About the Project
Different psychological states, such as perception, attention or memory, arise moment-by-moment from the dynamic collective behaviour of neurons participating in functional networks. Discovering the structure of these functional networks may therefore give us deep insight into how we perceive, pay attention or remember.
For over 100 years, neuroscientists and psychologists have been trying to decode the collective electrical activity of neural networks from electro-encephalogram (EEG) recordings taken at the scalp surface. Our state of the art approach involves graph-based models using a new, highly efficient implementation of Granger causality that has an analytical solution, which allows rapid estimation of network parameters (eg Gao et al., 2015, Scientific Reports, 5, 10399). So far we have applied this technique ‘offline’ to perform network estimation after data collection has taken place, but with further optimisation a possible spin-off is that the technique may be able to perform network estimation ‘online’ in real-time during data collection. This would provide researchers with a new, statistically robust, visualisation tool that transforms EEG time-series data into a functional network model, allowing the relationship between functional brain networks and psychological states to be studied in real-time. To demonstrate the feasibility of this proposal, the PhD student will construct prototype visualisation software to display functional network structures in real-time, and will collect new data to examine how rapidly and how precisely the technique can estimate parameters for networks involved in the retrieval of information from long-term memory (eg Allan and Allen, 2005, J Neuroscience; Tsivilis et al., 2015, Frontiers in Human Neuroscience).
A key factor that could affect the reliability and speed of parameter estimation is how well the network functions each time it is engaged. To explore this factor, we will experimentally manipulate memory in two ways. First, we will employ well-established within-participant cognitive manipulations that enhance/degrade memory (eg depth of processing). This will allow us to examine within-participants effects of variability in memory performance upon the real-time operation of the modelling technique. Second, we will exploit ‘natural’ variability in memory accuracy by examining data collected across the adult lifespan from groups of healthy younger (18-35 years), middle aged (35-55 years) and older adults (55-85 years). This will allow us to examine across-participants effects of age-related memory decline upon the real-time operation of the modelling technique.
The project involves a cross-disciplinary collaboration between Dr Allan, a cognitive neuroscientist based in the School of Psychology, and Professor Schelter, a physicist based in the Institute for Complex Systems & Mathematical Biology. The project also has the backing of an industrial partner, Tau-Rx, a University of Aberdeen spinout company working on pharmacological treatments for dementia, and the student will be offered a substantial placement with Tau-Rx during their PhD where the opportunity will be given to apply skills learned to estimate functional network structure in patients and healthy controls from clinical EEG data.
The project will involve recording hi-density, 64-channel scalp EEG, within the Aberdeen School of Psychology EEG laboratories, from healthy adult volunteer subjects carrying out memory tasks. A key element of the project is to optimise and implement the Granger causality analysis upon data as it is being collected, and to display the outputs of the model within a bespoke visualisation tool. This will require training in the matlab and mathematical software packages, EEG data collection hardware and software, as well as the mathematical basis of the analysis, all of which can be provided by Dr Allan, Professor Schelter and their post-docs. The EEG Lab in the School of Psychology contains all the necessary EEG recording hardware, software and expertise to support the project. In Years 2–4 the student will receive further scientific and business-related training during a placement with the industrial partner, Tau-Rx. During their placement the student will work within a team of researchers involved in the analysis of EEG data from clinical trials of drug therapies for Alzheimer’s and fronto-temporal dementia.