Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions

OBJECTIVE

To investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy.

METHODS

Description of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR). The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation.

RESULTS

The proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients.

CONCLUSIONS

Numerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data.

SIGNIFICANCE

The proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.

A hierarchical atlas of the human cerebellum for functional precision mapping

The human cerebellum is activated by a wide variety of cognitive and motor tasks. Previous functional atlases have relied on single task-based or resting-state fMRI datasets. Here, we present a functional atlas that integrates information from 7 large-scale datasets, outperforming existing group atlasses. The new atlas has three further advantages: First, the regions are hierarchically organized across 3 levels, allowing analyses at the appropriate level of granularity. Second, we provide both asymmetric and symmetric versions of the atlas. The symmetric version, which is obtained by constraining the boundaries to be the same across hemispheres, is especially useful in studying functional lateralization. Finally, the atlas allows for precision mapping in individuals: The integration of the probabilistic group atlas with an individual localizer scan results in a marked improvement in prediction of individual boundaries. Overall, the new atlas is an important resource for the study of the interdigitated functional organization of the human cerebellum in health and disease.

The dynamics of attentional guidance by working memory contents

Working memory (WM) contents can guide attention toward matching sensory information in the environment, but there are mixed findings regarding whether only a single prioritized item or multiple items held in WM can guide attention. The present study examines the limit of WM-guided attention with a novel task procedure and mouse trajectory analysis. Specifically, we introduced a perceptual-matching task utilizing the continuous estimation procedure within the maintenance interval of a WM task for one or two colors. We found that the overall perceptual matching mouse trajectory were robustly biased toward the location of WM-match color on the color-wheel (i.e., attraction bias), but only at memory set size one. However, the analysis of circular mouse trajectory distributions, through hierarchical Bayesian modeling, revealed two separable central peaks at both memory set sizes. Furthermore, model-free analysis demonstrated that the perceptual matching mouse trajectory patterns were similar regardless of memory set sizes. Together, these results support the single-item account and highlight the utility of mouse trajectory analyses in hypothesis testing in experimental psychology.

Spatio-temporal pattern and associate factors of intestinal infectious diseases in Zhejiang Province, China, 2008-2021: a Bayesian modeling study

BACKGROUND

Despite significant progress in sanitation status and public health awareness, intestinal infectious diseases (IID) have caused a serious disease burden in China. Little was known about the spatio-temporal pattern of IID at the county level in Zhejiang. Therefore, a spatio-temporal modelling study to identify high-risk regions of IID incidence and potential risk factors was conducted.

METHODS

Reported cases of notifiable IID from 2008 to 2021 were obtained from the China Information System for Disease Control and Prevention. Moran's I index and the local indicators of spatial association (LISA) were calculated using Geoda software to identify the spatial autocorrelation and high-risk areas of IID incidence. Bayesian hierarchical model was used to explore socioeconomic and climate factors affecting IID incidence inequities from spatial and temporal perspectives.

