A Covariance Based Clustering for Tensor Objects

Clustering of tensors with limited sample size has become prevalent in a variety of application areas. Existing Bayesian model based clustering of tensors yields less accurate clusters when the tensor dimensions are sufficiently large, sample size is low and clusters of tensors mainly reveal difference in their variability. This article develops a clustering technique for high dimensional tensors with limited sample size when the clusters show difference in their covariances, rather than in their means. The proposed approach constructs several matrices from a tensor, referred to as transformed features, to adequately estimate its variability along different modes and implements a model-based approximate Bayesian clustering algorithm with the matrices thus constructed, in place with the original tensor data. Although some information in the data is discarded, we gain substantial computational efficiency and accuracy in clustering. Simulation study assesses the proposed approach along with its competitors in terms of estimating the number of clusters, identification of the modal cluster membership along with the probability of mis-classification in clustering (a measure of uncertainty in clustering). The proposed methodology provides novel insights into potential clinical subgroups for children with autism spectrum disorder based on resting-state electroencephalography activity.National Science Foundation Grant DMS-2220840, DMS-2210672 and Office of Naval Research Grant N00014-18-1-274

Multi-object Data Integration in the Study of Primary Progressive Aphasia

This article focuses on a multi-modal imaging data application where structural/anatomical information from grey matter (GM) and brain connectivity information in the form of a brain connectome network from functional magnetic resonance imaging (fMRI) are available for a number of subjects with different degrees of primary progressive aphasia (PPA), a neurodegenerative disorder (ND) measured through a speech rate measure on motor speech loss. The clinical/scientific goal in this study becomes the identification of brain regions of interest significantly related to the speech rate measure to gain insight into ND pathways. Viewing the brain connectome network and GM images as objects, we develop a flexible joint object response regression framework of network and GM images on the speech rate measure. A novel joint prior formulation is proposed on network and structural image coefficients in order to exploit network information of the brain connectome, while leveraging the topological linkages among connectome network and anatomical information from GM to draw inference on brain regions significantly related to the speech rate measure. The principled Bayesian framework allows precise characterization of the uncertainty in ascertaining a region being actively related to the speech rate measure. Our framework yields new insights into the relationship of brain regions with PPA, offering deeper understanding of neuro-degeneration pathways for PPA.National Science Foundation (DMS-2220840 and DMS-2210672), National Institutes of Health (NINDS R01NS050915, NIDCD K24DC015544, NIA P50AG023501

Mild hypoxic-ischemic encephalopathy (HIE): Timing and pattern of MRI brain injury

BACKGROUND: Mild hypoxic-ischemic encephalopathy (HIE) is increasingly recognized as a risk factor for neonatal brain injury. We examined the timing and pattern of brain injury in mild HIE. METHODS: This retrospective cohort study includes infants with mild HIE treated at 9 hospitals. Neonatal brain MRIs were scored by 2 reviewers using a validated classification system, with discrepancies resolved by consensus. Severity and timing of MRI brain injury (i.e., acute, subacute, chronic) was scored on the subset of MRIs that were performed at or before 8 days of age. RESULTS: Of 142 infants with mild HIE, 87 (61%) had injury on MRI at median age 5 (IQR 4-6) days. Watershed (23%), deep gray (20%) and punctate white matter (18%) injury were most common. Among the 125 (88%) infants who received a brain MRI at ≤8 days, mild (44%) injury was more common than moderate (11%) or severe (4%) injury. Subacute (37%) lesions were more commonly observed than acute (32%) or chronic lesions (1%). CONCLUSION: Subacute brain injury is common in newborn infants with mild HIE. Novel neuroprotective treatments for mild HIE will ideally target both subacute and acute injury mechanisms. IMPACT: Almost two-thirds of infants with mild HIE have evidence of brain injury on MRI obtained in the early neonatal period. Subacute brain injury was seen in 37% of infants with mild HIE. Neuroprotective treatments for mild HIE will ideally target both acute and subacute injury mechanisms

Network anatomy in logopenic variant of primary progressive aphasia

The logopenic variant of primary progressive aphasia (lvPPA) is a neurodegenerative syndrome characterized linguistically by gradual loss of repetition and naming skills resulting from left posterior temporal and inferior parietal atrophy. Here, we sought to identify which specific cortical loci are initially targeted by the disease (epicenters) and investigate whether atrophy spreads through predetermined networks. First, we used cross-sectional structural MRI data from individuals with lvPPA to define putative disease epicenters using a surface-based approach paired with an anatomically fine-grained parcellation of the cortical surface (i.e., HCP-MMP1.0 atlas). Second, we combined cross-sectional functional MRI data from healthy controls and longitudinal structural MRI data from individuals with lvPPA to derive the epicenter-seeded resting-state networks most relevant to lvPPA symptomatology and ascertain whether functional connectivity in these networks predicts longitudinal atrophy spread in lvPPA. Our results show that two partially distinct brain networks anchored to the left anterior angular and posterior superior temporal gyri epicenters were preferentially associated with sentence repetition and naming skills in lvPPA. Critically, the strength of connectivity within these two networks in the neurologically-intact brain significantly predicted longitudinal atrophy progression in lvPPA. Taken together, our findings indicate that atrophy progression in lvPPA, starting from inferior parietal and temporoparietal junction regions, predominantly follows at least two partially nonoverlapping pathways, which may influence the heterogeneity in clinical presentation and prognosis

