Voxelwise Multivariate Statistics and Brain-Wide Machine Learning Using the Full Diffusion Tensor

  • Anne-Laure Fouque
  • Pierre Fillard
  • Anne Bargiacchi
  • Arnaud Cachia
  • Monica Zilbovicius
  • Benjamin Thyreau
  • Edith Le Floch
  • Philippe Ciuciu
  • Edouard Duchesnay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

In this paper, we propose to use the full diffusion tensor to perform brain-wide score prediction on diffusion tensor imaging (DTI) using the log-Euclidean framework., rather than the commonly used fractional anisotropy (FA). Indeed, scalar values such as the FA do not capture all the information contained in the diffusion tensor. Additionally, full tensor information is included in every step of the pre-processing pipeline: registration, smoothing and feature selection using voxelwise multivariate regression analysis. This approach was tested on data obtained from 30 children and adolescents with autism spectrum disorder and showed some improvement over the FA-only analysis.

Keywords

Autism Spectrum Disorder Autism Spectrum Disorder Feature Selection Fractional Anisotropy Tensor Space 
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 2011

Authors and Affiliations

  • Anne-Laure Fouque
    • 1
    • 2
  • Pierre Fillard
    • 1
    • 3
  • Anne Bargiacchi
    • 2
  • Arnaud Cachia
    • 4
  • Monica Zilbovicius
    • 2
  • Benjamin Thyreau
    • 1
  • Edith Le Floch
    • 1
    • 2
  • Philippe Ciuciu
    • 1
  • Edouard Duchesnay
    • 1
    • 2
  1. 1.CEA, Neurospin, LNAOSaclayFrance
  2. 2.INSERM-CEA U.1000 Imaging and PsychiatryOrsayFrance
  3. 3.INRIA Saclay-Île-de-France, ParietalSaclayFrance
  4. 4.Laboratory of Pathophysiology of Psychiatric Diseases, Sainte-Anne HospitalUMR 894 INSERM - Paris Descartes UniversityParisFrance

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