Multiparametric Analysis Combining DSC-MR Perfusion and [18F]FET-PET is Superior to a Single Parameter Approach for Differentiation of Progressive Glioma from Radiation Necrosis.

PURPOSE: Perfusion-weighted (PWI) magnetic resonance imaging (MRI) and O­(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) positron emission tomography (PET) are both useful for discrimination of progressive disease (PD) from radiation necrosis (RN) in patients with gliomas. Previous literature showed that the combined use of FET-PET and MRI-PWI is advantageous; hhowever the increased diagnostic performances were only modest compared to the use of a single modality. Hence, the goal of this study was to further explore the benefit of combining MRI-PWI and [18F]FET-PET for differentiation of PD from RN. Secondarily, we evaluated the usefulness of cerebral blood flow (CBF), mean transit time (MTT) and time to peak (TTP) as previous studies mainly examined cerebral blood volume (CBV). METHODS: In this single center study, we retrospectively identified patients with WHO grades II-IV gliomas with suspected tumor recurrence, presenting with ambiguous findings on structural MRI. For differentiation of PD from RN we used both MRI-PWI and [18F]FET-PET. Dynamic susceptibility contrast MRI-PWI provided normalized parameters derived from perfusion maps (r(relative)CBV, rCBF, rMTT, rTTP). Static [18F]FET-PET parameters including mean and maximum tumor to brain ratios (TBRmean, TBRmax) were calculated. Based on histopathology and radioclinical follow-up we diagnosed PD in 27 and RN in 10 cases. Using the receiver operating characteristic (ROC) analysis, area under the curve (AUC) values were calculated for single and multiparametric models. The performances of single and multiparametric approaches were assessed with analysis of variance and cross-validation. RESULTS: After application of inclusion and exclusion criteria, we included 37 patients in this study. Regarding the in-sample based approach, in single parameter analysis rTBRmean (AUC = 0.91, p < 0.001), rTBRmax (AUC = 0.89, p < 0.001), rTTP (AUC = 0.87, p < 0.001) and rCBVmean (AUC = 0.84, p < 0.001) were efficacious for discrimination of PD from RN. The rCBFmean and rMTT did not reach statistical significance. A classification model consisting of TBRmean, rCBVmean and rTTP achieved an AUC of 0.98 (p < 0.001), outperforming the use of rTBRmean alone, which was the single parametric approach with the highest AUC. Analysis of variance confirmed the superiority of the multiparametric approach over the single parameter one (p = 0.002). While cross-validation attributed the highest AUC value to the model consisting of TBRmean and rCBVmean, it also suggested that the addition of rTTP resulted in the highest accuracy. Overall, multiparametric models performed better than single parameter ones. CONCLUSION: A multiparametric MRI-PWI and [18F]FET-PET model consisting of TBRmean, rCBVmean and PWI rTTP significantly outperformed the use of rTBRmean alone, which was the best single parameter approach. Secondarily, we firstly report the potential usefulness of PWI rTTP for discrimination of PD from RN in patients with glioma; however, for validation of our findings the prospective studies with larger patient samples are necessary.

How many data clusters are in the Galaxy data set?: Bayesian cluster analysis in action.

In model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (Stat Model 1:287-304) compares maximum likelihood and Bayesian analyses of the Galaxy data set and expresses reservations about the Bayesian approach due to the fact that the prior assumptions imposed remain rather obscure while playing a major role in the results obtained and conclusions drawn. The aim of the paper is to address Aitkin's concerns about the Bayesian approach by shedding light on how the specified priors influence the number of estimated clusters. We perform a sensitivity analysis of different prior specifications for the mixtures of finite mixture model, i.e., the mixture model where a prior on the number of components is included. We use an extensive set of different prior specifications in a full factorial design and assess their impact on the estimated number of clusters for the Galaxy data set. Results highlight the interaction effects of the prior specifications and provide insights into which prior specifications are recommended to obtain a sparse clustering solution. A simulation study with artificial data provides further empirical evidence to support the recommendations. A clear understanding of the impact of the prior specifications removes restraints preventing the use of Bayesian methods due to the complexity of selecting suitable priors. Also, the regularizing properties of the priors may be intentionally exploited to obtain a suitable clustering solution meeting prior expectations and needs of the application.

