Frontal theta brain activity varies as a function of surgical experience and task error

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Abstract

Objective: Investigations into surgical expertise have almost exclusively focused on overt behavioral characteristics with little consideration of the underlying neural processes. Recent advances in neuroimaging technologies, for example, wireless, wearable scalp-recorded electroencephalography (EEG), allow an insight into the neural processes governing performance. We used scalp-recorded EEG to examine whether surgical expertise and task performance could be differentiated according to an oscillatory brain activity signal known as frontal theta - a putative biomarker for cognitive control processes. Design, setting, and participants: Behavioral and EEG data were acquired from dental surgery trainees with 1 year (n=25) and 4 years of experience (n=20) while they performed low and high difficulty drilling tasks on a virtual reality surgical simulator. EEG power in the 4-7 Hz range in frontal electrodes (indexing frontal theta) was examined as a function of experience, task difficulty and error rate. Results: Frontal theta power was greater for novices relative to experts (p=0.001), but did not vary according to task difficulty (p=0.15) and there was no Experience × Difficulty interaction (p=0.87). Brain-behavior correlations revealed a significant negative relationship between frontal theta and error in the experienced group for the difficult task (r=-0.594, p=0.0058), but no such relationship emerged for novices. Conclusion: We find frontal theta power differentiates between surgical experiences but correlates only with error rates for experienced surgeons while performing difficult tasks. These results provide a novel perspective on the relationship between expertise and surgical performance.

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Balkhoyor, A. M., Awais, M., Biyani, S., Schaefer, A., Craddock, M., Jones, O., … Mushtaq, F. (2020). Frontal theta brain activity varies as a function of surgical experience and task error. BMJ Surgery, Interventions, and Health Technologies, 2(1). https://doi.org/10.1136/bmjsit-2020-000040

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