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Motor disability is a dominant and restricting symptom in multiple sclerosis, yet its neuroimaging correlates are not fully understood. We apply statistical and machine learning techniques on multimodal neuroimaging data to discriminate between multiple sclerosis patients and healthy controls and to predict motor disability scores in the patients. We examine the data of sixty-four multiple sclerosis patients and sixty-five controls, who underwent the MRI examination and the evaluation of motor disability scales. The modalities used comprised regional fractional anisotropy, regional grey matter volumes, and functional connectivity. For analysis, we employ two approaches: high-dimensional support vector machines run on features selected by Fisher Score (aiming for maximal classification accuracy), and low-dimensional logistic regression on the principal components of data (aiming for increased interpretability). We apply analogous regression methods to predict symptom severity. While fractional anisotropy provides the classification accuracy of 96.1% and 89.9% with both approaches respectively, including other modalities did not bring further improvement. Concerning the prediction of motor impairment, the low-dimensional approach performed more reliably. The first grey matter volume component was significantly correlated (R = 0.28-0.46, p < 0.05) with most clinical scales. In summary, we identified the relationship between both white and grey matter changes and motor impairment in multiple sclerosis. Furthermore, we were able to achieve the highest classification accuracy based on quantitative MRI measures of tissue integrity between patients and controls yet reported, while also providing a low-dimensional classification approach with comparable results, paving the way to interpretable machine learning models of brain changes in multiple sclerosis. [ABSTRACT FROM AUTHOR]

The human brain represents a complex computational system, the function and structure of which may be measured using various neuroimaging techniques focusing on separate properties of the brain tissue and activity. We capture the organization of white matter fibers acquired by diffusion-weighted imaging using probabilistic diffusion tractography. By segmenting the results of tractography into larger anatomical units, it is possible to draw inferences about the structural relationships between these parts of the system. This pipeline results in a structural connectivity matrix, which contains an estimate of connection strength among all regions. However, raw data processing is complex, computationally intensive, and requires expert quality control, which may be discouraging for researchers with less experience in the field. We thus provide brain structural connectivity matrices in a form ready for modelling and analysis and thus usable by a wide community of scientists. The presented dataset contains brain structural connectivity matrices together with the underlying raw diffusion and structural data, as well as basic demographic data of 88 healthy subjects. Measurement(s) Diffusion Weighted Imaging Technology Type(s) Magnetic Resonance Imaging Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Czech Republic [ABSTRACT FROM AUTHOR]

Objective: The accuracy of phase-contrast magnetic resonance imaging (PC-MRI) measurement is investigated using a computational fluid dynamics (CFD) model with the objective to determine the magnitude of the flow underestimation due to turbulence behind a narrowed valve in a phantom experiment. Materials and methods: An acrylic stationary flow phantom is used with three insertable plates mimicking aortic valvular stenoses of varying degrees. Positive and negative horizontal fluxes are measured at equidistant slices using standard PC-MRI sequences by 1.5T and 3T systems. The CFD model is based on the 3D lattice Boltzmann method (LBM). The experimental and simulated data are compared using the Bland-Altman-derived limits of agreement. Based on the LBM results, the turbulence is quantified and confronted with the level of flow underestimation. Results: LBM gives comparable results to PC-MRI for valves up to moderate stenosis on both field strengths. The flow magnitude through a severely stenotic valve was underestimated due to signal void in the regions of turbulent flow behind the valve, consistently with the level of quantified turbulence intensity. Discussion: Flow measured by PC-MRI is affected by noise and turbulence. LBM can simulate turbulent flow efficiently and accurately, it has therefore the potential to improve clinical interpretation of PC-MRI. [ABSTRACT FROM AUTHOR]

One of the recognised complications of catheter ablation is pulmonary venous stenosis. The aim of this study was to compare two methods of evaluation of pulmonary venous diameter for follow-up assessment of the above complication: (1) a linear approach evaluating two main diameters of the vein, (2) semiautomatically measured cross-sectional area (CSA). The study population consists of 29 patients. All subjects underwent contrast-enhanced magnetic resonance angiography (CeMRA) of the pulmonary veins (PVs) before and after the ablation; 14 patients were also scanned 3 months later. PV diameter was evaluated from two-dimensional multiplanar reconstructions by measuring either the linear diameter or CSA. A comparison between pulmonary venous CSA and linear measurements revealed a systematic difference in absolute values. This difference was not significant when comparing the relative change CSA and quadratic approximation using linear extents (linear approach). However, a trend towards over-estimation of calibre reduction was documented for the linear approach. Using CSA assessment, significant PV stenosis was found in ten PVs (8%) shortly after ablation. Less significant PV stenosis, ranging from 20 to 50% was documented in other 18 PVs (15%). CeMRA with CSA assessment of the PVs is suitable method for evaluation of PV diameters. [ABSTRACT FROM AUTHOR]

Presents a letter to the editor about percutaneous shunt creation for hemodialysis using uncovered metallic stents.