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Article Dans Une Revue Scientific Reports Année : 2022

Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming

Résumé

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 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.
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Dates et versions

hal-03590714 , version 1 (28-02-2022)

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CC0 - Transfert dans le Domaine Public

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Majd Abdallah, Valentin Iovene, Gaston Zanitti, Demian Wassermann. Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming. Scientific Reports, 2022, ⟨10.1038/s41598-022-21801-4⟩. ⟨hal-03590714⟩
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