RESULTS

From 2008 to 2021, a total of 101 cholera, 55,298 bacterial dysentery, 131 amoebic dysentery, 5297 typhoid, 2102 paratyphoid, 27,947 HEV, 1,695,925 hand, foot and mouth disease (HFMD), and 1,505,797 other infectious diarrhea (OID) cases were reported in Zhejiang Province. The hot spots for bacterial dysentery, OID, and HEV incidence were found mainly in Hangzhou, while high-high cluster regions for incidence of enteric fever and HFMD were mainly located in Ningbo. The Bayesian model showed that Areas with a high proportion of males had a lower risk of BD and enteric fever. People under the age of 18 may have a higher risk of IID. High urbanization rate was a protective factor against HFMD (RR = 0.91, 95% CI: 0.88, 0.94), but was a risk factor for HEV (RR = 1.06, 95% CI: 1.01-1.10). BD risk (RR = 1.14, 95% CI: 1.10-1.18) and enteric fever risk (RR = 1.18, 95% CI:1.10-1.27) seemed higher in areas with high GDP per capita. The greater the population density, the higher the risk of BD (RR = 1.29, 95% CI: 1.23-1.36), enteric fever (RR = 1.12, 95% CI: 1.00-1.25), and HEV (RR = 1.15, 95% CI: 1.09-1.21). Among climate variables, higher temperature was associated with a higher risk of BD (RR = 1.32, 95% CI: 1.23-1.41), enteric fever (RR = 1.41, 95% CI: 1.33-1.50), and HFMD (RR = 1.22, 95% CI: 1.08-1.38), and with lower risk of HEV (RR = 0.83, 95% CI: 0.78-0.89). Precipitation was positively correlated with enteric fever (RR = 1.04, 95% CI: 1.00-1.08), HFMD (RR = 1.03, 95% CI: 1.00-1.06), and HEV (RR = 1.05, 95% CI: 1.03-1.08). Higher HFMD risk was also associated with increasing relative humidity (RR = 1.20, 95% CI: 1.16-1.24) and lower wind velocity (RR = 0.88, 95% CI: 0.84-0.92).

CONCLUSIONS

There was significant spatial clustering of IID incidence in Zhejiang Province from 2008 to 2021. Spatio-temporal patterns of IID risk could be largely explained by socioeconomic and meteorological factors. Preventive measures and enhanced monitoring should be taken in some high-risk counties in Hangzhou city and Ningbo city.

Familiarity enhances mnemonic precision but impairs mnemonic accuracy in visual working memory

Prior stimulus familiarity has a variety of effects on visual working memory representations and processes. However, it is still unclear how familiarity interacts with the veridical correspondence between mnemonic representation and external stimuli. Here, we examined the effect of familiarity on two aspects of mnemonic correspondence, precision and accuracy, in visual working memory. Specifically, we used a hierarchical Bayesian method to model task performance in a change detection task with celebrity lookalikes (morphed faces between celebrities and noncelebrities with various ratios) as the memory stimuli. We found that familiarity improves memory precision by sharpening mnemonic representation but impairs memory accuracy by biasing mnemonic representation toward familiar faces (i.e., celebrity faces). These findings provide an integrated account of the puzzling celebrity sighting phenomena with the dissociable effects on mnemonic imprecision and bias and further highlight the importance of assessing these two aspects of memory correspondence in future research.

Effectiveness of hierarchical Bayesian models for citizen science data with missing values: A case study on the factors influencing beach litter in Shimane Prefecture, Japan

Citizen science can play an important role in addressing the issue of marine debris. However, citizen science data are often composed of inconsistent methods compared to data collected by experts. In this study, we applied beach cleanup data, collected in different survey years at different survey sites, to a hierarchical Bayesian model to elucidate the factors affecting the distribution of beach litter. The results showed the model accounting for differences between years had a smaller Watanabe-Akaike Information criterion than the model that did not account for it, indicating better accuracy of the model. The amount of beach litter was influenced by current velocity and bay openness, and these effects varied across years. The results indicate that citizen science data, which may contain missing values due to various constraints such as economic and human resources, can make an important contribution toward solving marine debris issues by flexible statistical analysis methods.

CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data

Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.

Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006-2020: a Bayesian modeling study of national mortality registries

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death in China. No previous study has reported CVD mortality at county-level, and little was known about the nonmedical ecological factors of CVD mortality at such small scale in mainland China. Understanding the spatiotemporal variations of CVD mortality and examining its nonmedical ecological factors would be of great importance to tailor local public health policies.

METHODS

By using national mortality registration data in China, this study used hierarchical spatiotemporal Bayesian model to demonstrate spatiotemporal distribution of CVD mortality in 2844 counties during 2006 to 2020 and investigate how nonmedical ecological determinants have affected CVD mortality inequities from the spatial perspectives.

