dc.contributor.author | Pina Paredes, Violeta | |
dc.date.accessioned | 2022-05-31T11:55:30Z | |
dc.date.available | 2022-05-31T11:55:30Z | |
dc.date.issued | 2022-04-14 | |
dc.identifier.citation | Pina 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.uri | http://hdl.handle.net/10481/75140 | |
dc.description | This 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.abstract | Structural 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.sponsorship | PSI2017-84556-P funded
by MCIN/AEI/10.13039/501100011033 | es_ES |
dc.description.sponsorship | “ERDF A way of
making Europe | es_ES |
dc.description.sponsorship | European Union’s Horizon 2020
Research and Innovation Program under grant agreement
number 825903 (euCanSHare project) | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Frontiers | es_ES |
dc.rights | Atribución 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | School-aged children | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Mathematical performance | es_ES |
dc.subject | sMRI | es_ES |
dc.subject | Radiomics | es_ES |
dc.title | Mathematical Abilities in School-Aged Children: A Structural Magnetic Resonance Imaging Analysis With Radiomics | es_ES |
dc.type | journal article | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/825903 | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.3389/fnins.2022.819069 | |
dc.type.hasVersion | VoR | es_ES |