A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

This article describes the R package mcglm implemented for fitting multivariate covariance generalized linear models (McGLMs). McGLMs provide a general statistical modeling framework for normal and non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function and a matrix linear predictor involving known symmetric matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modeled by means of a link function and a linear predictor. The models are fitted using an estimating function approach based on second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, genetic, spatial and spatio-temporal structures. The mcglm package allows a flexible specification of the mean and covariance structures, and explicitly deals with multivariate response variables, through a user friendly formula interface similar to the ordinary glm function. Illustrations in this article cover a wide range of applications from the traditional one response variable Gaussian mixed models to multivariate spatial models for areal data using the multivariate Tweedie distribution. Additional features, such as robust and bias-corrected standard errors for regression parameters, residual analysis, measures of goodness-of-fit and model selection using the score information criterion are discussed through six worked examples. The mcglm package is a full R implementation based on the Matrix package which provides efficient access to BLAS (basic linear algebra subroutines), Lapack (dense matrix), TAUCS (sparse matrix) and UMFPACK (sparse matrix) routines for efficient linear algebra in R.

The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.

We describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. Our main result is based on the observation that, by conditioning on the unobserved QTL genotypes, the problem can be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTL and the phenotype. The second part involves only the location of the QTL in the genome. We developed a simple Monte Carlo algorithm to implement Bayesian QTL analysis. This algorithm simulates multiple versions of complete genotype information on a genomewide grid of locations using information in the marker genotype data. Weights are assigned to the simulated genotypes to capture information in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations and effect sizes. One advantage of this approach is that only the weights are recomputed as the analyst considers different candidate models. This device allows the analyst to focus on modeling and model comparisons. The proposed framework can accommodate multiple interacting QTL, nonnormal and multivariate phenotypes, covariates, missing genotype data, and genotyping errors in any type of inbred line cross. A software tool implementing this procedure is available. We demonstrate our approach to QTL analysis using data from a mouse backcross population that is segregating multiple interacting QTL associated with salt-induced hypertension.

Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence

Statistical significance tests are unnecessary even when properly done and properly interpreted: Reply to commentaries

Bayesian model selection for group studies — Revisited

Schizophrenia is a chronic neurobiological disorder whose early detection has attracted significant attention from the clinical, psychiatric, and also artificial intelligence communities. This latter approach has been mainly focused on the analysis of neuroimaging and genetic data. A less explored strategy consists in exploiting the power of natural language processing (NLP) algorithms applied over narrative texts produced by schizophrenic subjects. In this paper, a novel dataset collected from a proper field study is presented. Also, grammatical traits discovered in narrative documents are used to build computational representations of texts, allowing an automatic classification of discourses generated by schizophrenic and non-schizophrenic subjects. The attained results showed that the use of the proposed computational representations along with machine learning techniques enables a novel and precise strategy to automatically detect texts produced by schizophrenic subjects.

Soft real-time applications lack a formal methodology for their design optimization. Well-established techniques from hard real-time systems cannot be directly applied to soft real-time applications, without losing key benefits of the soft real-time paradigm. We introduce a statistical analysis framework that is well-suited for discovering opportunities for optimization of soft real-time applications. We demonstrate how programmers can use the analysis provided by our framework to perform aggressive soft real-time design optimizations on their applications. The paper introduces the Context Execution Tree (CET) representation for capturing the statistical properties of function calls in the context of their execution in the program. The CET is constructed from an offline-profile of the application. Statistical measures are coupled with techniques that extract runtime distinguishable call-chains. This combination of techniques is applied to the CET to find statistically significant patterns of activity that i) expose slack in the execution of the application with respect to its soft real-time requirements, and ii) can be predicted with low overhead and high reliability during the normal execution of the application.

Statistical hypothesis testing is one of the key steps in modern medical research. Initially, scientists formulate a research hypothesis based on which the statistical hypothesis is then developed and statistically tested. This review provides the null and alternative hypotheses’ compiling examples for different research questions and the general algorithm for their testing using t-test. The authors also describe type I errors, which are necessary to interpret p-values estimated from statistical tests, and type II errors, which are used to assess study power. The article focuses on effect size and its calculation methods, and the difference between statistically significant and clinically significant effects. The associations between effect size, sample size, and type II error are also discussed.