Meeting human resources for health staffing goals by 2018: a quantitative analysis of policy options in Zambia

<p>Abstract</p> <p>Background</p> <p>The Ministry of Health (MOH) in Zambia is currently operating with fewer than half of the health workers required to deliver basic health services. The MOH has developed a human resources for health (HRH) strategic plan to address the crisis through improved training, hiring, and retention. However, the projected success of each strategy or combination of strategies is unclear.</p> <p>Methods</p> <p>We developed a model to forecast the size of the public sector health workforce in Zambia over the next ten years to identify a combination of interventions that would expand the workforce to meet staffing targets. The key forecasting variables are training enrolment, graduation rates, public sector entry rates for graduates, and attrition of workforce staff. We model, using Excel (Office, Microsoft; 2007), the effects of changes in these variables on the projected number of doctors, clinical officers, nurses and midwives in the public sector workforce in 2018.</p> <p>Results</p> <p>With no changes to current training, hiring, and attrition conditions, the total number of doctors, clinical officers, nurses, and midwives will increase from 44% to 59% of the minimum necessary staff by 2018. No combination of changes in staff retention, graduation rates, and public sector entry rates of graduates by 2010, without including training expansion, is sufficient to meet staffing targets by 2018 for any cadre except midwives. Training enrolment needs to increase by a factor of between three and thirteen for doctors, three and four for clinical officers, two and three for nurses, and one and two for midwives by 2010 to reach staffing targets by 2018. Necessary enrolment increases can be held to a minimum if the rates of retention, graduation, and public sector entry increase to 100% by 2010, but will need to increase if these rates remain at 2008 levels.</p> <p>Conclusions</p> <p>Meeting the minimum need for health workers in Zambia this decade will require an increase in health training school enrolment. Supplemental interventions targeting attrition, graduation and public sector entry rates can help close the gap. HRH modelling can help MOH policy makers determine the relative priority and level of investment needed to expand Zambia's workforce to target staffing levels.</p

Modeling Region-Referenced Longitudinal Functional Electroencephalography Data

Highly structured data collected in a variety of biomedical applications such as electroencephalography (EEG) are discrete samples of a smooth functional process observed across both temporal and spatial dimensions. EEG data is conceptualized as region-referenced longitudinal functional data in which the functional dimension captures local signal dynamics, the longitudinal dimension tracks changes over the course of an experiment, and the regional dimension indexes spatial information across electrodes on the scalp. This complex data structure exhibits intricate dependencies with rich information but its dimensionality and size produce significant obstacles for interpretation, estimation, and inference. Motivated by a series of EEG studies in children with autism spectrum disorder (ASD), a set of computationally efficient methods for these high-dimensional data structures are proposed that both maintain information along each dimension and yield interpretable components and inferences. The first half of the work considers decompositions of the total variation. To begin, a multi-dimensional functional principal components analysis (MD-FPCA) is introduced which decomposes the total variation into subject- and electrode-level components and for each level employs a two-stage functional principal components decomposition sequentially across functional and longitudinal time. Next, a hybrid principal components analysis (HPCA) for region-referenced longitudinal functional EEG data is proposed which utilizes both vector and functional principal components analyses and does not collapse information along any of the three dimensions of the data. The second half of the work shifts to modeling associations and introduces a covariate-adjusted region-referenced generalized functional linear model (CARR-GFLM) for modeling scalar outcomes from region-referenced functional predictors. CARR-GFLM utilizes a tensor basis formed from one-dimensional discrete and continuous bases to estimate functional effects across a discrete regional domain while simultaneously adjusting for additional non-functional covariates, such as age. Proposed methods not only help identify neurodevelopmental differences between typically developing and ASD children but can also be used to study the heterogeneity within children with ASD. The performance of all proposed methods is studied via extensive simulations

Sketching in High Dimensional Regression With Big Data Using Gaussian Scale Mixture Priors

Bayesian computation of high dimensional linear regression models with popular Gaussian scale mixture prior distributions using Markov Chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive due to the heavy computational cost that grows in the cubic order of p, with p as the number of features. Although a few recently developed algorithms allow computational efficiency in presence of a small to moderately large sample size, the computational issues are considerably less explored when sample size n is also large, except for a few recent articles. In this article we propose a sketching approach to compress the n original samples by a random linear transformation to m samples in p dimensions, and compute Bayesian regression with Gaussian scale mixture prior distributions with the randomly compressed response vector and feature matrix. Our proposed approach yields computational complexity growing in the cubic order of m. Our detailed empirical investigation with the Horseshoe prior from the class of Gaussian scale mixture priors shows closely similar inference and a considerable reduction in per iteration computation time of the proposed approach compared to the regression with the full sample. One notable contribution of this article is to derive posterior contraction rate for high dimensional feature coefficient with a general class of shrinkage priors on the coefficients under data compression/sketching. In particular, we characterize the dimension of the compressed response vector m as a function of the sample size, number of features and sparsity in the regression to guarantee accurate estimation of feature coefficients asymptotically, even after data sketching.NSF DMS-2220840, DMS-221067

Bayesian adaptive design for covariate-adaptive historical control information borrowing.

Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study-level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate-adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate-dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo-controlled randomized trials on vertebral fracture risk in post-menopausal women