Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.

In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple non-stationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through a Vecchia-type approximation of our prior that retains full spatial rank and can be constructed for a wide class of spatial correlation functions. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses and several alternatives. Finally, we illustrate our methods in an analysis of cortical surface functional magnetic resonance imaging task contrast data from a large cohort of children enrolled in the adolescent brain cognitive development study.

A framework of NGO inside and outside strategies in the commercial determinants of health: findings from a narrative review.

BACKGROUND: Public health scholarship has uncovered a wide range of strategies used by industry actors to promote their products and influence government regulation. Less is known about the strategies used by non-government organisations to attempt to influence commercial practices. This narrative review applies a political science typology to identify a suite of 'inside' and 'outside' strategies used by NGOs to attempt to influence the commercial determinants of health. METHODS: We conducted a systematic search in Web of Science, ProQuest and Scopus. Articles were eligible for inclusion if they comprised an empirical study, explicitly sought to examine 'NGOs', were in English, and identified at least one NGO strategy aimed at commercial and/or government policy and practice. RESULTS: One hundred forty-four studies met the inclusion criteria. Eight industry sectors were identified: extractive, tobacco, food, alcohol, pharmaceuticals, weapons, textiles and asbestos, and a small number of general studies. We identified 18 types of NGO strategies, categorised according to the target (i.e. commercial actor or government actor) and type of interaction with the target (i.e. inside or outside). Of these, five NGO 'inside' strategies targeted commercial actors directly: 1) participation in partnerships and multistakeholder initiatives; 2) private meetings and roundtables; 3) engaging with company AGMs and shareholders; 4) collaborations other than partnerships; and 5) litigation. 'Outside' strategies targeting commercial actors through the mobilisation of public opinion included 1) monitoring and reporting; 2) protests at industry sites; 3) boycotts; 4) directly engaging the public; and 5) creative use of alternative spaces. Four NGO 'inside' strategies directly targeting government actors included: 1) lobbying; 2) drafting legislation, policies and standards; 3) providing technical support and training; and 4) litigation. NGO 'outside' strategies targeting government included 1) protests and public campaigns; 2) monitoring and reporting; 3) forum shifting; and 4) proposing and initiating alternative solutions. We identified three types of NGO impact: substantive, procedural, and normative. CONCLUSION: The analysis presents a matrix of NGO strategies used to target commercial and government actors across a range of industry sectors. This framework can be used to guide examination of which NGO strategies are effective and appropriate, and which conditions enable NGO influence.

Radiologist age and diagnostic errors.

PURPOSE: Previous investigations into the causes of error by radiologists have addressed work schedule, volume, shift length, and sub-specialization. Studies regarding possible associations between radiologist errors and radiologist age and timing of residency training are lacking in the literature, to our knowledge. The aim of our study was to determine if radiologist age and residency graduation date is associated with diagnostic errors. METHODS: Our retrospective analysis included 1.9 million preliminary interpretations (out of a total of 5.2 million preliminary and final interpretations) of imaging examinations by 361 radiologists in a US-based national teleradiology practice between 1/1/2019 and 1/1/2020. Quality assurance data regarding the number of radiologist errors was generated through client facility feedback to the teleradiology practice. With input from both the client radiologist and the teleradiologist, the final determination of the presence, absence, and severity of a teleradiologist error was determined by the quality assurance committee of radiologists within the teleradiology company using standardized criteria. Excluded were 3.2 million final examination interpretations and 93,963 (1.8%) of total examinations from facilities reporting less than one discrepancy in examination interpretation in 2019. Logistic regression with covariates radiologist age and residency graduation date was performed for calculation of relative risk of overall error rates and by major imaging modality. Major errors were separated from minor errors as those with a greater likelihood of affecting patient care. Logistic regression with covariates radiologist age, residency graduation date, and log total examinations interpreted was used to calculate odds of making a major error to that of making a minor error. RESULTS: Mean age of the 361 radiologists was 51.1 years, with a mean residency graduation date of 2001. Mean error rate for all examinations was 0.5%. Radiologist age at any residency graduation date was positively associated with major errors (p < 0.05), with a relative risk 1.021 for each 1-year increase in age and relative risk 1.235 for each decade as well as for minor errors (p < 0.05, relative risk 1.007 for each year, relative risk 1.082 for each decade). By major imaging modality, radiologist age at any residency graduation date was positively associated with computed tomography (CT) and X-ray (XR) major and minor error, magnetic resonance imaging (MRI) major error, and ultrasound (US) minor error (p < 0.05). Radiologist age was positively associated with odds of making a major vs. minor error (p < 0.05). CONCLUSIONS: The mean error rate for all radiologists was low. We observed that increasing age at any residency graduation date was associated with increasing relative risk of major and minor errors as well as increasing odds of a major vs. minor error among providers. Further study is needed to corroborate these results, determine clinical relevance, and highlight strategies to address these findings.

