Longitudinal study on the impact of short-term radiological interpretation training on resting-state brain network hubs

Abstract

Radiological expertise develops through extensive experience in specific imaging modalities. While previous research has focused on long-term learning and neural mechanisms of expertise, the effects of short-term radiological training on resting-state neural networks remain underexplored. This study investigates the impact of four weeks of radiological interpretation training on resting-state neural networks in 32 radiology interns. Using behavioral assessments and resting-state fMRI data, a Recursive Feature Elimination Support Vector Machine (RFE-SVM) model achieved 82% accuracy in classifying data from the pre- and post-training phases. Key brain regions linked to attention, decision-making, working memory, and visual processing were identified, providing insights into how short-term training reshapes intrinsic brain networks and facilitates rapid adaptation to new skills. These findings also lay a theoretical foundation for designing more effective training programs.