Automated Diagnosis of Alzheimer’s Disease by Integrating Genetic Biomarkers and Tissue Density Information

  • Andrés Ortiz
  • Miguel Moreno-Estévez
  • Juan M. Górriz
  • Javier Ramírez
  • María J. García-Tarifa
  • Jorge Munilla
  • Nuria Haba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9107)

Abstract

Computer aided diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer’s Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper presents a straightfoward approach to determine the most discrimanative brain regions, defined by the Automated Anatomical Labelling (AAL), based on the measurements of the tissue density at the different brain areas. Statistical analysis of GM and WM densities reveal significant differences between controls (CN) and AD at specific brain areas associated to tissue density diminishing due to neurodegeneration. The proposed method has been evaluated using a large dataset from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation proved that computed WM/GM densities are discriminative enough to differentiate between CN/AD. Moreover, fusing density measurements with ApoE genetic information help to increase the diagnosis accuracy.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andrés Ortiz
    • 1
  • Miguel Moreno-Estévez
    • 1
  • Juan M. Górriz
    • 2
  • Javier Ramírez
    • 2
  • María J. García-Tarifa
    • 1
  • Jorge Munilla
    • 1
  • Nuria Haba
    • 1
  1. 1.Communications Engineering DepartmentUniversity of MálagaMálagaSpain
  2. 2.Department of Signal Theory, Communications and NetworkingUniversity of GranadaGranadaSpain

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