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dc.contributor.authorPina Paredes, Violeta 
dc.date.accessioned2022-05-31T11:55:30Z
dc.date.available2022-05-31T11:55:30Z
dc.date.issued2022-04-14
dc.identifier.citationPina V... [et al.] (2022) Mathematical Abilities in School-Aged Children: A Structural Magnetic Resonance Imaging Analysis With Radiomics. Front. Neurosci. 16:819069. doi: [10.3389/fnins.2022.819069]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/75140
dc.descriptionThis research was supported by grant PSI2017-84556-P funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe,” and the European Union’s Horizon 2020 Research and Innovation Program under grant agreement number 825903 (euCanSHare project).es_ES
dc.description.abstractStructural magnetic resonance imaging (sMRI) studies have shown that children that differ in some mathematical abilities show differences in gray matter volume mainly in parietal and frontal regions that are involved in number processing, attentional control, and memory. In the present study, a structural neuroimaging analysis based on radiomics and machine learning models is presented with the aim of identifying the brain areas that better predict children’s performance in a variety of mathematical tests. A sample of 77 school-aged children from third to sixth grade were administered four mathematical tests: Math fluency, Calculation, Applied problems and Quantitative concepts as well as a structural brain imaging scan. By extracting radiomics related to the shape, intensity, and texture of specific brain areas, we observed that areas from the frontal, parietal, temporal, and occipital lobes, basal ganglia, and limbic system, were differentially related to children’s performance in the mathematical tests. sMRI-based analyses in the context of mathematical performance have been mainly focused on volumetric measures. However, the results for radiomics-based analysis showed that for these areas, texture features were the most important for the regression models, while volume accounted for less than 15% of the shape importance. These findings highlight the potential of radiomics for more in-depth analysis of medical images for the identification of brain areas related to mathematical abilities.es_ES
dc.description.sponsorshipPSI2017-84556-P funded by MCIN/AEI/10.13039/501100011033es_ES
dc.description.sponsorship“ERDF A way of making Europees_ES
dc.description.sponsorshipEuropean Union’s Horizon 2020 Research and Innovation Program under grant agreement number 825903 (euCanSHare project)es_ES
dc.language.isoenges_ES
dc.publisherFrontierses_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectSchool-aged childrenes_ES
dc.subjectMachine learninges_ES
dc.subjectMathematical performancees_ES
dc.subjectsMRIes_ES
dc.subjectRadiomicses_ES
dc.titleMathematical Abilities in School-Aged Children: A Structural Magnetic Resonance Imaging Analysis With Radiomicses_ES
dc.typejournal articlees_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/825903es_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.3389/fnins.2022.819069
dc.type.hasVersionVoRes_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España