Daytime, evening, and overnight: the 24-h radiology cycle and impact on interpretative accuracy.

PURPOSE: To assess the influence of time of day when a study is interpreted on discrepancy rates for common and advanced studies performed in the acute community setting. METHODS: This retrospective study used the databank of a U.S. teleradiology company to retrieve studies between 2012 and 2016 with a preliminary report followed by a final report by the on-site client hospital. Neuroradiology, abdominal radiology, and musculoskeletal radiology studies were included. Teleradiologists were fellowship trained in one of these subspecialty areas. Daytime, evening, and overnight times were defined. Associations between major and minor discrepancies, time of day, and whether the study was common or advanced were tested with significance set at p = .05. RESULTS: A total of 5,883,980 studies were analyzed. There were 8444 major discrepancies (0.14%) and 17,208 minor discrepancies (0.29%). For common studies, daytime (0.13%) and evening (0.13%) had lower major discrepancy rates compared to overnight (0.14%) (daytime to overnight, RR = 0.57, 95%CI: 0.45, 0.72, p < 0.01 and evening to overnight, RR = 0.57, 95%CI: 0.49,0.67, p < 0.01). Minor discrepancy rates for common studies were decreased for evening (0.29%) compared to overnight (0.30%) (RR = 0.89, 95%CI: 0.80,0.99, p = 0.029). For advanced studies, daytime (.15%) had lower major discrepancy rates compared to evening (0.20%) and overnight (.23%) (daytime to evening, RR = 0.77, 95%CI: 0.61, 0.97, p = 0.028 and daytime to overnight, RR = 0.66, 95%CI: 0.50, 0.87, p ≤ 0.01). CONCLUSION: Significantly higher major discrepancy rates for studies interpreted overnight suggest the need for radiologists to exercise greater caution when interpreting studies overnight and may require practice management strategies to help optimize overnight work conditions. The lower major discrepancy rates on advanced studies interpreted during the daytime suggest the need for reserving advanced studies for interpretation during the day when possible.

A spatial Bayesian latent factor model for image-on-image regression.

Image-on-image regression analysis, using images to predict images, is a challenging task, due to (1) the high dimensionality and (2) the complex spatial dependence structures in image predictors and image outcomes. In this work, we propose a novel image-on-image regression model, by extending a spatial Bayesian latent factor model to image data, where low-dimensional latent factors are adopted to make connections between high-dimensional image outcomes and image predictors. We assign Gaussian process priors to the spatially varying regression coefficients in the model, which can well capture the complex spatial dependence among image outcomes as well as that among the image predictors. We perform simulation studies to evaluate the out-of-sample prediction performance of our method compared with linear regression and voxel-wise regression methods for different scenarios. The proposed method achieves better prediction accuracy by effectively accounting for the spatial dependence and efficiently reduces image dimensions with latent factors. We apply the proposed method to analysis of multimodal image data in the Human Connectome Project where we predict task-related contrast maps using subcortical volumetric seed maps.

Interpretations of Examinations Outside of Radiologists' Fellowship Training: Assessment of Discrepancy Rates Among 5.9 Million Examinations From a National Teleradiology Databank.

