BackgroundAlzheimer’s disease (AD) is a progressive, incurable neurodegenerative disease and the most common type of dementia. It cannot be prevented, cured or drastically slowed, even though AD research has increased in the past 5-10 years. Instead of focusing on the brain volume or on the single brain structures like hippocampus, this paper investigates the relationship and proximity between regions in the brain and uses this information as a novel way of classifying normal control (NC), mild cognitive impaired (MCI), and AD subjects.MethodsA longitudinal cohort of 528 subjects (170 NC, 240 MCI, and 114 AD) from ADNI at baseline and month 12 was studied. We investigated a marker based on Procrustes aligned center of masses and the percentile surface connectivity between regions. These markers were classified using a linear discriminant analysis in a cross validation setting and compared to whole brain and hippocampus volume.ResultsWe found that both our markers was able to significantly classify the subjects. The surface connectivity marker showed the best results with an area under the curve (AUC) at 0.877 (p<0.001), 0.784 (p<0.001), 0,766 (p<0.001) for NC-AD, NC-MCI, and MCI-AD, respectively, for the functional regions in the brain. The surface connectivity marker was able to classify MCI-converters with an AUC of 0.599 (p<0.05) for the 1-year period.ConclusionOur results show that our relative proximity markers include more information than whole brain and hippocampus volume. Our results demonstrate that our proximity markers have the potential to assist in early diagnosis of AD.

We present a fully automated framework for scoring a patient's risk of cardiovascular disease (CVD) and mortality from a standard lateral radiograph of the lumbar aorta. The framework segments abdominal aortic calcifications for computing a CVD risk score and performs a survival analysis to validate the score. Since the aorta is invisible on X-ray images, its position is reasoned from 1) the shape and location of the lumbar vertebrae and 2) the location, shape, and orientation of potential calcifications. The proposed framework follows the principle of Bayesian inference, which has several advantages in the complex task of segmenting aortic calcifications. Bayesian modeling allows us to compute CVD risk scores conditioned on the seen calcifications by formulating distributions, dependencies, and constraints on the unknown parameters. We evaluate the framework on two datasets consisting of 351 and 462 standard lumbar radiographs, respectively. Promising results indicate that the framework has potential applications in diagnosis, treatment planning, and the study of drug effects related to CVD.

The aim of this study is to investigate new methods for describing the progression of atherosclerosis based on novel information of the growth patterns of individual abdominal aortic calcifications (AACs) over time. Lateral X-ray images were used due to their low cost, fast examination time, and wide-spread use, which facilitates a large statistical model (n > 100) based on longitudinal data. The examined cohort consisted of 103 post-menopausal women aged 62.4 years (± 7.0 years) with an average number of AACs of (4.7 ± 8.0) at baseline. The subjects had X-ray images taken in 1992-1993 (baseline) and again in 2000-2001 (follow-up). The growth patterns of the individual AACs were derived based on registered baseline and follow-up images. Area, height, width, centre of mass position, and movement of the centre of mass, and upper and lower boundary of the matched AACs were measured. The AACs occurred first, mainly, on the posterior aortic wall. The AACs grew on average 41 in the longitudinal direction and 21 in the radial direction. A correlation of 0.48 (P < 0.001) between growth in width and height of the AACs was present. The centre of mass of the AACs moved 0.60 mm (P < 0.001) downstream in the aorta, on average. The growth patterns of AACs may give new insights into the progression of atherosclerosis. The downstream asymmetry in the growth patterns indicates variability in microscopic environments around the AACs.

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We present a method for prediction of atherosclerotic growth based on a training set of 229 2D manually annotated baseline and corresponding follow-up calcifications from lateral X-ray images over an 8 year period. The prediction uses affine shape analysis based on singular value decomposition where non-rigid shapes are modeled as projections of rigid high-dimensional shapes. The SVD prediction was compared to growth based on dilation and predictive conditional PCA. The SVD model yields the largest Jaccard score indicating that the SVD model captures the most of the growth. We also found that small circular shapes grow the most which is likely due to the higher growth potential in smaller calcifications. Furthermore, we are able to predict the biological risk factors better by a joint shape and biology model that suggests a relationship between the shapes and the risk factors.

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