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.
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Da Mota, B., Fritsch, V., Varoquaux, G., Frouin, V., Poline, JB., Thirion, B. (2013). Enhancing the Reproducibility of Group Analysis with Randomized Brain Parcellations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40763-5_73
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