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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: Statistical modelling of functional magnetic resonance imaging data is challenging as the data are both spatially and temporally correlated. Spatially, measurements are taken at thousands of contiguous regions, called voxels, and temporally measurements are taken at hundreds of time points at each voxel. Recent advances in Bayesian hierarchical modelling have addressed the challenges of spatiotemporal structure in functional magnetic resonance imaging data with models incorporating both spatial and temporal priors for signal and noise. Whereas there has been extensive research on modelling the functional magnetic resonance imaging signal (i.e. the convolution of the experimental design with the functional choice for the haemodynamic response function) and its spatial variability, less attention has been paid to realistic modelling of the temporal dependence that typically exists within the functional magnetic resonance imaging noise, where a low order auto‐regressive process is typically adopted. Furthermore, the auto‐regressive order is held constant across voxels (e.g. AR(1) at each voxel). Motivated by an event‐related functional magnetic resonance imaging experiment, we propose a novel hierarchical Bayesian model with automatic selection of the auto‐regressive orders of the noise process that vary spatially over the brain. With simulation studies we show that our model is more statistically efficient and we apply it to our motivating example. [ABSTRACT FROM AUTHOR]

Summary: Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging. With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta‐analysis to identify the brain regions that are consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field cannot fully account for the spatial nature of neuroimaging meta‐analysis data or the heterogeneity observed among WM studies. In this work, we propose a fully Bayesian random‐effects metaregression model based on log‐Gaussian Cox processes, which can be used for meta‐analysis of neuroimaging studies. An efficient Markov chain Monte Carlo scheme for posterior simulations is presented which makes use of some recent advances in parallel computing using graphics processing units. Application of the proposed model to a real data set provides valuable insights regarding the function of the WM. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Motivation: ChIP-Seq is the standard method to identify genome-wide DNA-binding sites for transcription factors (TFs) and histone modifications. There is a growing need to analyze experiments with biological replicates, especially for epigenomic experiments where variation among biological samples can be substantial. However, tools that can perform group comparisons are currently lacking.Results: We present a peak-calling prioritization pipeline (PePr) for identifying consistent or differential binding sites in ChIP-Seq experiments with biological replicates. PePr models read counts across the genome among biological samples with a negative binomial distribution and uses a local variance estimation method, ranking consistent or differential binding sites more favorably than sites with greater variability. We compared PePr with commonly used and recently proposed approaches on eight TF datasets and show that PePr uniquely identifies consistent regions with enriched read counts, high motif occurrence rate and known characteristics of TF binding based on visual inspection. For histone modification data with broadly enriched regions, PePr identified differential regions that are consistent within groups and outperformed other methods in scaling False Discovery Rate (FDR) analysis.Availability and implementation: http://code.google.com/p/pepr-chip-seq/.Contact: sartorma@umich.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary. The prognosis for patients with high grade gliomas is poor, with a median survival of 1 year. Treatment efficacy assessment is typically unavailable until 5-6 months post diagnosis. Investigators hypothesize that quantitative magnetic resonance imaging can assess treatment efficacy 3 weeks after therapy starts, thereby allowing salvage treatments to begin earlier. The purpose of this work is to build a predictive model of treatment efficacy by using quantitative magnetic resonance imaging data and to assess its performance. The outcome is 1-year survival status. We propose a joint, two-stage Bayesian model. In stage I, we smooth the image data with a multivariate spatiotemporal pairwise difference prior. We propose four summary statistics that are functionals of posterior parameters from the first-stage model. In stage II, these statistics enter a generalized non-linear model as predictors of survival status. We use the probit link and a multivariate adaptive regression spline basis. The hybrid Metropolis-within-Gibbs algorithm and reversible jump Markov chain Monte Carlo methods are applied iteratively between the two stages to estimate the posterior distribution. Through both simulation studies and model performance comparisons we find that we can achieve higher overall correct classification rates by accounting for the spatiotemporal correlation in the images and by allowing for a more complex and flexible decision boundary provided by the generalized non-linear model. [ABSTRACT FROM AUTHOR]

