Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI... Show moreAlzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly. Show less
Carotid atherosclerosis, a disease in which plaque builds up inside the vessel wall, is a major cause of ischemic stroke. Traditionally, atherosclerosis risk stratification is heavily based on... Show moreCarotid atherosclerosis, a disease in which plaque builds up inside the vessel wall, is a major cause of ischemic stroke. Traditionally, atherosclerosis risk stratification is heavily based on the percentage of stenosis. However, a growing body of evidence suggests that luminal stenosis may not be the only cause of symptoms but the plaque composition may be more likely to impact the disease outcome. High-resolution vessel wall magnetic resonance imaging (VWMRI) is one of the most promising modalities for visualizing and evaluating carotid atherosclerotic plaque. The quantitative assessment of carotid atherosclerotic disease requires vessel wall segmentation and plaque classification, which is generally performed by manual delineations. However, manual contour tracing is labor-intensive, time-consuming and subject to inter-observer and inter-scan variability, which makes manual image analysis impractical for studies where large volume of data needs to be processed. Therefore, the main goal of this thesis is to: 1) develop approaches to automatically, robustly and reproducibly segment the carotid vessel wall and classify the atherosclerotic plaque from multi-spectral VWMRI; 2) validate the developed methods with reference standard; 3) extract the imaging biomarkers that can assist carotid artery disease evaluation. Show less
Boesveld, I.C.; Bruijnzeels, M.A.; Hitzert, M.; Hermus, M.A.A.; Pal-de Bruin, K.M. van der; Akker-van Marle, M.E. van den; ... ; Wiegers, T.A. 2017
We propose a novel classification method that integrates into existing agile software development practices by collecting data records generated by software and tools used in the development... Show moreWe propose a novel classification method that integrates into existing agile software development practices by collecting data records generated by software and tools used in the development process. We extract features from the collected data and create visualizations that provide insights, and feed the data into a prediction framework consisting of a deep neural network. The features and results are validated against conceptual frameworks that model the development methodologies as similar processes in other contexts. Initial results show that the visualization and prediction techniques provide promising outcomes that may help development teams and management gain better understanding of past events and future risks. Show less
