{"DOI":"10.1101/2022.02.25.481931","abstract":"AbstractInferring 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 modelling the uncertainty inherent to neuroimaging data. We demonstrate the language's capabilities in conducting comprehensive neuroimaging meta-analysis through use-case examples that address questions of structure-function associations. Specifically, we infer the specific functional roles of three canonical brain networks, support the role of the visual word-form area in visuospatial attention, and investigate the heterogeneous organization of the fronto-parietal control network.","author":[{"family":"Abdallah","given":"Majd"},{"family":"Iovene","given":"Valentin"},{"family":"Zanitti","given":"Gaston"},{"family":"Wassermann","given":"Demian"}],"id":"unknown","issued":{"date-parts":[[2022,2,28]]},"publisher":"Cold Spring Harbor Laboratory","title":"Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming","type":"post"}