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Open AccessArticle

Resting-State Functional Connectivity in Mathematical Expertise

by 1,†, 1,2,†, 3 and 4,5,*
1
Department of Electronics and Information Engineering, Korea University, Sejong 30019, Korea
2
Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong 30019, Korea
3
Research Institute for Cognition and Robotics (CoR-Lab), Machine Learning Group Bielefeld University, 33615 Bielefeld, Germany
4
Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea
5
Partner Group of the Max Planck Institute for Human Cognitive and Brain Sciences at the Department for Brain and Cognitive Sciences, DGIST, Daegu 42988, Korea
*
Author to whom correspondence should be addressed.
These two authors contributed equally.
Academic Editor: Simone Grimm
Brain Sci. 2021, 11(4), 430; https://doi.org/10.3390/brainsci11040430
Received: 11 March 2021 / Revised: 25 March 2021 / Accepted: 26 March 2021 / Published: 28 March 2021
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. View Full-Text
Keywords: resting-state functional connectivity; mathematicians; expertise; neural efficiency; machine learning; support vector machine resting-state functional connectivity; mathematicians; expertise; neural efficiency; machine learning; support vector machine
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MDPI and ACS Style

Shim, M.; Hwang, H.-J.; Kuhl, U.; Jeon, H.-A. Resting-State Functional Connectivity in Mathematical Expertise. Brain Sci. 2021, 11, 430. https://doi.org/10.3390/brainsci11040430

AMA Style

Shim M, Hwang H-J, Kuhl U, Jeon H-A. Resting-State Functional Connectivity in Mathematical Expertise. Brain Sciences. 2021; 11(4):430. https://doi.org/10.3390/brainsci11040430

Chicago/Turabian Style

Shim, Miseon; Hwang, Han-Jeong; Kuhl, Ulrike; Jeon, Hyeon-Ae. 2021. "Resting-State Functional Connectivity in Mathematical Expertise" Brain Sci. 11, no. 4: 430. https://doi.org/10.3390/brainsci11040430

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