Randomized parcellation based inference.
Résumé
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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persistence.pdf (684.73 Ko)
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supplementary_material.pdf (127.01 Ko)
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supplementary_material1.pdf (106.19 Ko)
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| Origine | Fichiers produits par l'(les) auteur(s) |
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| Format | Autre |
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| Origine | Fichiers produits par l'(les) auteur(s) |
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