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

项目：Mujica-Parodi， L, Millett， D. E.和Shaw， s .建议号：1141995INTELLECTUAL meriti顽固性癫痫通常通过手术切除假定的致痫灶来治疗，但仍有相当一部分患者目前无法通过标准诊断技术（EEG， MRI， PET）精确识别病灶。从动态系统和统计物理中提取的信息理论方法已经应用于脑电图来识别癫痫发作，最近已被应用于癫痫发作病灶的识别，并取得了一定的成功。然而，由于EEG的空间分辨率较差，这些技术的临床应用失败了。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信息理论方法，如时延嵌入，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应用，该技术将脑电图中发现的对电路失调的敏感性与功能磁共振成像中发现的空间分辨率结合起来，从而为未来的临床研究确定一个全新的方向。在本应用中，我们将开发将功率谱尺度不变性应用于fMRI时间序列的技术，我们之前已经证明了这种方法在识别和解剖定位旁边缘电路失调方面具有实用性。这些技术将在癫痫灶的识别中进行测试，并通过与标准神经心理学评估、颅内监测和/或手术结果的比较来验证。更广泛的影响大约有300万美国人患有癫痫；据估计，在目前的技术水平下，这些人中有三分之二没有得到成功的治疗。早期成功控制这种疾病至关重要，因为反复发作会导致不可逆转的脑损伤和死亡。因为目前的技术水平不能够以成功的手术干预所需的高度空间分辨率清楚地识别癫痫灶；如果成功，我们提出的方向将彻底改变顽固性癫痫的治疗。该提案的独特之处在于，它是有机跨学科的，将动态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的治疗问题。

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.