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Computer Aided Diagnosis of Schizophrenia Based on Local-Activity Measures of Resting-State fMRI

  • Alexandre Savio
  • Darya Chyzhyk
  • Manuel Graña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8480)

Abstract

Resting state functional Magnetic Resonance Imaging (rs-fMRI) is increasingly used for the identification of image biomarkers of brain diseases or psychiatric conditions, such as Schizophrenia. One approach is to perform classification experiments on the data, using feature extraction methods that allow to localize the discriminant locations in the brain, so that further studies may assess the clinical value of such locations. The classification accuracy results ensure that the located brain regions have some relation to the disease. In this paper we explore the discriminant value of brain local activity measures for the classification of Schizophrenia patients. The extensive experimental work, carried out on a publicly available database, provides evidence that local activity measures such as Regional Homogeneity (ReHo) may be useful for such purposes.

Keywords

Feature Selection Random Forest Functional Connectivity Feature Extraction Method Functional Magnetic Resonance Imaging 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexandre Savio
    • 1
  • Darya Chyzhyk
    • 1
  • Manuel Graña
    • 1
  1. 1.Computational Intelligence GroupUniversity of the Basque Country (UPV/EHU)San SebastiánSpain

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