"Control Theory for Brain Modelling and Analysis"

“大脑建模与分析的控制理论”

• 批准号: 2595464
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2595464
• 关键词: Control; Theory; Brain; Modelling; Analysis

This project aims to develop and analyse accurate computer models simulating epileptic seizures in the human brain, and apply control theory techniques to advise novel treatment strategies for epilepsy, as would be needed to implement direct closed-loop stimulation of the brain by a future implantable device.Epilepsy is a relatively common, life-limiting and sometimes life-threatening condition. First-line treatment is with medication, but this is not without side effects, and some cases can remain intractable. Some patients are willing to resort to surgical treatment in order to control their condition. This can be effective, but is not guaranteed to work, is invasive, and carries its own set of risks.Already there are a number of open-loop devices used in the treatment of severe neurological diseases, such as electrical deep brain stimulation for Parkinson's disease. Research is underway to apply closed-loop techniques to the treatment of epilepsy. The question then becomes about how an implementation in a medical device could effectively stimulate the brain in response to real time sensor data. As a general guiding principle of medicine, one should "first do no harm". Modelling and experimental work has previously suggested that closed-loop control, if applied with the wrong parameters, may be liable to cause, worsen or prolong the seizure activity it is intended to treat. Any form of stimulation has the potential to adversely affect normal brain function during and following its application. In particular, direct electrical stimulation may cause permanent damage to brain tissue. For these reasons there is a genuine interest both in minimising episodes of intervention overall and in ensuring that, when it is applied, it is both beneficial and safe to do so. Additionally, but not inconsequentially, there would also be practical engineering benefits from reducing the overall power consumption of any potential implantable device that might eventually arise from this line of research. In particular, it is intended that the project should ask: what techniques from control theory are suitable for analysing models of the human brain; how we can design effective stimulation strategies to control epilepsy with such techniques; how to make concepts and techniques from control theory applicable to large-scale models of the brain; and how to encode uncertainty in the brain's behaviour using mathematical models of the brain and answer the previous questions while analysing the effect of uncertainty.Candidate methods to be investigated for possible use include barrier certificates, Gaussian processes, stochastic optimal control, recurrent neural networks, model reduction, abstraction-based verification, and compositional synthesis methods.

该项目旨在开发和分析模拟人类大脑癫痫发作的精确计算机模型，并应用控制理论技术为癫痫提供新的治疗策略，这将需要通过未来的植入式设备对大脑进行直接闭环刺激。癫痫是一种相对常见的，限制生命，有时甚至危及生命的疾病。一线治疗是药物治疗，但这并非没有副作用，有些病例可能仍然难以治愈。有些病人愿意采取手术治疗以控制病情。这可能是有效的，但不能保证工作，是侵入性的，并带有自己的一系列风险。已经有一些开环设备用于治疗严重的神经系统疾病，如帕金森氏症的脑深部电刺激。目前正在研究将闭环技术应用于癫痫的治疗。然后，问题变成了医疗设备中的实现如何能够响应于真实的时间传感器数据而有效地刺激大脑。作为医学的一般指导原则，应该“首先不伤害”。建模和实验工作以前表明，闭环控制，如果应用错误的参数，可能会导致，恶化或延长癫痫发作活动，它是打算治疗。任何形式的刺激有可能产生不利影响正常的大脑功能期间和之后的应用。特别是，直接电刺激可能会对脑组织造成永久性损伤。出于这些原因，我们真正关心的是尽量减少总体干预事件，并确保在实施干预时既有益又安全。此外，但并非不相干的是，降低任何潜在的植入式器械的总功耗也将带来实际的工程效益，这些器械最终可能来自这一研究领域。特别是，它的目的是，该项目应该问：从控制理论的技术是适合于分析人类大脑的模型;我们如何能够设计有效的刺激策略，以控制癫痫与这些技术;如何使概念和技术从控制理论适用于大规模模型的大脑;以及如何使用大脑的数学模型对大脑行为中的不确定性进行编码，并在分析不确定性的影响时回答前面的问题。有待研究的可能使用的候选方法包括屏障证书，高斯过程，随机最优控制，递归神经网络，模型简化，基于抽象的验证和合成方法。