Using Network Dynamic fMRI for Pre-surgical Localization of Epileptogenic Foci

PI: Mujica-Parodi， lilianne提案号：1264440智力优势：本提案，我们将开发一种基于神经生物学的仪器来识别癫痫发作病灶。癫痫会逐渐破坏大脑；因此，早期干预对于最大限度地提高完全恢复的机会至关重要。对于大约三分之一的癫痫不能通过药物治疗的患者，最常见的治疗选择是手术切除被认为是癫痫病灶的大脑区域。但是，该过程假定可以精确地识别焦点。当无法通过神经影像学的标准技术识别时，就像在所有难治性局灶性癫痫患者中约有一半的患者那样，选择要么不治疗，要么手术试验和错误，这两种方法都可能导致与疾病本身一样严重的神经退行性变。从动态系统和统计物理中提取的熵方法已被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，并取得了一些成功。然而，由于脑电图的空间分辨率差，无法进入通常与癫痫发作有关的皮层下区域，这些技术的临床应用失败了。FMRI具有识别神经外科目标所需的三维全脑覆盖和空间分辨率。不幸的是，血液动力学时间序列通常太短、太稀疏，不允许大多数标准信息理论方法的应用，而功能磁共振成像采集和处理技术在多大程度上保持信号动力学是一个尚未解决的基本工程问题。拟议的三年研究计划提供全面优化，包括在采集、硬件、软件和分析技术方面的创新，并由四个部分组成。首先，我们将评估和改进fMRI动态的保真度。这包括fMRI动态幻影的仪器开发，首次允许在图像处理管道的每个阶段对已知的“血流动力学”输入和信号输出进行定量比较/校正，以及使用颅内脑电图（“金标准”）、头皮脑电图和fMRI从相同空间坐标获得的时间序列之间进行定量比较/校正。其次，为了帮助识别网络异常，我们将推导出规范的掩模来控制刺激或默认网络激活模式。第三，我们将研究信号复杂性异常与网络连通性之间的关系，后者对于在突触水平上理解癫痫易感性的病因具有重要意义。最后，我们将使用支持向量机开发自动算法来识别假定的癫痫病灶，通过颅内电极和/或手术切除后的癫痫发作自由来确认。如果成功，我们提出的方向将最终为神经外科医生提供难治性隐源性癫痫手术治疗的潜在革命性进展，从而满足NSF资助机制的使命，旨在支持“基础工程和科学知识的重大进步”，而不是渐进式的改进。该建议将计算技术和仪器的发展与直接临床应用相结合。更广泛的影响：2007年，国会授权国家科学院、国家工程院和医学研究所组成一个委员会，以应对在日益全球化的经济中保持科学创新和经济竞争力的相关挑战。对于这一提案，我们将重点讨论具体建议。一项行动项目是通过加强科学教师本身的科学和工程教育，加强儿童在K-12科学和技术方面的准备。行动项目将通过开发1-6年级的动手工程设计和创新课程来解决。该课程将在社会经济多元化的学校进行反复测试和完善，并通过我们的网站进行传播，并将包括后续的NWEA个性化评估，以衡量提高学生STEM成绩的有效性。通过在实验室中训练学生在自己的研究中进行工程设计和创新，然后训练他们在更基础的水平上教授教师和小学生相同的概念工具，我们能够最大限度地将我们的研究和教育目标结合起来。

PI: Mujica-Parodi, LilianneProposal Number: 1264440Intellectual Merit: For this proposal, we will develop a neurobiologically-based instrument to identify seizure foci in epilepsy. Seizures gradually destroy the brain; therefore, early intervention is critical to maximize chances of full recovery. For the roughly one third of all patients whose seizures cannot be managed through medication, the most common treatment option is surgical removal of the brain areas thought to be seizure foci. However, the procedure assumes that foci can be identified with precision.When they cannot be identified through standard techniques in neuroimaging, as in about half of all patients with medication-resistant focal epilepsy, the options are either no treatment or surgical trial and error, both of which can lead to neurodegeneration as severe as the disease itself. Entropic 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 and inability to access sub-cortical regions commonly implicated in seizures. FMRI has the three-dimensional whole brain coverage and spatial resolution required for identifying neurosurgical targets. Unfortunately, hemodynamic time-series are typically too short and sparse to permit application of most standard information theoretic methods, and the degree to which fMRI acquisition and processing techniques preserve signal dynamics is a fundamental engineering question that remains unaddressed. The proposed three-year research plan provides for comprehensive optimization, including innovations in acquisition, hardware, software, and analytical techniques, and is comprised of four parts. First, we will assess and improve the fidelity of fMRI dynamics. This includes both instrumentation development of a Dynamic Phantom for fMRI, for the first time permitting quantitative comparison/correction between known "hemodynamic" inputs and signal outputs at each stage of the image processing pipeline, as well as between time-series acquired from the same spatial coordinates using intracranial EEG (the "gold standard"), scalp EEG, and fMRI. Second, in order to aid in identification of network abnormalities, we will derive normative masks to control for stimulus or default-network activation patterns. Third, we will investigate the relationship between abnormalities of signal complexity and network connectivity, the latter of which has critical implications for understanding the etiology of seizure vulnerability at the synaptic level. Finally, we will use support vector machine to develop automated algorithms for identification of putative seizure foci, as confirmed by intracranial electrodes and/or seizure freedom following surgical resection. If successful, our proposed direction would culminate in providing neurosurgeons with a potentially revolutionary advance in surgical treatment of intractable cryptogenic epilepsy, and thus satisfies the mission of the NSF funding mechanism, designed to support "significant advancement of fundamental engineering and scientific knowledge" rather than incremental improvements. This proposal integrates development of computational techniques and instrumentation with direct clinical applications.Broader Impact: In 2007, the National Academy of Science, National Academy of Engineering, andInstitute of Medicine were charged by Congress to form a committee to address the challenges associated with maintaining scientific innovation and economic competitiveness within an increasingly global economy. For this proposal, we will focus on addressing specific recommendations. an action item was to strengthen children's K-12 preparation in science and technology by enhancing the science and engineering education of the science teachers themselves. The action item will be addressed through the development of a hands-on engineering design and innovation curriculum for grades 1-6. This curriculum will be iteratively tested and refined in a socioeconomically-diverse school, disseminated through our website, and will include follow-up NWEA individualized assessment to measure efficacy in improving student STEM performance. By training students in the lab to develop engineering design and innovation in their own research, and then training them to teach teachers and elementary school students the same conceptual tools at a more basic level, we are able to integrate our research and educational goals to the fullest extent possible.