BACKGROUND. In community settings, radiologists commonly function as multispecialty radiologists, interpreting examinations outside of their area of fellowship training. OBJECTIVE. The purpose of this article was to compare discrepancy rates for preliminary interpretations of acute community-setting examinations that are concordant versus discordant with interpreting radiologists' area of fellowship training. METHODS. This retrospective study used the databank of a U.S. teleradiology company that provides preliminary interpretations for client community hospitals. The analysis included 5,883,980 acute examinations performed from 2012 to 2016 that were preliminarily interpreted by 269 teleradiologists with a fellowship of neuroradiology, abdominal radiology, or musculoskeletal radiology. When providing final interpretations, client on-site radiologists voluntarily submitted quality assurance (QA) requests if preliminary and final interpretations were discrepant; the teleradiology company's QA committee categorized discrepancies as major (n = 8444) or minor (n = 17,208). Associations among examination type (common vs advanced), relationship between examination subspecialty and the teleradiologist's fellowship (concordant vs discordant), and major and minor discrepancies were assessed using three-way conditional analyses with generalized estimating equations. RESULTS. For examinations with a concordant subspecialty, the major discrepancy rate was lower for common than for advanced examinations (0.13% vs 0.26%; relative risk [RR], 0.50, 95% CI, 0.42-0.60; p < .001). For examinations with a discordant subspecialty, the major discrepancy rate was lower for common than advanced examinations (0.14% vs 0.18%; RR, 0.81; 95% CI, 0.72-0.90; p < .001). For common examinations, the major discrepancy rate was not different between examinations with concordant versus discordant subspecialty (0.13% vs 0.14%; RR, 0.90; 95% CI, 0.81-1.01; p = .07). For advanced examinations, the major discrepancy rate was higher for examinations with concordant versus discordant subspecialty (0.26% vs 0.18%; RR, 1.45; 95% CI, 1.18-1.79; p < .001). The minor discrepancy rate was higher among advanced examinations for those with concordant versus discordant subspecialty (0.34% vs 0.29%; RR, 1.17; 95% CI, 1.00-1.36; p = .04), but not different for other comparisons (p > .05). CONCLUSION. Major and minor discrepancy rates were not higher for acute community-setting examinations outside of interpreting radiologists' fellowship training. Discrepancy rates increased for advanced examinations. CLINICAL IMPACT. The findings support multispecialty radiologist practice in acute community settings. Efforts to match examination and interpreting radiologist sub-specialty may not reduce diagnostic discrepancies.

Exposure-Focused CBT Outperforms Relaxation-Based Control in an RCT of Treatment for Child and Adolescent Anxiety.

OBJECTIVE: The relative contribution of individual cognitive behavioral therapy (CBT) components to treatment outcomes for child anxiety disorders (CADs) is unclear. Recent meta-analyses suggest that exposure may be the primary active ingredient in CBT for CADs, and that relaxation may be relatively less effective. This brief report tests the hypothesis that exposure-focused CBT (EF-CBT) would outperform a relaxation-based active therapy control (Relaxation Mentorship Training; RMT) for the treatment of CADs. METHOD: Participants were 102 youth with CADs (mean age = 11.91, 26 males; 76.4% White, 14.7% Multiracial, 3.9% Black, 3.9% Asian, 0.9% other/do not wish to identify) as part of an ongoing neuroimaging randomized controlled trial. Participants were randomly assigned (ratio 2:1) to receive 12 sessions of EF-CBT (n = 70) or RMT (n = 32). Clinical improvement was measured at Week 12 (Clinical Global Impression - Improvement scale; CGI-I); treatment response was defined as receiving a rating of "very much" or "much improved" on the CGI-I. Anxiety severity was measured at Weeks 1, 6, 9, 12 (Pediatric Anxiety Rating Scale; PARS). Outcome measures were completed by an independent evaluator unaware of condition. RESULTS: EF-CBT exhibited 2.98 times higher odds of treatment completion than RMT; 13 treatment non-completers were included in analyses. Estimated treatment response rates were higher for EF-CBT (57.3%) than for RMT (19.2%). Longitudinal analyses indicated that EF-CBT was associated with faster and more pronounced anxiety reductions than RMT on the PARS (Hedges' g = .77). CONCLUSIONS: Results suggest that EF-CBT without relaxation is effective for CADs, and more effective than a relaxation-based intervention.

Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation.

Parametric response mapping (PRM) is a novel computed tomography (CT) technology that has shown potential for assessment of bronchiolitis obliterans syndrome (BOS) after hematopoietic stem cell transplantation (HCT). The primary aim of this study was to evaluate whether variations in image acquisition under real-world conditions affect the PRM measurements of clinically diagnosed BOS. CT scans were obtained retrospectively from 72 HCT recipients with BOS and graft-versus-host disease from Fred Hutchinson Cancer Research Center, Karolinska Institute, and the University of Michigan. Whole lung volumetric scans were performed at inspiration and expiration using site-specific acquisition and reconstruction protocols. PRM and pulmonary function measurements were assessed. Patients with moderately severe BOS at diagnosis (median forced expiratory volume at 1 second [FEV1] 53.5% predicted) had similar characteristics between sites. Variations in site-specific CT acquisition protocols had a negligible effect on the PRM-derived small airways disease (SAD), that is, BOS measurements. PRM-derived SAD was found to correlate with FEV1% predicted and FEV1/ forced vital capacity (R = -0.236, P = .046; and R = -0.689, P < .0001, respectively), which suggests that elevated levels in the PRM measurements are primarily affected by BOS airflow obstruction and not CT scan acquisition parameters. Based on these results, PRM may be applied broadly for post-HCT diagnosis and monitoring of BOS.

Lung T1 mapping magnetic resonance imaging in the assessment of pulmonary disease in children with cystic fibrosis: a pilot study.

BACKGROUND: Assessment tools for early cystic fibrosis (CF) lung disease are limited. Detecting early pulmonary disease is crucial to increasing life expectancy by starting interventions to slow the progression of the pulmonary disease with the many treatment options available. OBJECTIVE: To compare the utility of lung T1-mapping MRI with ultrashort echo time (UTE) MRI in children with cystic fibrosis in detecting early stage lung disease and monitoring pulmonary exacerbations. MATERIALS AND METHODS: We performed a prospective study in 16 children between September 2017 and January 2018. In Phase 1, we compared five CF patients with normal spirometry (mean 11.2 years) to five age- and gender-matched healthy volunteers. In Phase 2, we longitudinally evaluated six CF patients (median 11 years) in acute pulmonary exacerbation. All children had non-contrast lung T1-mapping and UTE MRI and spirometry testing. We compared the mean normalized T1 value and percentage lung volume without T1 value in CF patients and healthy subjects in Phase 1 and during treatment in Phase 2. We also performed cystic fibrosis MRI scoring. We evaluated differences in continuous variables using standard statistical tests. RESULTS: In Phase 1, mean normalized T1 values of the lung were significantly lower in CF patients in comparison to healthy controls (P=0.02) except in the right lower lobe (P=0.29). The percentage lung volume without T1 value was also significantly higher in CF patients (P=0.006). UTE MRI showed no significant differences between CF patients and healthy volunteers (P=0.11). In Phase 2, excluding one outlier case who developed systemic disease in the course of treatment, the whole-lung T1 value increased (P=0.001) and perfusion scoring improved (P=0.02) following therapy. We observed no other significant changes in the MRI scoring. CONCLUSION: Lung T1-mapping MRI can detect early regional pulmonary CF disease in children and might be helpful in the assessment of acute pulmonary exacerbations.

Effects of Tumor Burden on Reference Tissue Standardized Uptake for PET Imaging: Modification of PERCIST Criteria.

