Resting-State Functional Connectivity in Mathematical Expertise
DC Field | Value | Language |
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dc.contributor.author | Shim, Miseon | - |
dc.contributor.author | Hwang, Han-Jeong | - |
dc.contributor.author | Kuhl, Ulrike | - |
dc.contributor.author | Jeon, Hyeon-Ae | - |
dc.date.accessioned | 2021-11-22T00:40:34Z | - |
dc.date.available | 2021-11-22T00:40:34Z | - |
dc.date.created | 2021-08-30 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 2076-3425 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/128294 | - |
dc.description.abstract | To what extent are different levels of expertise reflected in the functional connectivity of the brain? We addressed this question by using resting-state functional magnetic resonance imaging (fMRI) in mathematicians versus non-mathematicians. To this end, we investigated how the two groups of participants differ in the correlation of their spontaneous blood oxygen level-dependent fluctuations across the whole brain regions during resting state. Moreover, by using the classification algorithm in machine learning, we investigated whether the resting-state fMRI networks between mathematicians and non-mathematicians were distinguished depending on features of functional connectivity. We showed diverging involvement of the frontal-thalamic-temporal connections for mathematicians and the medial-frontal areas to precuneus and the lateral orbital gyrus to thalamus connections for non-mathematicians. Moreover, mathematicians who had higher scores in mathematical knowledge showed a weaker connection strength between the left and right caudate nucleus, demonstrating the connections' characteristics related to mathematical expertise. Separate functional networks between the two groups were validated with a maximum classification accuracy of 91.19% using the distinct resting-state fMRI-based functional connectivity features. We suggest the advantageous role of preconfigured resting-state functional connectivity, as well as the neural efficiency for experts' successful performance. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Resting-State Functional Connectivity in Mathematical Expertise | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hwang, Han-Jeong | - |
dc.identifier.doi | 10.3390/brainsci11040430 | - |
dc.identifier.scopusid | 2-s2.0-85103951062 | - |
dc.identifier.wosid | 000642781400001 | - |
dc.identifier.bibliographicCitation | BRAIN SCIENCES, v.11, no.4 | - |
dc.relation.isPartOf | BRAIN SCIENCES | - |
dc.citation.title | BRAIN SCIENCES | - |
dc.citation.volume | 11 | - |
dc.citation.number | 4 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.subject.keywordAuthor | resting-state functional connectivity | - |
dc.subject.keywordAuthor | mathematicians | - |
dc.subject.keywordAuthor | expertise | - |
dc.subject.keywordAuthor | neural efficiency | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | support vector machine | - |
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