Linking genetics, 3D sterophotogrammetry and neuroimaging to clinical subtypes in epilepsy
There are many important things about epilepsy that we do not understand, such as: how does one epilepsy differ from another, and how are they similar; can we predict outcomes such as treatment resistance, psychosis, depression and sudden death? If we had better understanding of what underlies epilepsy, we would be better placed to explain these important aspects, and so offer better care. Separately, imaging and genetics research have already greatly enhanced understanding of epilepsy, for example showing the causes of epilepsy in an important minority of people. Genetic variation is one contributing factor to the different types and aspects of epilepsy. Such genetic differences may manifest as alterations in the brain’s structure and function (detectable using neuroimaging) but also as changes in face shape (measured using 3D sterophotogrammetry). During human development, face and brain maturation rely on similar genetic pathways. Therefore, alterations in face shape are expected to predict changes in brain structure and vice versa. By bringing information on imaging and genetics together, we expect to learn more about what causes specific types of epilepsy and how, and which individuals might be at more risk of adverse outcomes such as why antiepileptic drugs might not work for some people. Thus, by analysing multi-modal imaging and genetics data with machine learning methods this project will contribute to establishing precision medicine in the treatment of epilepsies.
Aims and Objectives:
We aim to combine data from genetics, 3D sterophotogrammetry and neuroimaging to identify new biological and imaging markers for the epilepsies, with potential for direct benefit to individuals through improved understanding. More precisely, we hypothesise that imaging and joint imaging-genetic analyses will uncover novel biology associated with common syndrome types and multidrug resistance (MDR). The objectives of the project are: 1. Identify genetic patterns that reflect 3D face shapes in people with epilepsy (PWE) 2. Identify genetic patterns that reflect regional features of brain scans, e.g., gray matter, in PWE 3. Identify 3D face shape patterns that reflect regional features of brain scans in PWE 4. Link the identified patterns to clinical features of epilepsy.