Michele Guindani

A class of covariate-dependent spatiotemporal covariance functions for the analysis of daily ozone concentration

In geostatistics, it is common to model spatially distributed phenomena through an underlying stationary and isotropic spatial process. However, these assumptions are often untenable in practice because of the influence of local effects in the correlation structure. Therefore, it has been of prolonged interest in the literature to provide flexible and effective ways to model non-stationarity in the spatial effects. Arguably, due to the local nature of the problem, we might envision that the correlation structure would be highly dependent on local characteristics of the domain of study, namely the latitude, longitude and altitude of the observation sites, as well as other locally defined covariate information. In this work, we provide a flexible and computationally feasible way for allowing the correlation structure of the underlying processes to depend on local covariate information. We discuss the properties of the induced covariance functions and discuss methods to assess its dependence on local covariate information by means of a simulation study and the analysis of data observed at ozone-monitoring stations in the Southeast United States.

A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses

In this paper we present a novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide a joint analytical framework that allows to detect regions of the brain which exhibit neuronal activity in response to a stimulus and, simultaneously, infer the association, or clustering, of spatially remote voxels that exhibit fMRI time series with similar characteristics. We start by modeling the data with a hemodynamic response function (HRF) with a voxel-dependent shape parameter. We detect regions of the brain activated in response to a given stimulus by using mixture priors with a spike at zero on the coefficients of the regression model. We account for the complex spatial correlation structure of the brain by using a Markov random field (MRF) prior on the parameters guiding the selection of the activated voxels, therefore capturing correlation among nearby voxels. In order to infer association of the voxel time courses, we assume correlated errors, in particular long memory, and exploit the whitening properties of discrete wavelet transforms. Furthermore, we achieve clustering of the voxels by imposing a Dirichlet process (DP) prior on the parameters of the long memory process. For inference, we use Markov Chain Monte Carlo (MCMC) sampling techniques that combine Metropolis-Hastings schemes employed in Bayesian variable selection with sampling algorithms for nonparametric DP models. We explore the performance of the proposed model on simulated data, with both block- and event-related design, and on real fMRI data.

Time-dependence of graph theory metrics in functional connectivity analysis.

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations.

An Integrative Bayesian Modeling Approach to Imaging Genetics

In this article we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance imaging (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs’ activations with available genetic information from single nucleotide polymorphisms (SNPs) on the subjects. For this task, we develop a hierarchical mixture model that includes several innovative characteristics: it incorporates the selection of ROIs that discriminate the subjects into separate groups; it allows the mixture components to depend on selected covariates; it includes prior models that capture structural dependencies among the ROIs. Applied to the schizophrenia dataset, the model leads to the simultaneous selection of a set of discriminatory ROIs and the relevant SNPs, together with the reconstruction of the correlation structure of the selected regions. To the best of our knowledge, our work represents the first attempt at a rigorous modeling strategy for imaging genetics data that incorporates all such features.

Rosiglitazone-loaded nanospheres for modulating macrophage-specific inflammation in obesity.

PPARγ nuclear receptor agonists have been shown to attenuate macrophage inflammatory responses implicated in the metabolic complications of obesity and in atherosclerosis. However, PPARγ agonists currently in clinical use, including rosiglitazone (RSG), are often associated with severe side effects that limit their therapeutic use. Here, 200nm PLGA/PVA nanospheres were formulated for the systemic delivery of RSG specifically to macrophages. RSG was encapsulated with over 50% efficiency in the hydrophobic PLGA core and released specifically within the acidifying macrophage phagosomes. In bone marrow derived macrophages, RSG-loaded nanoparticles (RSG-NPs) induce a dose dependent upregulation (1.5 to 2.5-fold) of known PPARγ target genes, with maximal induction at 5μM; and downregulate the expression of genes related to the inflammatory process, with a maximum effect at 10μM. In Ldlr(-/-) mice fed high fat diet, treatment with RSG-NPs alleviated inflammation in white adipose tissue and liver but, unlike treatment with free RSG, did not alter genes associated with lipid metabolism or cardiac function, indicating a reduction in the RSG side effect profile. These biocompatible, biodegradable RSG-NPs represent a preliminary step towards the specific delivery of nuclear receptor agonists for the treatment of macrophage-mediated inflammatory conditions associated with obesity, atherosclerosis and other chronic disease states.

