Corrigendum: Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis (Frontiers in Neuroscience, (2023), 17, (1152619), 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.