Purpose To examine the effect metabolic burden (tumor and/or cardiac myocyte uptake) has on fluorine 18 fluorodeoxyglucose (FDG) distribution in organs and tissues of interest. Materials and Methods Positron emission tomographic (PET)/computed tomographic (CT) scans at the Ann Arbor Veterans Affairs hospital from January to July 2015 were reviewed. A total of 107 scans (50 patients; mean age, 64.3 years ± 13.2 [standard deviation]) had metabolic tissue burden assessed by using total lesion glycolysis (TLG) obtained from autosegmentation of the tumor and/or cardiac tissue. Standardized uptake value (SUV) and subsequent normalized SUV uptake in target organs and tissues were compared with 436 FDG PET/CT scans previously reported in 229 patients as a function of TLG to describe the effect(s) that metabolic burden has on reference tissue (blood pool, liver, and brain) FDG uptake. Subsequent regression by using linear mixed-effects models was used. If the slope of the regression was significantly (P < .05) different than zero, then an effect from TLG was present. Results There was a negative inverse relationship (P < .0001) between FDG uptake within reference tissues (blood pool, liver, and brain) and TLG in comparison to the study population at similar blood glucose levels. This TLG effect was no longer statistically significant (P > .05) when FDG uptake was normalized to a reference tissue (eg, blood pool or liver). Conclusion Metabolic tissue burden can have a significant effect on SUV measurements for PET imaging. This effect can be mitigated by normalizing FDG uptake to a reference tissue. © RSNA, 2018.

Impact of Clinical History on Maximum PI-RADS Version 2 Score: A Six-Reader 120-Case Sham History Retrospective Evaluation.

Purpose To assess the impact of clinical history on the maximum Prostate Imaging Recording and Data System (PI-RADS) version 2 (v2) score assigned to multiparametric magnetic resonance (MR) imaging of the prostate. Materials and Methods This retrospective cohort study included 120 consecutively selected multiparametric prostate MR imaging studies performed between November 1, 2016, and December 31, 2016. Sham clinical data in four domains (digital rectal examination, prostate-specific antigen level, plan for biopsy, prior prostate cancer history) were randomly assigned to each case by using a balanced orthogonal design. Six fellowship-trained abdominal radiologists independently reviewed the sham data, actual patient age, and each examination while they were blinded to interreader scoring, true clinical data, and histologic findings. Readers were told the constant sham histories were true, believed the study to be primarily investigating interrater agreement, and were asked to assign a maximum PI-RADS v2 score to each case. Linear regression was performed to assess the association between clinical variables and maximum PI-RADS v2 score designation. Intraclass correlation coefficients (ICCs) were obtained to compare interreader scoring. Results Clinical information had no significant effect on maximum PI-RADS v2 scoring for any of the six readers (P = .09-.99, 42 reader-variable pairs). Distributions of maximum PI-RADS v2 scores in the research context were similar to the distribution of the scores assigned clinically and had fair-to-excellent pairwise interrater agreement (ICC range: 0.53-0.76). Overall interrater agreement was good (ICC: 0.64; 95% confidence interval: 0.57, 0.71). Conclusion Clinical history does not appear to be a substantial bias in maximum PI-RADS v2 score assignment. This is potentially important for clinical nomograms that plan to incorporate PI-RADS v2 score and clinical data into their algorithms (ie, PI-RADS v2 scoring is not confounded by clinical data).

Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets.

Time series analysis of fMRI data: Spatial modelling and Bayesian computation.

Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. Spatial models and Bayesian approaches for inference in such models have advantages over more traditional mass univariate approaches; however, a major challenge for such analyses is the required computation. As a result, the neuroimaging community has embraced approximate Bayesian inference based on mean-field variational Bayes (VB) approximations. These approximations are implemented in standard software packages such as the popular statistical parametric mapping software. While computationally efficient, the quality of VB approximations remains unclear even though they are commonly used in the analysis of neuroimaging data. For reliable statistical inference, it is important that these approximations be accurate and that users understand the scenarios under which they may not be accurate. We consider this issue for a particular model that includes spatially varying coefficients. To examine the accuracy of the VB approximation, we derive Hamiltonian Monte Carlo (HMC) for this model and conduct simulation studies to compare its performance with VB in terms of estimation accuracy, posterior variability, the spatial smoothness of estimated images, and computation time. As expected, we find that the computation time required for VB is considerably less than that for HMC. In settings involving a high or moderate signal-to-noise ratio (SNR), we find that the 2 approaches produce very similar results suggesting that the VB approximation is useful in this setting. On the other hand, when one considers a low SNR, substantial differences are found, suggesting that the approximation may not be accurate in such cases and we demonstrate that VB produces Bayes estimators with larger mean squared error. A comparison of the 2 computational approaches in an application examining the hemodynamic response to face perception in addition to a comparison with the traditional mass univariate approach in this application is also considered. Overall, our work clarifies the usefulness of VB for the spatiotemporal analysis of fMRI data, while also pointing out the limitation of VB when the SNR is low and the utility of HMC in this case.