RESULTS

During 2006-2020, the age-standardized mortality rate (ASMR) of CVD decreased from 284.77 per 100,000 in 2006 to 241.34 per 100,000 in 2020. Among 2844 counties, 1144 (40.22%) were hot spots counties with a higher CVD mortality risk compared to the national average and located mostly in northeast, north central, and westernmost regions; on the contrary, 1551 (54.53%) were cold spots counties and located mostly in south and southeast coastal counties. CVD mortality risk decreased from 2006 to 2020 was larger in counties where CVD mortality rate had been higher in 2006 in most of the counties, vice versa. Nationwide, nighttime light intensity (NTL) was the major influencing factor of CVD mortality, a higher NTL appeared to be negatively associated with a lower CVD mortality, with one unit increase in NTL, and the CVD mortality risk will decrease 11% (relative risk of NTL was estimated as 0.89 with 95% confidence interval of 0.83-0.94).

CONCLUSIONS

Substantial between-county discrepancies of CVD mortality distribution were observed during past 15 years in mainland China. Nonmedical ecological determinants were estimated to significantly explain the overall and local spatiotemporal patterns of this CVD mortality risk. Targeted considerations are needed to integrate primary care with clinical care through intensifying further strategies to narrow unequally distribution of CVD mortality at local scale. The approach to county-level analysis with small area models has the potential to provide novel insights into Chinese disease-specific mortality burden.

Impact of regional heterogeneity on the severity of COVID-19

The main objective of the study is to assess the impact of regional heterogeneity on the severity of COVID-19 in Japan. We included 27,865 cases registered between January 2020 and February 2021 in the COVID-19 Registry of Japan, to examine the relationship between the National Early Warning Score (NEWS) of COVID-19 patients on the day of admission and the prefecture where the patients live. A hierarchical Bayesian model was used to examine the random effect of each prefecture in addition to the patients' backgrounds. Additionally, we compared the results of two models; one model included the number of beds secured for COVID-19 patients in each prefecture as one of the fixed effects, and the other model did not. The results indicated that the prefecture had a substantial impact on the severity of COVID-19 on admission, even when considering the effect of the number of beds separately. Our analysis revealed a possible association between regional heterogeneity and increased/decreased risk of severe COVID-19 infection on admission. This heterogeneity was derived not only from the number of beds secured in each prefecture but also from other factors.

Tree manipulation experiment for the short-term effect of tree cutting on N2O emission: A evaluation using Bayesian hierarchical modeling

Considerable uncertainty exists with regard to the effects of thinning and harvesting on N₂O emissions as a result of changes caused in the belowground environment by tree cutting. To evaluate on the effects of changes in the belowground environment on N₂O emissions from soils, we conducted a tree manipulation experiment in a Japanese cedar (Cryptomeria japonica) stand without soil compaction or slash falling near measurement chambers and measured N₂O emission at distances of 50 and 150 cm from the tree stem (stump) before and after cutting. In addition, we inferred the effects of logging on the emission using a hierarchical Bayesian (HB) model. Our results showed that tree cutting stimulated N₂O emission from soil and that the increase in N₂O emission depended on the distance from the stem (stump); increase in N₂O emission was greater at 50 than at 150 cm from the stem. Tree cutting caused the estimated N₂O emission at 0-40 cm from the stem to double (the % increase in N₂O emission by tree cutting was 54%-213%, 95% predictive credible interval) when soil temperature was 25 °C and WFPS was 60%. Posterior simulation of the HB model predicted that 30% logging would cause a 57% (47%-67%) increase in N₂O emission at our study site (2000 trees ha^-1) considering only the effects of belowground changes by tree cutting during the measurement period.

Predicting the impact of outdoor vector control interventions on malaria transmission intensity from semi-field studies

BACKGROUND

Semi-field experiments with human landing catch (HLC) measure as the outcome are an important step in the development of novel vector control interventions against outdoor transmission of malaria since they provide good estimates of personal protection. However, it is often infeasible to determine whether the reduction in HLC counts is due to mosquito mortality or repellency, especially considering that spatial repellents based on volatile pyrethroids might induce both. Due to the vastly different impact of repellency and mortality on transmission, the community-level impact of spatial repellents can not be estimated from such semi-field experiments.

