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：
9318585
负责人：
SYDNEY S CASH
金额：
$ 30.91万
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 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9318585
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 Medical Medication Management Methodology Methods Modeling Names Neurologic Neurons Neurosciences Noise Operative Surgical Procedures Outcome Pathology Pathway Analysis Patient Care Patients Pattern Pharmacology 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 cohesion 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（由适用提供）：癫痫是最常见的神经系统综合症之一，影响美国估计有300万人。在这些患者的三分之一中，癫痫发作无法控制最大药物管理。定义癫痫病皮层和驱动癫痫发作的倡议和扩散的神经元网络动力学的复杂性使理解和治疗癫痫成为独特的挑战。在此提案中，跨学科研究团队将应对这一挑战。组装的研究人员将临床专业知识和数据记录功能与复杂的网络分析和统计建模技术相结合。利用侵入性的脑电压记录，将在自发性癫痫发作期间从患者人群中推断出动态功能网络。为了表征这些动态网络，将开发出新的数据分析和统计建模技术，以应对临床人类数据的独特挑战。这些技术将用于了解在人类皮质癫痫发作动力学的噪声，真实世界环境中，在噪声，现实世界中，良好连接的子集的突然爆炸性出现。了解癫痫发作和终止的快速网络组织将激发癫痫的新疗法，以及在爆炸性渗透的出现理论研究领域中的动机发展和应用。拟议的研究将以三种方式提高科学知识和理解。首先，新型动态网络分析技术在临床癫痫发作数据中的开发和应用将更深入地了解人类癫痫和癫痫发作计划和终止的网络相互作用。其次，拟议的研究需要新工具来表征和跟踪噪声，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经常性社区模式的研究中的开放问题和意外方向。本提案中开发的所有动态网络分析工具将免费提供，以供其他研究人员申请和开发。第三，通过利用复杂的神经生理学数据，拟议的研究将基础嘈杂的现实现象中的爆炸性渗透领域，并动机新的发展和应用对这项新兴科学至关重要。拟议的研究产生了三个更广泛的影响。首先，人类癫痫发作数据的动态网络分析和统计模型将提供新的方法来改善患者对医学性难治性癫痫的护理。特别是，通过前瞻性和回顾性研究，将采用动态网络分析和建模技术来识别主要的外科手术靶标，并预测哪些患者将（也不会受益）从手术中受益。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中具有一般适用性。这些工具可以广泛地应用于系统神经科学中 - 阐明了健康的大脑功能并存在于病理学中的脑动力学 - 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，仅举几例）。第三，拟议的研究将为研究生的转化神经科学提供独特的培训机会，并特别强调临床数据，网络推断和动态网络分析以及统计建模。这些学员将在临床，统计和计算神经科学方面发展独特的跨学科技能。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures.

DOI：
10.1371/journal.pcbi.1011188
发表时间：
2023-06
期刊：
PLoS computational biology
影响因子：
4.3
作者：
通讯作者：

A procedure to increase the power of Granger-causal analysis through temporal smoothing.

DOI：
10.1016/j.jneumeth.2018.07.010
发表时间：
2018-10-01
期刊：
Journal of neuroscience methods
影响因子：
3
作者：
Spencer E;Martinet LE;Eskandar EN;Chu CJ;Kolaczyk ED;Cash SS;Eden UT;Kramer MA
通讯作者：
Kramer MA