My research project
Switch It Up: Characterizing, modelling, and optimizing the neural networks underpinning task switching
I am a PhD student studying neural network dynamics at the University of Surrey and Imperial College London. During the course of my PhD I plan to use multi-modal imaging techniques and mathematical modeling to determine optimal stimulation parameters. I will construct a mathematical model of the brain during a task switching paradigm using fMRI, high-density EEG, and DTI data to pinpoint optimal nodes for stimulation. Then, using stimulation, I hope to lower the switch cost in task-switching paradigms. This could improve task performance, and have translation to disorders characterized by pathological cognitive inflexibility (OCD, MDD, etc). This would not only result in further validation of the mechanisms determining tDCS's effects, but further expand the potential of this rapidly developing technique.
The spread of diseases like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in human populations involve a large number of variables, making it difficult to predict how it will spread across communities and populations. Reduced representation simulations allow us to reduce the complexity of disease spread and model transmission based on a few key variables. Here we have created a Viral Transmission Education Simulator (VTES) that simulates the spread of disease through the interactions between circles representing individual people bouncing around a bounded, 2D plane. Infections are transmitted via person-to-person contact and the course of an outbreak can be tracked over time. Using this approach, we are able to simulate the influence of variables like infectivity, population density, and social distancing on the course of an outbreak. We also describe how VTES's code can be used to calculate R0 for the simulated pandemic. VTES is useful for modeling how small changes in variables that influence disease transmission can have large changes on the outcome of an epidemic. Additionally, VTES serves as an educational tool where users can easily visualize how disease spreads, and test how interventions, like masking, can influence an outbreak. VTES is designed to be simple and clear to encourage user modifications. These properties make VTES an educational tool that uses accessible, clear code and dynamic simulations to provide a richer understanding of the behaviors and factors underpinning a pandemic. VTES is available from: https://github.com/sstagg/disease-transmission.