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  Resting-state functional connectivity in mathematical expertise

Shim, M., Hwang, H.-J., Kuhl, U., & Jeon, H.-A. (2021). Resting-state functional connectivity in mathematical expertise. Brain Sciences, 11(4): 430. doi:10.3390/brainsci11040430.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0008-CBBC-0 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-4A7E-4
Genre: Journal Article

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 Creators:
Shim, Miseon1, Author
Hwang, Han-Jeong1, Author
Kuhl, Ulrike1, Author
Jeon, Hyeon-Ae1, Author           
Affiliations:
1External Organizations, ou_persistent22              

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Free keywords: Resting-state functional connectivity; Mathematicians; Expertise; Neural efficiency; Machine learning; Support vector machine
 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.

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Language(s): eng - English
 Dates: 2021-03-252021-03-112021-03-262021-03-28
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3390/brainsci11040430
PMID: 33800679
PMC: PMC8065786
BibTex Citekey: brainsci11040430
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Project name : -
Grant ID : NRF-2019M3C7A1031995; NRF-2020R1A2C2099568
Funding program : Basic Science Research Program
Funding organization : National Research Foundation of Korea (NRF)
Project name : -
Grant ID : 2017M3A9G8084463; 2020R1A4A1017775
Funding program : Bio and Medical Technology Development Program
Funding organization : MSIT
Project name : -
Grant ID : 2017-0-00451
Funding program : Institute for Information and Communications Technology Planning and Evaluation (IITP)
Funding organization : Korea Government

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Title: Brain Sciences
Source Genre: Journal
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Publ. Info: Basel, Switzerland : Multidisciplinary Digital Publishing Institute (MDPI)
Pages: - Volume / Issue: 11 (4) Sequence Number: 430 Start / End Page: - Identifier: ISSN: 2076-3425
CoNE: https://pure.mpg.de/cone/journals/resource/2076-3425