The work is motivated by a quantitative magnetic resonance imaging study of the differential tumour-healthy tissue change in contrast uptake induced by radiation. The goal is to determine the time in which there is maximal contrast uptake (a surrogate for permeability) in the tumour relative to healthy tissue. A notable feature of the data is its spatial heterogeneity. Zhang and co-workers have discussed two parallel approaches to 'denoise' a single image of change in contrast uptake from baseline to one follow-up visit of interest. In this work we extend the image model to explore the longitudinal profile of the tumour-healthy tissue contrast uptake in multiple images over time. We fit a two-stage model. First, we propose a longitudinal image model for each subject. This model simultaneously accounts for the spatial and temporal correlation and denoises the observed images by borrowing strength both across neighbouring pixels and over time. We propose to use the Mann-Whitney U-statistic to summarize the tumour contrast uptake relative to healthy tissue. In the second stage, we fit a population model to the U-statistic and estimate when it achieves its maximum. Our initial findings suggest that the maximal contrast uptake of the tumour core relative to healthy tissue peaks around 3 weeks after initiation of radiotherapy, though this warrants further investigation. [ABSTRACT FROM AUTHOR]

The aim of this article is to develop a spatial model for multi-subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi-subject data, some work on spatial modeling of single-subject data, and some recent work on spatial modeling of multi-subject data. However, there has been no work on spatial models that explicitly account for inter-subject variability in activation locations. In this article, we use the idea of activation centers and model the inter-subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical framework which allows us to draw inferences at all levels: the population level, the individual level, and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question that is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass-univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data. [ABSTRACT FROM AUTHOR]

Many hormones are secreted in pulses. The pulsatile relationship between hormones regulates many biological processes. To understand endocrine system regulation, time series of hormone concentrations are collected. The goal is to characterize pulsatile patterns and associations between hormones. Currently each hormone on each subject is fitted univariately. This leads to estimates of the number of pulses and estimates of the amount of hormone secreted; however, when the signal-to-noise ratio is small, pulse detection and parameter estimation remains difficult with existing approaches. In this article, we present a bivariate deconvolution model of pulsatile hormone data focusing on incorporating pulsatile associations. Through simulation, we exhibit that using the underlying pulsatile association between two hormones improves the estimation of the number of pulses and the other parameters defining each hormone. We develop the one-to-one, driver–response case and show how birth–death Markov chain Monte Carlo can be used for estimation. We exhibit these features through a simulation study and apply the method to luteinizing and follicle stimulating hormones. [ABSTRACT FROM AUTHOR]

Many challenges arise in the analysis of pulsatile, or episodic, hormone concentration time series data. Among these challenges is the determination of the number and location of pulsatile events and the discrimination of events from noise. Analyses of these data are typically performed in two stages. In the first stage, the number and approximate location of the pulses are determined. In the second stage, a model (typically a deconvolution model) is fit to the data conditional on the number of pulses. Any error made in the first stage is carried over to the second stage. Furthermore, current methods, except two, assume that the underlying basal concentration is constant. We present a fully Bayesian deconvolution model that simultaneously estimates the number of secretion episodes, as well as their locations, and a nonconstant basal concentration. This model obviates the need to determine the number of events a priori. Furthermore, we estimate probabilities for all “candidate” event locations. We demonstrate our method on a real data set. [ABSTRACT FROM AUTHOR]

Many hormones are secreted into the circulatory system in pulsatile manner and are cleared exponentially. The most common method of analyzing these systems is to deconvolve the hormone concentration into secretion function and clearance function. Accurate estimation of the model parameters depends on the number and location of the secretion pulses. To date,deconvolution analysis assumes the number and approximate location of these pulses are known a priori. In this article, we present novel Bayesian approach to deconvolution that jointly models the number of pulses along with all other model parameters. Our method stochastically searches for the secretion pulses. This is accomplished by viewing the set of parameters that define the pulses as point process. Pulses are determined by birth-death process which is embedded in Markov chain Monte Carlo algorithm. This idea originated with Stephens (2000, Annals of Statistics 28, 40-74) in the context of finite mixture model density estimation,where the number of mixture components is unknown. There are several advantages that our model enjoys over the traditional frequentist approaches. These advantages are highlighted with four datasets consisting of serum concentration levels of luteinizing hormone obtained from ovariectomized ewes. [ABSTRACT FROM AUTHOR]

Reviewed: The Men Stood Like Iron: How the Iron Brigade Won Its Name. Herdegen, Lance J.

Reviewed: On Many a Bloody Field: Four Years in the Iron Brigade. Gaff, Alan D.

Examines the association of survival in advanced HIV type 1 infection with the T lymphocyte activation and virus chemokine coreceptor usage. Determinants of HIV-1 disease progression; Cause of T cell immune dysfunction; Measurement of the immune activation of CD4 and CD8 T cells.