METHODS

We present a new stochastic model that is able to estimate for any product inhibiting outdoor biting, its repelling effect versus its killing and disarming (preventing host-seeking until the next night) effects, based only on time-stratified HLC data from controlled semi-field experiments. For parameter inference, a Bayesian hierarchical model is used to account for nightly variation of semi-field experimental conditions. We estimate the impact of the products on the vectorial capacity of the given Anopheles species using an existing mathematical model. With this methodology, we analysed data from recent semi-field studies in Kenya and Tanzania on the impact of transfluthrin-treated eave ribbons, the odour-baited Suna trap and their combination (push-pull system) on HLC of Anopheles arabiensis in the peridomestic area.

RESULTS

Complementing previous analyses of personal protection, we found that the transfluthrin-treated eave ribbons act mainly by killing or disarming mosquitoes. Depending on the actual ratio of disarming versus killing, the vectorial capacity of An. arabiensis is reduced by 41 to 96% at 70% coverage with the transfluthrin-treated eave ribbons and by 38 to 82% at the same coverage with the push-pull system, under the assumption of a similar impact on biting indoors compared to outdoors.

CONCLUSIONS

The results of this analysis of semi-field data suggest that transfluthrin-treated eave ribbons are a promising tool against malaria transmission by An. arabiensis in the peridomestic area, since they provide both personal and community protection. Our modelling framework can estimate the community-level impact of any tool intervening during the mosquito host-seeking state using data from only semi-field experiments with time-stratified HLC.

Vulnerable road-user deaths in Brazil: a Bayesian hierarchical model for spatial-temporal analysis

Reducing the road traffic injuries burden is relevant to many sustainable development goals (SDG), in particular SDG3 - to establish good health and well-being. To describe the spatial-temporal trends and identify hotspot regions for fatal road traffic injuries, a Bayesian hierarchical Poisson model was used to analyze data on vulnerable road users (bicyclist, motorcyclist and pedestrians) in Brazil from 1999 to 2016. During the study period, mortality rates for bicyclists remained almost unchanged (0.6 per 100,000 people) but rose dramatically for motorcyclists (from 1.0 in 1999 to 6.0 per 100,000 people in 2016) and decreased for pedestrians (from 6.3 to 3.0 per 100,000 people). Spatial analyses accounting for socio-economic factors showed that the central and northeastern microregions of Brazil are hotspot areas for fatal injuries among motorcyclists while the southern areas are for pedestrians.

Dataset for estimating occurrence probability of causations for plugged, abandoned and decommissioned oil and gas wells

This article contains the dataset on the failure frequencies of the barrier and mechanical plugs in place within the hydrocarbon-containing wellbore during plugging and abandonment operation. The interpretation and application of this data can be found in the research article (“https://doi.org/10.1016/j.psep.2019.09.015” Babaleye et al., 2019). These datasets were collected through a comprehensive hazard identification technique workshop involving 10 engineers and academics with considerable years of field experience. The data were collected based on how likely it is for each causation to occur and these likelihoods are ranked from 1 to 10. The process is experience-driven and is complemented by a 1–10 rating of the duration of leak of hydrocarbon before remediation, should the leak reach the mudline. The ranked data was a representative of raw failure data (failure rate or mean time to failure (MTTF)) for each causation and are coded in MATLAB using gamma distribution based on hierarchical Bayesian analysis. The dataset offers unique opportunity for reuse due to its accessibility and discreteness.

Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth

We focus on electro-/magnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial and deep areas without specific a priori knowledge of the number or location of the activity. To enable source distinguishability for any depth, we introduce a randomized multiresolution scanning (RAMUS) approach in which the MAP estimate of the brain activity is varied during the reconstruction process. RAMUS aims to provide a robust and accurate imaging outcome for the whole brain, while maintaining the computational cost on an appropriate level. The inverse gamma (IG) distribution is applied as the primary hyperprior in order to achieve an optimal performance for the deep part of the brain. In this proof-of-the-concept study, we consider the detection of simultaneous thalamic and somatosensory activity via numerically simulated data modeling the 14-20 ms post-stimulus somatosensory evoked potential and field response to electrical wrist stimulation. Both a spherical and realistic model are utilized to analyze the source reconstruction discrepancies. In the numerically examined case, RAMUS was observed to enhance the visibility of deep components and also marginalizing the random effects of the discretization and optimization without a remarkable computation cost. A robust and accurate MAP estimate for the primary current density was obtained in both superficial and deep parts of the brain.

