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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Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Summary
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
Volume
17 - 2023
Edited and reviewed by
Xi Jiang, University of Electronic Science and Technology of China, China
Updates
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.cnChenwang Jin jin1115@xjtu.edu.cn
†These authors have contributed equally to this work
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.