Randomized parcellation based inference

نویسندگان

  • Benoit Da Mota
  • Virgile Fritsch
  • Gaël Varoquaux
  • Tobias Banaschewski
  • Gareth J. Barker
  • Arun L. W. Bokde
  • Uli Bromberg
  • Patricia J. Conrod
  • Jürgen Gallinat
  • Hugh Garavan
  • Jean-Luc Martinot
  • Frauke Nees
  • Tomás Paus
  • Zdenka Pausova
  • Marcella Rietschel
  • Michael N. Smolka
  • Andreas Ströhle
  • Vincent Frouin
  • Jean-Baptiste Poline
  • Bertrand Thirion
چکیده

Neuroimaging group analyses are used to relate inter-subject signal differences observed in brain imaging with behavioral or genetic variables and to assess risks factors of brain diseases. The lack of stability and of sensitivity of current voxel-based analysis schemes may however lead to non-reproducible results. We introduce a new approach to overcome the limitations of standard methods, in which active voxels are detected according to a consensus on several random parcellations of the brain images, while a permutation test controls the false positive risk. Both on synthetic and real data, this approach shows higher sensitivity, better accuracy and higher reproducibility than state-of-the-art methods. In a neuroimaging-genetic application, we find that it succeeds in detecting a significant association between a genetic variant next to the COMT gene and the BOLD signal in the left thalamus for a functional Magnetic Resonance Imaging contrast associated with incorrect responses of the subjects from a Stop Signal Task protocol.

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عنوان ژورنال:
  • NeuroImage

دوره 89  شماره 

صفحات  -

تاریخ انتشار 2014