Classification of Alzheimer's Disease from structural MRI using sparse logistic regression with optional spatial regularization

In this paper, we apply Sparse Logistic Regression Classifiers to the classification of 69 Alzheimer's Disease and 60 normal control subjects based on voxel-wise grey matter volumes derived from structural MRI. Methods such as standard logistic regression cannot be used in such problems because of the large number of voxels in comparison to the number of training subjects. Sparse Logistic Regression (SLR) addresses this issue by incorporating a sparsity penalty into the log-likelihood, which effects an automatic feature selection within the classification framework. We apply two different formulations of sparse logistic regression and compare their classification accuracy with that of Penalized Logistic Regression (PLR) and Maximum uncertainty Linear Discriminant Analysis (MLDA). In the first approach, we use the original formulation of SLR in which correlated voxels are forced to have similar weights. In the second approach we use a spatially regularized formulation, SRSLR, to force the discriminating vector to be spatially smooth when viewed as an image. Evaluation of the methods using cross-validation shows similar classification accuracies for SLR and SRSLR, with both performing better than PLR and MLDA. In addition, SRSLR produced classifiers that were spatially smoother than those produced by SLR, which may better reflect the regional effects of Alzheimer's Disease. What do you want to do ? New mail Cop

Automated detection of depression from brain structural magnetic resonance imaging (sMRI) scans

 Automated sMRI-based depression detection system is developed whose components include acquisition and preprocessing, feature extraction, feature selection, and classification. The core focus of the research is on the establishment of a new feature selection algorithm that quantifies the most relevant brain volumetric feature for depression detection at an individual level

The anthropometric, environmental and genetic determinants of right ventricular structure and function

BACKGROUND Measures of right ventricular (RV) structure and function have significant prognostic value. The right ventricle is currently assessed by global measures, or point surrogates, which are insensitive to regional and directional changes. We aim to create a high-resolution three-dimensional RV model to improve understanding of its structural and functional determinants. These may be particularly of interest in pulmonary hypertension (PH), a condition in which RV function and outcome are strongly linked. PURPOSE To investigate the feasibility and additional benefit of applying three-dimensional phenotyping and contemporary statistical and genetic approaches to large patient populations. METHODS Healthy subjects and incident PH patients were prospectively recruited. Using a semi-automated atlas-based segmentation algorithm, 3D models characterising RV wall position and displacement were developed, validated and compared with anthropometric, physiological and genetic influences. Statistical techniques were adapted from other high-dimensional approaches to deal with the problems of multiple testing, contiguity, sparsity and computational burden. RESULTS 1527 healthy subjects successfully completed high-resolution 3D CMR and automated segmentation. Of these, 927 subjects underwent next-generation sequencing of the sarcomeric gene titin and 947 subjects completed genotyping of common variants for genome-wide association study. 405 incident PH patients were recruited, of whom 256 completed phenotyping. 3D modelling demonstrated significant reductions in sample size compared to two-dimensional approaches. 3D analysis demonstrated that RV basal-freewall function reflects global functional changes most accurately and that a similar region in PH patients provides stronger survival prediction than all anthropometric, haemodynamic and functional markers. Vascular stiffness, titin truncating variants and common variants may also contribute to changes in RV structure and function. CONCLUSIONS High-resolution phenotyping coupled with computational analysis methods can improve insights into the determinants of RV structure and function in both healthy subjects and PH patients. Large, population-based approaches offer physiological insights relevant to clinical care in selected patient groups.Open Acces