Alladi Ramakrishnan Hall

A matter of time: Analysing time-varying functional brain networks in EEG task-related data

Nitin Williams

University of Helsinki, Helsinki, Finland

Imagine you are seated in a room performing a cognitive task, for e.g. recalling a memory. What processes are occurring in your brain to enable this? Converging evidence suggests that when a task is performed, the brain moves rapidly through a series of functional states, with each state represented by a functional network of brain regions. Due to its high temporal resolution, EEG (Electroencephalography) is well suited to provide a window to this process. However, there is a lack of analysis methods to reveal these time-varying networks in EEG task-related data. This talk will present a pipeline we have developed for this purpose. In the pipeline, we determine functional networks from brief time segments (using sparse Multi-Variate Auto-Regressive (MVAR) modelling), group these networks into a small number of functional states (using k-means clustering) and characterise the sequence of states (as a Markov chain). After validating our pipeline on simulated EEG data, we applied it to experimental EEG data from an established cognitive experiment with two conditions, i.e. the oddball task. Using only the description of EEG activity obtained by applying the pipeline, we demonstrate statistically significant discrimination between trials of the two conditions. These results provide powerful proof-of-concept for this approach to modeling the data. This in turn paves the way for its use in cognitive neuroscience, to test rich hypotheses on sub-second changes in functional networks as a task is being performed.