Using real-time intracranial EEG and EEG-fMRI to investigate dynamic connectivity and epileptogenic activity in epilepsy disease

Epilepsy is increasingly understood as a disease where abnormal large-scale brain network properties and their dynamics are
responsible for epileptic events[1,2]. In addition, there is increasing access to computational and theoretical models that can
describe conditions that should reduce or increase epileptic brain activity, which can be mapped onto measures of brain activity
from Electroencephalography (EEG; obtained on the scalp or intracranially) and functional Magnetic Resonance Imaging (fMRI)
[3]. The measurement of both of these simultaneously is technically challenging, but together they provide a high resolution
spatial and dynamic readout of brain activity with the ability to measure periods of pathological brain dynamics in epilepsy as
demonstrated by the supervisory team. e have a number of ways in which we can alter brain network activity in terms of connectivity and dynamics including
transcranial electrical stimulation (TES) [4], biofeedback [5] and cognitive tasks [6]. However, there is currently very little work
that addresses the need to optimise these approaches given the difficulty of finding appropriate parameters that are individual
specific in the context of very large parameter spaces.
The Automatic Neuroscientist (AN) uses Bayesian optimisation (as a real-time supervised learning algorithm developed by
supervisor Leech) which functions on a closed loop search through a large task space [7]. This alternative framework might
resolve the problem discussed above by constructing neuroadaptive paradigms combined with real-time analysis. The algorithm
can be characterised by automatic selection from a sample space from which it progressively learns and can use its knowledge of
the space to optimises the subsequent selection. In this study, the EEG-fMRI data or iEEG data would therefore be analysed in
real time to iteratively update the cognitive task selection based upon the real time results from previous tasks. This approach is
faster than testing all possible tasks while able to provide more information than simply testing at random. It allows the
algorithm to build upon its pre-existing understanding of functional organisations by testing and refining in iterative cycles
producing a robust model across a highly dimensional space and optimising task suggestion for optimal brain dynamics.
Previously this approach has been used to maximise the activity in a brain region related to a cognitive process. Here, the target
(cost function) would instead be a modulation of epileptic activity such as the rate of interictal epileptiform discharges as
different types of cognitive tasks are performed.