485 research outputs found
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
Many analyses of neuroimaging data involve studying one or more regions of
interest (ROIs) in a brain image. In order to do so, each ROI must first be
identified. Since every brain is unique, the location, size, and shape of each
ROI varies across subjects. Thus, each ROI in a brain image must either be
manually identified or (semi-) automatically delineated, a task referred to as
segmentation. Automatic segmentation often involves mapping a previously
manually segmented image to a new brain image and propagating the labels to
obtain an estimate of where each ROI is located in the new image. A more recent
approach to this problem is to propagate labels from multiple manually
segmented atlases and combine the results using a process known as label
fusion. To date, most label fusion algorithms either employ voting procedures
or impose prior structure and subsequently find the maximum a posteriori
estimator (i.e., the posterior mode) through optimization. We propose using a
fully Bayesian spatial regression model for label fusion that facilitates
direct incorporation of covariate information while making accessible the
entire posterior distribution. We discuss the implementation of our model via
Markov chain Monte Carlo and illustrate the procedure through both simulation
and application to segmentation of the hippocampus, an anatomical structure
known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure
Soft Null Hypotheses: A Case Study of Image Enhancement Detection in Brain Lesions
This work is motivated by a study of a population of multiple sclerosis (MS)
patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)
to identify active brain lesions. At each visit, a contrast agent is
administered intravenously to a subject and a series of images is acquired to
reveal the location and activity of MS lesions within the brain. Our goal is to
identify and quantify lesion enhancement location at the subject level and
lesion enhancement patterns at the population level. With this example, we aim
to address the difficult problem of transforming a qualitative scientific null
hypothesis, such as "this voxel does not enhance", to a well-defined and
numerically testable null hypothesis based on existing data. We call the
procedure "soft null hypothesis" testing as opposed to the standard "hard null
hypothesis" testing. This problem is fundamentally different from: 1) testing
when a quantitative null hypothesis is given; 2) clustering using a mixture
distribution; or 3) identifying a reasonable threshold with a parametric null
assumption. We analyze a total of 20 subjects scanned at 63 visits (~30Gb), the
largest population of such clinical brain images
Estimation of survival of left truncated and right censored data under increasing hazard
When subjects are recruited through a cross-sectional survey they have already experienced the initiation of the event of interest, say the onset of a disease. This method of recruitment results in the fact that subjects with longer duration of the disease have a higher chance of being selected. It follows that censoring in such a case is not non-informative. The application of standard techniques for right-censored data thus introduces a bias to the analysis; this is referred to as length-bias. This paper examines the case where the subjects are assumed to enter the study at a uniform rate, allowing for the analysis in a more efficient unconditional manner. In particular, a new method for unconditional analysis is developed based on the framework of a conditional estimator. This new method is then applied to the several data sets and compared with the conditional technique of Tsai [23]
A BROAD SYMMETRY CRITERION FOR NONPARAMETRIC VALIDITY OF PARAMETRICALLY-BASED TESTS IN RANDOMIZED TRIALS
Summary. Pilot phases of a randomized clinical trial often suggest that a parametric model may be an accurate description of the trial\u27s longitudinal trajectories. However, parametric models are often not used for fear that they may invalidate tests of null hypotheses of equality between the experimental groups. Existing work has shown that when, for some types of data, certain parametric models are used, the validity for testing the null is preserved even if the parametric models are incorrect. Here, we provide a broader and easier to check characterization of parametric models that can be used to (a) preserve nonparametric validity of testing the null hypothesis, i.e., even when the models are incorrect, and (b) increase power compared to the non- or semiparametric bounds when the models are close to correct. We demonstrate our results in a clinical trial of depression in Alzheimer\u27s patients
The emergent integrated network structure of scientific research
The practice of scientific research is often thought of as individuals and
small teams striving for disciplinary advances. Yet as a whole, this endeavor
more closely resembles a complex system of natural computation, in which
information is obtained, generated, and disseminated more effectively than
would be possible by individuals acting in isolation. Currently, the structure
of this integrated and innovative landscape of scientific ideas is not well
understood. Here we use tools from network science to map the landscape of
interconnected research topics covered in the multidisciplinary journal PNAS
since 2000. We construct networks in which nodes represent topics of study and
edges give the degree to which topics occur in the same papers. The network
displays small-world architecture, with dense connectivity within scientific
clusters and sparse connectivity between clusters. Notably, clusters tend not
to align with assigned article classifications, but instead contain topics from
various disciplines. Using a temporal graph, we find that small-worldness has
increased over time, suggesting growing efficiency and integration of ideas.
Finally, we define a novel measure of interdisciplinarity, which is positively
associated with PNAS's impact factor. Broadly, this work suggests that complex
and dynamic patterns of knowledge emerge from scientific research, and that
structures reflecting intellectual integration may be beneficial for obtaining
scientific insight
Estimating effects by combining instrumental variables with case-control designs: the role of principal stratification
The instrumental variable framework is commonly used in the estimation of causal effects from cohort samples. In the case of more efficient designs such as the case-control study, however, the combination of the instrumental variable and complex sampling designs requires new methodological consideration. As the prevalence of Mendelian randomization studies is increasing and the cost of genotyping and expression data can be high, the analysis of data gathered from more cost-effective sampling designs is of prime interest. We show that the standard instrumental variable analysis is not applicable to the case-control design and can lead to erroneous estimation and inference. We also propose a method based on principal stratification for the analysis of data arising from the combination of case-control sampling and instrumental variable design and illustrate it with a study in oncology
Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA
A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).
Keywords: Brain; Cortical thickness; Gray matter; Mega-analysis; Neuroimaging; Schizophrenia; Volum
Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe)
We propose a new method for the simultaneous selection and estimation of
multivariate sparse additive models with correlated errors. Our method called
Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe)
simultaneously selects among null, linear, and smooth non-linear effects for
each predictor while incorporating joint estimation of the sparse residual
structure among responses, with the motivation that accounting for
inter-response correlation structure can lead to improved accuracy in variable
selection and estimation efficiency. CoMPAdRe is constructed in a
computationally efficient way that allows the selection and estimation of
linear and non-linear covariates to be conducted in parallel across responses.
Compared to single-response approaches that marginally select linear and
non-linear covariate effects, we demonstrate in simulation studies that the
joint multivariate modeling leads to gains in both estimation efficiency and
selection accuracy, of greater magnitude in settings where signal is moderate
relative to the level of noise. We apply our approach to protein-mRNA
expression levels from multiple breast cancer pathways obtained from The Cancer
Proteome Atlas and characterize both mRNA-protein associations and
protein-protein subnetworks for each pathway. We find non-linear mRNA-protein
associations for the Core Reactive, EMT, PIK-AKT, and RTK pathways
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