Bayesian Inference of Microbial Community Structure from Metagenomic Data Using BioMiCo

Microbial samples taken from an environment often represent mixtures of communities, where each community is composed of overlapping assemblages of species. Such data represent a serious analytical challenge, as the community structures will be present as complex mixtures, there will be very large numbers of component species, and the species abundance will often be sparse over samples. The structure and complexity of these samples will vary according to both biotic and abiotic factors, and classical methods of data analysis will have a limited value in this setting. A novel Bayesian modeling framework, called BioMiCo, was developed to meet this challenge. BioMiCo takes abundance data derived from environmental DNA, and models each sample by a two-level mixture, where environmental OTUs contribute community structures, and those structures are related to the known biotic and abiotic features of each sample. The model is constrained by Dirichlet priors, which induces compact structures, minimizes variance, and maximizes model interpretability. BioMiCo is trained on a portion of the data, and once trained a BioMiCo model can be employed to make predictions about the features of new samples. This chapter provides a set of protocols that illustrate the application of BioMiCo to real inference problems. Each protocol is designed around the analysis of a real dataset, which was carefully chosen to illustrate specific aspects of real data analysis. With these protocols, users of BioMiCo will be able to undertake basic research into the properties of complex microbial systems, as well as develop predictive models for applied microbiomics.

A hierarchical Bayesian model for decomposing the impacts of human activities and climate change on water resources in China

Human activities and climate change are two key factors influencing the variation of the total amount of available surface and groundwater, hereafter termed water resources. Quantitatively separating their impacts remains a challenge. To this end, we used time-varying Budyko-type equations and a hierarchical Bayesian model in this paper to separate their impacts in 31 provincial-level divisions of China. The time-varying Budyko-type equations treat the Budyko equation parameter w as a variable, which depends on human activities (represented by per capita gross regional production) and climate change (represented by temperature and precipitation). The hierarchical model quantifies the uncertainty of parameters and the interrelation between covariates across regions in China. The results show that the time-varying Budyko-type equation can improve the fitting capability for water resources in China. The hierarchical Bayesian model, which considered spatial dependence, reduced the uncertainty of the parameters compared to spatially independent counterparts. For most regions of China, human activities reduce water resources while climate change increases them. Southeastern China is the most influenced area, and its water resources decreased approximately 50 mm because of human activities. This study can provide a basis for water resource management under climate change and human activity constraints in China.

On the importance of avoiding shortcuts in applying cognitive models to hierarchical data

Psychological experiments often yield data that are hierarchically structured. A number of popular shortcut strategies in cognitive modeling do not properly accommodate this structure and can result in biased conclusions. To gauge the severity of these biases, we conducted a simulation study for a two-group experiment. We first considered a modeling strategy that ignores the hierarchical data structure. In line with theoretical results, our simulations showed that Bayesian and frequentist methods that rely on this strategy are biased towards the null hypothesis. Secondly, we considered a modeling strategy that takes a two-step approach by first obtaining participant-level estimates from a hierarchical cognitive model and subsequently using these estimates in a follow-up statistical test. Methods that rely on this strategy are biased towards the alternative hypothesis. Only hierarchical models of the multilevel data lead to correct conclusions. Our results are particularly relevant for the use of hierarchical Bayesian parameter estimates in cognitive modeling.

Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models

Traffic crashes are more likely to occur at intersections where the traffic environment is complicated. In this study, a hybrid approach combining cluster analysis and hierarchical Bayesian models is developed to examine driver injury severity patterns in intersection-related crashes based on two-year crash data in New Mexico. Three clusters are defined by K-means cluster analysis based on weather and roadway environmental conditions in order to reveal drivers' risk compensation instability under diverse external environment. Hierarchical Bayesian random intercept models are developed for each of the three clusters as well as the whole dataset to identify the contributing factors on multilevel driver injury outcomes: property damage only (Level I), complaint of injury and visible injury (Level II), and incapacitating injury and fatality (Level III). Model comparison with an ordinary multinomial logistic model omitting crash data hierarchical features and cross-level interactions verifies the suitability and effectiveness of the proposed hybrid approach. Results show that a number of crash-level variables (time period, weather, light condition, area, and road grade), vehicle/driver-level variables (traffic controls, vehicle action, vehicle type, seatbelt used, driver age, drug/alcohol impaired, and driver age) along with some cross-level interactions (i.e., left turn and night, drug and dark) impose significantly influence driver injury severity. This study provides insightful understandings of the effects of these variables on driver injury severity in intersection-related crashes and beneficial references for developing effective countermeasures for severe crash prevention.

Ambient air quality and spatio-temporal patterns of cardiovascular emergency department visits

BACKGROUND

Air pollutants have been associated with various adverse health effects, including increased rates of hospital admissions and emergency room visits. Although numerous time-series studies and case-crossover studies have estimated associations between day-to-day variation in pollutant levels and mortality/morbidity records, studies on geographic variations in emergency department use and the spatial effects in their associations with air pollution exposure are rare.

METHODS

We focused on the elderly who visited emergency room for cardiovascular related disease (CVD) in 2011. Using spatially and temporally resolved multi-pollutant exposures, we investigated the effect of short-term exposures to ambient air pollution on emergency department utilization. We developed two statistical models with and without spatial random effects within a hierarchical Bayesian framework to capture the spatial heterogeneity and spatial autocorrelation remaining in emergency department utilization.

RESULTS

Although the cardiovascular effect of spatially homogeneous pollutants, such as PM2.5 and ozone, was unchanged, we found the cardiovascular effect of NO[Formula: see text] was pronounced after accounting for the spatially correlated structure in emergency department utilization. We also identified areas with high ED utilization for CVD among the elderly and assessed the uncertainty associated with risk estimates.

CONCLUSIONS

We assessed the short-term effect of multi-pollutants on cardiovascular risk of the elderly and demonstrated the use of community multiscale air quality model-derived spatially and temporally resolved multi-pollutant exposures to an epidemiological study. Our results indicate that NO[Formula: see text] was significantly associated with the elevated ED utilization for CVD among the elderly.

Hierarchical Bayesian models for the autonomic-based concealed information test

The concealed information test (CIT) is a psychophysiological memory detection technique for examining whether an examinee recognizes crime-relevant information. In current statistical analysis practice, the autonomic responses are usually transformed into Z scores within individuals to remove inter- and intra-individual variability. However, this conventional procedure leads to overestimation of the effect size, specifically the standardized mean difference of the autonomic responses between the crime-relevant information and the crime-irrelevant information. In this study, we attempted to resolve this problem by modeling inter- and intra-individual variability directly using hierarchical Bayesian modeling. Five models were constructed and applied to CIT data obtained from 167 participants. The validity of the CIT was confirmed using Bayesian estimates of the effect sizes, which are more accurate and interpretable than conventional effect sizes. Moreover, hierarchical Bayesian modeling provided information that is not available from the conventional statistical analysis procedure.

Computational Psychosomatics and Computational Psychiatry: Toward a Joint Framework for Differential Diagnosis

This article outlines how a core concept from theories of homeostasis and cybernetics, the inference-control loop, may be used to guide differential diagnosis in computational psychiatry and computational psychosomatics. In particular, we discuss 1) how conceptualizing perception and action as inference-control loops yields a joint computational perspective on brain-world and brain-body interactions and 2) how the concrete formulation of this loop as a hierarchical Bayesian model points to key computational quantities that inform a taxonomy of potential disease mechanisms. We consider the utility of this perspective for differential diagnosis in concrete clinical applications.

