"Control Theory for Brain Modelling and Analysis"

该项目旨在开发和分析模拟人类大脑中癫痫发作的准确计算机模型，并应用控制理论技术为癫痫提供新的治疗策略，这是未来可植入设备对大脑进行直接闭环刺激所必需的。癫痫是一种相对常见的、限制生命的疾病，有时甚至危及生命。一线治疗是药物治疗，但这并不是没有副作用，一些病例可能仍然难以治愈。一些患者愿意通过手术治疗来控制自己的病情。这可能是有效的，但不能保证有效，是侵入性的，而且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疾病，例如帕金森氏症的脑深部电刺激。将闭环技术应用于癫痫治疗的研究正在进行中。然后，问题变成了医疗设备中的实现如何有效地刺激大脑，以响应实时传感器数据。作为医学的总的指导原则，一个人应该先不做坏事。以前的模拟和实验工作表明，如果应用了错误的参数，闭环控制可能会导致、恶化或延长它打算治疗的癫痫发作活动。任何形式的刺激都有可能在其应用期间和之后对正常的大脑功能产生不利影响。特别是，直接电刺激可能会对脑组织造成永久性损害。出于这些原因，在总体上将干预次数降至最低，并确保在实施干预时既有益又安全，这两方面都是真正有利益的。此外，但并非无关紧要的是，降低任何潜在的植入性设备的总功耗也将带来实际的工程好处，这些设备最终可能会产生于这一系列研究。特别是，这个项目应该问：控制理论中的哪些技术适合于分析人脑模型；我们如何设计有效的刺激策略来利用这些技术控制癫痫；如何使控制理论中的概念和技术适用于大规模的大脑模型；以及如何使用大脑的数学模型来编码大脑行为中的不确定性，并在分析不确定性的影响时回答前面的问题。可能使用的研究方法包括屏障证书、高斯过程、随机最优控制、递归神经网络、模型简化、基于抽象的验证和成分合成方法。

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.