CAREER: Mathematical Modeling and Computational Studies of Human Seizure Initiation and Spread

职业：人类癫痫发作和传播的数学建模和计算研究

• 批准号: 1451384
• 负责人: Mark Kramer
• 金额: $ 45万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451384&HistoricalAwards=false
• 关键词: CAREER; Mathematical; Modeling; Computational; Studies

Epilepsy - the condition of recurrent unprovoked seizures - is a brain disorder that affects 3 million people in the United States. Although the symptoms of epilepsy have been observed for millennia, the brain processes that support human seizures remain poorly understood. This lack of understanding has a profound clinical impact; in one-third of patients with epilepsy, seizures are not adequately controlled. Animal studies provide powerful methods to uncover the potential mechanisms for epilepsy, yet how the results from these studies relate to human epilepsy remains unclear. Although some mechanisms of epilepsy may be consistent in animal models and humans, differences occur, and these differences are critical to understanding and treating human epilepsy. The PI's goal is to improve understanding of the mechanisms that drive human seizures and thereby advance therapeutic management of this disease. To do so, brain voltage recordings made directly from human patients will be analyzed. Motivated by these patient data, mathematical models will be developed that describe the activity of individual neurons and small populations of interacting neurons. The mathematical models will then be used to study the biological mechanisms that support the different brain voltage rhythms that appear during seizure, and how these rhythms move across the surface of the brain. Ultimately, these mathematical models will provide new insights into human epilepsy, and help identify novel approaches to improve patient care. The PI will also include integration of research data and methods into an undergraduate course in computational neuroscience, publish a textbook and online course in neuronal data analysis, and provide undergraduate and graduate research training in computational neuroscience, with a specific emphasis on clinical data and computational modeling.The PI aims to improve understanding of the ionic and neuronal mechanisms that govern the brain's stereotyped spatiotemporal dynamics during human seizure. To do so, a computational modeling framework will be developed that incorporates individual neuron dynamics in cortical and subcortical structures and ion concentration dynamics in the extracellular space. Model behavior will be explored through simulation and dynamical systems techniques, and model features will be constrained to match microelectrode array recordings of seizures in human patients. The modeling framework will be used to test the hypothesized scenario that a class of cortical interneurons serve as the first line of defense against the outbreak of seizure, but eventually fails upon entering depolarization block. Concomitant with this failure, another circuit activates to the support large amplitude, spike-and-wave dynamics, which appear as traveling waves that sweep across the cortical surface. Two main research goals are the focus of the project. First the modeling of human seizure data will provide new insights into the mechanisms of medically refractory epilepsy, and help identify biological targets for novel pharmacological approaches to improve patient care. Second, to understand brain function and dysfunction, a deeper knowledge of cortical and subcortical neuronal dynamics combined with ion concentration dynamics is required. In this project, the stereotyped dynamical state of seizure motivates models that implement these dynamics to examine principles that support spatiotemporal patterns in the human brain. Educationally the PI will develop new interdisciplinary training in computational neuroscience. This will be done through integration of research data, analysis methods and computational technology in the undergraduate classroom, publication of a textbook and development of an online course describing cases studies in neural data analysis, and directed graduate and undergraduate research in computational neuroscience.

癫痫是一种反复发作的无端发作，是一种脑部疾病，在美国影响着300万人。虽然癫痫的症状已经被观察了几千年，但支持人类癫痫发作的大脑过程仍然知之甚少。这种理解的缺乏具有深远的临床影响；在三分之一的癫痫患者中，癫痫发作没有得到充分控制。动物研究为揭示癫痫的潜在机制提供了有力的方法，但这些研究结果与人类癫痫的关系尚不清楚。虽然癫痫的一些机制在动物模型和人类中可能是一致的，但存在差异，这些差异对于理解和治疗人类癫痫至关重要。PI的目标是提高对驱动人类癫痫发作机制的理解，从而推进这种疾病的治疗管理。为此，将对直接从人类患者身上采集的脑电压记录进行分析。在这些患者数据的激励下，将开发数学模型来描述单个神经元和相互作用的小群体神经元的活动。然后，这些数学模型将用于研究支持癫痫发作期间出现的不同脑电压节律的生物机制，以及这些节律如何在大脑表面移动。最终，这些数学模型将为人类癫痫提供新的见解，并帮助确定改善患者护理的新方法。该项目还将包括将研究数据和方法整合到计算神经科学的本科课程中，出版一本关于神经元数据分析的教科书和在线课程，并提供计算神经科学的本科生和研究生研究培训，特别强调临床数据和计算建模。PI旨在提高对控制人类癫痫发作期间大脑时空动态的离子和神经元机制的理解。为此，将开发一个计算建模框架，将皮质和皮质下结构中的单个神经元动力学和细胞外空间中的离子浓度动力学结合起来。模型行为将通过仿真和动态系统技术进行探索，模型特征将被限制为与人类患者癫痫发作的微电极阵列记录相匹配。建模框架将用于测试假设的场景，即一类皮层中间神经元作为防止癫痫发作的第一道防线，但最终在进入去极化块时失败。与此同时，另一个电路被激活，以支持大振幅，尖峰-波动力学，它以行波的形式扫过皮层表面。两个主要的研究目标是该项目的重点。首先，人类癫痫发作数据的建模将为医学上难治性癫痫的机制提供新的见解，并有助于确定新的药理方法的生物学靶点，以改善患者护理。其次，为了理解大脑功能和功能障碍，需要对皮层和皮层下神经元动力学以及离子浓度动力学有更深入的了解。在这个项目中，癫痫发作的刻板动态状态激发了实现这些动态的模型，以检查支持人脑时空模式的原则。在教育方面，PI将在计算神经科学方面发展新的跨学科培训。这将通过在本科课堂上整合研究数据、分析方法和计算技术、出版教科书和开发描述神经数据分析案例研究的在线课程，以及指导研究生和本科生在计算神经科学方面的研究来实现。