First-episode psychotic disorders in the wake of the COVID-19 pandemic: a descriptive review of casereports.

OBJECTIVE: Since the onset of COVID-19 pandemic, many case reports and case series dealt with new-onset psychotic disorders in patients either infected with SARS-CoV-2 or thematically linked to the pandemic, but without an infection. Our aim was to provide a comprehensive collection of these reports to illustrate the nature of these psychoses. METHODS: We conducted a literature search in MEDLINE, Embase, PsycINFO, using search terms regarding first-episode psychotic disorders in the context of corona. RESULTS: 96 case reports or case series covering 146 patients (62 without and 84 with SARS-CoV-2 infection) were found. Compared to patients without infection, patients with infection showed significantly more often visual hallucinations (28.6% vs 8.1%), confusion (36.9% vs 11.3%), an acute onset of illness (88.5% vs 59.6%) and less often depression (13.1% vs 35.5%) and a delusional content related to the pandemic (29.5% vs 78.3%). Both groups had an equally favourable outcome with a duration of psychosis ≤2 weeks in half and full remission in two-thirds of patients. In patients with infection, signs of inflammation were reported in 78.3% and increased CRP in 58.6%. While reports on patients with infection are continuously published, no report about patients without infection was found after July 2020. CONCLUSION: Cases without infection were considered reactive and originated all from the first wave of the corona pandemic. In cases with infection, inflammation was considered as the main pathogenetic factor but was not found in all patients. Diagnosis was impeded by the overlap of psychosis with delirium.

Lack of association between cortical amyloid deposition and glucose metabolism in early stage Alzheimer´s disease patients.

BACKGROUND: Beta amyloid (Aß) causes synaptic dysfunction leading to neuronal death. It is still controversial if the magnitude of Aß deposition correlates with the degree of cognitive impairment. Diagnostic imaging may lead to a better understanding the role of Aß in development of cognitive deficits. The aim of the present study was to investigate if Aß deposition in the corresponding brain region of early stage Alzheimer´s disease (AD) patients, directly correlates to neuronal dysfunction and cognitive impairment indicated by reduced glucose metabolism. PATIENTS AND METHODS: In 30 patients with a clinical phenotype of AD and amyloid positive brain imaging, 2-[18F] fluoro-2-deoxy-d-glucose (FDG) PET/CT was performed. We extracted the average [18F] flutemetamol (Vizamyl) uptake for each of the 16 regions of interest in both hemispheres and computed the standardized uptake value ratio (SUVR) by dividing the Vimazyl intensities by the mean signal of positive and negative control regions. Data were analysed using the R environment for statistical computing and graphics. RESULTS: Any negative correlation between Aß deposition and glucose metabolism in 32 dementia related and corresponding brain regions in AD patients was not found. None of the correlation coefficient values were statistically significant different from zero based on two-sided p- value. CONCLUSIONS: Regional Aß deposition did not correlate negatively with local glucose metabolism in early stage AD patients. Our findings support the role of Aß as a valid biomarker, but does not permit to conclude that Aß is a direct cause for an aberrant brain glucose metabolism and neuronal dysfunction.

Semi-parametric Regression under Model Uncertainty: Economic Applications.

Economic theory does not always specify the functional relationship between dependent and explanatory variables, or even isolate a particular set of covariates. This means that model uncertainty is pervasive in empirical economics. In this paper, we indicate how Bayesian semi-parametric regression methods in combination with stochastic search variable selection can be used to address two model uncertainties simultaneously: (i) the uncertainty with respect to the variables which should be included in the model and (ii) the uncertainty with respect to the functional form of their effects. The presented approach enables the simultaneous identification of robust linear and nonlinear effects. The additional insights gained are illustrated on applications in empirical economics, namely willingness to pay for housing, and cross-country growth regression.

From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering.

In model-based clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al. (Stat Comput 26:303-324, 2016) are sparse finite mixtures, where the prior distribution on the weight distribution of a mixture with K components is chosen in such a way that a priori the number of clusters in the data is random and is allowed to be smaller than K with high probability. The number of clusters is then inferred a posteriori from the data. The present paper makes the following contributions in the context of sparse finite mixture modelling. First, it is illustrated that the concept of sparse finite mixture is very generic and easily extended to cluster various types of non-Gaussian data, in particular discrete data and continuous multivariate data arising from non-Gaussian clusters. Second, sparse finite mixtures are compared to Dirichlet process mixtures with respect to their ability to identify the number of clusters. For both model classes, a random hyper prior is considered for the parameters determining the weight distribution. By suitable matching of these priors, it is shown that the choice of this hyper prior is far more influential on the cluster solution than whether a sparse finite mixture or a Dirichlet process mixture is taken into consideration.

