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Voxelwise multivariate statistics and brain-wide machine learning using the full diffusion tensor.

Anne-Laure Fouque Pierre Fillard 1 Anne Bargiacchi Arnaud Cachia Monica Zilbovicius Benjamin Thyreau Edith Le Floch 2 Philippe Ciuciu 2 Edouard Duchesnay 2
1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
2 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur
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.
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https://hal.inria.fr/hal-00700801
Contributor : Pierre Fillard <>
Submitted on : Wednesday, May 23, 2012 - 11:47:54 PM
Last modification on : Thursday, June 18, 2020 - 6:48:02 PM

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  • HAL Id : hal-00700801, version 1
  • PUBMED : 21995007

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Anne-Laure Fouque, Pierre Fillard, Anne Bargiacchi, Arnaud Cachia, Monica Zilbovicius, et al.. Voxelwise multivariate statistics and brain-wide machine learning using the full diffusion tensor.. Med Image Comput Comput Assist Interv, Springer, 2011, 14 (Pt 2), pp.9-16. ⟨hal-00700801⟩

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