Assessment of surgical skills based on virtual reality (VR) technology has received major attention in recent years, with special focus placed on experience discrimination via hand motion analysis. Although successful, this approach is restricted from extracting additional important information about the trainee's hand kinematics. In this study, we investigate the role of hand motion connectivity in the performance of a laparoscopic cholecystectomy on a VR simulator. Two groups were considered: experienced residents and beginners. The connectivity pattern of each subject was evaluated by analyzing their hand motion signals with multivariate autoregressive (MAR) models. Our analysis included the entire as well as key phases of the operation. The results revealed that experienced residents outperformed beginners in terms of the number, magnitude and covariation of the MAR weights. The magnitude of the coherence spectra between different combinations of hand signals was in favor of the experienced group. Yet, the more challenging (in terms of hand movement activity) an operational phase was, the more connections were generated, with experienced subjects performing more coordinated gestures per phase. The proposed approach provides a suitable basis for hand motion analysis of surgical trainees and could be utilized in future VR simulators for skill assessment.