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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NeuroImage, Elsevier, 2013, epub ahead of print. 〈10.1016/j.neuroimage.2013.11.012〉
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Dernière modification le : lundi 4 juin 2018 - 15:42:02
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Benoit Da Mota, Virgile Fritsch, Gaël Varoquaux, Tobias Banaschewski, Gareth J. Barker, et al.. Randomized parcellation based inference.. NeuroImage, Elsevier, 2013, epub ahead of print. 〈10.1016/j.neuroimage.2013.11.012〉. 〈hal-00915243〉

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