Generalized Species Sampling Priors With Latent Beta Reinforcements

Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of nonexchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables. We compare their theoretical clustering properties with those of the Dirichlet process and the two parameters Poisson–Dirichlet process. The proposed construction provides a complete characterization of the joint process, differently from existing work. We then propose the use of such process as prior distribution in a hierarchical Bayes’ modeling framework, and we describe a Markov chain Monte Carlo sampler for posterior inference. We evaluate the performance of the prior and the robustness of the resulting inference in a simulation study, providing a comparison with popular Dirichlet process mixtures and hidden Markov models. Finally, we develop an application to the detection of chromosomal aberrations in breast cancer by leveraging array comparative genomic hybridization (CGH) data. Supplementary materials for this article are available online.

The role of the gastrointestinal microbiome in infectious complications during induction chemotherapy for acute myeloid leukemia

Despite increasing data on the impact of the microbiome on cancer, the dynamics and role of the microbiome in infection during therapy for acute myelogenous leukemia (AML) are unknown. Therefore, the authors sought to determine correlations between microbiome composition and infectious outcomes in patients with AML who were receiving induction chemotherapy (IC).

Body image screening for cancer patients undergoing reconstructive surgery.

Body image is a critical issue for cancer patients undergoing reconstructive surgery, as they can experience disfigurement and functional impairment. Distress related to appearance changes can lead to various psychosocial difficulties, and patients are often reluctant to discuss these issues with their healthcare team. Our goals were to design and evaluate a screening tool to aid providers in identifying patients who may benefit from referral for specialized psychosocial care to treat body image concerns.

A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data

In this paper we propose a unified, probabilistically coherent framework for the analysis of task-related brain activity in multi-subject fMRI experiments. This is distinct from two-stage “group analysis” approaches traditionally considered in the fMRI literature, which separate the inference on the individual fMRI time courses from the inference at the population level. In our modeling approach we consider a spatiotemporal linear regression model and specifically account for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject nonparametric variable selection prior. For posterior inference, in addition to Markov chain Monte Carlo sampling algorithms, we develop suitable variational Bayes algorithms. We show on simulated data that variational Bayes inference achieves satisfactory results at more reduced computational costs than using MCMC, allowing scalability of our methods. In an application to data collected to assess brain responses to emotional stimuli our method correctly detects activation in visual areas when visual stimuli are presented.

Incidence of Atypical Femur Fractures in Cancer Patients: The MD Anderson Cancer Center Experience.

Atypical femoral fractures (AFFs) are rare adverse events attributed to bisphosphonate (BP) use. Few cases of AFF in cancer have been described; the aim of this study is to identify the incidence and risk factors for AFF in a large cancer center. This retrospective study was conducted at the MD Anderson Cancer Center. The incidence rate of AFF among BP users was calculated from January 1, 2004 through December 31, 2013. The control group (n = 51) included 2 or 3 patients on BPs matched for age (≤1 year) and gender. Logistic regression analysis was used to assess the relationship between clinical characteristics and AFF. Twenty‐three AFF cases were identified radiographically among 10,587 BP users, the total BP exposure was 53,789 months (4482 years), and the incidence of AFF in BP users was 0.05 cases per 100,000 person‐years. Meanwhile, among 300,553 patients who did not receive BPs there were 2 cases of AFF as compared with the 23 cases noted above. The odds ratio (OR) of having AFF in BP users was 355.58 times higher (95% CI, 84.1 to 1501.4, p < 0.0001) than the risk in non‐BP users. The OR of having AFF in alendronate users was 5.54 times greater (OR 5.54 [95% CI, 1.60 to 19.112, p = 0.007]) than the odds of having AFF among other BP users. Patients who were on zoledronic acid (ZOL) had smaller odds of developing AFF compared with other BP users in this matched case control sample. AFFs are rare, serious adverse events that occur in patients with cancer who receive BP therapy. Patients with cancer who receive BPs for prior osteoporosis therapy or for metastatic cancer are at higher risk of AFF.