Classical measures of functional connectivity assume that the stationarity of the time courses and the time-invariance of functional connectivity under investigation. These assumptions may not be valid in the real cases. Also, they are bivariate measures and may not provide the directional information flow between brain units. A new approach is proposed to tackle these problems. A statistics reasoning shows that the short-length time course is more likely to be stationary than the long-length time course. Thus, the entire time course under investigation is divided into short segments with the proper length. Magnitude squared coherence (in spectrum domain) is computed to assess functional connectivity on these segments, hence, provides a dynamic measure of functional connectivity. The averaged magnitude squared coherence over the segments gives an overall measure of functional connectivity. This approach has been applied to several neuroimaging data analysis. The results and the interpretations / predictions are in good agreement. Mutual coherence (in time domain) is computed to assess functional connectivity, hence, provides an insight on directional information flow. By using grid computing, this approach will be extended from the bivariate to the multivariate.

When psychiatrists see a patient, they consider a diagnosis, estimate a prognosis and treat accordingly, but very few of these decisions are informed by objective tests. Recent advances in neuroimaging data analysis have shown that brain scans can make powerful diagnostic and prognostic predictions in patients with psychosis and depression.

Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics. The PARAFAC2 model jointly analyzes data from multiple time windows revealing subject-mode patterns, evolving spatial regions (also referred to as networks) and temporal patterns. We compare the PARAFAC2 model with matrix factorization-based approaches relying on independent components, namely, joint independent component analysis (ICA) and independent vector analysis (IVA), commonly used in neuroimaging data analysis. We assess the performance of the methods in terms of capturing evolving networks through extensive numerical experiments demonstrating their modeling assumptions. In particular, we show that (i) PARAFAC2 provides a compact representation in all modes, i.e., subjects, time, and voxels, revealing temporal patterns as well as evolving spatial networks, (ii) joint ICA is as effective as PARAFAC2 in terms of revealing evolving networks but does not reveal temporal patterns, (iii) IVA's performance depends on sample size, data distribution and covariance structure of underlying networks. When these assumptions are satisfied, IVA is as accurate as the other methods, (iv) when subject-mode patterns differ from one time window to another, IVA is the most accurate. Furthermore, we analyze real fMRI data collected during a sensory motor task, and demonstrate that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured, which includes primary and secondary motor regions, cerebellum, and temporal lobe, revealing a meaningful spatial map and its temporal change.

Postanesthesia Care Unit Visitation, Anxiety, and Experimental Design

The main objective of this paper is to present first and second-order sensitivity analysis of vibro-acoustic systems in the statistical energy analysis (SEA) frame work. Equations for computing these sensitivities for a general SEA model are obtained from two different approaches: (1) direct method and (2) adjoint method. The above equations are applied to a simple model of three plates, joined in the form of a ‘Z’, to minimize the total energy of one of the plates. It has been verified that these approaches lead to the same results and the difference between them is only with respect to the computational efficiency. The design sensitivity results calculated from the proposed analytical methods are compared with those obtained from the finite difference method, which show good agreement. The results of this paper can be useful to optimization of vibro-acoustic systems at the drawing board stage in the SEA framework.

Bicistronic reporter assay systems have become a mainstay of molecular biology. While the assays themselves encompass a broad range of diverse and unrelated experimental protocols, the numerical data garnered from these experiments often have similar statistical properties. In general, a primary dataset measures the paired expression of two internally controlled reporter genes. The expression ratio of these two genes is then normalized to an external control reporter. The end result is a ‘ratio of ratios’ that is inherently sensitive to propagation of the error contributed by each of the respective numerical components. The statistical analysis of this data therefore requires careful handling in order to control for the propagation of error and its potentially misleading effects. A careful survey of the literature found no consistent method for the statistical analysis of data generated from these important and informative assay systems. In this report, we present a detailed statistical framework for the systematic analysis of data obtained from bicistronic reporter assay systems. Specifically, a dual luciferase reporter assay was employed to measure the efficiency of four programmed −1 frameshift signals. These frameshift signals originate from the L-A virus, the SARS-associated Coronavirus and computationally identified frameshift signals from two Saccharomyces cerevisiae genes. Furthermore, these statistical methods were applied to prove that the effects of anisomycin on programmed −1 frameshifting are statistically significant. A set of Microsoft Excel spreadsheets, which can be used as templates for data generated by dual reporter assay systems, and an online tutorial are available at our website (http://dinmanlab.umd.edu/statistics). These spreadsheets could be easily adapted to any bicistronic reporter assay system.