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Multivariate Statistical Analysis of Whole Brain Structural Networks Obtained Using Probabilistic Tractography

  • Emma C. Robinson
  • Michel Valstar
  • Alexander Hammers
  • Anders Ericsson
  • A. David Edwards
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)

Abstract

This paper presents a new framework for the analysis of anatomical connectivity derived from diffusion tensor MRI. The framework has been applied to estimate whole brain structural networks using diffusion data from 174 adult subjects. In the proposed approach, each brain is first segmented into 83 anatomical regions via label propagation of multiple atlases and subsequent decision fusion. For each pair of anatomical regions the probability of connection and its strength is then estimated using a modified version of probabilistic tractography. The resulting brain networks have been classified according to age and gender using non-linear support vector machines with GentleBoost feature extraction. Classification performance was tested using a leave-one-out approach and the mean accuracy obtained was 85.4%.

Keywords

Support Vector Machine Fractional Anisotropy Multivariate Statistical Analysis Brain Network Orientation Distribution Function 
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-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Emma C. Robinson
    • 1
  • Michel Valstar
    • 1
  • Alexander Hammers
    • 2
  • Anders Ericsson
    • 1
  • A. David Edwards
    • 3
  • Daniel Rueckert
    • 1
  1. 1.Department of Computing, Imperial College LondonUK
  2. 2.MRC Clinical Sciences Centre and Division of Neuroscience, Faculty of Medicine, Imperial College LondonUK
  3. 3.Department of Paediatrics, Imperial College LondonUK

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