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. METHODS: We harmonized data from 1489 participants (9-27 years, 67% male), from six heterogeneous samples. In the resulting dataset, we tested relations between distinct dimensions of antisociality and heart rate, pre-ejection period (PEP), respiratory sinus arrhythmia, respiration rate, skin conductance levels, testosterone, basal cortisol, and the cortisol awakening response (CAR), and test the role of age throughout adolescence and young adulthood. RESULTS: Three dimensions of antisociality were uncovered: 'callous-unemotional (CU)/manipulative traits', 'intentional aggression/conduct', and 'reactivity/impulsivity/irritability'. Shorter PEPs and higher testosterone were related to CU/manipulative traits, and a higher CAR is related to both CU/manipulative traits and intentional aggression/conduct. These effects were stable across age. CONCLUSIONS: Across a heterogeneous sample and consistent across development, the CAR may be a valuable measure to link to CU/manipulative traits and intentional aggression, while sympathetic arousal and testosterone are additionally valuable to understand CU/manipulative traits. Together, these findings deepen our understanding of the fundamental mechanisms underlying different components of antisociality. Finally, we illustrate the potential of using current statistical techniques for combining multiple datasets to draw robust conclusions about biobehavioral associations.

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. This model is particularly useful for contingency tables in which both the rows and the columns are characterized as profiles of sets of response variables. The parameters are estimated under a Poisson sampling scheme using a generalized EM algorithm. The performance of the model is tested in a Monte Carlo experiment, and an empirical data set is analyzed to illustrate the 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. Generalized estimating equations are most efficient when the working correlation form is chosen correctly and are not feasible when the within-subject variable is non-factorial. Assumptions and limitations of another common approach, ANOVA for repeated measurements, are even more worrisome: listwise deletion when data are missing, the sphericity assumption, inability to model an unevenly spaced time variable and time-varying covariates, and the limitation to normally distributed dependent variables. This paper introduces ClusterBootstrap, an R package for the analysis of hierarchical data using generalized linear models with the cluster bootstrap (GLMCB). Being a bootstrap method, the technique is relatively assumption-free, and it has already been shown to be comparable, if not superior, to GEE in its performance. The paper has three goals. First, GLMCB will be introduced. Second, there will be an empirical example, using the ClusterBootstrap package for a Gaussian and a dichotomous dependent variable. Third, GLMCB will be compared to mixed models in a Monte Carlo experiment. Although GLMCB can be applied to a multitude of hierarchical data forms, this paper discusses it in the context of the analysis of repeated measurements or longitudinal data. It will become clear that the GLMCB is a promising alternative to mixed models and the ClusterBootstrap package an easy-to-use R implementation of the technique.

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. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population.

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 = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly.

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. METHODS & PROCEDURES: We followed clinically referred children with DLD (n = 114, 49% girls) and without DLD (n = 214, 58% girls) between the ages of 8 and 16 years across an 18-month period. Participants completed self-report questionnaires at three time points. Parents of children with DLD reported on their child's communication problems. OUTCOMES & RESULTS: Multilevel analyses confirmed higher levels of depressive symptoms in youngsters with DLD compared with peers without DLD, with a decrease across time in the DLD group. In both groups, higher levels of approach and increasing avoidant strategies aimed at distraction or trivializing a problem explained lower depressive symptoms, whereas more worry and externalizing strategies contributed to more depressive symptoms. Within the DLD group, semantic language problems were associated with higher depressive symptoms. However, this relation was mediated by the tendency to worry or use externalizing strategies. CONCLUSIONS & IMPLICATIONS: Results suggest that interventions for children with DLD should focus on enhancing their adaptive ER strategies to help them cope with daily stressors just as in the general population.

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. The full potential of anatomical MRI scans for AD diagnosis might thus not yet have been used optimally. In this study, we therefore combined multiple anatomical MRI measures to improve diagnostic classification of AD. For 21 clinically diagnosed AD patients and 21 cognitively normal controls, we calculated (i) cortical thickness, (ii) cortical area, (iii) cortical curvature, (iv) grey matter density, (v) subcortical volumes, and (vi) hippocampal shape. These six measures were used separately and combined as predictors in an elastic net logistic regression. We made receiver operating curve plots and calculated the area under the curve (AUC) to determine classification performance. AUC values for the single measures ranged from 0.67 (cortical thickness) to 0.94 (grey matter density). The combination of all six measures resulted in an AUC of 0.98. Our results demonstrate that the different anatomical MRI measures contain complementary information. A combination of these measures may therefore improve accuracy of AD diagnosis in clinical practice. Hum Brain Mapp 37:1920-1929, 2016. © 2016 Wiley Periodicals, Inc.

