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

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,...

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Bibliographic Details
Published in:Scientific reports 2022-11, Vol.12 (1), p.19431-18, Article 19431
Main Authors: Abdallah, Majd, Iovene, Valentin, Zanitti, Gaston, Wassermann, Demian
Format: Article
Language:English
Subjects:
631/114/1314
631/114/1751
631/114/2164
631/114/2401
631/114/2415
631/378/2649
Artificial Intelligence
Automation
Bioengineering
Brain - diagnostic imaging
Brain research
Cognitive Sciences
Computer Science
Functional Neuroimaging
Humanities and Social Sciences
Hypotheses
Image processing
Imaging
Language
Life Sciences
Logic
Logic in Computer Science
Logic programming
Medical imaging
Meta-analysis
multidisciplinary
Nervous system
Neuroimaging
Neuroimaging - methods
Neurons and Cognition
Neurosciences
Science
Science (multidisciplinary)
Semantics
Structure-function relationships
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Summary: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 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 frontoparietal control network.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-21801-4