  With the growing number of published functional magnetic resonance imaging
(fMRI) studies, meta-analysis databases and models have become an integral part
of brain mapping research. Coordinate-based meta-analysis (CBMA) databases are
built by automatically extracting both coordinates of reported peak activations
and term associations using natural language processing (NLP) techniques.
Solving term-based queries on these databases make it possible to obtain
statistical maps of the brain related to specific cognitive processes. However,
with tools like Neurosynth, only singleterm queries lead to statistically
reliable results. When solving richer queries, too few studies from the
database contribute to the statistical estimations. We design a probabilistic
domain-specific language (DSL) standing on Datalog and one of its probabilistic
extensions, CP-Logic, for expressing and solving rich logic-based queries. We
encode a CBMA database into a probabilistic program. Using the joint
distribution of its Bayesian network translation, we show that solutions of
queries on this program compute the right probability distributions of voxel
activations. We explain how recent lifted query processing algorithms make it
possible to scale to the size of large neuroimaging data, where state of the
art knowledge compilation (KC) techniques fail to solve queries fast enough for
practical applications. Finally, we introduce a method for relating studies to
terms probabilistically, leading to better solutions for conjunctive queries on
smaller databases. We demonstrate results for two-term conjunctive queries,
both on simulated meta-analysis databases and on the widely-used Neurosynth
database.
