Measuring cognitive load is important for surgical education and patient safety. Traditional approaches of measuring cognitive load of surgeons utilise behavioural metrics to measure performance and surveys and questionnaires to collect reports of subjective experience. These have disadvantages such as sporadic data, occasionally intrusive methodologies, subjective or misleading self-reporting. In addition, traditional approaches use subjective metrics that cannot distinguish between skill levels. Functional neuroimaging data was collected using a high density, wireless NIRS device from sixteen surgeons (11 attending surgeons and 5 surgery resident) and 17 students while they performed two laparoscopic tasks (Peg transfer and String pass). Participant’s subjective mental load was assessed using the NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. The Prefrontal cortex (PFC) activations were greater in students who reported higher-than-median task load, as measured by the NASA-TLX survey. However in the case of attending surgeons the opposite tendency was observed, namely higher activations in the lower v higher task loaded subjects. We found that response was greater in the left PFC of students particularly near the dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict the differences in skill and task load using machine learning while focussing on the effects of NIRS channel separation distance on the results. Our results showed that the classification of skill level and subjective task load could be predicted based on PFC activation with an accuracy of nearly 90%. Our finding shows that there is sufficient information available in the optical signals to make accurate predictions about the surgeons’ subjective experiences and skill levels. The high accuracy of results is encouraging and suggest the integration of the strategy developed in this study as a promising approach to design automated, more accurate and objective evaluation methods.

Ayna nöronlar, 1990'lı yılların başında Parma Üniversitesi'nde Rizzolatti ve ekibi tarafından ilk kez makak maymunlarında keşfedilmiştir. İnsanlarda ayna nöronların araştırıldığı çalışmalarda, makaklarda olduğu gibi invazif yöntemlerin kullanılması mümkün olmadığı için nörogörüntüleme ve elektrofizyolojik teknikler gibi non-invazif yöntemler kullanılmıştır. Bu nöron topluluğu, keşfinden bu yana eylemlerin tanınması, yorumlanması, taklit edilmesi, empati, öğrenme ve hafıza gibi birçok karmaşık işlevler ile ilişkilendirilmiştir. Bu durum, ayna nöron sisteminin biliş ve eylem arasında bir çeşit köprü oluşturduğunu düşündürmektedir. Ayna nöronların bilişteki olası rolü, bu sistemin nörolojik ve psikiyatrik hastalıklara bir şekilde etki ettiğini göstermektedir. Bu çerçevede bu derlemede, Parkinson hastalığı, Alzheimer hastalığı, amyotrofik lateral skleroz, otizm spektrum bozuklukları ve psikotik bozukluklardaki ayna nöron sistemi patolojisi ile konjenital ayna hareket bozukluğunun ayna nöron sistemi ile olası ilişkisine dair çalışmalara yer verilmiştir. Ayna nöronların keşfinden sonra beyin organizasyonu hakkındaki yeni görüşler ışığında nörolojik ve psikiyatrik hastalıkların yeniden yorumlanması, klinik belirtilerinin daha iyi anlaşılması ve rehabilitasyonu için yeni yollar açmaya yardımcı olabilir.

Aim Measuring cognitive load is important for surgical education and patient safety. Traditional approaches of measuring cognitive load of surgeons utilise behavioural metrics to measure performance and surveys and questionnaires to collect reports of subjective experience. There is a need for more automated, more accurate and objective evaluation methods. Method Functional neuroimaging data was collected using wireless NIRS device from sixteen surgeons (11 attending surgeons and 5 surgery resident) and 17 students while they performed two laparoscopic tasks (Peg transfer and String pass). Participant’s subjective mental load was assessed using NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. Results The Prefrontal cortex (PFC) activations were greater in students who reported higher-than-median task load, as measured by the NASA-TLX survey. However, in the case of attending surgeons the opposite tendency was observed, namely higher activations in lower v higher task loaded subjects. We found response was greater in the left PFC of students particularly near dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict differences in skill and task load using machine learning while focusing on the effects of NIRS channel separation distance on the results. Our results showed that the classification of skill level and subjective task load could be predicted based on PFC activation with an accuracy of nearly 90%. Conclusions The high accuracy of results is encouraging and suggest the integration of the strategy developed in this study as a promising approach to design automated, more accurate and objective evaluation methods.