Section: New Results

Randomized parcellation-based inference

Participants : Gaël Varoquaux, Bertrand Thirion, Benoit Da Mota, Virgile Fritsch.

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 (see Fig. 3 ). 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.

More details can be found in [55] .

Figure 3. Overview of the randomized parcellation based inference framework on an example with few parcels. The variability of the parcels definition is used to obtain voxel-level statistics.