  Typical cohorts in brain imaging studies are not large enough for systematic
testing of all the information contained in the images. To build testable
working hypotheses, investigators thus rely on analysis of previous work,
sometimes formalized in a so-called meta-analysis. In brain imaging, this
approach underlies the specification of regions of interest (ROIs) that are
usually selected on the basis of the coordinates of previously detected
effects. In this paper, we propose to use a database of images, rather than
coordinates, and frame the problem as transfer learning: learning a
discriminant model on a reference task to apply it to a different but related
new task. To facilitate statistical analysis of small cohorts, we use a sparse
discriminant model that selects predictive voxels on the reference task and
thus provides a principled procedure to define ROIs. The benefits of our
approach are twofold. First it uses the reference database for prediction, i.e.
to provide potential biomarkers in a clinical setting. Second it increases
statistical power on the new task. We demonstrate on a set of 18 pairs of
functional MRI experimental conditions that our approach gives good prediction.
In addition, on a specific transfer situation involving different scanners at
different locations, we show that voxel selection based on transfer learning
leads to higher detection power on small cohorts.
