Computational basis of decision-making impairment in multiple sclerosis

Background: Multiple sclerosis (MS) is commonly associated with decision-making, neurocognitive impairments, and mood and motivational symptoms. However, their relationship may be obscured by traditional scoring methods. Objectives: To study the computational basis underlying decision-making impairments in MS and their interaction with neurocognitive and neuropsychiatric measures. Methods: Twenty-nine MS patients and 26 matched control subjects completed a computer version of the Iowa Gambling Task (IGT). Participants underwent neurocognitive evaluation using an expanded version of the Brief Repeatable Battery. Hierarchical Bayesian Analysis was used to estimate three established computational models to compare parameters between groups. Results: Patients showed increased learning rate and reduced loss-aversion during decision-making relative to control subjects. These alterations were associated with: (1) reduced net gains in the IGT; (2) processing speed, executive functioning and memory impairments; and (3) higher levels of depression and current apathy. Conclusion: Decision-making deficits in MS patients could be described by the interplay between latent computational processes, neurocognitive impairments, and mood/motivational symptoms.

Caesarean Delivery Use in Ethiopia: A Spatial and Hierarchical Bayesian Analysis

Background: Access to emergency obstetric care (EmOC) is very important for reducing maternal mortality. A geographically linked data analysis using population and health facility data is potentially valuable to map caesarean delivery use, and to identify inequalities in service access and provision. Thus, this study aimed to assess the spatial patterns of caesarean delivery use, and to identify associated factors among pregnant women in Ethiopia. Method: A secondary data analysis of the 2016 Ethiopia Demographic and Health Survey linked with the 2014 Ethiopian Service Provision Assessment was conducted. A hierarchical Bayesian analysis was carried out using the SAS MCMC procedure. A spatial analysis was performed to identify the hot spot areas of caesarean delivery use with ArcGIS. Results: Women who had four or more ANC visits were 4.077 (95% Credible Interval (CrI): 1.909, 8.179) times more likely to use caesarean delivery compared to those who had no ANC visits. Pregnant women living in rural areas were 60% less likely to deliver via caesarean section. About 50% of the variability in the rate of caesarean delivery was accounted for by location. The spatial analysis found that Addis Ababa, Dire Dawa and the Harari region had clusters of high caesarean delivery rates. Conclusion: There were significant variations in the use of caesarean delivery services across the different regions of Ethiopia. The findings have important policy implications. The Ethiopian government has to increase the distribution of EmOC facilities and/or to establish a faster transportation system to allow pregnant women to reach EmOC facilities when caesarean delivery is indicated.

Prediction of water consumption by consumer categories: a case study

This study introduces a new Bayesian model for predicting water consumption in a medium-sized municipality in the State of São Paulo, Brazil. For the study, a stratified random sample of water consumption for consumers in different consumer categories (residential, industrial, public and commercial) is selected for 55 monthly consecutive measurements of water consumption and the proposed model is compared with some usual existing time series models (moving average models and ARIMA models) commonly used in forecasts. The Bayesian model for the consumption data assumes the presence of a random effect that captures the possible dependence between the monthly consumption for the different categories. A hierarchical Bayesian analysis is done using MCMC (Markov Chain Monte Carlo) methods to generate samples of the joint posterior distribution of interest. A detailed discussion of the results obtained is presented, showing the advantages and disadvantages of each model proposed in terms of feasibility for the municipality's water supply company. The results of this study can be generalized to water consumption data for any municipality.

Anxiety Modulates Preference for Immediate Rewards Among Trait-Impulsive Individuals: A Hierarchical Bayesian Analysis

Trait impulsivity—defined by strong preference for immediate over delayed rewards and difficulties inhibiting prepotent behaviors—is observed in all externalizing disorders, including substance-use disorders. Many laboratory tasks have been developed to identify decision-making mechanisms and correlates of impulsive behavior, but convergence between task measures and self-reports of impulsivity are consistently low. Long-standing theories of personality and decision-making predict that neurally mediated individual differences in sensitivity to (a) reward cues and (b) punishment cues (frustrative nonreward) interact to affect behavior. Such interactions obscure one-to-one correspondences between single personality traits and task performance. We used hierarchical Bayesian analysis in three samples with differing levels of substance use ( N = 967) to identify interactive dependencies between trait impulsivity and state anxiety on impulsive decision-making. Our findings reveal how anxiety modulates impulsive decision-making and demonstrate benefits of hierarchical Bayesian analysis over traditional approaches for testing theories of psychopathology spanning levels of analysis.

Accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis

The regulation of mRNA controls both overall gene expression as well as the distribution of mRNA isoforms encoded by the gene. Current algorithmic approaches focus on characterization of significant differential expression or alternative splicing events or isoform distribution without integrating both events. Here, we present Hierarchical Bayesian Analysis of Differential Expression and ALternative SPlicing (HBA-DEALS), which simultaneously characterizes differential expression and splicing in cohorts. HBA-DEALS attains state of the art or better performance for both expression and splicing, and allows genes to be characterized as having differential gene expression (DGE), differential alternative splicing (DAST), both, or neither. Based on an analysis of Genotype-Tissue Expression (GTEx) data we demonstrate the existence of sets of genes that show predominant DGE or DAST across a comparison of 20 tissue types, and show that these sets have pervasive differences with respect to gene structure, function, membership in protein complexes, and promoter architecture.