Removal of T-Fasteners Immediately After Percutaneous Gastrostomy Tube Placement: Experience in 488 Patients.

OBJECTIVE: The purpose of this article is to evaluate the safety of T-fastener removal immediately after percutaneous gastrostomy tube placement by assessing difference in complication rates between early and delayed gastropexy removal. MATERIALS AND METHODS: A retrospective review was performed of the electronic medical records of all patients who underwent percutaneous gastrostomy at our institution from January 2015 to June 2017. A total of 722 patients successfully underwent gastrostomy tube insertion during this period. Two hundred twenty-two patients were excluded from analysis on the basis of our exclusion criteria. Twelve patients were lost to follow-up. The remaining patients were divided into two groups: group 1 included 350 patients who had their T-fasteners released immediately after gastrostomy, and group 2 included 138 patients whose T-fasteners were left in place for 2 weeks. Electronic medical records were reviewed for postprocedure complications for up to 1 month. Complications were classified according to Society of Interventional Radiology classification. Statistical analysis was performed using a logistic regression model with calculation of odds ratios, power, p values, and 95% CIs. RESULTS: Seven (2.0%) major and 24 (6.8%) minor complications occurred in group 1. Two (1.4%) major and 11 (7.9%) minor complications occurred in group 2. There was no statistically significant difference in complication rates between immediate and delayed gastropexy removal. CONCLUSION: In our study, immediate removal of T-fasteners was not associated with a higher complication rate.

Rheumatic heart disease in Timor-Leste school students: an echocardiography-based prevalence study.

OBJECTIVES: To determine the prevalence of rheumatic heart disease (RHD) in school-aged children and young people in Timor-Leste. DESIGN: Prospective cross-sectional survey. Echocardiography was performed by Australian cardiologists to determine the presence of RHD. Demographic data were also collected. Patients in whom RHD was detected were entered into a register to allow monitoring of adherence to secondary prophylaxis; the first dose of benzathine penicillin G (BPG) was administered on the day of screening. SETTING: Schools in urban (Dili) and rural (Ermera) Timor-Leste. PARTICIPANTS: School students aged 5-20 years. OUTCOME MEASURES: Definite and borderline RHD, as defined by World Heart Federation echocardiographic criteria. RESULTS: 1365 participants were screened; their median age was 11 years (IQR, 9-14 years), and 53% were girls. The estimated prevalence of definite RHD was 18.3 cases per 1000 population (95% CI, 12.3-27.0 per 1000), and of definite or borderline RHD 35.2 per 1000 (95% CI, 26.5-46.4 per 1000). Definite (adjusted odds ratio [aOR], 3.5; 95% CI, 1.3-9.4) and definite or borderline RHD (aOR, 2.7; 95% CI, 1.4-5.2) were more prevalent among girls than boys. Eleven children (0.8%) had congenital heart disease. Of the 25 children in whom definite RHD was identified, 21 (84%) received education and a first dose of BPG on the day of screening; all 25 have since received education about primary care for RHD and have commenced penicillin prophylaxis. CONCLUSIONS: The rates of RHD in Timor-Leste are among the highest in the world, and prevalence is higher among girls than boys. Community engagement is essential for ensuring follow-up and the effective delivery of secondary prophylaxis.

Multivoxel Pattern Analysis Does Not Provide Evidence to Support the Existence of Basic Emotions.

