Improved Source Localization for Pediatric Epilepsy

改进小儿癫痫源定位

• 批准号: 8401525
• 负责人: Damon Hyde
• 金额: $ 17.35万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-01-15 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8401525
• 关键词: Accounting; Affect; Algorithms; Animals; Auditory; Boston; Brain; Brain region; Cerebrospinal Fluid; Childhood; Clinical; Curative Surgery; Data; Decision Making; Development; Development Plans; Diagnosis; Diagnostic; Disease Progression; Effectiveness; Electrical Engineering; Electrocorticogram; Electrodes; Electroencephalogram; Electroencephalography; Electromagnetics; Epilepsy; Excision; Functional Magnetic Resonance Imaging; Goals; Gold; Head; Health; Health Sciences; Health Technology; Human; Image; Image Analysis; Individual; International; Knowledge; Lead; Learning; Lesion; Magnetic Resonance Imaging; Maps; Measurement; Measures; Medical; Metabolism; Methods; Metric; Modality; Modeling; Monitor; Motor; Nature; Nerve Tissue; Neurology; Neurosciences; Neurosurgeon; Operating Rooms; Operative Surgical Procedures; Outcome; Patients; Pediatric Hospitals; Pharmaceutical Preparations; Pharmacotherapy; Physiological; Positioning Attribute; Procedures; Process; Proteins; Research; Research Personnel; Scalp structure; Seizures; Shadowing (Histology); Signal Transduction; Source; Statistical Models; Stimulus; Structure; Surgically-Created Resection Cavity; System; Techniques; Testing; Time; Translating; Visual; Work; base; bioimaging; career; career development; clinical application; clinical care; cranium; effective therapy; experience; falls; improved; minimally invasive; neuroimaging; outcome forecast; pre-clinical; reconstruction; relating to nervous system; screening; simulation; single photon emission computed tomography; software development; symposium

DESCRIPTION (provided by applicant): I am an electrical engineer whose research focuses on the use of advanced algorithmic techniques to improve the reconstruction of biomedical images from physical data. My graduate work focused on the development of algorithms to improve pre-clinical imaging of fluorescent proteins in small animals. Since the fall of 2008, I have held a postdoctoral position at Children's Hospital Boston studying the use of scalp EEG data to non-invasively localize seizure foci for the planning of surgical interventions. My immediate career goals are to establish myself in the field of functional neuroimaging using electroencephalography. In the near term, I seek to demonstrate that these techniques can offer a non- invasive approach to the current clinical gold standard of electrocorticography. In the longer term, I want to translate high leadcount EEG to being a part of the standard clinical care of epilepsy. Additionally, I want to expand my research into the application of EEG localization to more general neuroscience topics such as auditory and visual evoked stimuli. This K25 will give me the clinical and medical experience necessary to complete a successful RO1 application, and transition to being an independent researcher. The proposed career development plan incorporates a combination of classroom learning and practical clinical shadowing to develop a greater understanding of basic neurology and its clinical applications. I will take several classes in neurology from the Harvard/MIT Health Science and Technology (HST) division. Additionally, I will shadow both epileptologists and neurosurgeons at Children's Hospital to attain a greater understanding of clinical procedure and the ways in which various sources of information affect diagnostic decision making. I will regularly attend surgical planning conferences, and observe the final resection procedures. Professionally, I will attend local presentations on neurology and neuroimaging, and a minimum of two national and international conferences on imaging and epilepsy per year. In as many as 30% of epilepsy patients, their condition is poorly controlled with existing medications. For these individuals, surgical resection of afflicted nervous tissue may be the only effective treatment approach. However, poor localization of the seizure focus means only a small number of these patients ever see an operating room. Current state-of-the-art clinical procedure uses a wide range of structural and functional tests to localize seizure activity. The current gold standard for localization of the epileptic focus is electroencephalography (ECoG). However, because of the highly invasive nature of this procedure only a small number of patients are evaluated. Advanced algorithmic processing of scalp EEG data has the potential to offer similar accuracy from a noninvasive screening technique. This would allow far more patients to be comprehensively screened for surgical potential, both after conventional drug therapy has failed and early in the progression of the disease, before epileptic networks have developed which may reduce the effectiveness of surgical interventions. The EEG signal is highly dependent upon the physical, and thereby electrical, structure of the head. The use of structural information as prior knowledge in the source localization problem can be essential to enhancing the capability for identifying and localizing seizure foci. The long term goal is to improve scalp based EEG source localization to a point where the use of highly invasive sub-dural electrodes can be reduced or eliminated. Furthermore, highly accurate localization would potentially enable minimally invasive treatment approaches to be pursued, improving patient prognosis. The specific aims of this proposal are to 1) Construct and evaluate improved patient-specific modeling of electrical propagation. We will examine improved methods for modeling the skull and cerebrospinal fluid regions of the brain using new MR imaging approaches such as ultrashort echo time (UTE) imaging, and model based methods to account for partial volume effects in sulci. 2) Develop statistical models for data fusion within the EEG source localization problem. Constructed from structural and functional information extracted from MR imaging studies, these will be used in a Bayesian inversion framework to obtain statistically optimal maps of source activity. 3) Evaluate and quantify the benefits of high lead-count EEG systems. We will use the 128-lead EEG system currently at Children's Hospital to perform additional EEG studies and compare the accuracy of source localizations to those obtained with subsampled electrode data. We expect the additional information provided by the higher number of electrodes to yield increased localization accuracy, and thereby improved diagnoses. 4) Evaluate the relationship between localization and surgical outcome. Using quantitative metrics presented in Specific Aim 3, we will evaluate the interaction between accuracy and surgical outcome. Additionally, we will investigate whether a metric applied to the localization itself can act as a statistically significant predictor of surgical outcome.