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). We assessed, including and excluding replenishment samples, whether the demographic composition of the samples changed between the first and fifth waves. Replenishment samples were tailored to ensure the sample remained representative of the smoking population. We also constructed a multivariable survival model of attrition that included all five waves with replenishment samples. RESULTS: Of the original 1820 respondents recruited in 2008, 46% participated again in 2012. Demographic differences between waves due to attrition were generally small and replenishment samples tended to minimize them further. The multivariable survival analysis revealed that only two of the 10 variables analysed were significant predictors of attrition: a weak effect for gender (men dropped out more often) and weak to moderate effects for age (respondents aged 15-24 years dropped out more than aged 25-39 years, who dropped out more than those aged 40+ years). CONCLUSIONS: Weak to moderate attrition effects were found for men and younger age groups. This information could be used to minimize respondent attrition. Our findings suggest that sampling weights and tailored replenishment samples can effectively compensate for attrition effects. This is already being done for the International Tobacco Control Netherlands Survey, including the categories that significantly predicted attrition in this study.

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." In addition, the added value of different intensities of therapist support was investigated. METHODS: Participants (N=354) were aged 16 years or older with self-reported ED symptoms, including symptoms of anorexia nervosa, bulimia nervosa, and binge eating disorder. Participants were recruited via the website of Featback and the website of a Dutch pro-recovery-focused e-community for young women with ED problems. Participants were randomized to: (1) Featback, consisting of psychoeducation and a fully automated self-monitoring and feedback system, (2) Featback supplemented with low-intensity (weekly) digital therapist support, (3) Featback supplemented with high-intensity (3 times a week) digital therapist support, and (4) a waiting list control condition. Internet-administered self-report questionnaires were completed at baseline, post-intervention (ie, 8 weeks after baseline), and at 3- and 6-month follow-up. The primary outcome measure was ED psychopathology. Secondary outcome measures were symptoms of depression and anxiety, perseverative thinking, and ED-related quality of life. Statistical analyses were conducted according to an intent-to-treat approach using linear mixed models. RESULTS: The 3 Featback conditions were superior to a waiting list in reducing bulimic psychopathology (d=-0.16, 95% confidence interval (CI)=-0.31 to -0.01), symptoms of depression and anxiety (d=-0.28, 95% CI=-0.45 to -0.11), and perseverative thinking (d=-0.28, 95% CI=-0.45 to -0.11). No added value of therapist support was found in terms of symptom reduction although participants who received therapist support were significantly more satisfied with the intervention than those who did not receive supplemental therapist support. No significant differences between the Featback conditions supplemented with low- and high-intensity therapist support were found regarding the effectiveness and satisfaction with the intervention. CONCLUSIONS: The fully automated Internet-based self-monitoring and feedback intervention Featback was effective in reducing ED and comorbid psychopathology. Supplemental therapist support enhanced satisfaction with the intervention but did not increase its effectiveness. Automated interventions such as Featback can provide widely disseminable and easily accessible care. Such interventions could be incorporated within a stepped-care approach in the treatment of EDs and help to bridge the gap between mental disorders and mental health care services. TRIAL REGISTRATION: Netherlands Trial Registry: NTR3646; http://www.trialregister.nl/trialreg/admin/ rctview.asp?TC=3646 (Archived by WebCite at http://www.webcitation.org/6fgHTGKHE).

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. In this randomized single-blinded placebo-controlled crossover study (12 healthy young men), five sets of resting-state fMRI (RSFMRI), pseudocontinuous arterial spin labeling (PCASL), and corresponding salivary cortisol samples were acquired over 4h, at given intervals under pharmacokinetically-controlled infusion of subanesthetic ketamine (20 & 40mg/70kg/h). An identical procedure was repeated under a sham placebo condition. Differences in the profile of ketamine versus placebo effect over time were examined. Compared to placebo, ketamine mimicked a stress-like response (increased cortisol, reduced calmness and alertness, and impaired working memory). Ketamine effects on the brain included a transient prefrontal hyperperfusion and a dose-related reduction of relative hippocampal perfusion, plus emerging hyperconnectivity between the hippocampus and the occipital, cingulate, precuneal, cerebellar and basal ganglia regions. The spatiotemporal profiles of ketamine effects on different hippocampal subnetworks suggest a topographically dissociable change in corticohippocampal functional connectivity. We discuss our findings in the context of the negative feedback inhibition theory of the hippocampal stress-control. This pilot study provides a methodological framework for multimodal functional neuroimaging under resting-state conditions, which may be generalized for translational studies of glutamatergic- or stress-related etiology of neuropsychiatric disorders.

