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 questio...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-11-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21801-4 |
| _version_ | 1811230451510018048 |
|---|---|
| author | Majd Abdallah Valentin Iovene Gaston Zanitti Demian Wassermann |
| author_facet | Majd Abdallah Valentin Iovene Gaston Zanitti Demian Wassermann |
| author_sort | Majd Abdallah |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-04-12T10:29:33Z |
| format | Article |
| id | doaj.art-b2fe950d08c6417c9ef08400e8ffad6e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T10:29:33Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b2fe950d08c6417c9ef08400e8ffad6e2022-12-22T03:36:53ZengNature PortfolioScientific Reports2045-23222022-11-0112111810.1038/s41598-022-21801-4Meta-analysis of the functional neuroimaging literature with probabilistic logic programmingMajd Abdallah0Valentin Iovene1Gaston Zanitti2Demian Wassermann3Inria, CEA, Neurospin, MIND Team, Université Paris SaclayInria, CEA, Neurospin, MIND Team, Université Paris SaclayInria, CEA, Neurospin, MIND Team, Université Paris SaclayInria, CEA, Neurospin, MIND Team, Université Paris SaclayAbstract 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.https://doi.org/10.1038/s41598-022-21801-4 |
| spellingShingle | Majd Abdallah Valentin Iovene Gaston Zanitti Demian Wassermann Meta-analysis of the functional neuroimaging literature with probabilistic logic programming Scientific Reports |
| title | Meta-analysis of the functional neuroimaging literature with probabilistic logic programming |
| title_full | Meta-analysis of the functional neuroimaging literature with probabilistic logic programming |
| title_fullStr | Meta-analysis of the functional neuroimaging literature with probabilistic logic programming |
| title_full_unstemmed | Meta-analysis of the functional neuroimaging literature with probabilistic logic programming |
| title_short | Meta-analysis of the functional neuroimaging literature with probabilistic logic programming |
| title_sort | meta analysis of the functional neuroimaging literature with probabilistic logic programming |
| url | https://doi.org/10.1038/s41598-022-21801-4 |
| work_keys_str_mv | AT majdabdallah metaanalysisofthefunctionalneuroimagingliteraturewithprobabilisticlogicprogramming AT valentiniovene metaanalysisofthefunctionalneuroimagingliteraturewithprobabilisticlogicprogramming AT gastonzanitti metaanalysisofthefunctionalneuroimagingliteraturewithprobabilisticlogicprogramming AT demianwassermann metaanalysisofthefunctionalneuroimagingliteraturewithprobabilisticlogicprogramming |