Student: Zachary Cohen
1st Supervisor: David Carmichael, King’s College London
2nd Supervisor: Rob Leech, King’s College London
Clinical Champion: Mark Richardson, King’s College London
Epilepsy has been 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) and functional Magnetic Resonance Imaging (fMRI) . The measurement of both of these simultaneously is technically challenging, but together they provide a high spatial and dynamic readout of brain activity with the ability to measure periods of pathological brain dynamics in epilepsy . One of the technical challenges is the introduction of artefacts into the EEG when in the MRI environment due to motion and the switched magnetic field gradients required for functional image acquisition which has been the subject of ongoing research .
We have a number of ways in which we can alter brain network activity in terms of connectivity and dynamics including transcranial electrical stimulation (TES) , biofeedback  and neurofeedback with EEG .
However, most experimental work to date has attempted to either; a) apply these approaches and subsequently measure treatment outcome, or, b) measure functional network dynamics without applying modulations. These elements need to be combined so that the acute relationship between applied modulations, alterations in brain dynamics and their effect on epileptic activity can be characterised and understood mechanistically. This would represent a significant step towards understanding how these approaches could be used effectively for therapy.
To achieve this we need a system where we can reliably measure and extract information from EEG and fMRI in such as brain connectivity and the rate of epileptic events in real-time during experimental manipulations including electrical stimulation. Recent developments in deep learning (DL) using neural networks hold promise in this regard because (following training) they can be applied very fast.
In the first part of this project we will develop such a system, which requires solving a number of key technical challenges. In the second part of the project, we will use this knowledge to perform experiments using simultaneous EEG and fMRI where we will use experimental manipulations to achieve target brain states (in terms of brain connectivity and the rate of epileptic events) using real-time experimental manipulations including biofeedback and/or transcranial electrical stimulation.
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