Project ID: SST1
This project aims to address key problems arising in the training of laparoscopic surgeons, by providing quantifiable measures of laparoscopy aptitude, making laparoscopy easier to learn and helping develop standardised training certification. This will help increase training efficiency, as well as shed light on the underlying brain mechanisms of motor skill learning.
Laparoscopic surgery (LS) has significant clinical and economic benefits over open surgery and is becoming increasingly routine in many surgical conditions. Despite its advantages in terms of patient safety and healthcare efficiency, LS can be frustrating for the surgeon and requires new training methods.
Laparoscopic surgery (LS) novices who progress toward expertise acquire an internal model of how their actions and instruments interact in the surgical environment. The brain uses internal models to predict the sensory outcomes of motor commands and shifts in underlying brain activity can be characterised by functional neuroimaging.
The project intends to determine how changes in neuronal synchronisation, cerebral hemodynamics and inter-area functional connectivity track changes in skill and cognitive load. We will also assess the ability of hybrid neurofeedback to enhance LS learning, lower cognitive load and promote surgeons’ well-being.
The student will become part of a dynamic team of researchers at NTU, leveraging existing state-of-the-art hardware, software and data to develop novel . They will capitalise on the combined expertise of the supervisory team in the fields of neuroimaging, machine learning, mental state decoding, medical devices, and biopsychology.
This work will foster long-term, interdisciplinary research collaboration at NTU with the goal of developing advanced neuromodulation techniques to improve motor skill training and wellbeing. The proposed study represents a low risk and potentially very high return on investment. By fostering this interdisciplinary collaboration, NTU will pave the way for a longer-term stream of high-quality outputs, grant income, engineer-psychologist-physician collaborations and commercial applications, which extend well beyond this specific project.
Supervisor: Dr Ahmet Omurtag
Co-supervisor: Dr Alexander Sumich
Co-supervisor: Dr Zohreh Zakeri
The candidate we seek will be a creative and rigorous and evaluative thinker with strong programming experience (Matlab preferred but not required), and have a background in some of the following: signal analysis, neuroscience and/or psychology, human subjects research. We are particularly interested in working with people who share a common enthusiasm and motivation for helping to improve healthcare through biomedical innovation.
How to apply
How to apply
Please visit our how to apply page for a step-by-step guide and make an application and include the project ID in your application
Application deadline: Thursday 8 June 2023.
Interviews will take place in mid-June 2023
Fees and funding
This is an NTU Studentship funded opportunity.
Guidance and support
Find out about guidance and support for PhD students.