More efficient approaches to the exponentiated half-logistic distribution based on record values

The exponentiated half-logistic distribution has various shapes depending on its shape parameter. Therefore, this paper proposes more efficient approach methods for estimating shape parameters in the presence of a nuisance parameter, that is, a scale parameter, from Bayesian and non-Bayesian perspectives if record values have an exponentiated half-logistic distribution. In the frequentist approach, estimation methods based on pivotal quantities are proposed which require no complex computation unlike the maximum likelihood method. In the Bayesian approach, a robust estimation method is developed by constructing a hierarchical structure of the parameter of interest. In addition, two approaches address how the nuisance parameter can be dealt with and verifies that the proposed methods are more efficient than existing methods through Monte Carlo simulations and analyses based on real data.

Bayesian EEG source localization using a structured sparsity prior

This paper deals with EEG source localization. The aim is to perform spatially coherent focal localization and recover temporal EEG waveforms, which can be useful in certain clinical applications. A new hierarchical Bayesian model is proposed with a multivariate Bernoulli Laplacian structured sparsity prior for brain activity. This distribution approximates a mixed ℓ₂₀ pseudo norm regularization in a Bayesian framework. A partially collapsed Gibbs sampler is proposed to draw samples asymptotically distributed according to the posterior of the proposed Bayesian model. The generated samples are used to estimate the brain activity and the model hyperparameters jointly in an unsupervised framework. Two different kinds of Metropolis-Hastings moves are introduced to accelerate the convergence of the Gibbs sampler. The first move is based on multiple dipole shifts within each MCMC chain, whereas the second exploits proposals associated with different MCMC chains. Experiments with focal synthetic data shows that the proposed algorithm is more robust and has a higher recovery rate than the weighted ℓ₂₁ mixed norm regularization. Using real data, the proposed algorithm finds sources that are spatially coherent with state of the art methods, namely a multiple sparse prior approach and the Champagne algorithm. In addition, the method estimates waveforms showing peaks at meaningful timestamps. This information can be valuable for activity spread characterization.

Reporting of core items in hierarchical Bayesian analysis for aggregating N-of-1 trials to estimate population treatment effects is suboptimal

OBJECTIVES

N-of-1 trials can be aggregated to estimate population treatment effects using hierarchical Bayesian models. It is very important to report core items in hierarchical Bayesian analysis. In this study, we assessed reporting of items in hierarchical Bayesian analysis for aggregating N-of-1 trials to estimate population treatment effects.

STUDY DESIGN AND SETTING

This was a systematic literature review of aggregating N-of-1 trials by hierarchical Bayesian models to estimate population treatment effects. A comprehensive search was performed to collect eligible articles. Pilot studies, formal N-of-1 trials and reports in which the data were reanalyzed using hierarchical Bayesian methods, were included. The information of reported items related with hierarchical Bayesian analysis was extracted by two independent reviewers. The guideline "ROBUST," developed for reporting Bayesian analysis of clinical studies, was published in Journal of Clinical Epidemiology in 2005. We assessed the included reports using ROBUST criteria and 18 other important items.

RESULTS

After careful screening, 11 studies were identified to be eligible for inclusion. There were three pilot studies, four formal trials, and four reports in which the data were reanalyzed using hierarchical Bayesian methods. The number of reported items in ROBUST criteria ranged from six to seven, with a median number of six. Five of eleven included articles reported all items of the ROBUST criteria. But for justification and sensitivity analysis in prior distribution items, other items were reported in all of the included articles. Software and analysis data set items were reported the most frequently in additional items excluded from the ROBUST criteria. Less than half of the studies reported the other additional items.

CONCLUSION

Reporting of core items in hierarchical Bayesian analysis for aggregating N-of-1 trials to estimate population treatment effects is suboptimal. A PRISMA-like guidance on reviews of Bayesian N-of-1 trials may be required in the future.

