A main goal in current neuroscience is understanding of how different cognitive functions originate in the brain. To achieve this, several projects developed in the last decade sparked large heterogeneous databases. But at present, there is no unifying framework to represent theories. Progress has been made through meta-analysis but some limitations remain to be addressed. With Neurosynth, for example, we can't analyse whether, in the literature, terms dubbed as synonyms are really used in an exchangeable manner across articles. Furthermore, the type of queries that can be used can only be based on propositional logic. In this work, we address these issues by introducing NeuroLang, a probabilistic language based on Datalog$^{+/-}$ that was intended as a first-order logic-based tool. We will use NeuroLang's ability to combine structured knowledge and its power to perform queries based on first-order logic to perform an analysis of the relationship between ontological properties. We will show how, by analysing the changes in the statistical power of meta-analysis data, we can confirm the hierarchization of terms in ontologies.