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.

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.

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. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models. For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model. In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice.

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.

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.

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. In this study we investigated early CAA -related alterations in the white matter. Methods and Results We investigated diffusion magnetic resonance imaging ( dMRI ) data for 15 symptomatic and 11 presymptomatic HCHWA -D mutation carriers and 30 noncarrier control participants using 4 different approaches. We looked at (1) the relation between age and global dMRI measures for mutation carriers versus controls, (2) voxel-wise d MRI , (3) independent component-clustered dMRI measures, and (4) structural connectomics between presymptomatic or symptomatic carriers and controls. Fractional anisotropy decreased, and mean diffusivity and peak width of the skeletonized mean diffusivity increased significantly over age for mutation carriers compared with controls. In addition, voxel-wise and independent component-wise fractional anisotropy, and mean diffusivity, and structural connectomics were significantly different between HCHWA -D patients and control participants, mainly in the periventricular frontal and occipital regions and in the occipital lobe. We found no significant differences between presymptomatic carriers and control participants. Conclusions The d MRI technique is sensitive in detecting alterations in symptomatic HCHWA -d carriers but did not show alterations in presymptomatic carriers. This result indicates that d MRI may be less suitable for identifying early white matter changes in CAA .

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.

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.

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. METHODS: Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS: Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION: Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

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.

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. However, the IPC model is not able to represent both quantities at the same time. We then derived a new parametrization of the model, the bivariate IPC (BIPC) model, which is able to represent both the marginal probabilities and the association structure. Like the standard IPC model, the results of the BIPC model can be displayed in a biplot, from which the effects of predictors on the binary responses and on their association can be read. We will illustrate our findings with a psychological example relating personality traits to depression and anxiety disorders.

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).

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.

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. We performed a longitudinal cohort study with repeated assessments after 2 and 4 years in a sample of 737 persons with a 6-month recency dysthymic and/or major depressive disorder, a sample of 1150 remitted persons with a history of previous depressive disorders; and a sample of 626 persons with no 6-month recency depressive or anxiety disorders and no previous depressive disorders. Experiential avoidance predicted onset, relapse as well as maintenance of depressive disorders during the 4-year follow-up period. However, after controlling for rumination, worry and neuroticism, experiential avoidance no longer significantly predicted onset, relapse or maintenance of depressive disorders in contrast to repetitive thinking in the form of rumination or worry. Experiential avoidance also did not mediate or moderate the effect of rumination, worry and neuroticism.

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. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

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.

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. At each wave psychological distress was measured with the Inventory of Depressive Symptomatology, Beck Anxiety Inventory, and Fear Questionnaire. At T0 childhood maltreatment types were measured with a semi-structured interview (Childhood Trauma Interview) and personality traits with the NEO-Five Factor Inventory. RESULTS: Using latent variable analyses, we found that severity of childhood maltreatment (emotional neglect and abuse in particular) predicted higher initial levels of psychological distress and that this effect was mediated by maladaptive personality types. Differences in trajectories of distress between persons with varying levels of childhood maltreatment remained significant and stable over time. LIMITATIONS: Childhood maltreatment was assessed retrospectively and maladaptive personality types and level of psychological distress at study entry were assessed concurrently. CONCLUSIONS: Routine assessment of maladaptive personality types and possible childhood emotional maltreatment in persons with severe and prolonged psychological distress seems warranted to identify persons who may need a different or more intensive treatment.

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.

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.