Randomized parcellation based inference

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.

Authors

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

Keywords

• Group analysis
• Parcellation
• Reproducibility
• Multiple comparisons
• Permutations