two-level Mokken scale analysis is a generalization of Mokken scale analysis for multi-rater data. The bias of estimated scalability coefficients for two-level Mokken scale analysis, the bias of their estimated standard errors, and the coverage of the confidence intervals has been investigated, under various testing conditions. It was found that the estimated scalability coefficients were unbiased in all tested conditions.

A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data.

Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this article, a latent block distance-association model is proposed to apply block clustering to the outcomes of two categorical variables while the cluster centers are represented in a low dimensional space in terms of a distance-association model.

ClusterBootstrap: An R package for the analysis of hierarchical data using generalized linear models with the cluster bootstrap.

In the analysis of clustered or hierarchical data, a variety of statistical techniques can be applied. Most of these techniques have assumptions that are crucial to the validity of their outcome. Mixed models rely on the correct specification of the random effects structure.

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification.

Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population.

Multiple Approaches to Diffusion Magnetic Resonance Imaging in Hereditary Cerebral Amyloid Angiopathy Mutation Carriers.

Background Cerebral amyloid angiopathy ( CAA ) is a major cause of lobar intracerebral hemorrhage in elderly adults; however, presymptomatic diagnosis of CAA is difficult. Hereditary cerebral hemorrhage with amyloidosis-Dutch type ( HCHWA -D) is a rare autosomal-dominant disease that leads to pathology similar to sporadic CAA . Presymptomatic HCHWA -D mutation carriers provide a unique opportunity to study CAA -related changes before any symptoms have occurred.

Depressive symptoms and emotion regulation strategies in children with and without developmental language disorder: a longitudinal study.

Background: Depressive symptoms are common in children with developmental language disorder (DLD). However, risk and protective factors contributing to these problems are currently underspecified.

Aims: The current longitudinal study examined the role of emotion-regulation (ER) strategies in the severity of depressive symptoms in children with and without DLD, taking into account the severity of communication problems of children with DLD.

Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

Background/objective: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known.

A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease.

Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.

Properties of Ideal Point Classification Models for Bivariate Binary Data.

The ideal point classification (IPC) model was originally proposed for analysing multinomial data in the presence of predictors. In this paper, we studied properties of the IPC model for analysing bivariate binary data with a specific focus on three quantities: (1) the marginal probabilities; (2) the association structure between the two binary responses; and (3) the joint probabilities. We found that the IPC model with a specific class point configuration represents either the marginal probabilities or the association structure.

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging.

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173).

Web-Based Fully Automated Self-Help With Different Levels of Therapist Support for Individuals With Eating Disorder Symptoms: A Randomized Controlled Trial.

Background: Despite the disabling nature of eating disorders (EDs), many individuals with ED symptoms do not receive appropriate mental health care. Internet-based interventions have potential to reduce the unmet needs by providing easily accessible health care services.

Objective: This study aimed to investigate the effectiveness of an Internet-based intervention for individuals with ED symptoms, called "Featback.

Is Experiential Avoidance a Mediating, Moderating, Independent, Overlapping, or Proxy Risk Factor in the Onset, Relapse and Maintenance of Depressive Disorders?

Our study aim was to investigate how experiential avoidance 'works together' with bordering psychological constructs (i.e., rumination, worry and neuroticism) in predicting the onset, relapse and maintenance of depressive disorders.

Attrition analysed in five waves of a longitudinal yearly survey of smokers: findings from the ITC Netherlands survey.

Background: Attrition bias can affect the external validity of findings. This article analyses attrition bias and assesses the effectiveness of replenishment samples on demographic and smoking-related characteristics for the International Tobacco Control Netherlands Survey, a longitudinal survey among smokers.

Methods: Attrition analyses were conducted for the first five survey waves (2008-12).

Combining multiple anatomical MRI measures improves Alzheimer's disease classification.

Several anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD patients from controls. These anatomical MRI measures have so far mainly been used separately.

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease.

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study.

Childhood maltreatment, maladaptive personality types and level and course of psychological distress: A six-year longitudinal study.

Background: Childhood maltreatment and maladaptive personality are both cross-sectionally associated with psychological distress. It is unknown whether childhood maltreatment affects the level and longitudinal course of psychological distress in adults and to what extent this effect is mediated by maladaptive personality.

Methods: A sample of 2947 adults aged 18-65, consisting of healthy controls, persons with a prior history or current episode of depressive and/or anxiety disorders according to the Composite Interview Diagnostic Instrument were assessed in six waves at baseline (T0) and 1 (T1), 2 (T2), 4 (T4) and 6 years (T6) later.

Estimating subgroup effects using the propensity score method: a practical application in outcomes research.

Objective: Our aim was to demonstrate the feasibility of the univariate and generalized propensity score (PS) method in subgroup analysis of outcomes research.

Methods: First, to estimate subgroup effects, we tested the performance of 2 different PS methods, using Monte Carlo simulations: (1) the univariate PS with additional adjustment on the subgroup; and (2) the generalized PS, estimated by crossing the treatment options with a subgroup variable. The subgroup effects were estimated in a linear regression model using the 2 PS adjustments.

Ketamine interactions with biomarkers of stress: a randomized placebo-controlled repeated measures resting-state fMRI and PCASL pilot study in healthy men.

Ketamine, an NMDA receptor antagonist, is increasingly used to study the link between glutamatergic signaling dysregulation and mood and chronic pain disorders. Glutamatergic neurotransmission and stress corticosteroids (cortisol in human) are critical for Ca(2+) mediated neuroplasticity and behavioral adaptation. The mechanisms of action of glutamatergic neurotransmission and stress corticosteroids on the NMDA-receptors of the hippocampus have been long investigated in animals, but given little attention in human studies.

A longitudinal study of experiential avoidance in emotional disorders.

The aim of this study was to examine the degree in which measurements of trait experiential avoidance (EA) are affected by current emotional disorder and whether EA is a causal factor in the course of emotional disorders (anxiety and depressive disorders) and the development of comorbidity among emotional disorders. In a sample of 2,316 adults aged 18 to 65, consisting of healthy controls, persons with a prior history of emotional disorders, and persons with a current emotional disorder, DSM-IV-based emotional disorders (CIDI: Composite Interview Diagnostic Instrument) were assessed at T2 and 2 (T4) and 4years later (T6) and experiential avoidance (AAQ: Acceptance and Action Questionnaire) at T2 and T4. Results showed that EA scores were stable over a 2-year period notwithstanding state fluctuations because of current emotional disorder.

Development of equity preferences in boys and girls across adolescence.

The aim of the current study was to examine the development of equity preferences across adolescence, for boys and girls separately. Participants from 8 to 18 years old (M = 14.09 years; N = 1,216) played four economic allocation games.