描述（由申请人提供）：我是一名电气工程师，研究重点是使用先进的算法技术来改进从物理数据重建生物医学图像。我的研究生工作集中在算法的发展，以提高荧光蛋白在小动物的临床前成像。自2008年秋季以来，我在波士顿儿童医院（Children’s Hospital Boston）担任博士后，研究利用头皮脑电图数据非侵入性定位癫痫病灶，以规划手术干预。我近期的职业目标是利用脑电图在功能神经成像领域建立自己的地位。在短期内，我试图证明这些技术可以提供一种非侵入性的方法，以达到目前临床的皮质电成像金标准。从长远来看，我希望将高导联脑电图转化为癫痫标准临床护理的一部分。此外，我想将我的研究扩展到脑电图定位应用到更一般的神经科学主题，如听觉和视觉诱发刺激。这个K25将给我完成成功的RO1申请所需的临床和医学经验，并过渡到成为一名独立的研究人员。建议的职业发展计划包括课堂学习和实际临床实习相结合，以发展对基础神经病学及其临床应用的更好理解。我将参加哈佛/麻省理工学院健康科学与技术（HST）部门的几门神经病学课程。此外，我将跟随儿童医院的癫痫学家和神经外科医生，以获得对临床程序和各种信息来源影响诊断决策的方式的更好理解。我将定期参加手术计划会议，并观察最后的切除过程。在专业方面，我将参加当地关于神经病学和神经影像学的报告，以及每年至少两次关于影像学和癫痫的国家和国际会议。在多达30%的癫痫患者中，他们的病情用现有药物控制得很差。对于这些个体，手术切除受影响的神经组织可能是唯一有效的治疗方法。然而，癫痫病灶定位不佳，这意味着只有一小部分患者能进手术室。目前最先进的临床程序使用广泛的结构和功能测试来定位癫痫活动。目前癫痫病灶定位的金标准是脑电图（ECoG）。然而，由于这种手术的高度侵入性，只有少数患者得到评估。头皮脑电图数据的高级算法处理有可能提供与非侵入性筛查技术相似的准确性。这将允许更多的患者在常规药物治疗失败后和疾病进展早期，在癫痫网络形成之前进行全面的手术潜力筛查，这可能会降低手术干预的有效性。脑电图信号高度依赖于头部的物理结构，因此也依赖于头部的电结构。在源定位问题中使用结构信息作为先验知识对于增强识别和定位癫痫病灶的能力至关重要。长期目标是改善基于头皮的脑电图源定位，以减少或消除高侵入性硬膜下电极的使用。此外，高度准确的定位将有可能使微创治疗方法得以实现，改善患者预后。本提案的具体目的是1)构建和评估改进的针对患者的电传播模型。我们将研究使用新的磁共振成像方法（如超短回波时间（UTE）成像）和基于模型的方法来解释脑沟部分体积效应的颅骨和脑脊液区域建模的改进方法。2)针对脑电源定位问题，建立数据融合的统计模型。根据从磁共振成像研究中提取的结构和功能信息构建，这些信息将用于贝叶斯反演框架，以获得统计上最优的震源活动图。3)评估和量化高导联数脑电图系统的效益。我们将使用目前儿童医院的128导联脑电图系统进行额外的脑电图研究，并将源定位的准确性与通过亚采样电极数据获得的准确性进行比较。我们期望更多电极提供的额外信息能够提高定位的准确性，从而提高诊断。4)评估定位与手术结果的关系。使用具体目标3中提出的定量指标，我们将评估准确性和手术结果之间的相互作用。此外，我们将研究应用于定位本身的指标是否可以作为手术结果的统计显著预测因子。