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Enhancing the Reproducibility of Group Analysis with Randomized Brain Parcellations
Benoit Da Mota1,2, Virgile Fritsch1,2, Gaël Varoquaux1,2, Vincent Frouin2, Jean-Baptiste Poline2,3, and Bertrand Thirion1,2
1Parietal Team, INRIA Saclay-Île-de-France, Saclay, France
mota@inria.fr
2CEA, DSV, I2BM, Neurospin bât 145, 91191, Gif-Sur-Yvette, France
3Henry H. Wheeler Jr. Brain Imaging Center, University of California at Berkeley, USA
Abstract. Neuroimaging group analyses are used to compare the inter-subject variability observed in brain organization with behavioural 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. A new approach is introduced 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 syntetic and real data, this approach shows higher sensitivity, better recovery and higher reproducibility than standard methods and succeeds in detecting a significant association in an imaging-genetic study between a genetic variant next to the COMT gene and a region in the left thalamus on a functional Magnetic Resonance Imaging contrast.
Keywords: neuroimaging, group analysis, parcellation, reproducibility
LNCS 8150, p. 591 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013