Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features

Lysophosphatidic acid and sphingosine-1-phosphate promote morphogenesis and block invasion of prostate cancer cells in three-dimensional organotypic models

Normal prostate and some malignant prostate cancer (PrCa) cell lines undergo acinar differentiation and form spheroids in three-dimensional (3-D) organotypic culture. Acini formed by PC-3 and PC-3M, less pronounced also in other PrCa cell lines, spontaneously undergo an invasive switch, leading to the disintegration of epithelial structures and the basal lamina, and formation of invadopodia. This demonstrates the highly dynamic nature of epithelial plasticity, balancing epithelial-to-mesenchymal transition against metastable acinar differentiation. This study assessed the role of lipid metabolites on epithelial maturation. PC-3 cells completely failed to form acinar structures in delipidated serum. Adding back lysophosphatidic acid (LPA) and sphingosine-1-phosphate (S1P) rescued acinar morphogenesis and repressed invasion effectively. Blocking LPA receptor 1 (LPAR1) functions by siRNA (small interference RNA) or the specific LPAR1 inhibitor Ki16425 promoted invasion, while silencing of other G-protein-coupled receptors responsive to LPA or S1P mainly caused growth arrest or had no effects. The G-proteins Gα12/13 and Gαi were identified as key mediators of LPA signalling via stimulation of RhoA and Rho kinases ROCK1 and 2, activating Rac1, while inhibition of adenylate cyclase and accumulation of cAMP may be secondary. Interfering with these pathways specifically impeded epithelial polarization in transformed cells. In contrast, blocking the same pathways in non-transformed, normal cells promoted differentiation. We conclude that LPA and LPAR1 effectively promote epithelial maturation and block invasion of PrCa cells in 3-D culture. The analysis of clinical transcriptome data confirmed reduced expression of LPAR1 in a subset of PrCa's. Our study demonstrates a metastasis-suppressor function for LPAR1 and Gα12/13 signalling, regulating cell motility and invasion versus epithelial maturation

In vivo NMR as a tool for probing molecular structure and dynamics in intact Chlamydomonas reinhardtii cells

Solid state NMR/Biophysical Organic Chemistr

Fast multiatlas selection using composition of transformations for radiation therapy planning

In radiation therapy, multiatlas segmentation is recognized as being accurate, but is generally not considered scalable since the highest accuracy is achieved only when using a large atlas database. The fundamental problem is to use such a large database, to accurately represent the population variability, while conserving a relatively small computational cost. A method based on the composition of transformations is proposed to address this issue. The main novelties and key contributions of this paper are the definition of a transitivity error function and the presentation of an image clustering scheme that is based solely on the computed registration transformations. Leave-one-out experiments conducted on a database of N=50 MR prostate scans demonstrate that a reduction of (N−1)=49x in the number of pre-alignment registrations, and of 3.2x in term of total registration effort, is possible without significant impact on segmentation quality