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Supervisor: Amy McTague, Ronit Pressler
Project Description:
Background:
Epilepsies presenting in the first year of life represent a spectrum from self-limited seizures to severe treatment-resistant seizures with developmental delay. In well over 50% of infants a genetic diagnosis is made which informs prognosis and genetic counselling, and allows access to targeted treatments and emerging novel therapies. However, a major challenge in clinical practice is that early intervention requires early diagnosis. Furthermore, there is a wide phenotypic spectrum from mild self-limited epilepsy to severe life-limiting developmental and epileptic encephalopathies, for example KCNQ2 and SCN1A. Even if diagnosed early, this makes prognostic counselling impossible and leaves families grappling with uncertainty. EEG is a non-invasive tool which has the potential to become valuable biomarker to determine outcome in these children early. This will have implications for families but also for the development of precision medicine.
The Gene-STEPs study an international study (partially funded by Great Ormond Street Hospital Children’s Charity) is currently underway to implement rapid genetic diagnosis with tailored management in children with seizure onset under 12 months of age, delineating their epilepsy and developmental trajectories. This prospective observational study is recruiting infants from four centres (GOSH, Boston, Melbourne, and Toronto) to assess the impact of rapid trio whole genome sequencing on the patient journey including treatment choice, epilepsy, EEG and developmental outcomes. At GOSH, 80 patients have already been recruited of a total of 120 infants to be recruited by December 2025. Across Gene-STEPs consortium a total of 360 children will be included. This gives a unique opportunity to identify EEG biomarkers and correlate with phenotypic severity in early onset genic epilepsy.
Aims/Objectives:
The primary objective of this research proposal is to identify outcome measures and biomarkers for early onset genetic epilepsy syndrome by analysing EEG patterns and their correlation with clinical data. The student, under appropriate supervision, will undertake the following tasks:
1. Analyse EEG data from at least 120 participants of the Gene-STEPs study to identify background pattern and epileptiform abnormalities.
2. Establish correlations between EEG data and other clinical variables and outcome measures.
3. Apply advanced machine learning algorithms for the recognition of EEG patterns that may facilitate prognosis, clinical trial recruitment and design.
Methods:
Data Collection: Whole genome sequencing, clinical data including EEGs and neurodevelopmental data are currently acquired and will be available at the beginning of this PhD. Ethical approval is in place.
EEG Data Analysis: The study will employ state-of-the-art signal processing techniques to analyse the EEG data and identify patterns during sleep and wakefulness.
Correlation Analysis: Statistical analyses will be performed to establish correlations between EEG data and other findings from the Gene-STEPs study.
Machine Learning Implementation: Advanced machine learning models will be developed and applied to recognise specific EEG patterns that may serve as potential biomarkers.
Timeline:
Oct 2025-May 2026: Training in method (EEG) and work on database
May 2026-Oct 2027: Data analysis
Nov 2027-Aug 2028: writing up
References:
Pressler & Lagae. 2020 doi:10.1016/j.neuropharm.2019.107854.
McTague A. 2019. doi:10.1111/dmcn.14203.
McTague A, et al. 2022. doi:10.1016/j.ejmg.2022.104531.
Goodspeed et al. 2023 doi: 10.1177/08830738231177386.
Contact Information:
Ronit Pressler