A Greedy Best-First Search Algorithm for Accurate Functional Brain Mapping

Detecting the most relevant brain regions for explaining the distinction between conditions is one of the most sought after goals in cognitive neuroimaging research. A popular approach for achieving this goal is the multivariate pattern analysis (MVPA) which is commonly conducted through the searchlight procedure due to its advantages such as being intuitive and flexible with regards to search space size. However, the searchlight approach suffers from a number of limitations that lead to misidentification of truly informative voxels or clusters of voxels which in turn results in imprecise information maps. These limitations mainly stem from the fact that the information value of the search spheres are assigned to the voxel at the center of them, as well as the requirement of manual assignment of searchlight radius. This issue becomes more severe when larger searchlight radius values are selected which makes truly informative voxels less likely to be identified. In this paper we propose a datadriven algorithm for creating the information map of the brain while alleviating the above mentioned issues.