Mass Meta-analysis in Talairach Space

We provide a method for mass meta-analysis in a neuroinformatics database containing stereotaxic Talairach coordinates from neuroimaging experiments. Database labels are used to group the individual experiments, e.g., according to cognitive function, and the consistent pattern of the experiments within the groups are determined. The method voxelizes each group of experiments via a kernel density estimation, forming probability density volumes. The values in the probability density volumes are compared to null-hypothesis distributions generated by resamplings from the entire unlabeled set of experiments, and the distances to the nullhypotheses are used to sort the voxels across groups of experiments. This allows for mass meta-analysis, with the construction of a list with the most prominent associations between brain areas and group labels. Furthermore, the method can be used for functional labeling of voxels.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Finn Årup Nielsen
• Keywords: Kernel Density Estimation, Mass Meta-analysis, NIPS 2004, NIPS 2007, Probability Density Volumes