Stratifying patient response to epilepsy treatment using computational models of brain networks

癫痫是最常见的严重神经系统疾病。大约60%的患者在抗癫痫药物（AED）干预后癫痫发作得到控制。在难治性局灶性癫痫患者中，约50%的癫痫发作在手术干预后得到控制。治疗后持续性癫痫发作的机制尚不清楚，目前没有可靠的生物标志物来预测个体患者的治疗结果。脑成像为脑疾病的体内非侵入性生物标志物的开发提供了无与伦比的机会。癫痫是由异常的大脑网络引起的;这些网络可以使用复杂的MRI技术进行建模。拟议项目的目标是开发基于大脑网络的癫痫患者医疗和手术治疗结果的标志物。大脑网络的分析越来越依赖于神经科学家和计算数学家之间的合作。拟议的项目将在利物浦大学的一名神经成像师（主要监督人）和纽卡斯尔大学的一名计算数学家（次要监督人）的监督下进行，他们在癫痫脑网络方面有着杰出的出版历史。此外，这项工作将带来利物浦和纽卡斯尔大学与利物浦沃尔顿中心NHS基金会信托基金（WCFT）之间的合作，这是一个神经和神经外科治疗中心，专门从事癫痫护理。WCFT代表我们的非学术合作伙伴。先进的MRI已经并将继续从WCFT的癫痫患者中收集，为开发基于网络的治疗结果措施提供了独特的机会。将使用弥散张量成像和静息态功能MRI数据对三个癫痫队列中获得的脑结构和功能网络进行建模：（i）接受术前成像、颞叶手术和术后随访的颞叶癫痫患者;（ii）在诊断时接受成像并在开始AED治疗后1年随访的新诊断局灶性癫痫患者;和（iii）正在招募进行成像的特发性全身性癫痫患者，包括对AED治疗有反应和无反应的患者。健康志愿者也进行了成像比较。将在这些样本中构建大规模的脑网络来预测术后结果（队列1）和AED治疗结局（队列2和3）。学生将主要在利物浦大学转化医学研究所工作，并在主管1的指导下工作，经常访问计算机学院与主管2一起工作，纽卡斯尔大学，将提供网络分析培训。学生还将获得与WCFT的荣誉合同，允许使用信托的设施，并有机会深入了解大型神经和神经外科医院的临床活动。成功的学生将在多个充满活力和活跃的研究环境中发展高度跨学科的技能，包括医学成像，临床神经科学和计算。他们将与学术研究人员和执业临床医生接触。重要的是，学生将开始一个直接与临床相关的项目，并有可能告知癫痫患者的管理。

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.