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

• 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
• 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

 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综合征之一，在美国估计有300万人受到影响。在这些患者中，有三分之一的患者癫痫发作无法控制，尽管进行了最大限度的药物治疗。神经元网络动力学的复杂性定义了致痫皮质并驱动癫痫的启动和传播，这使得对癫痫的理解和治疗成为一个独特的挑战。在这项提案中，一个跨学科的研究团队将应对这一挑战。聚集的研究人员将临床专业知识和数据记录能力与复杂的网络分析和统计建模技术相结合。利用有创的脑电压记录，将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，将开发新的数据分析和统计建模技术，以解决临床人类数据的独特挑战。这些技术将被应用于理解在嘈杂的真实环境中人类大脑皮层癫痫发作动力学中突然出现的连接良好的节点子集(也称为社区)。了解癫痫发作和终止时的快速网络组织将启发新的癫痫治疗策略，并推动爆炸渗流这一新兴理论研究领域的发展和应用。 拟议的研究将在三个方面促进科学知识和理解。首先，新的动态网络分析技术在临床癫痫数据中的开发和应用将使我们更深入地了解人类癫痫以及癫痫发作开始和终止背后的网络相互作用。其次，这项拟议的研究需要新的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时和反复出现的社区模式研究中的公开问题和未探索的方向。本提案中开发的所有动态网络分析工具将免费提供给其他研究人员应用和开发。第三，通过利用复杂的神经生理学数据，拟议的研究将为嘈杂的真实世界现象中的爆炸性渗流领域奠定基础，并推动对这一新兴科学至关重要的新发展和新应用。 这项拟议的研究有三个更广泛的影响。首先，人类癫痫发作数据的动态网络分析和统计建模将为改善难治性癫痫患者的护理提供新的方法。特别是，通过前瞻性和回溯性研究，动态网络分析和建模技术将被应用于确定原则上的手术目标，并预测哪些患者将--或将不--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具有普遍适用性。这些工具可以广泛应用于系统神经科学中--阐明健康大脑功能的基础和病理学中存在的大脑动力学--以及出现动态网络的许多其他科学领域(例如，细胞生物学、生态学、社会科学、分布式计算等)。第三，拟议的研究将为研究生提供翻译神经科学方面的独特培训机会，特别强调临床数据、网络推理和动态网络分析以及统计建模。这些受训人员将在临床、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

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

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