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

癫痫是一种大脑疾病，在美国有300万人受到影响。尽管癫痫的症状已经观察了数千年，但支持人类癫痫发作的大脑过程仍然知之甚少。这种缺乏了解对临床产生了深远的影响；在三分之一的癫痫患者中，癫痫发作没有得到充分的控制。动物研究为揭示癫痫的潜在机制提供了强有力的方法，但这些研究的结果如何与人类癫痫有关仍不清楚。虽然癫痫的某些机制在动物模型和人类中可能是一致的，但也会出现差异，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的机制的理解，从而促进对这种疾病的治疗管理。为了做到这一点，我们将分析直接从人类患者那里获得的大脑电压记录。在这些患者数据的激励下，将开发描述单个神经元和少量相互作用的神经元的活动的数学模型。然后，这些数学模型将被用来研究支持癫痫发作期间出现的不同大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。PI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，重点是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刻板印象的时空动力学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以及细胞外空间的离子浓度动力学结合在一起。模型行为将通过模拟和动力学系统技术进行探索，模型特征将受到限制，以匹配人类患者癫痫发作的微电极阵列记录。该模型框架将被用来测试假设的情景，即一类皮质中间神经元作为癫痫发作的第一道防线，但最终在进入去极化阻滞剂时失败。伴随着这一故障，另一个回路被激活以支持大幅度的尖峰和波动力学，这些动力学表现为横扫大脑皮层表面的行波。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学方法的生物靶点，以改善患者护理。其次，为了了解大脑的功能和功能障碍，需要对皮质和皮质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，癫痫发作的刻板动力学状态激发了实现这些动力学的模型，以检查支持人脑时空模式的原理。在教育方面，PI将在计算神经科学方面开发新的跨学科培训。这将通过在本科生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，并指导计算神经科学的研究生和本科生研究来实现。

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.