CRCNS: Dynamic network analysis of human seizures for therapeutic intervention

CRCNS：人类癫痫发作的动态网络分析用于治疗干预

基本信息

批准号：
9116972
负责人：
SYDNEY S CASH
金额：
$ 31万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9116972
关键词：
Address Affect Area Brain Brain region Cellular biology Clinical Clinical Data Communities Complex Coupling Data Data Analyses Detection Development Devices Ecology Environment Epilepsy Event Evolution Excision Human Interdisciplinary Study Intervention Knowledge Measurement Measures Medication Management Methodology Methods Modeling Names Neurologic Neurons Neurosciences Noise Operative Surgical Procedures Outcome Pathology Pathway Analysis Patient Care Patients Pattern Population Dynamics Prospective Studies Recurrence Refractory Research Research Personnel Retrospective Studies Science Scientific Advances and Accomplishments Seizures Social Sciences Statistical Models Structure Syndrome System Techniques Theoretical Studies Therapeutic Intervention Translating United States base brain tissue cluster computing computational neuroscience critical period flexibility graduate student human data improved network models neurophysiology novel novel strategies patient population programs skills success targeted treatment theories tool tool development training opportunity translational neuroscience treatment strategy voltage

项目摘要

DESCRIPTION (provided by applicant): Epilepsy is one of the most common neurological syndromes, affecting an estimated 3 million people in the United States. In one-third of these patients, seizures cannot be controlled despite maximal medication management. The complexity of the neuronal network dynamics that define the epileptogenic cortex and drive seizure initiation and spread makes understanding and treating epilepsy a unique challenge. In this proposal, an interdisciplinary research team will address this challenge. The assembled researchers integrate clinical expertise and data recording capabilities with sophisticated network analysis and statistical modeling techniques. Utilizing invasive brain voltage recordings, dynamic functional networks will be inferred from a population of patients during spontaneous seizures. To characterize these dynamic networks, new data analysis and statistical modeling techniques tailored to address the unique challenges of the clinical human data will be developed. These techniques will be applied to understand the sudden, explosive emergence of well-connected subsets of nodes (a.k.a., communities) in the noisy, real-world environment of human cortical seizure dynamics. Understanding the rapid network organization at seizure onset and termination will inspire new treatment strategies for epilepsy, and motivate developments and applications in the emerging theoretical research field of explosive percolation. The proposed research will advance scientific knowledge and understanding in three ways. First, the development and application of novel dynamic network analysis techniques to clinical seizure data will provide a deeper understanding of human epilepsy and the network interactions that underlie seizure initiation and termination. Second, the proposed research requires new tools to characterize and track community structure in noisy, dynamic networks. Development of these tools will help to address open questions and unexplored directions in the study of transient and recurrent community patterns emergent in dynamic networks. All dynamic network analysis tools developed in this proposal will be made freely available for other researchers to apply and develop. Third, by utilizing complex neurophysiological data, the proposed research will ground the field of explosive percolation in noisy real-world phenomena, and motivate new developments and applications critical to this emerging science. There are three broader impacts of the proposed research. First, the dynamic network analysis and statistical modeling of human seizure data will provide new approaches to improve patient care of medically refractory epilepsy. In particular, through prospective and retrospective studies, the dynamic network analysis and modeling techniques will be applied to identify principled surgical targets, and predict which patients will - and will not - benefit from surgery. Second, the dynamic community detection tools and statistical models developed will have general applicability across many domains of science. These tools can be applied broadly within systems neuroscience - to elucidate brain dynamics underlying healthy brain function and present in pathology - and in many other scientific fields (e.g., cell biology, ecology, social sciences, distributed computing, to name a few) in which dynamic networks appear. Third, the proposed research will provide unique training opportunities for graduate students in translational neuroscience, with a specific emphasis on clinical data, network inference and dynamical network analysis, and statistical modeling. These trainees will develop unique interdisciplinary skills in clinical, statistical, and computational neuroscience.

描述（由申请人提供）：癫痫是最常见的神经系统综合征之一，在美国估计有 300 万人受到影响，尽管采取了最大程度的药物管理，但其中三分之一的患者仍无法控制癫痫发作。定义致癫痫皮层并驱动癫痫发作和扩散的网络动力学使得理解和治疗癫痫成为一个独特的挑战，在这项提案中，一个跨学科的研究小组将解决这一挑战，将临床专业知识和数据记录能力与复杂的技术相结合。利用侵入性脑电压记录，可以从自发性癫痫发作期间的患者群体中推断出动态功能网络，为了表征这些动态网络，我们将采用新的数据分析和统计建模技术来解决临床的独特挑战。这些技术将用于了解在人类皮质癫痫动态的嘈杂的现实环境中突然、爆炸性地出现的连接良好的节点子集（也称为社区）。发作发病和终止将激发新的癫痫治疗策略，并促进爆炸渗透这一新兴理论研究领域的发展和应用。拟议的研究将从三个方面推进科学知识和理解，首先，新型动态网络分析技术的开发和应用将提供对人类癫痫以及癫痫发作和终止背后的网络相互作用的更深入的了解。拟议的研究需要新的工具来描述和跟踪噪声动态网络中的社区结构，这些工具的开发将有助于解决动态网络中出现的瞬态和循环社区模式研究中的开放性问题和未探索的方向。本提案中制定的将是第三，通过利用复杂的神经生理学数据，拟议的研究将为嘈杂的现实世界现象中的爆炸渗透领域奠定基础，并激发对这一新兴科学至关重要的新发展和应用。拟议研究具有三个更广泛的影响：首先，人类癫痫数据的动态网络分析和统计模型将为改善医学难治性癫痫患者的护理提供新方法，特别是通过前瞻性和回顾性研究、动态网络分析和统计模型。建模技术将用于确定原则性手术目标，并预测哪些患者会从手术中受益，哪些患者不会受益。其次，开发的动态社区检测工具和统计模型将在许多科学领域具有普遍适用性，这些工具可以广泛应用于系统神经科学（阐明健康大脑功能和病理学中存在的大脑动力学）以及许多其他科学领域。（例如，细胞生物学、生态学、社会科学、分布式计算等）其中出现动态网络第三，拟议的研究将为转化神经科学的研究生提供独特的培训机会，特别强调临床数据。网络推理和这些学员将发展临床、统计和计算神经科学方面独特的跨学科技能。