Saarimaki et al. (2015) published a paper claiming to find the neural "fingerprints" for anger, fear, disgust, happiness, sadness, and surprise using multivariate pattern analysis. There are 2 ways in which Saarimaki et al.'s interpretation mischaracterizes their actual findings. The first is statistical: a pattern that successfully distinguishes the members of one category from the members of another (with an accuracy greater than that which might be expected by chance) is not a "fingerprint" (i.e., an essence); it is an abstract, statistical summary of a variable population of instances. The second way in which Saarimaki et al.'s interpretation mischaracterizes their results is conceptual: their findings do not actually meet the specific criteria for basic emotion theory. Instead, their findings are more consistent with a theory of constructed emotion. In our view, Saarimaki et al. is elegant in method and important in that it demonstrates empirical support for a theory of emotion that relies on population thinking; it is also an example of how essentialism-the belief that all instances of a category possesses necessary features that define what is, and what is not, a category member-contributes to a fundamental misunderstanding of the neural basis of emotion.

Using Cox cluster processes to model latent pulse location patterns in hormone concentration data.

Many hormones, including stress hormones, are intermittently secreted as pulses. The pulsatile location process, describing times when pulses occur, is a regulator of the entire stress system. Characterizing the pulse location process is particularly difficult because the pulse locations are latent; only hormone concentration at sampled times is observed. In addition, for stress hormones the process may change both over the day and relative to common external stimuli. This potentially results in clustering in pulse locations across subjects. Current approaches to characterizing the pulse location process do not capture subject-to-subject clustering in locations. Here we show how a Bayesian Cox cluster process may be adapted as a model of the pulse location process. We show that this novel model of pulse locations is capable of detecting circadian rhythms in pulse locations, clustering of pulse locations between subjects, and identifying exogenous controllers of pulse events. We integrate our pulse location process into a model of hormone concentration, the observed data. A spatial birth-and-death Markov chain Monte Carlo algorithm is used for estimation. We exhibit the strengths of this model on simulated data and adrenocorticotropic and cortisol data collected to study the stress axis in depressed and non-depressed women.

The coordinate-based meta-analysis of neuroimaging data.

Neuroimaging meta-analysis is an area of growing interest in statistics. The special characteristics of neuroimaging data render classical meta-analysis methods inapplicable and therefore new methods have been developed. We review existing methodologies, explaining the benefits and drawbacks of each. A demonstration on a real dataset of emotion studies is included. We discuss some still-open problems in the field to highlight the need for future research.

Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods.

The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doubly-stochastic property, i.e., it is an hierarchical combination of a Poisson process at the first level and a Gaussian Process at the second level. Various methods have been proposed to estimate such a process, including traditional likelihood-based approaches as well as Bayesian methods. We focus here on Bayesian methods and several approaches that have been considered for model fitting within this framework, including Hamiltonian Monte Carlo, the Integrated nested Laplace approximation, and Variational Bayes. We consider these approaches and make comparisons with respect to statistical and computational efficiency. These comparisons are made through several simulation studies as well as through two applications, the first examining ecological data and the second involving neuroimaging data.

A Bayesian model of category-specific emotional brain responses.

Understanding emotion is critical for a science of healthy and disordered brain function, but the neurophysiological basis of emotional experience is still poorly understood. We analyzed human brain activity patterns from 148 studies of emotion categories (2159 total participants) using a novel hierarchical Bayesian model. The model allowed us to classify which of five categories--fear, anger, disgust, sadness, or happiness--is engaged by a study with 66% accuracy (43-86% across categories). Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures. The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks. The model provides a precise summary of the prototypical patterns for each emotion category, and demonstrates that a sufficient characterization of emotion categories relies on (a) differential patterns of involvement in neocortical systems that differ between humans and other species, and (b) distinctive patterns of cortical-subcortical interactions. Thus, these findings are incompatible with several contemporary theories of emotion, including those that emphasize emotion-dedicated brain systems and those that propose emotion is localized primarily in subcortical activity. They are consistent with componential and constructionist views, which propose that emotions are differentiated by a combination of perceptual, mnemonic, prospective, and motivational elements. Such brain-based models of emotion provide a foundation for new translational and clinical approaches.