387 research outputs found
Ethnic differences in adiposity and diabetes risk â insights from genetic studies
Type 2 diabetes is more common in non-Europeans and starts at a younger age and at lower BMI cut-offs. This review discusses the insights from genetic studies about pathophysiological mechanisms which determine risk of disease with a focus on the role of adiposity and body fat distribution in ethnic disparity in risk of type 2 diabetes. During the past decade, genome-wide association studies (GWAS) have identified more than 400 genetic variants associated with the risk of type 2 diabetes. The Eurocentric nature of these genetic studies have made them less effective in identifying mechanisms that make non-Europeans more susceptible to higher risk of disease. One possible mechanism suggested by epidemiological studies is the role of ethnic difference in body fat distribution. Using genetic variants associated with an ability to store extra fat in a safe place, which is subcutaneous adipose tissue, we discuss how different ethnic groups could be genetically less susceptible to type 2 diabetes by developing a more favourable fat distribution
Neuroconductor: an R platform for medical imaging analysis
Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience
Longitudinal multivariate tensor- and searchlight-based morphometry using permutation testing
Tensor based morphometry [1] was used to detect
statistically significant regions of neuroanatomical
change over time in a comparison between 36 probable
Alzheimer's Disease patients and 20 age- and sexmatched
controls. Baseline and twelve-month repeat
Magnetic Resonance images underwent tied spatial
normalisation [10] and longitudinal high-dimensional
warps were then estimated. Analyses involved univariate
and multivariate data derived from the longitudinal
deformation fields. The most prominent findings were
expansion of the fluid spaces, and contraction of the
hippocampus and temporal region. Multivariate measures
were notably more powerful, and have the potential to
identify patterns of morphometric difference that would
be overlooked by conventional mass-univariate analysis
Multiscale Systematic Risk
Cataloged from PDF version of article.In this paper we propose a new approach to estimating systematic risk (the beta of an asset). The proposed method is based on a wavelet multiscaling approach that decomposes a given time series on a scale-by-scale basis. The empirical results from different economies show that the relationship between the return of a portfolio and its beta becomes stronger as the wavelet scale increases. Therefore, the predictions of the CAPM model should be investigated considering the multiscale nature of risk and return. (C) 2004 Elsevier Ltd. All rights reserved
Longitudinal Voxel-based morphometry with unified segmentation: evaluation on simulated Alzheimerâs disease
The goal of this work is to evaluate Voxel-Based Morphometry and three longitudinally-tailored methods
of VBM.We use a cohort of simulated images produced by deforming original scans using a Finite Element Method,
guided to emulate Alzheimer-like changes. The simulated images provide quite realistic data with a known pattern of
spatial atrophy, with which VBMâs findings can be meaningfully compared. We believe this is the first evaluation of VBM for which anatomically-plausible âgold-standardâ results are available. The three longitudinal VBM methods
have been implemented within the unified segmentation framework of SPM5; one of the techniques is a newly
developed procedure, which shows promising potential
Subâphenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets
Objective
This study performed individual-centric, data-driven calculations of propensity for coronary heart disease (CHD) and type 2 diabetes (T2D), utilizing magnetic resonance imaging-acquired body composition measurements, for sub-phenotyping of obesity and nonalcoholic fatty liver disease (NAFLD).
Methods
A total of 10,019 participants from the UK Biobank imaging substudy were included and analyzed for visceral and abdominal subcutaneous adipose tissue, muscle fat infiltration, and liver fat. An adaption of the k-nearest neighbors algorithm was applied to the imaging variable space to calculate individualized CHD and T2D propensity and explore metabolic sub-phenotyping within obesity and NAFLD.
Results
The ranges of CHD and T2D propensity for the whole cohort were 1.3% to 58.0% and 0.6% to 42.0%, respectively. The diagnostic performance, area under the receiver operating characteristic curve (95% CI), using disease propensities for CHD and T2D detection was 0.75 (0.73-0.77) and 0.79 (0.77-0.81). Exploring individualized disease propensity, CHD phenotypes, T2D phenotypes, comorbid phenotypes, and metabolically healthy phenotypes were found within obesity and NAFLD.
Conclusions
The adaptive k-nearest neighbors algorithm allowed an individual-centric assessment of each individualâs metabolic phenotype moving beyond discrete categorizations of body composition. Within obesity and NAFLD, this may help in identifying which comorbidities a patient may develop and consequently enable optimization of treatment
A spatial variation model of white matter microstructure
In this study, we introduce a new technique to model the variation of microstructural parameters across speci c brain regions. We use a simple model of di usion in each voxel, but model the variation of parameters across the region using penalised splines. We t the whole region model directly to the di usion-weighted signals. We test the tech- nique on the mid-sagittal section of the corpus callosum (CC) using a di usion MRI data set with distinct age groups. The method detects di erences and separates the groups
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