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

1)对研究背景的简要描述，包括潜在的影响仅在英国就有45万人患有癫痫，每年约有3万人患上癫痫。使用抗癫痫药物的药物干预导致60%的缓解率，留下40%的抗药性癫痫。前颞叶切除术(ATLR)是一种治疗颞叶癫痫的外科手术，其缓解率高达77%。ATLR未得到充分利用，主要是因为担心不利影响，高达30%的人获得语言缺陷，对他们的生活质量产生负面影响。这意味着较少的患者选择这种治疗，这种治疗很有可能使他们的癫痫发作消失。核磁共振成像(MRI)是术前常规使用的评估病变位置和语言。这些扫描没有揭示语言缺陷的结构性或功能性原因。语言障碍的一个潜在解释是脑白质束受损。弥散磁共振成像能够绘制白质束图，利用这一点，已经表明，在语言任务中，白质在功能激活区域到优势半球之间的更大偏侧化与手术后更大的命名下降有关。这个项目旨在研究白质束，这些白质束被证明与语言有关，并贯穿于颞叶。如果这个项目能够预测任何单一或组合的白质(WM)束准确地预测语言衰退，我们就可以向外科医生概述这一点，以最大限度地减少语言缺陷。这种副作用的减轻将使ATLR成为对许多人更有吸引力的选择，从而导致更具成本效益的治疗并提高他们的生活质量。2)目的和目标本项目可分为两个总体目标，分目标1)回顾调查哪个WM束与ATLRa术后所见的语言障碍有关。评估回顾数据的质量b.调查哪些处理方法使所有的回顾数据具有可比性c.开发一种反映文献的自动纤维束成像算法以重建所有患者的WM束2)使用1.基于避免对语言功能至关重要的区域来计划ATLR，并评估术后的影响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，确定如何最好地避开关键区域3)研究方法的新颖性在ATLR中还没有研究到这种程度的语言和WM捆绑。发现一个或多个WM束导致语言障碍的损害不仅有助于改善患者的生活，还有助于澄清目前对白质束及其功能的理解。此外，目前还没有反映我们关注的捆绑包的当前文献的自动跟踪图像术算法。这种工具的开发将使更多的研究小组能够为与白质束及其与语言的关系有关的项目进行研究。这反过来将提高对这些联系如何与EPSRC的战略和研究领域保持一致的理解本项目与EPSRC医疗技术的研究主题一致，特别是通过将潜在赤字降至最低，进一步为个人量身定做手术来优化治疗。此外，该项目与EPSRC的另外两个研究领域相匹配：1)医学成像，因为该项目依赖于多模式磁共振数据。2)软件工程，因为我们将开发一种新的算法，用于自动跟踪语言相关的捆绑包。5)任何参与的公司或合作者，Sjoerd B.Vos，Peter N.Taylor，Pamela J.Thompson，Sallie Baxendale，Jane de Tisi，Gavin P.Winston，John S.Duncan UCLH，NewCastle Uni，癫痫学会，癫痫研究英国

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