Meta-analysis of the functional neuroimaging literature with probabilistic logic programming.

ABSTRACT: Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple neuroscience concepts and expressing a restricted range of questions. Here, we expand the scope of neuroimaging meta-analysis by designing NeuroLang: a domain-specific language to express and test hypotheses using probabilistic first-order logic programming. By leveraging formalisms found at the crossroads of artificial intelligence and knowledge representation, NeuroLang provides the expressivity to address a larger repertoire of hypotheses in a meta-analysis, while seamlessly modeling the uncertainty inherent to neuroimaging data. We demonstrate the language's capabi

SUBMITTER: Abdallah M 

PROVIDER: S-EPMC9653422 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature