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Hospital de Órbigo, Spain

Bejarano B.,University of Navarra | Bianco M.,San Raffaele Scientific Institute | Gonzalez-Moron D.,University of Navarra | Sepulcre J.,University of Navarra | And 8 more authors.
BMC Neurology | Year: 2011

Background: The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS).Methods: We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center.Results: We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later.Conclusions: The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability. © 2011 Bejarano et al; licensee BioMed Central Ltd. Source


Sepulveda M.,University of Barcelona | Ros C.,University of Barcelona | Martinez-Lapiscina E.H.,University of Barcelona | Sola-Valls N.,University of Barcelona | And 9 more authors.
Multiple Sclerosis | Year: 2016

Since a decline in the ovary function might impact the reproductive potential in women with multiple sclerosis (MS), we investigated the pituitary-ovary axis and ovarian reserve, including anti-Müllerian hormone (AMH) levels and ultrasound imaging of the ovaries, of 25 relapsing-remitting MS patients and 25 age-matched healthy controls. Mean levels of pituitary-gonadal hormones and age-adjusted parameters of ovarian reserve markers were not significantly different between both groups. Patients with higher disease activity (annualized relapse rate >0.5; n=9) had significantly lower AMH levels, total antral follicle count and ovarian volume, than those with lower disease activity. The finding of poorer ovarian reserve associated with higher disease activity should be taken into consideration since it may negatively impact the reproductive prognosis. © SAGE Publications. Source


Rodrigues P.,Mint Labs | Prats-Galino A.,LSNA | Villoslada P.,Center for Neuroimmunology | Falcon C.,Medical Imaging Platform | Prckovska V.,Center for Neuroimmunology
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention | Year: 2013

Brain networks are becoming forefront research in neuroscience. Network-based analysis on the functional and structural connectomes can lead to powerful imaging markers for brain diseases. However, constructing the structural connectome can be based upon different acquisition and reconstruction techniques whose information content and mutual differences has not yet been properly studied in a unified framework. The variations of the structural connectome if not properly understood can lead to dangerous conclusions when performing these type of studies. In this work we present evaluation of the structural connectome by analysing and comparing graph-based measures on real data acquired by the three most important Diffusion Weighted Imaging techniques: DTI, HARDI and DSI. We thus come to several important conclusions demonstrating that even though the different techniques demonstrate differences in the anatomy of the reconstructed fibers the respective connectomes show variations of 20%. Source


Rodrigues P.,Mint Labs | Prats-Galino A.,LSNA | Villoslada P.,Center for Neuroimmunology | Falcon C.,Medical Imaging Platform | Prckovska V.,Center for Neuroimmunology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Brain networks are becoming forefront research in neuroscience. Network-based analysis on the functional and structural connectomes can lead to powerful imaging markers for brain diseases. However, constructing the structural connectome can be based upon different acquisition and reconstruction techniques whose information content and mutual differences has not yet been properly studied in a unified framework. The variations of the structural connectome if not properly understood can lead to dangerous conclusions when performing these type of studies. In this work we present evaluation of the structural connectome by analysing and comparing graph-based measures on real data acquired by the three most important Diffusion Weighted Imaging techniques: DTI, HARDI and DSI. We thus come to several important conclusions demonstrating that even though the different techniques demonstrate differences in the anatomy of the reconstructed fibers the respective connectomes show variations of 20%. © 2013 Springer-Verlag. Source


Velez de Mendizabal N.,University of Navarra | Velez de Mendizabal N.,University of the Basque Country | Carneiro J.,Instituto Gulbenkian Of Ciencia | Sole R.V.,University Pompeu Fabra | And 9 more authors.
BMC Systems Biology | Year: 2011

Background: The relapsing-remitting dynamics is a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). Although current understanding of both cellular and molecular mechanisms involved in the pathogenesis of autoimmune diseases is significant, how their activity generates this prototypical dynamics is not understood yet. In order to gain insight about the mechanisms that drive these relapsing-remitting dynamics, we developed a computational model using such biological knowledge. We hypothesized that the relapsing dynamics in autoimmunity can arise through the failure in the mechanisms controlling cross-regulation between regulatory and effector T cells with the interplay of stochastic events (e.g. failure in central tolerance, activation by pathogens) that are able to trigger the immune system.Results: The model represents five concepts: central tolerance (T-cell generation by the thymus), T-cell activation, T-cell memory, cross-regulation (negative feedback) between regulatory and effector T-cells and tissue damage. We enriched the model with reversible and irreversible tissue damage, which aims to provide a comprehensible link between autoimmune activity and clinical relapses and active lesions in the magnetic resonances studies in patients with Multiple Sclerosis. Our analysis shows that the weakness in this negative feedback between effector and regulatory T-cells, allows the immune system to generate the characteristic relapsing-remitting dynamics of autoimmune diseases, without the need of additional environmental triggers. The simulations show that the timing at which relapses appear is highly unpredictable. We also introduced targeted perturbations into the model that mimicked immunotherapies that modulate effector and regulatory populations. The effects of such therapies happened to be highly dependent on the timing and/or dose, and on the underlying dynamic of the immune system.Conclusion: The relapsing dynamic in MS derives from the emergent properties of the immune system operating in a pathological state, a fact that has implications for predicting disease course and developing new therapies for MS. © 2011 Vélez de Mendizábal et al; licensee BioMed Central Ltd. Source

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