Novel DWI Methods to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery.

最大限度减少小儿癫痫手术术后缺陷的新 DWI 方法。

• 批准号: 10298205
• 负责人: Jeong-Won Jeong
• 金额: $ 43.41万
• 依托单位: WAYNE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10298205
• 关键词: Age; Area; Benefits and Risks; Biological Markers; Brain; Childhood; Classification; Clinical; Clinical Management; Contralateral; Crowding; Data; Detection; Development; Diffusion Magnetic Resonance Imaging; Drug Evaluation; Drug resistance; Electric Stimulation; Epilepsy; Equilibrium; Evaluation; Excision; Family; Freedom; Funding; Goals; Gold; Hearing; Image; Imaging Device; Language; Left; Methods; Motor; Network-based; Neurocognitive; Operative Surgical Procedures; Outcome; Partial Epilepsies; Pathway interactions; Patients; Pharmaceutical Preparations; Postoperative Period; Probability; Protocols documentation; Provider; Sampling; Seizures; Severities; Speech; Structure; Surgical margins; Techniques; Therapeutic; Time; Translating; Treatment/Psychosocial Effects; United States National Institutes of Health; Vision; Visual; base; brain tissue; childhood epilepsy; cognitive regression; cohort; connectome; convolutional neural network; deep learning; functional improvement; imaging biomarker; improved; individual patient; insight; language impairment; novel; preservation; prospective; quantitative imaging; relating to nervous system; risk benefit ratio; risk minimization; side effect; tool; tractography; white matter

Project Summary/Abstract For the millions of epilepsy patients with drug-resistant seizures, surgical resection of epileptogenic brain tissue is often the only remaining therapeutic option. Especially in young patients who do not obtain seizure control or suffer from unacceptable side effects from medications, there are further concerns about the effect of seizures on development: even brief but repetitive seizures cause cognitive regression and detrimental psychosocial effects. This motivates a particular urgency to investigate a more structured, quantitative, and non-invasive tool, which is capable of informing families and providers to decide timely surgery by accurately providing the probabilities of both favorable and unfavorable postoperative outcomes using data from preoperative imaging analysis at the whole-brain level. The overall goal of this project is to develop a novel tool of benefit-risk analysis for the presurgical evaluation of pediatric drug-resistant focal epilepsy. Toward this goal, we will validate a state- of-the-art deep learning-based diffusion MRI technique to provide the resection margin (i.e., the distance between epileptogenic area and eloquent area) resulting in maximized benefits (i.e., seizure freedom and long- term neurocognitive improvement) and minimized risk (i.e., deficits in eloquent functions including motor/language/hearing/vision). With NIH support, we have established diffusion-weighted imaging maximum a posteriori probability (DWI-MAP) analysis with Kalman filter, which can provide individual patients with the optimal resection margin, yielding successful avoidance of motor/language/visual deficits in 93%/91%/90% of patients with ≥75% of patients benefiting from seizure freedom. Recently, we have also found that deep convolutional neural network (DCNN) can provide an excellent accuracy (94-100%) to classify true positive tracts of eloquent brain areas, suggesting that DCNN-based tract classification may outperform the DWI-MAP in detecting diverse function-specific white matter pathways. Aim 1 of this project will investigate if a combination of DCNN-based tract classification with Kalman filter even better predicts the resection margin, resulting in seizure freedom and avoidance of functional deficits at a large cohort. Aim 2 will investigate if an advanced DWI approach integrating DCNN and DWI connectome helps decide timely surgery by providing 1) preoperative imaging markers underlying high likelihood of postoperative neurocognitive improvements and 2) mechanistic insight in structural brain reorganization associated with postoperative verbal IQ improvement. The results of this project are expected to ultimately improve clinical management of pediatric epilepsy by translating deep learning- based diffusion MRI technique to optimize the surgical margin, predict the postoperative neurocognitive outcome, and determine specific mechanism of postoperative brain reorganization, which will be validated for optimizing clinical benefit-risk analysis before surgical intervention.

项目概要/摘要 对于数百万耐药性癫痫发作的癫痫患者来说，手术切除致癫痫脑组织 通常是唯一剩下的治疗选择。特别是对于癫痫发作未得到控制或未获得控制的年轻患者 遭受不可接受的药物副作用，人们进一步担心癫痫发作的影响 关于发育：即使是短暂但重复的癫痫发作也会导致认知衰退和有害的社会心理 影响。这促使我们迫切需要研究一种更加结构化、定量和非侵入性的工具， 它能够通过准确提供信息来通知家庭和提供者决定及时手术 使用术前影像数据得出有利和不利术后结果的概率 全脑水平的分析。该项目的总体目标是开发一种新的收益-风险分析工具 用于儿科耐药局灶性癫痫的术前评估。为了实现这一目标，我们将验证一个状态- 最先进的基于深度学习的扩散 MRI 技术，提供切除边缘（即距离 致癫痫区域和雄辩区域之间），从而实现利益最大化（即无癫痫发作和长期治疗） 术语神经认知改善）和最小化风险（即口才功能缺陷，包括 运动/语言/听力/视觉）。在 NIH 的支持下，我们建立了扩散加权成像最大 使用卡尔曼滤波器进行后验概率 (DWI-MAP) 分析，可以为个体患者提供 最佳切除边缘，成功避免了 93%/91%/90% 的运动/语言/视觉缺陷 ≥75%的患者从无癫痫发作中受益。最近我们还发现，深 卷积神经网络 (DCNN) 可以提供出色的准确率 (94-100%) 来分类真阳性区域 雄辩的大脑区域，表明基于 DCNN 的束分类可能优于 DWI-MAP 检测多种功能特异性白质通路。该项目的目标 1 将调查组合是否 基于 DCNN 的管道分类与卡尔曼滤波器的结合甚至可以更好地预测切除边缘，从而 在一大群人中实现无癫痫发作并避免功能缺陷。目标 2 将调查高级 DWI 是否 整合 DCNN 和 DWI 连接组的方法通过提供 1) 术前帮助决定及时手术 术后神经认知改善的可能性很高的影像学标志物和 2) 机制 深入了解与术后言语智商改善相关的大脑结构重组。这样做的结果 预计该项目将通过转化深度学习最终改善小儿癫痫的临床管理 基于扩散MRI技术来优化手术切缘，预测术后神经认知结果， 确定术后脑重组的具体机制，并对其进行验证以优化 手术干预前的临床获益-风险分析。