  Despite the digital nature of magnetic resonance imaging, the resulting
observations are most frequently reported and stored in text documents. There
is a trove of information untapped in medical health records, case reports, and
medical publications. In this paper, we propose to mine brain medical
publications to learn the spatial distribution associated with anatomical
terms. The problem is formulated in terms of minimization of a risk on
distributions which leads to a least-deviation cost function. An efficient
algorithm in the dual then learns the mapping from documents to brain
structures. Empirical results using coordinates extracted from the
brain-imaging literature show that i) models must adapt to semantic variation
in the terms used to describe a given anatomical structure, ii) voxel-wise
parameterization leads to higher likelihood of locations reported in unseen
documents, iii) least-deviation cost outperforms least-square. As a proof of
concept for our method, we use our model of spatial distributions to predict
the distribution of specific neurological conditions from text-only reports.
