Neural mass modelling from EEG in children with epilepsy

  • Research type

    Research Study

  • Full title

    Neural mass modelling from sleep EEG in children and young people with epilepsy - a pilot study (NESEE)

  • IRAS ID

    339729

  • Contact name

    Samantha Chan

  • Contact email

    samantha.chan@stgeorges.nhs.uk

  • Sponsor organisation

    St George's University Hospitals NHS Foundation Trust

  • Duration of Study in the UK

    1 years, 0 months, 0 days

  • Research summary

    Despite advances in treatments to prevent seizures, about a third of patients with epilepsy remain with seizures after trying multiple treatments. At present, doctors choose a treatment based on matching the characteristics of a patient and their seizures to the characteristics of known epilepsy syndromes, along with looking at the patient’s EEG (brainwave tracing), if this is available. However, individual patients often do not behave in a typical way, and simply looking at an EEG does not fully utilise the available information. Furthermore, there is little scientific evidence on how best to use treatments in combination.

    Using sleep EEG data collected from children with epilepsy, we have developed a ‘model’ – a system of virtual brain cells which communicate in ways that can be expressed in numbers. By changing these numbers, we can create a simulation of the EEG data, which gives us information on how close the brain is to a seizure.

    In this study, we will recruit 10 children with epilepsy who have undergone a sleep EEG prior to and after a change in treatment, or who are due a follow up EEG after a treatment change. Using these data, we will create a bespoke mathematical model for each child which will allow us to see how the activity of their virtual brain cells has changed with the change in treatment. We will correlate these changes to whether the child and their family believe the treatment has been helpful.

    The findings from this study will help refine our model and will provide evidence that it can work in the real world. Using this data, we will apply for more funding so we can automate the process of creating ‘virtual brains’ for patients with epilepsy to try treatments out on their behalf.

  • REC name

    London - Brighton & Sussex Research Ethics Committee

  • REC reference

    25/LO/0636

  • Date of REC Opinion

    24 Nov 2025

  • REC opinion

    Further Information Favourable Opinion