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. We further explored whether the subgroup variable should be included in the univariate PS. Second, the 2 methods were compared using data from a large effectiveness study on psychotherapy in personality disorders. Using these data we tested the differences between short-term and long-term treatment, with the severity of patients' problems defining the subgroups of interest. RESULTS: The Monte Carlo simulations showed minor differences between both PS methods, with the bias and mean squared error overall marginally lower for the generalized PS. When considering the univariate PS, the subgroup variable can be excluded from the PS estimation and only adjusted for in the outcome equation. When applied to the psychotherapy data, the univariate and generalized PS estimations gave similar results. CONCLUSION: The results support the use of the generalized PS as a feasible method, compared with the univariate PS, to find certain subgroup effects in nonrandomized outcomes research.

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. Analyses revealed a decrease in equity preferences with age and this decrease was stronger for boys than for girls. There was also an age-related increase in the preference for efficient outcomes (i.e., maximization of total available resources), which was again stronger for boys than for girls. Overall, although equity remains as a strong social norm, adolescents are decreasingly strict in adhering to the equity norm and show increasing flexibility in equity preferences.

Spatial heterogeneity of the relation between resting-state connectivity and blood flow: an important consideration for pharmacological studies.

Resting state fMRI (RSfMRI) and arterial spin labeling (ASL) provide the field of pharmacological Neuroimaging tool for investigating states of brain activity in terms of functional connectivity or cerebral blood flow (CBF). Functional connectivity reflects the degree of synchrony or correlation of spontaneous fluctuations--mostly in the blood oxygen level dependent (BOLD) signal--across brain networks; but CBF reflects mean delivery of arterial blood to the brain tissue over time. The BOLD and CBF signals are linked to common neurovascular and hemodynamic mechanisms that necessitate increased oxygen transportation to the site of neuronal activation; however, the scale and the sources of variation in static CBF and spatiotemporal BOLD correlations are likely different. We tested this hypothesis by examining the relation between CBF and resting-state-network consistency (RSNC)--representing average intranetwork connectivity, determined from dual regression analysis with eight standard networks of interest (NOIs)--in a crossover placebo-controlled study of morphine and alcohol. Overall, we observed spatially heterogeneous relations between RSNC and CBF, and between the experimental factors (drug-by-time, time, drug and physiological rates) and each of these metrics. The drug-by-time effects on CBF were significant in all networks, but significant RSNC changes were limited to the sensorimotor, the executive/salience and the working memory networks. The post-hoc voxel-wise statistics revealed similar dissociations, perhaps suggesting differential sensitivity of RSNC and CBF to neuronal and vascular endpoints of drug actions. The spatial heterogeneity of RSNC/CBF relations encourages further investigation into the role of neuroreceptor distribution and cerebrovascular anatomy in predicting spontaneous fluctuations under drugs.

Adolescents' increasing stress response to social evaluation: pubertal effects on cortisol and alpha-amylase during public speaking.

Stress responses to social evaluation are thought to increase during adolescence, which may be due to pubertal maturation. However, empirical evidence is scarce. This study is the first to investigate the relation between pubertal development and biological responses to a social-evaluative stressor longitudinally. Participants performed the Leiden Public Speaking Task twice, with a 2-year interval (N = 217; age at Time 1: 8-17 years). The results support an increase in sensitivity to social evaluation during adolescence. The overall cortisol and alpha-amylase responses increased-both between and within participants-and were more strongly related to self-reported pubertal development than to age. The cortisol response shifted from speech delivery toward anticipation. The alpha-amylase response increased in both phases.

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. We aim to test these questions using socio-economic, questionnaire (including experience sampling methods), behavioral, brain (fMRI, sMRI, EEG), hormonal, and genetic measures in four large cohorts including adolescents and young adults. Two cohorts are designed as test and replication cohorts to test the developmental trajectory of self-regulation, including adolescents of different socioeconomic status thereby bridging individual, family, and societal perspectives. The third cohort consists of an entire social network to examine how neural and self-regulatory development influences and is influenced by whom adolescents and young adults choose to interact with. The fourth cohort includes youth with early signs of antisocial and delinquent behavior to understand patterns of societal development in individuals at the extreme ends of self-regulation and societal participation, and examines pathways into and out of delinquency. We will complement the newly collected cohorts with data from existing large-scale population-based and case-control cohorts. The study is embedded in a transdisciplinary approach that engages stakeholders throughout the design stage, with a strong focus on citizen science and youth participation in study design, data collection, and interpretation of results, to ensure optimal translation to youth in society.

A three-year longitudinal functional magnetic resonance imaging study of performance monitoring and test-retest reliability from childhood to early adulthood.

