Perturbation-based Biomarkers for Tracking Brain Dynamics
A state transition is when the fundamental dynamical state of a system transitions from one type to another (e.g. a static state to an oscillatory state). Brain state transitions such as from a resting state to a seizure state, are often used to describe pathological brain dynamics observed in neurological diseases such as epilepsy. Epilepsy is a highly patient-specific disease which is characterised by seizure transitions, which are poorly understood. Brain imaging technology, such as Electroencephalography (EEG), has been used to diagnose and study such pathological changes. When the brain gets closer to a seizure transition, biomarkers such as the variance and autocorrelation of the EEG data, have been observed to increase in epileptic EEG data. The underlying mechanism, however, cannot be mathematically verified because the principles governing brain dynamics are poorly described mathematically. In this project, the performances of these biomarkers as indicators of state transitions were evaluated in both computational and experimental data including animal data and human data. These techniques could be used to suggestively track brain dynamics for seizure prediction and epilepsy diagnosis and prognosis.
I moved to Australia after obtaining my bachelor's degree in Biomedical Engineering. I spent 3 years at the University of Melbourne for the Master in Biomedical Engineering. After that, I wanted to explore the software side of the engineering world, so I obtained another Master's degree in IT from the University of Melbourne. I started my PhD in Biomedical Engineering in 2019 and I have been enjoying what I do since then.
I have always been fascinated by the brain. As an engineer, I would like to adopt engineering approaches to understand how the brain works and maybe more interestingly why sometimes it does not work. As complex as the brain can get, I am always searching for a simpler explanation to describe brain dynamics. I am always excited about dealing with the most complex system in the universe.
Wei Qin, PhD Candidate