Mr Luke Tait
Telephone: 01392 727464
Extension: (Streatham) 7464
I am a 4th year PhD student in Mathematics based in the Living Systems Institute, a member of the University of Exeter Alzheimer’s Society Doctoral Training Centre, and associated with the EPSRC Centre for Predictive Modelling in Healthcare.
My research involves mathematical analysis and modelling of the brain to understand neurological disorders such as Alzheimer's disease and frontotemporal dementia. Specifically, my interests include the following:
- Computational modelling to understand how neuronal dynamics and network oscillations are altered in animal models of dementia, particularly in the entorhinal cortex, the region of the brain responsible for building a map of space.
- Analysis of non-invasive electrophysiological data such as resting state EEG recorded from humans in order to develop biomarkers of Alzheimer's disease with the aims of aiding diagnosis in a clinical setting.
- Solving the EEG inverse problem and source space localization to gain insight into the mechanisms underlying alterations to neuronal oscillations, functional connectivity and network topology, and responses to visual stimuli in Alzheimer's disease.
- Identifying translational electrophysiological biomarkers of Alzheimer's disease between animal models of dementia and humans.
Whilst my background is in mathematics and physics, during my PhD I have also gained experience with experimental electrophysiological tools, having spent approximately six months performing patch clamp experiments from brain slices with the Exeter Applied Neurophysiology Group.
L. Tait, G. Stothart, E. Coulthard, J.T. Brown, N. Kazanina, M. Goodfellow. Network Substrates of Cognitive Impairment in Alzheimer's Disease, Submitted to NeuroImage: Clinical
L. Tait, K. Wedgwood, K. Tsaneva-Atanasova, J.T. Brown, M. Goodfellow (2018). Control of clustered action potential firing in a mathematical model of entorhinal cortex stellate cells. Journal of Theoretical Biology 449:23-34
G. Stothart, G. Petkov, N. Kazanina, M. Goodfellow, L. Tait, J.T. Brown (2016) Graph-theoretical measures provide translational markers of large-scale brain network disruption in human dementia patients and animal models of dementia. International Journal of Psychophysiology 108:71