Previous cross-sectional functional magnetic resonance imaging studies have shown that performance monitoring functions continue to develop well into adolescence, associated with increased activation in brain regions important for cognitive control (prefrontal cortex, anterior cingulate cortex, and parietal cortex). To date, however, the development of performance monitoring has not yet been studied longitudinally, which leaves open the question whether changes can be detected within individuals over time. In the present study, human boys and girls, between ages 8 and 27 years, performed a child-friendly rule-switch task in the scanner on two occasions ∼3.5 years apart. Change versus stability was examined using two methods: (1) repeated-measures analyses and (2) test-retest reliabilities of blood oxygenation level-dependent responses. Results showed that with increasing age, participants performed better on the task. The changes in neural activation associated with the processing of performance feedback were, however, more reliably correlated with changes in performance than with age. Test-retest reliability was at least fair to good for adults and adolescents, but poor to fair for the youngest age group. Substantially more variability was observed in the pattern and magnitude of children compared with adults, which may be interpreted as proxy for developmental change. Together, the results show that (1) change within individuals is variable, and more so for children than for adolescents and adults, and (2) performance is a better predictor for change in neural activation over time. These findings set the stage for studying developmental change in the perspective of multiple predictors, rather than solely by divisions based on age groups.

Healthier food choices as a result of the revised healthy diet programme Krachtvoer for students of prevocational schools.

BACKGROUND: Krachtvoer is a Dutch healthy diet programme for prevocational schools, developed in 2001 and revised for a broader target group in 2007, based on the findings of an evaluation of the first version. The goal of this study was to report on the short- and longer-term total and subgroup effects of the revised programme on students' fruit, fruit juice, breakfast, and snack consumption. METHODS: Schools were randomized to the experimental condition, teaching the Krachtvoer programme, or to the control condition teaching the regular nutrition lessons. Self-reported consumption of fruit, fruit juice, breakfast and snacks was measured at baseline directly before programme implementation, one to four weeks after finishing programme implementation, and after six months. Mixed linear and logistic regression analyses were conducted. RESULTS: In total 1117 students of 13 experimental schools and 758 students of 11 control schools participated in the study. Short- and longer-term favourable intervention effects were found on fruit consumption (mean difference between experimental and control group 0.15 servings at both posttests). Regarding fruit juice consumption, only short-term favourable effects were revealed (mean difference between experimental and control group 0.05 glasses). Intervention effects on breakfast intakes were limited. No changes in snack frequency were reported, but students made healthier snack choices as a result of the programme. Some favourable as well as unfavourable effects occurred in subgroups of students. CONCLUSIONS: The effects on fruit consumption and snack choices justify the current nationwide dissemination of the programme. Achieving changes in breakfast consumption may, however, require other strategies.

The longitudinal relationship between emotion awareness and internalising symptoms during late childhood.

Emotion awareness, the ability to reflect upon the own emotions, is assumed to contribute to better mental health. However, empirical support for this relationship has only been cross-sectional. In this study we examined the extent to which individual differences in changes in emotion awareness over time can explain individual differences in changes in symptoms of internalising problems (depression, fear, worrying and ruminative thoughts). Children and young teenagers (368 boys and 295 girls) were asked four times to fill out self-report questionnaires, with a 6-month time interval between each time. The mean age was 10 years during the first data collection. Longitudinal multilevel analyses showed that the variance in emotion awareness trends was highly predictive for the variance in trends for internalizing problems over time. The ability to differentiate discrete emotions was a strong predictor and negatively contributed to all internalising symptoms. In addition, a diminished tendency to address and value emotions contributed to more depressive symptoms; whereas hiding the own emotions contributed to more worrying and ruminative thoughts. The outcomes show that individual differences in emotion awareness over time make a strong, and, above all, negative contribution to the prediction of the individual differences in various internalizing symptoms. The fact that several aspects of emotional (dys)functioning are uniquely related to different kinds of internalizing problems gives valuable and useful information not only theoretically but also clinically about the distinctive nature of these problems.

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. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

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. For estimating standard errors, the delta method and the cluster bootstrap were compared. The cluster bootstrap structurally underestimated the standard errors of the scalability coefficients, with low coverage values. Except for unequal numbers of raters across subjects and small sets of items, the delta method standard error estimates had negligible bias and good coverage. Post hoc simulations showed that the cluster bootstrap does not correctly reproduce the sampling distribution of the scalability coefficients, and an adapted procedure was suggested. In addition, the delta method standard errors can be slightly improved if the harmonic mean is used for unequal numbers of raters per subject rather than the arithmetic mean.