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Randomized parcellation based inference.

Benoit da Mota 1 Virgile Fritsch 1, * Gaël Varoquaux 1, 2 Tobias Banaschewski 3 Gareth J. Barker 4 Arun L. W. Bokde 5 Uli Bromberg 6 Patricia J. Conrod 7 Jürgen Gallinat 8 Hugh Garavan 9, 10 Jean-Luc Martinot 11 Frauke Nees 3 Tomáš Paus 12, 13, 14 Zdenka Pausova 15 Marcella Rietschel 16 Michael N. Smolka 17 Andreas Ströhle 18 Vincent Frouin 19 Jean-Baptiste Poline 2 Bertrand Thirion 1, 19 
Abstract : 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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Contributor : Virgile Fritsch Connect in order to contact the contributor
Submitted on : Monday, January 6, 2014 - 1:11:30 PM
Last modification on : Friday, December 9, 2022 - 12:19:40 PM
Long-term archiving on: : Sunday, April 6, 2014 - 10:11:00 PM


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Benoit da Mota, Virgile Fritsch, Gaël Varoquaux, Tobias Banaschewski, Gareth J. Barker, et al.. Randomized parcellation based inference.. NeuroImage, 2013, epub ahead of print. ⟨10.1016/j.neuroimage.2013.11.012⟩. ⟨hal-00915243⟩



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