Stratifying patient response to epilepsy treatment using computational models of brain networks
Lead Research Organisation:
University of Liverpool
Department Name: Institute of Translational Medicine
Abstract
Epilepsy is the most common serious neurological disorder. Approximately 60% of patients will have seizures controlled after anti-epileptic drug (AED) intervention. In patients with refractory focal epilepsy, around 50% will have seizures controlled after surgical intervention. The mechanisms underlying persistent seizures after treatment are not well understood, and there are currently no reliable biomarkers to predict treatment outcome in individual patients. Brain imaging offers unparalleled opportunities for the development of in-vivo non-invasive biomarkers in brain disorders. Epilepsy is caused by abnormal brain networks; these networks can be modelled using sophisticated MRI techniques. The goal of the proposed project will be to develop brain network-based markers of medical and surgical treatment outcome in patients with epilepsy.
The analysis of brain networks is increasingly reliant on the collaboration between neuroscientists and computational mathematicians. The proposed project will take place under the supervision of a neuroimager at the University of Liverpool (primary supervisor) and a computational mathematician at Newcastle University (secondary supervisor) who have a distinguished publication history in brain networks in epilepsy. Furthermore, this work will bring collaboration between the Universities of Liverpool and Newcastle and the Walton Centre NHS Foundation Trust (WCFT) in Liverpool, which is a neurological and neurosurgical treatment centre, specialising in epilepsy care. The WCFT represents our non-academic partner. Advanced MRI has been, and is continuing to be, collected from patients with epilepsy at the WCFT, providing a unique opportunity to develop network-based measures of treatment outcome.
Structural and functional brain networks will be modelled using diffusion tensor imaging and resting-state functional MRI data acquired in three epilepsy cohorts: (i) patients with temporal lobe epilepsy who had preoperative imaging, temporal lobe surgery and postoperative follow up; (ii) patients with newly diagnosed focal epilepsy who received imaging at the point of diagnosis and follow up one year after starting AED treatment; and (iii) patients with idiopathic generalised epilepsy who are in the process of being recruited for imaging and include those who have, and those who have not, responded to AED therapy. Healthy volunteers have also undergone imaging for comparison. Large-scale brain networks will be constructed in these samples to predict postoperative outcome (Cohort 1) and AED therapy outcome (Cohorts 2 & 3).
The student will be primarily based at the Institute of Translational Medicine, University of Liverpool and work under the guidance of Supervisor 1, with frequent visits to work with Supervisor 2 at the School of Computing, Newcastle University who will provide training in network analysis. The student will also receive an honorary contract with the WCFT, permitting use of the Trust's facilities and the opportunity to gain insights into the clinical activities of a large neurological and neurosurgical hospital. The successful student will develop a highly interdisciplinary skillset encompassing medical imaging, clinical neuroscience, and computing in multiple vibrant and active research environments. They will engage with both academic researchers and practicing clinicians. Importantly, the student will embark on a project that is directly clinically relevant and has the potential to inform the management of people with epilepsy.
The analysis of brain networks is increasingly reliant on the collaboration between neuroscientists and computational mathematicians. The proposed project will take place under the supervision of a neuroimager at the University of Liverpool (primary supervisor) and a computational mathematician at Newcastle University (secondary supervisor) who have a distinguished publication history in brain networks in epilepsy. Furthermore, this work will bring collaboration between the Universities of Liverpool and Newcastle and the Walton Centre NHS Foundation Trust (WCFT) in Liverpool, which is a neurological and neurosurgical treatment centre, specialising in epilepsy care. The WCFT represents our non-academic partner. Advanced MRI has been, and is continuing to be, collected from patients with epilepsy at the WCFT, providing a unique opportunity to develop network-based measures of treatment outcome.
Structural and functional brain networks will be modelled using diffusion tensor imaging and resting-state functional MRI data acquired in three epilepsy cohorts: (i) patients with temporal lobe epilepsy who had preoperative imaging, temporal lobe surgery and postoperative follow up; (ii) patients with newly diagnosed focal epilepsy who received imaging at the point of diagnosis and follow up one year after starting AED treatment; and (iii) patients with idiopathic generalised epilepsy who are in the process of being recruited for imaging and include those who have, and those who have not, responded to AED therapy. Healthy volunteers have also undergone imaging for comparison. Large-scale brain networks will be constructed in these samples to predict postoperative outcome (Cohort 1) and AED therapy outcome (Cohorts 2 & 3).
The student will be primarily based at the Institute of Translational Medicine, University of Liverpool and work under the guidance of Supervisor 1, with frequent visits to work with Supervisor 2 at the School of Computing, Newcastle University who will provide training in network analysis. The student will also receive an honorary contract with the WCFT, permitting use of the Trust's facilities and the opportunity to gain insights into the clinical activities of a large neurological and neurosurgical hospital. The successful student will develop a highly interdisciplinary skillset encompassing medical imaging, clinical neuroscience, and computing in multiple vibrant and active research environments. They will engage with both academic researchers and practicing clinicians. Importantly, the student will embark on a project that is directly clinically relevant and has the potential to inform the management of people with epilepsy.
