  Researchers in neuroscience have a growing number of datasets available to
study the brain, which is made possible by recent technological advances. Given
the extent to which the brain has been studied, there is also available
ontological knowledge encoding the current state of the art regarding its
different areas, activation patterns, key words associated with studies, etc.
Furthermore, there is an inherent uncertainty associated with brain scans
arising from the mapping between voxels -- 3D pixels -- and actual points in
different individual brains. Unfortunately, there is currently no unifying
framework for accessing such collections of rich heterogeneous data under
uncertainty, making it necessary for researchers to rely on ad hoc tools. In
particular, one major weakness of current tools that attempt to address this
kind of task is that only very limited propositional query languages have been
developed. In this paper, we present NeuroLang, an ontology language with
existential rules, probabilistic uncertainty, and built-in mechanisms to
guarantee tractable query answering over very large datasets. After presenting
the language and its general query answering architecture, we discuss
real-world use cases showing how NeuroLang can be applied to practical
scenarios for which current tools are inadequate.
