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Emerging Imaging

Engineering simultaneous EEG-fMRI for image guided modulation of brain dynamics

Project ID:

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) [3]. 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 [1]. 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 [4].

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) [5], biofeedback [6] and neurofeedback with EEG [7].

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.

  1. Centeno M, Carmichael DW. Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions. Front Neurol. 2014;5:93. Published 2014 Jul 4. doi:10.3389/fneur.2014.00093
  2. Lopes MA, Richardson MP, Abela E, et al. An optimal strategy for epilepsy surgery: Disruption of the rich-club?. PLoS Comput Biol. 2017;13(8):e1005637. Published 2017 Aug 17. doi:10.1371/journal.pcbi.1005637
  3. Centeno M, Tierney TM, Perani S, Shamshiri EA, St Pier K, Wilkinson C, Konn D, Vulliemoz S, Grouiller F, Lemieux L, Pressler RM, Clark CA, Cross JH, Carmichael DW. Combined electroencephalography-functional magnetic resonance imaging and electrical source imaging improves localization of pediatric focal epilepsy. Ann Neurol. 2017 Aug;82(2):278-287.
  4. Maziero D, Velasco TR, Hunt N, et al. Towards motion insensitive EEG-fMRI: Correcting motion-induced voltages and gradient artefact instability in EEG using an fMRI prospective motion correction (PMC) system. Neuroimage. 2016;138:13-27. doi:10.1016/j.neuroimage.2016.05.003
  5. Violante IR, Li LM, Carmichael DW, Lorenz R, Leech R, Hampshire A, Rothwell JC, Sharp DJ. Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance. Elife. 2017 Mar 14;6.
  6. Nagai Y, Aram J, Koepp M, et al. Epileptic Seizures are Reduced by Autonomic Biofeedback Therapy Through Enhancement of Fronto-limbic Connectivity: A Controlled Trial and Neuroimaging Study. EBioMedicine. 2018;27:112-122. doi:10.1016/j.ebiom.2017.12.012
  7. Strehl U, Birkle SM, Wörz S, Kotchoubey B. Sustained Reduction of Seizures in Patients with Intractable Epilepsy after Self-Regulation Training of Slow Cortical Potentials – 10 Years After. Frontiers in Human Neuroscience. 2014 ; 8:604.
  8. Hao, Y., Khoo, H. M., von Ellenrieder, N., Zazubovits, N., & Gotman, J. (2018). DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning. NeuroImage: Clinical, 17, 962–975.
  9. Kotwas I, McGonigal A, Trebuchon A, Bastien-Toniazzo M, Nagai Y, Bartolomei F, Micoulaud-Franchi JA. (2016) Self-control of epileptic seizures by nonpharmacological strategies. Epilepsy Behav. Jan 15;55:157-164.
  10. Lorenz R, Monti RP, Violante IR, et al. The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI. Neuroimage. 2016;129:320-334. doi:10.1016/j.neuroimage.2016.01.032

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