@Article{Illan2014,
  author =      {Illan, Ignacio A. and G{\'{o}}rriz, Juan M. and Ram{\'{\i}}rez, Javier and Meyer-Base, Anke},
  title =       {Spatial component analysis of {MRI} data for Alzheimer's disease diagnosis: a {Bayes}ian network approach.},
  journal =     {Frontiers in Computational Neuroscience},
  year =        {2014},
  volume =      {8},
  pages =       {156},
  abstract =    {This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is used to model the dependencies between affected regions of AD. The structure of relations between affected regions allows to detect neurodegeneration with an estimated performance of 88\% on more than 400 subjects and predict neurodegeneration with 80\% accuracy, supporting the conclusion that modeling the dependencies between components increases the recognition of different patterns of brain degeneration in AD.},
  doi =         {10.3389/fncom.2014.00156},
  institution = {Department of Scientific Computing, Florida State University Tallahassee, FL, USA.},
  language =    {eng},
  medline-pst = {epublish},
  owner =       {pakitochus},
  pmid =        {25505408},
  timestamp =   {2016.02.02}
}

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