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

1)研究背景的简要描述，包括潜在影响仅在英国就有45万人患有癫痫，每年约有3万人患有癫痫。用抗癫痫药物进行药物干预，缓解率为60%，剩下40%为耐药癫痫。颞叶前切除术（atr）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，主要是因为担心不良影响，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。这意味着很少有患者选择这种治疗方法，因为这种治疗方法很有可能使他们免于癫痫发作。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质束受损。弥散核磁共振成像能够绘制白质束，利用它，研究表明，在语言任务中，白质在功能激活区域与主要半球之间的偏侧程度越大，手术后记忆力下降的程度就越大。这个项目旨在研究白质束，白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质（WM）束准确地预测语言衰退，我们可以向外科医生概述这一点，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并改善他们的生活质量。2)目的和目标本项目可分为两个总目标和子目标：1)回顾性研究哪些WM束与术后atlas观察到的语言缺陷有关。评估回顾性数据的质量b.研究何种处理方法使所有回顾性数据具有可比性c.开发一种反映文献的自动肌腱束造影算法，以重建所有患者的WM束2)使用1.的结果。在避免语言功能关键区域的基础上计划ATLR，并评估术后影响。评估哪些语言测试能最好地评估我们正在研究的内容b.与神经外科团队合作，确定如何最好地避免关键区域3)研究方法的新颖性在ATLR中，还没有研究对语言和WM束进行如此程度的调查。发现脑白质束或脑白质束导致语言缺陷的损伤不仅有助于改善患者的生活，而且有助于澄清目前对脑白质束及其功能的认识。此外，目前还没有能够反映我们所关注的bundle的当前文献的自动轨迹图算法。这样一个工具的开发将允许更多的研究小组对白质束及其与语言的关系相关的项目进行调查。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，特别是在通过将潜在缺陷最小化，进一步为个体量身定制手术来优化治疗方面。此外，该项目还与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态MRI数据。2)软件工程，因为我们将开发一种新的算法来自动跟踪与语言相关的束。5)任何涉及的公司或合作者joerd 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) 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