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Brain areas related to mathematical abili- ties in children have been mainly assessed through their activation in fMRI, while volume-based analysis have been employed in sMRI to discover structural differ- ences. However, a recent technique in precision medicine allows to enhance the sMRI analysis by extracting a large number of features, also called radiomics, related to shape, intensity and texture from specific areas. In the present study, a structural neuroimaging analysis based on radiomics and machine learning models is pre- sented with the aim of identifying brain areas related to different mathematical tests. A total of 77 school- aged children from third to sixth grade were adminis- tered four mathematical tests: Math Fluency, Calcu- lation, Applied Problems and Concepts as well as a structural brain imaging scan. The results confirmed and extended the involvement of brain areas found in sMRI and fMRI literature such as the frontal, parietal, temporal and occipital cortex, as well as basal ganglia and limbic system areas. For these areas, texture fea- tures were the most informative while volume represented less than 15% of the shape information. These findings emphasize the potential of radiomics for a more in-depth analysis of medical images for the identifi- cation of brain areas related to mathematical perfor- mance. The code used to obtain these results can be found at github.com/vicmancr/MathBrainRadiomics.
Keywords
Children, Machine Learning, Mathematical Performance, Radiomics, sMRI
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This preprint is under consideration at Brain Structure and Function. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 13 Apr, 2021
No community comments so far
Editorial decision: Major Revision and Re-Review Needed
On 30 May, 2021
Reviews received
Received 23 Apr, 2021
Reviewers invited
Invitations sent on 10 Apr, 2021
Editor assigned
On 08 Apr, 2021
First submitted
On 08 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Head & Neck Surgery
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Brain areas related to mathematical abili- ties in children have been mainly assessed through their activation in fMRI, while volume-based analysis have been employed in sMRI to discover structural differ- ences. However, a recent technique in precision medicine allows to enhance the sMRI analysis by extracting a large number of features, also called radiomics, related to shape, intensity and texture from specific areas. In the present study, a structural neuroimaging analysis based on radiomics and machine learning models is pre- sented with the aim of identifying brain areas related to different mathematical tests. A total of 77 school- aged children from third to sixth grade were adminis- tered four mathematical tests: Math Fluency, Calcu- lation, Applied Problems and Concepts as well as a structural brain imaging scan. The results confirmed and extended the involvement of brain areas found in sMRI and fMRI literature such as the frontal, parietal, temporal and occipital cortex, as well as basal ganglia and limbic system areas. For these areas, texture fea- tures were the most informative while volume represented less than 15% of the shape information. These findings emphasize the potential of radiomics for a more in-depth analysis of medical images for the identifi- cation of brain areas related to mathematical perfor- mance. The code used to obtain these results can be found at github.com/vicmancr/MathBrainRadiomics.
Figure 1
Figure 2
Figure 3
Figure 4
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Figure 6
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