Distinguishing TLE and TLE+ using MEG virtual sensors

使用 MEG 虚拟传感器区分 TLE 和 TLE

• 批准号: 10447404
• 负责人: Jeffrey Tenney
• 金额: $ 23.85万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10447404
• 关键词: Algorithms; Antiepileptic Agents; Brain; Brain region; Child; Clinical; Clinical Research; Data; Double-Blind Method; Drug resistance; Electrodes; Electroencephalography; Epilepsy; Evaluation; Excision; Failure; Focal Seizure; Freedom; Goals; Implanted Electrodes; Individual; Inferior; Insula of Reil; Intractable Epilepsy; Lateral; Lobectomy; Location; Magnetoencephalography; Measures; Methods; Mission; Modeling; Modification; Monitor; Morbidity - disease rate; National Institute of Neurological Disorders and Stroke; Operative Surgical Procedures; Outcome; Outcomes Research; Participant; Patients; Pattern; Phase; Population; Postoperative Period; Prognosis; Public Health; Reader; Reporting; Research; Risk; Sampling; Sampling Biases; Seizures; Sensitivity and Specificity; Source; Structure; Techniques; Temporal Lobe; Temporal Lobe Epilepsy; Testing; Time; Treatment Failure; Treatment outcome; Visual; base; connectome; density; improved; indexing; individual patient; innovation; mortality; novel; pediatric patients; predict clinical outcome; programs; sensor; source localization; surgery outcome; targeted treatment; tool; virtual

Project Summary/Abstract The most common drug resistant epilepsy involves the temporal lobe (TLE) and a quarter of patients continue to suffer from seizures after temporal lobectomy, with children designated as temporal plus epilepsy (TLE+) having a five-times increased risk of post-operative surgical failure (continued seizures). The long-term goal of our research program is to develop brain connectivity tools that optimize the use of targeted therapies, including surgery. The objective of the proposed study is to use visual analysis and connectomics of MEG virtual sensor waveforms to predict the presence of spikes on intracranial EEG (iEEG) and pre-surgically distinguish between patients with TLE and TLE+. The central hypothesis is that user-defined virtual sensor beamforming (UDvs- beamforming), using expert reader analysis and connectomics, are superior to ECD, current density source modeling, and conventional beamforming for non-invasively differentiating between TLE and TLE+. The rationale is that new, validated MEG methods would improve pre-surgical planning of iEEG and better identify patients at risk for worsened outcomes. The research proposed in this application is innovative because (1) while MEG connectivity measures have been studied in epilepsy, its use in pre-surgical evaluation for TLE/TLE+ would be novel, (2) our approach can characterize both local and widespread connectivity patterns without sampling bias, and (3) results can be collected on an individual patient basis which will facilitate immediate integration into existing clinical pipelines. This contribution is significant because it is the first step in a program of research that is expected to improve pre-surgical planning for patients with drug resistant epilepsy and improve understanding of treatment failure and outcomes in epilepsy.

项目摘要/摘要 最常见的耐药癫痫累及颞叶(TLE)，四分之一的患者仍在继续 颞叶切除术后癫痫发作，儿童被指定为颞叶加癫痫(TLE+) 术后手术失败的风险增加五倍(持续癫痫发作)。的长期目标是 我们的研究计划是开发大脑连接工具，以优化靶向治疗的使用，包括 做手术。提出的研究目的是使用脑磁图虚拟传感器的视觉分析和连接学 波形预测颅内脑电(IEEG)上棘波的存在和手术前区分 TLE和TLE+患者。中心假设是用户定义的虚拟传感器波束形成(UDVS- 波束成形)，使用专家读取器分析和连接学，优于ECD，电流密度源 对TLE和TLE+进行非侵入性区分的建模和常规波束形成。其基本原理是 新的、有效的脑磁图方法将改进iEEG的术前计划，并在 结果恶化的风险。本申请中提出的研究具有创新性，因为(1)虽然MEG 已经研究了癫痫的连通性测量，它在TLE/TLE+手术前评估中的应用将是 新颖，(2)我们的方法可以在没有抽样偏差的情况下表征局部和广泛的连通性模式， 以及(3)可以按患者个人收集结果，这将有助于立即整合到 现有的临床管道。这一贡献意义重大，因为这是一项研究计划的第一步 预计将改善耐药癫痫患者的手术前计划，并提高对 癫痫的治疗失败和结果。