Exploring multidimensional brain mechanisms in robot-assisted surgical simulation

• Published in: NeuroImage (Orlando, Fla.) Vol. 317; p. 121294
• Main Authors: Cui, Haoxin, Liang, Yujing, Sun, Fankai, Li, Desheng, Wang, Xiangqing, Wang, Rong, Zheng, Nan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.08.2025; Elsevier Limited; Elsevier
• Subjects: Adult; Brain; Brain - physiology; Brain mechanisms; Clinical Competence; Computer Simulation; Cortex (parietal); Design; EEG; Electroencephalography; Electroencephalography - methods; Experiments; Female; Humans; Male; Nerve Net - physiology; Neural networks; Occipital lobe; Prefrontal cortex; Principal components analysis; Radiology/Diagnostic Imaging; Robot-assisted surgery; Robotic surgery; Robotic Surgical Procedures - education; Robotic Surgical Procedures - methods; Simulation; Skills; Skills assessment; Statistical analysis; Surgeons; Training; Urology; Robot-assisted surgery; EEG; Brain mechanisms; Skills assessment
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2025.121294

The introduction of robotic-assisted surgical systems has revolutionized surgical procedures; however, current training programs often overlook the role of brain activity during surgery, making it challenging to detect cognitive differences between surgeons. To address this gap, this paper designed an experimental task closely resembling real surgical scenarios using a robotic surgical simulation system. The study introduced Principal Component Analysis (PCA) weights and Mahalanobis distance as metrics for identifying cognitive differences, with a focus on investigating the brain mechanisms underlying varying levels of surgical proficiency in terms of frequency domain, neural connectivity, and graph theory. Frequency domain analyses revealed that experienced surgeons exhibited greater activation in the alpha bands of the prefrontal cortex (Fp1, Fp2), occipital cortex (O1, O2), and midline parietal cortex (Pz) during task execution, compared to less experienced surgeons. Connectivity analysis indicated that high-level surgeons demonstrated superior neural efficiency, characterized by weaker localized activity but enhanced global integration of brain regions. Graph theoretical analyses further highlighted differences in network organization, with higher-level surgeons achieving a balanced interplay between local specialization and global integration of brain networks. Finally, classification and ablation experiments confirmed that the EEG features identified in this study effectively differentiate surgeons based on their operational expertise. These findings provide valuable insights into the underlying brain mechanisms involved in surgical proficiency and offer potential applications for supporting surgeon training and objective assessment of surgical skills. This research paves the way for the development of more targeted training programs for robotic surgery, ultimately enhancing the effectiveness of skill development and performance evaluation. •Brain mechanism differences among surgeons of different levels were explored.•Surgeons were classified by clustered grouping of task scores instead of subject status.•Rates of change in PCA and Mahalanobis distance measured differences between groups.•High-level surgeons have higher α and β activation in key brain regions.•High-level surgeons exhibited weaker local activity but stronger integrated brain activity.