The fast without the spurious: developing a system for robust and rapid simultaneous EEG-fMRI measurements

To increase our understanding of how the brain works and how it goes wrong in people with neurological conditions such as epilepsy we need to develop better systems for making measurements of brain activity.
Electroencephalography (EEG) is an important modality in clinical and experimental neuroscience capable of measuring electrical changes occurring with sub-millisecond temporal resolution and a spatial resolution of centimetres while functional Magnetic Resonance Imaging (fMRI) can map haemodynamic changes over the entire the brain at a time-scale of seconds and spatial resolution of a few millimetres or better. Therefore together they can perform measurements across a greater range of brain activity occurring either at faster temporal or smaller spatial scales. However, during simultaneous EEG-fMRI acquisitions these both methods signal are degraded by noise related to motion, thereby significantly limiting the sensitivity of this type of study so far.
The purpose of this application is to build a robust system that can perform these simultaneous EEG and fMRI measurements of brain activity. To achieve this goal we will integrate new fast fMRI pulse sequences because images obtained in shorter time intervals are intrinsically less motion sensitive (like a faster shutter speed on a camera). Also better modelling of motion will be possible if we obtain more images per unit time because we can better separate and model the different sources of signal and noise that occur in different frequency ranges. In addition, we will optimise motion detection and prospective motion correction (PMC) using a camera system that tracks the subject's motion and updates the image acquisition process so that patient motion is supressed. When using these improvements to fMRI data acquisition we will need to develop novel EEG artefact correction methods for simultaneous in-scanner EEG recording which currently rely on the repetitive nature of the artefact in time. Both motion and PMC are likely to make the artefact more variable and so will require the development of novel correction methods.
Once we have developed this system we will apply it to ten patients with hard to treat epilepsy from Great Ormond Street Hospital who are being assessed for epilepsy surgery. This assessment aims to identify the epileptic brain regions and EEG-fMRI is a tool to help obtain this information. We will compare our current standard EEG-fMRI protocol which often suffers from degradation due to motion (particularly in young children) to our new robust EEG-fMRI incorporating PMC, fast fMRI and improved EEG artefact correction.

EEG-fMRI is used for the pre-surgical evaluation of focal epilepsy. The aim of this grant is to achieve motion insensitive, low noise, high temporal resolution, and simultaneous EEG-fMRI data acquisition. This will enable in the timescale of the grant:
- The improvement of EEG-fMRI as a clinical tool in terms of sensitivity specificity. This means better information for clinicians and parents to make life changing decisions.
- The study of seizures which represents an important clinical goal.
- Improvement of fMRI as a clinical tool by a reduction in movement sensitivity.
- Locally we aim to implementation EEG-fMRI as a clinical service at GOSH (which has the largest paediatric epilepsy surgery program in the UK) and this service will benefit from these developments.
- A reduction in the need for sedation and the amount of lost data as greater movement will be tolerated with acceptable image quality with significant cost implications.

Two strands of research are likely to coincide in the next 10 years. Firstly there is a great deal of research towards using different kinds of treatments for epilepsy that rely on local and acute delivery of therapy rather than the current global and chronic approaches available now (oral drug therapies and surgery). At the same time the understanding of epilepsy in terms as a disease involving a network of brain regions has increased in part due to imaging brain function. We therefore need to characterise and understand brain networks and their transition to epileptic states. The work in this grant aims to develop a better tool for this wider goal.
Recently there has been a large interest in so called 'resting state' fMRI where signals are recorded without a task and brain networks investigated by extracting regions that are temporally correlated and this technique has been applied to many patient groups such as dementia and has significant advantages in terms of simplicity and it can be applied across ages and intellectual abilities. However, there are a number of areas of uncertainty:
1. There is a poor definition of a 'resting state' and its consistency between centres.
2. There is an increased recognition that there are dynamic changes in 'resting state' activity which can measured can be seen at different temporal scales.
3. Physiological noise and motion cause uncertainty in the results which rely purely on correlations and so are sensitive to these noise sources.

The proposed developments in this grant aim to make a system where brain state can be characterised using EEG and so between subject differences can be accurately modelled. The combination of fast fMRI and EEG allows for the analysis of dynamic changes in brain networks over a range of temporal scales because of increased temporal sampling. The use of PMC and fast fMRI minimises the effects of motion and physiological noise therefore robustly revealing correlations due to brain activity. This EPSRC first grant can contribute to resting state fMRI connectivity with implications across a broad range of diseases for diagnosis and treatment monitoring and testing.
These developments might also have some commercial value for the manufacturers of MRI compatible EEG equipment and MRI scanners. The implementation of new EEG correction strategies maybe commercialised such as in brain products analyser software and / or as a free toolbox (e.g. as an additional tool interfaced with SPM www.fil.ion.ucl.ac.uk/spm). The development of fMRI protocols and their application within clinical populations can be of commercial interest (e.g. to Siemens medical).