Predictive statistical models linking antecedent meteorological conditions and waterway bacterial contamination in urban waterways

Although the relationships between meteorological conditions and waterway bacterial contamination are being better understood, statistical models capable of fully leveraging these links have not been developed for highly urbanized settings. We present a hierarchical Bayesian regression model for predicting transient fecal indicator bacteria contamination episodes in urban waterways. Canals, creeks, and rivers of the New York City harbor system are used to examine the model. The model configuration facilitates the hierarchical structure of the underlying system with weekly observations nested within sampling sites, which in turn were nested inside of the harbor network. Models are compared using cross-validation and a variety of Bayesian and classical model fit statistics. The uncertainty of predicted enterococci concentration values is reflected by sampling from the posterior predictive distribution. Issuing predictions with the uncertainty reasonably reflected allows a water manager or a monitoring agency to issue warnings that better reflect the underlying risk of exposure. A model using only antecedent meteorological conditions is shown to correctly classify safe and unsafe levels of enterococci with good accuracy. The hierarchical Bayesian regression approach is most valuable where transient fecal indicator bacteria contamination is problematic and drainage network data are scarce.

The effect of suspended sediment on fertilization success in the urchin Evechinus chloroticus: analysis of experimental data using hierarchical Bayesian methods

Terrestrial sediments are a significant stressor on coastal ecosystems, with both suspended and deposited sediment having adverse effects on aquatic organisms. However, information on the effect of suspended sediments on fertilization success for urchin species is lacking. Using sediment levels similar to those encountered in situ, a controlled experiment was conducted to test whether suspended sediment affects fertilization success in the urchin Evechinus chloroticus. Analyses used generalized linear mixed models (GLMMs) and hierarchical Bayesian (HB) regression. Both approaches showed a significant decrease in fertilization success with increased suspended sediment levels. Uncertainties in estimates were narrower for HB models, suggesting that this approach has advantages over GLMMs for sparse data problems sometimes encountered in laboratory experiments. Given future global change scenarios, this work is important for predicting the effects of stressors such as sedimentation that may ultimately impact marine populations.

Safety modeling of suburban arterials in Shanghai, China

As urbanization accelerates in Shanghai, land continues to develop along suburban arterials which results in more access points along the roadways and more congested suburban arterials; all these changes have led to deterioration in traffic safety. In-depth safety analysis is needed to understand the relationship between roadway geometric design, access features, traffic characteristics, and safety. This study examined 161 road segments (each between two adjacent signalized intersections) of eight suburban arterials in Shanghai. Information on signal spacing, geometric design, access features, traffic characteristics, and surrounding area types were collected. The effect of these factors on total crash occurrence was investigated. To account for the hierarchical data structure, hierarchical Bayesian models were developed for total crashes. To identify diverse effects on different crash injury severity, the total crashes were separated into minor injury and severe injury crashes. Bivariate hierarchical Bayesian models were developed for minor injury and severe injury to account for the correlation among different severity levels. The modeling results show that the density of signal spacing along arterials has a significant influence on minor injury, severe injury, and total crash frequencies. The non-uniform signal spacing has a significant impact on the occurrence of minor injury crashes. At the segment-level, higher frequencies of minor injury, severe injury, and total crashes tend to occur for the segments with curves, those with a higher density of access points, those with a higher percentage of heavy vehicles, and those in inner suburban areas. This study is useful for applications such as related engineering safety improvements and making access management policy.

Bayesian inference of structural brain networks

Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unfortunately, as of yet, none of the existing approaches provide an undisputed way of inferring brain networks from the streamline distributions which tractography produces. State-of-the-art methods rely on an arbitrary threshold or, alternatively, yield weighted results that are difficult to interpret. In this paper, we provide a generative model that explicitly describes how structural brain networks lead to observed streamline distributions. This allows us to draw principled conclusions about brain networks, which we validate using simultaneously acquired resting-state functional MRI data. Inference may be further informed by means of a prior which combines connectivity estimates from multiple subjects. Based on this prior, we obtain networks that significantly improve on the conventional approach.