EAGER: Using Network Dynamic fMRI for Pre-Surgical Localization of Epileptogenic Foci

EAGER：使用网络动态 fMRI 进行癫痫病灶的术前定位

• 批准号: 1141995
• 负责人: Lilianne Mujica-Parodi
• 金额: $ 30万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1141995&HistoricalAwards=false
• 关键词: EAGER; Using; Network; Dynamic; fMRI

PI: Mujica-Parodi, L., Millett, D. E. and Shaw, S.Proposal Number: 1141995INTELLECTUAL MERITIntractable epilepsy is often treated surgically through the ablation of presumed epileptogenic foci, yet there exists a significant portion of patients for whom foci cannot currently be identified with precision through standard diagnostic techniques (EEG, MRI, PET). Information-theoretic methods adapted from dynamical systems and statistical physics have been applied to EEG to identify seizures, and most recently have been applied to the identification of seizure foci, with some success. However, clinical adoption of these techniques has failed due to EEG's poor spatial resolution. While fMRI has excellent spatial resolution, its hemodynamic time-series are significantly too short and sparse to permit application of most standard information theoretic methods, such as time-delay embedding, fractal dimension, and entropy. The research proposed in this application is designed to establish the clinical utility of computational techniques that combine the sensitivity to circuit dysregulation found in EEG with the spatial resolution found in fMRI, thereby defining a fundamentally new direction for future clinical research. In this application, we will develop techniques for the application of power spectrum scale invariance to fMRI time-series, a method we have previously shown to have utility with respect to the identification and anatomical localization of dysregulation within the paralimbic circuit. These techniques will be tested in the identification of epileptogenic foci, validated by comparison with standard neuropsychological assessment, intracranial monitoring, and/or surgical outcomes.BROADER IMPACTSApproximately three million Americans suffer from epilepsy; it is estimated that fully two thirds of these individuals are unsuccessfully treated by the current state of the art. Successfully managing the disease early is critical, since repeated seizures can cause irreversible brain damage and death. Because the current state of the art is not capable of clearly identifying epileptogenic foci with the high degree of spatial resolution required for successful surgical intervention; if successful, our proposed direction would revolutionize treatment of intractable epilepsy. This proposal is unique in that it is organically interdisciplinary, integrating computational techniques adapted from dynamical systems and statistical physics with direct and immediate clinical applications, and thus fits within the scope of the National Science Foundation's GARDE program and EAGER funding mechanism by addressing the treatment of disability through the novel and transformative development of advanced engineering tools.

PI：Mujica-Parodi，L.，Millett，D. E.和Shaw，S.建议编号：1141995智力MERIT难治性癫痫通常通过消融假定的致癫痫灶进行手术治疗，然而，存在相当大一部分患者，目前通过标准诊断技术（EEG、MRI、PET）不能精确地识别病灶。从动力系统和统计物理学改编的信息理论方法已被应用于EEG以识别癫痫发作，并且最近已被应用于识别癫痫发作病灶，并取得了一些成功。然而，由于EEG的空间分辨率差，这些技术的临床采用失败了。虽然fMRI具有出色的空间分辨率，但其血流动力学时间序列明显太短且稀疏，无法应用大多数标准的信息理论方法，如时间延迟嵌入，分形维数和熵。本申请中提出的研究旨在建立计算技术的临床实用性，该技术将EEG中发现的电路失调的敏感性与fMRI中发现的空间分辨率相结合，从而为未来的临床研究定义了一个全新的方向。在这个应用程序中，我们将开发技术的应用功率谱标度不变性的功能磁共振成像时间序列，我们以前已经证明，具有实用性的识别和解剖定位的边缘回路内的失调的方法。这些技术将在致痫灶的识别中进行测试，通过与标准神经心理学评估、颅内监测和/或手术结果进行比较来验证。据估计，这些个体中有整整三分之二的人没有被现有技术成功地治疗。早期成功地控制疾病是至关重要的，因为反复发作会导致不可逆的脑损伤和死亡。由于目前的最新技术水平不能清楚地识别致痫灶与成功的手术干预所需的高度空间分辨率，如果成功，我们提出的方向将彻底改变治疗难治性癫痫。该提案的独特之处在于它是有机的跨学科，将动力系统和统计物理学的计算技术与直接和即时的临床应用相结合，因此符合美国国家科学基金会的GARDE计划和EAGER资助机制的范围，通过先进工程工具的新颖和变革性发展来解决残疾治疗问题。