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Multiple sclerosis (MS) is an inflammatory disease of central nervous system. Magnetic resonance (MR) images play an important role in diagnosis of MS because of the ability in detection of white matter lesions. Proper detection of lesions and their boundaries is crucial for diagnosis. T1 weighted images are the most preferred modal in diagnosis, but enriching them with information of other modals...
Artificial neural networks with radial basis functions are used to diagnose patients with multiple sclerosis. But the results of the training of this type of network varies greatly with the wavelet selected to compress the data and with the boundary conditions applied to the signal to compensate for the usage of a closed interval. In this paper we compare the results obtained for several wavelets...
Artificial neural networks with radial basis functions are used to diagnose patients with multiple sclerosis. But the training of this type of network requires a great amount of time. It would be advantageous if we could speed up this training. The Particle Swarm Optimization (PSO) algorithm was previously used in a particular case, but the results were poor. It was suggested to review this scenario...
Multiple sclerosis (MS) is a progressive neurological disorder affecting between 2 and 2.5 million people globally. Tests of mobility form part of clinical assessments of MS. Quantitative assessment of mobility using inertial sensors has the potential to provide objective, longitudinal monitoring of disease progression in patients with MS. The mobility of 21 patients (aged 25–59 years, 8 M, 13 F),...
Resting state functional connectivity is defined as correlations in brain activity measured by functional magnetic resonance imaging without any stimulation paradigm. Such connectivity is dynamic, even over the course of minutes, and the development of tools for its analysis is an important challenge in neuroscience. We propose a novel data-driven technique to extract connectivity patterns from dynamic...
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