£73,220.15 over 18 months
Awarded in 2018
EpiBioNet: Identifying imaging network biomarkers of antiepileptic drug treatment outcome
Dr Simon Keller
Dr Peter Taylor, Professor Tony Marson
University of Liverpool
Being able to predict whether a patient with epilepsy will respond to antiepileptic drug therapy will increase the likelihood of bringing seizures under control sooner through an earlier exploration of alternative or adjunctive treatments. Imaging the brain’s structural and functional networks may offer that possibility.
Dr Simon Keller
The first line of treatment for people with epilepsy is antiepileptic drug (AED) therapy. AED treatment fails to control seizures in over 30% of people with epilepsy. The reasons why treatment fails in these patients are unknown, and there is currently no way of predicting which patients will not respond to treatment. If we knew which patients would not respond to AEDs from the point of diagnosis of epilepsy it may be possible to explore alternative or adjunctive therapies sooner for these patients. Bringing seizures under control as early as possible is the most important treatment factor.
Looking at brain networks is crucial in epilepsy as epileptic seizures arise because of abnormal brain networks. Dr. Keller and colleagues will use specialised magnetic resonance imaging (MRI) scans to investigate how each part of the brain are connected i.e. how the brain is networked, in people with epilepsy. Investigation of brain networks using MRI provides information on abnormal brain structure and function that cannot be detected using standard clinical investigations. This study will apply sophisticated brain network imaging methods to MRI scans from people with a new diagnosis of focal epilepsy and people with idiopathic generalised epilepsy and compare them to people without epilepsy. The main goal of the study is to investigate whether abnormal brain connectivity underlies persistent seizures after AED therapy, and whether network-based MRI analyses can reliably identify the individual patients who will fail to respond to treatment. This work therefore seeks to identify reliable non-invasive brain imaging biomarkers of AED treatment, an important research benchmark in epilepsy.
Brain imaging represents an ideal method for the identification of biomarkers of treatment outcome given that most people with epilepsy routinely receive MRI in context of their clinical care. This research is important because if a reliable brain imaging biomarker of treatment outcome is identified, our methods could tell us which patients will not respond to first-line treatment. This information could be used by clinicians to counsel patients and consider alternative or adjunctive treatment options from an earlier stage.