Identifying Mixtures of Mixtures Using Bayesian Estimation.

The use of a finite mixture of normal distributions in model-based clustering allows us to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework, we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior, where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows us to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching issue and results in an identified model, our approach allows us to simultaneously (1) determine the number of clusters, (2) flexibly approximate the cluster distributions in a semiparametric way using finite mixtures of normals and (3) identify cluster-specific parameters and classify observations. The proposed approach is illustrated in two simulation studies and on benchmark datasets. Supplementary materials for this article are available online.

Treatment Duration With Long-Acting Injectable Antipsychotics After In-hospital Initiation: A Retrospective Cohort Study.

PURPOSE: Long-acting injectable (LAI) antipsychotics are recommended especially for patients with multiple admissions and poor adherence. The empirical basis of this strategy is a matter of debate. METHODS: In a retrospective cohort study extending over 6 years, all patients admitted for inpatient treatment with a diagnosis of psychotic disorders according to International Statistical Classification of Diseases and Related Health Problems, 10th Revision (F2) were screened for treatment episodes with a new start of an LAI. Indication for LAI treatment was based primarily on previous medication default. All-cause discontinuation was used as a measure of treatment efficiency. Patients with early dropout (termination of LAI treatment within 6 months) were compared with patients with longer treatment (treatment >6 months) for sociodemographic and treatment variables using bivariate and multivariate analyses. RESULTS: A total of 194 treatment episodes with new start of LAIs were identified. Almost one half dropped out within 6 months (early dropout: n = 95 [49%]; mean duration, 2.2 months). Termination of treatment was mainly due to patients' refusal to continue. However, almost a third of patients (61; 31.4%) had a treatment duration of more than 2 years. In a multivariate Cox regression model, longer treatment duration was associated with older age (P = 0.05), not being single (P = 0.04), fewer admissions during the year preceding the index episode (P = 0.02), and better ratings for adherence at the index episode (P = 0.03). CONCLUSIONS: There are both more patients than expected leaving the treatment early and more patients than expected staying for long periods, even among patients with a history of poor adherence.

Model-based clustering based on sparse finite Gaussian mixtures.

In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number of non-empty components visited during MCMC sampling. Specifying a shrinkage prior, namely the normal gamma prior, on the component means leads to improved parameter estimates as well as identification of cluster-relevant variables. After estimating the mixture model using MCMC methods based on data augmentation and Gibbs sampling, an identified model is obtained by relabeling the MCMC output in the point process representation of the draws. This is performed using [Formula: see text]-centroids cluster analysis based on the Mahalanobis distance. We evaluate our proposed strategy in a simulation setup with artificial data and by applying it to benchmark data sets.

Mood-dependent changes of serum lithium concentration in a rapid cycling patient maintained on stable doses of lithium carbonate.

OBJECTIVE: Serum lithium levels may be influenced by mood state. We report on a 58-year-old female patient suffering from rapid cycling bipolar disorder. Her serum lithium levels varied greatly, despite stable medication. METHODS: The patient was observed over a one-year period. RESULTS: The patient received a stable medication of lithium carbonate (450 mg), valproate (1500 mg), and clozapine (200 mg). Investigating mood and serum lithium levels over one year revealed six manic and six depressive phases. The mean lithium serum level was 0.67 mmol/L in the depressive states, 0.39 mmol/L in the manic states (t = 4.11, p = 0.001 versus depression), and 0.40 mmol/L in the euthymic states (t = 3.58, p = 0.003 versus depression). Noncompliance was ruled out. The patient gained up to 8 kg during manic phases, accompanied by pretibial edema. CONCLUSIONS: Changes in serum lithium concentration are probably not caused by altered lithium, but by water metabolism. During mania, body water increases, leading to dilution and therefore a reduction in serum lithium levels. As there is no proof for any other known cause of hypervolemia, we propose the hypothesis that the increase in body water is due to a variant of idiopathic edema.