Publication: Mathematical Abilities in School-Aged Children: A Structural Magnetic Resonance Imaging Analysis With Radiomics.
Authors
Pina, Violeta ; Campello, Víctor M. ; Lekadir, Karim ; Seguí, Santi ; Garcia Santos, Jose M. ; Fuentes Melero, Luis José
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Publisher
Frontiers Media
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DOI
https://doi.org/10.3389/fnins.2022.819069
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info:eu-repo/semantics/article
Description
© 2022 Pina, Campello, Lekadir, Seguí, García-Santos and Fuentes. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/
This document is the Published Manuscript version of a Published Work that appeared in final form in Frontiers in Neuroscience. To access the final edited and published work see https://doi.org/10.3389/fnins.2022.819069
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.
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Citation
Frontiers in Neuroscience, 2022, Vol. 16 : 819069
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