Accelerating regional atrophy rates in the progression from normal aging to Alzheimer’s disease

We investigated progression of atrophy in vivo, in Alzheimer’s disease (AD), and mild cognitive impairment (MCI). We included 64 patients with AD, 44 with MCI and 34 controls with serial MRI examinations (interval 1.8 ± 0.7 years). A nonlinear registration algorithm (fluid) was used to calculate atrophy rates in six regions: frontal, medial temporal, temporal (extramedial), parietal, occipital lobes and insular cortex. In MCI, the highest atrophy rate was observed in the medial temporal lobe, comparable with AD. AD patients showed even higher atrophy rates in the extramedial temporal lobe. Additionally, atrophy rates in frontal, parietal and occipital lobes were increased. Cox proportional hazard models showed that all regional atrophy rates predicted conversion to AD. Hazard ratios varied between 2.6 (95% confidence interval (CI) = 1.1–6.2) for occipital atrophy and 15.8 (95% CI = 3.5–71.8) for medial temporal lobe atrophy. In conclusion, atrophy spreads through the brain with development of AD. MCI is marked by temporal lobe atrophy. In AD, atrophy rate in the extramedial temporal lobe was even higher. Moreover, atrophy rates also accelerated in parietal, frontal, insular and occipital lobes. Finally, in nondemented elderly, medial temporal lobe atrophy was most predictive of progression to AD, demonstrating the involvement of this region in the development of AD

From "Low Hanging" to "User Ready": Initial Steps into a HealthGrid

Grids offer powerful infrastructures and promising concepts for the development and deployment of advanced applications in medical research and healthcare. The construction of HealthGrids in practice, however, is challenging due to reasons of scientific, technical, and cultural nature, among them the large gap between communities that develop and use the technology. Whereas grid developments focus mostly on functionality, usability issues are also very important to enable the potential of grids to be fully exploited by those who could mostly benefit from it, the end-users. In this paper we make a retrospective of our efforts to develop the Virtual Lab for functional Magnetic Resonance Imaging (fMRI). This project aims at providing for the end-users a grid-based system to facilitate research and clinical usage of fMRI data for study of brain activation. We present the evolution of this project in three phases coined "low hanging fruit", "trying out" and "end-user ready", and the lessons learnt in each one. The evolution of the software architecture, which had a large impact on the user front-end, is discussed in more detail. The current architecture facilitates the construction of front-ends that enable users to access the grid infrastructure from a single user-friendly GUI. All (local and grid) resources are accessed directly by the users from a virtual desktop implemented by the Virtual Resource Browser (VBrowser

W.J.: Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling

Abstract — This paper presents a new method for deformable model based segmentation of lumen and thrombus in abdominal aortic aneurysms from CT angiography scans. First the lumen is segmented based on two positions indicated by the user, and subsequently the resulting surface is used to initialise the automated thrombus segmentation method. For the lumen, the image-derived deformation term is based on a simple grey level model (bi-threshold). For the more complex problem of thrombus segmentation, a grey level modelling approach with a non-parametric pattern classification technique is used, namely k-nearest neighbours. The intensity profile sampled along the surface normal is used as classification feature. Manual segmentations are used for training the classifier: samples are collected inside, outside, and at the given boundary positions. The deformation is steered by the most likely class corresponding t

Usage of the Term Big Data in Biomedical Publications: A Text Mining Approach

In this study, we attempt to assess the value of the term Big Data when used by researchers in their publications. For this purpose, we systematically collected a corpus of biomedical publications that use and do not use the term Big Data. These documents were used as input to a machine learning classifier to determine how well they can be separated into two groups and to determine the most distinguishing classification features. We generated 100 classifiers that could correctly distinguish between Big Data and non-Big Data documents with an area under the Receiver Operating Characteristic (ROC) curve of 0.96. The differences between the two groups were characterized by terms specific to Big Data themes—such as ‘computational’, ‘mining’, and ‘challenges’—and also by terms that indicate the research field, such as ‘genomics’. The ROC curves when plotted for various time intervals showed no difference over time. We conclude that there is a detectable and stable difference between publications that use the term Big Data and those that do not. Furthermore, the use of the term Big Data within a publication seems to indicate a distinct type of research in the biomedical field. Therefore, we conclude that value can be attributed to the term Big Data when used in a publication and this value has not changed over time