White Matter Bundles and their Relationship to Language Decline Following Anterior Temporal Lobe Resection

前颞叶切除后白质束及其与语言衰退的关系

• 批准号: 2371255
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2371255
• 关键词: White; Matter; Bundles; their; Relationship

1) Brief description of the context of the research including potential impactEpilepsy in the UK alone affects 450,00 people with around 30,000 annually developing epilepsy. Pharmaceutical intervention with antiepileptic drugs results in a remission rate of 60%, leaving 40% with drug-resistant epilepsy. Anterior temporal lobe resection (ATLR) is a surgical intervention for temporal lobe epilepsy that results in a remission rate of up to 77%. ATLR is underutilised, principally because of concerns regarding adverse effects, with up to 30% acquiring a language deficit negatively impacting on their quality of life. This means that fewer patients opt for this treatment which has a good chance at rendering them seizure-free. Magnetic resonance imaging (MRI) is routinely used preoperative to assess lesion location and language. These scans do not reveal a structural or functional cause for the language deficit. One potential explanation for the language deficit seen is damage to white matter bundles. Diffusion MRI is able to map white matter tracts, using this it has been shown that the greater lateralisation of white matter between functionally activated areas during language tasks to the dominant hemisphere is associated with greater post-surgical naming decline. This project aims to investigate white matter bundles which are shown to be involved in language and run through the temporal lobe. If this project can predict any single or combination of white matter (WM) bundles accurately predicts language decline, we can outline this to surgeons to minimise language deficits. This alleviation of adverse side effects would make ATLR a more attractive choice for many, thus leading to a more cost-effective treatment and an improvement in their quality of life.2) Aims and objectivesThis project can be split into two general aims with sub-aims 1) Retrospectively investigate which WM bundle relates to the language deficit seen post-operatively from ATLRa. Assess the quality of retrospective data b. Investigate what processing methods make all retrospective data comparable c. Develop an automated tractography algorithm reflecting the literature to reconstruct WM bundles in all patients2) Using findings from 1. Plan ATLR based on avoiding areas critical to language function and assess the impact post-operativelya. Assess which language tests best assess what we are investigating b. Work with neurosurgical teams to identify how best to avoid critical regions 3) Novelty of the research methodologyThere has been no research that investigates language and WM bundles to this extent in ATLR. The discovery of damage to which WM bundle or bundles causes a language deficit would not only help improve patients' lives but also help clarify the current understanding of white matter bundles and their function. In addition, there is currently no automated tractography algorithm that reflects the current literature for the bundles we're focusing on. The development of such a tool would allow more research groups to investigate this for projects relating to white matter bundles and their relationship to language. This will, in turn, improve the understanding of how these connections work4) Alignment to EPSRC's strategies and research areasThis project aligns with the EPSRC research theme of healthcare technologies, particularly in optimising treatment by further tailoring surgery to the individual by minimising potential deficits. Furthermore, this project matches two additional EPSRC research areas: 1) Medical imaging as this project relies on multi-modal MRI data. 2) Software engineering as we will develop a new algorithm for automatic tractography of language-related bundles.5) Any companies or collaborators involvedSjoerd B. Vos, Peter N. Taylor, Pamela J. Thompson, Sallie Baxendale, Jane de Tisi, Gavin P. Winston, John S. Duncan UCLH, Newcastle Uni, The Epilepsy Society, Epilepsy Research UK

1)研究背景的简要描述，包括潜在的影响癫痫仅在英国就影响了450，000人，每年约有30，000人患癫痫。抗癫痫药物的药物干预导致60%的缓解率，剩下40%的耐药性癫痫。前颞叶切除术（ATLR）是一种治疗颞叶癫痫的手术干预，其缓解率高达77%。ATLR未得到充分利用，主要是因为担心其不良影响，高达30%的人患有语言缺陷，对他们的生活质量产生了负面影响。这意味着更少的患者选择这种治疗，这有很好的机会使他们免于痛苦。术前常规使用磁共振成像（MRI）评估病变位置和语言。这些扫描并没有揭示语言缺陷的结构或功能原因。语言缺陷的一个可能解释是白色物质束受损。扩散MRI能够映射白色物质束，使用此，已经表明，语言任务期间功能激活区域之间的白色物质向优势半球的更大偏侧化与更大的术后命名下降相关。本计画旨在研究白色物质束，这些物质束被证明与语言有关，并贯穿颞叶。如果这个项目可以预测任何单一或组合的白色物质（WM）束准确地预测语言下降，我们可以概述这对外科医生，以尽量减少语言缺陷。这种不良副作用的减轻将使ATLR成为许多人更有吸引力的选择，从而导致更具成本效益的治疗和生活质量的改善。2）目的和目标该项目可以分为两个总目标和子目标1）回顾性调查哪一个WM束与ATLRa术后观察到的语言缺陷有关。评估回顾性数据的质量B。调查什么样的处理方法使所有的回顾性数据具有可比性。开发一种自动纤维束成像算法，反映文献，以重建所有患者的WM束2）使用1.根据避免对语言功能至关重要的区域来规划ATLR，并评估术后影响a。评估哪种语言测试最能评估我们正在调查的内容B。与神经外科团队合作，确定如何最好地避免关键区域3）研究方法的新奇在ATLR中，还没有研究在这种程度上调查语言和工作记忆束。WM束或束导致语言缺陷的损害的发现不仅有助于改善患者的生活，而且有助于澄清目前对白色物质束及其功能的理解。此外，目前还没有自动化的纤维束成像算法，反映了目前的文献，我们关注的bundle.The这样一个工具的发展将允许更多的研究小组，以调查这一项目有关的白色物质束和他们的关系，语言。这将反过来提高对这些连接如何工作的理解4）与EPSRC的战略和研究领域保持一致该项目与EPSRC的医疗保健技术研究主题保持一致，特别是通过最大限度地减少潜在的缺陷，进一步为个人量身定制手术来优化治疗。此外，该项目还与EPSRC的两个额外研究领域相匹配：1）医学成像，因为该项目依赖于多模态MRI数据。2)软件工程，因为我们将开发一种新的算法，自动tractography的语言相关的语言。5）任何公司或合作者参与Sjoerd B。Vos，Peter N.放大图片作者：Taylor，Pamela J. Thompson，Sallie Mendale，Jane de Tisi，Gavin P. Winston，John S.邓肯UCLH，纽卡斯尔大学，癫痫学会，英国癫痫研究