This article is a correction to:
Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis
Hongmei Wang
Renhuan Yao3†
Xiaoyan Zhang
Minghao Dong
Chenwang Jin- 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
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
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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Keywords: degree centrality, visual expertise, object recognition, Support vector machine, radiologist
Citation: Wang H, Yao R, Zhang X, Chen C, Wu J, Dong M and Jin C (2023) Corrigendum: Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis. Front. Neurosci. 17:1241073. doi: 10.3389/fnins.2023.1241073
Received: 16 June 2023; Accepted: 20 June 2023;
Published: 07 July 2023.
Edited and reviewed by: Xi Jiang, University of Electronic Science and Technology of China, China
Copyright © 2023 Wang, Yao, Zhang, Chen, Wu, Dong and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Minghao Dong, dminghao@xidian.edu.cn; Chenwang Jin, jin1115@xjtu.edu.cn
†These authors have contributed equally to this work