Front. Neurosci. Frontiers in Neuroscience Front. Neurosci. 1662-453X Frontiers Media S.A. 10.3389/fnins.2023.1241073 Neuroscience Correction Corrigendum: Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis Wang Hongmei 1 2 † Yao Renhuan 3 † Zhang Xiaoyan 4 Chen Chao 5 Wu Jia 6 Dong Minghao 4 7 * Jin Chenwang 1 * 1Department of Radiology, First Affiliated Hospital of Xi'an, Jiaotong University, Xi'an, China 2Department of Medical Imaging, Inner Mongolia People's Hospital, Hohhot, China 3Department of Nuclear Medicine, Inner Mongolia People's Hospital, Hohhot, China 4Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China 5PLA Funding Payment Center, Beijing, China 6School of Foreign Languages, Northwestern Polytechnical University, Xi'an, Shaanxi, China 7Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, China

Edited and reviewed by: Xi Jiang, University of Electronic Science and Technology of China, China

 *Correspondence: Minghao Dong dminghao@xidian.edu.cn Chenwang Jin jin1115@xjtu.edu.cn

†These authors have contributed equally to this work

 07 07 2023 2023 17 1241073 16 06 2023 20 06 2023 Copyright © 2023 Wang, Yao, Zhang, Chen, Wu, Dong and Jin. 2023 Wang, Yao, Zhang, Chen, Wu, Dong and Jin

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 A corrigendum on Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis by Wang, H., Yao, R., Zhang, X., Chen, C., Wu, J., Dong, M., and Jin, C. (2023). Front. Neurosci. 17:1152619. doi: 10.3389/fnins.2023.1152619 degree centrality visual expertise object recognition Support vector machine radiologist section-at-acceptance Brain Imaging Methods

In the published article, there was an error in the legend for Figure 1 as published, G, H was incorrectly written as D–H.

The corrected legend appears below.

The pipeline of the rs-MRI data analysis. (A–C) The resting-state MRI data were collected and preprocessed following procedures described in the Methods. Then, the DC for each voxel was calculated and used for future feature selection. (D–F) Feature selection. Two-step feature selection was performed and the first level used a two-sample approach to perform the regional average feature. Then, RFE-SVM modeling with LOOCV was employed to search for the most remarkable features between groups. (G, H) SVM modeling. Reliable SVM classification results and the brain areas with robust differences in DC values between groups were obtained to reflect the alteration of dynamics in the whole-brain network. rs-MRI, resting-state MRI; fMRI, functional magnetic resonance imaging; DC, degree centrality; RFE-SVM, recursive feature elimination-support vector machine; SVM, support vector machine; LOOCV, leave-one-out cross-validation.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

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