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%的患者在手术干预后癫痫发作得到控制。治疗后持续性癫痫发作的机制尚不清楚，目前还没有可靠的生物标志物来预测个别患者的治疗结果。脑成像为开发大脑疾病的体内非侵入性生物标记物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核磁共振技术进行建模。拟议项目的目标将是开发基于大脑网络的癫痫患者内科和外科治疗结果的标记。对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(初级主管)和纽卡斯尔大学的一名计算数学家(二级主管)的监督下进行，这两人在癫痫的大脑网络方面有着杰出的出版历史。此外，这项工作将促进利物浦大学和纽卡斯尔大学与利物浦的沃尔顿中心NHS基金会信托基金(WCFT)之间的合作，WCFT是一家专门从事癫痫治疗的神经学和神经外科治疗中心。WCFT代表我们的非学术合作伙伴。先进的磁共振成像已经并将继续从WCFT的癫痫患者身上收集，这为开发基于网络的治疗结果测量提供了独特的机会。将利用扩散张量成像和在三个癫痫队列中获得的静息状态功能磁共振数据对大脑的结构和功能进行建模：(I)进行了术前成像、颞叶手术和术后随访的颞叶癫痫患者；(Ii)新诊断的局灶性癫痫患者在确诊时接受成像并在开始AED治疗一年后进行随访；(Iii)正在招募接受AED治疗的特发性全身性癫痫患者进行成像，其中包括对AED治疗有反应的患者和没有对AED治疗有反应的患者。健康的志愿者也接受了成像作为对比。将在这些样本中构建大规模的大脑网络，以预测术后结果(队列1)和AED治疗结果(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，在主管1的指导下工作，经常访问纽卡斯尔大学计算学院的主管2，后者将提供网络分析方面的培训。学生还将获得WCFT的荣誉合同，允许使用信托基金的设施，并有机会深入了解一家大型神经科和神经外科医院的临床活动。成功的学生将在多种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机。他们将与学术研究人员和执业临床医生进行接触。重要的是，学生将开始一个与临床直接相关的项目，并有可能为癫痫患者的管理提供信息。

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.