Frontiers in Computational Neuroscience (Nov 2014)

Spatial Component Analysis of MRI data for Alzheimer's Disease Diagnosis: a Bayesian network approach

  • Ignacio eA. Illán,
  • Juan Manuel Gorriz,
  • Javier eRamírez,
  • Anke eMeyer-Baese

DOI
https://doi.org/10.3389/fncom.2014.00156
Journal volume & issue
Vol. 8

Abstract

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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 modelling the dependencies between components increases the recognition of different patterns of brain degeneration in AD.

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