NCS-FO: Identification and control of neural cognitive systems

植入式设备为治疗对现有药物反应不佳的患者的神经疾病提供了另一种选择。一个很好的例子是治疗帕金森氏症的深部脑刺激器，非常成功。许多神经和精神障碍与认知缺陷有关，这些缺陷往往很难通过药物治疗来改善。在未来，使用可植入设备治疗认知缺陷是可能的，但这需要比目前可用的刺激器更先进的技术。该项目将为神经认知系统的接口技术奠定基础。能够操纵或控制任何类型的系统都需要系统的模型或数学描述。该项目将建立一个框架，直接从神经活动中得出这样的模型。它将进一步开发通过应用电微刺激将认知系统转移到期望的目标状态的策略，目的是允许纠正与疾病相关的适应不良状态。这个跨学科的项目涉及来自神经科学和工程学的研究人员，包括以下组成部分：1)系统识别：使用大量植入的电极，记录的神经活动将被用来识别能够捕捉(和预测)神经活动的时间模式的动力系统。重点将放在逼近非线性动力学的分段线性模型上。2)状态解码：借助于识别的模型，根据当前和最近的活动模式，可以识别神经系统的当前内部状态。3)系统控制：在系统当前状态偏离所需目标状态的情况下，所识别的模型与控制理论策略相结合可用于确定需要应用以将系统驱动至所需状态的刺激模式。该项目将使用简单的认知任务，如工作记忆和知觉决策任务，来提供一个原则证明，即认知神经系统的状态可以实时调整，以改善任务的整体表现。这是一个雄心勃勃的目标，在这种形式下，以前从未实现过，但一个成功的结果有可能彻底改变未来精神障碍的治疗方式。这项工作得到了神经和认知系统综合策略(NCS)项目的支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Implantable devices provide an alternative for treating neurological disorders in patients who do not respond well to available drugs. A good example are deep brain stimulators for the treatment of Parkinson’s Disease, which have been very successful. Many neurological and psychiatric disorders are associated with cognitive deficits, which tend to be difficult to ameliorate through drug therapy. In the future, it might be possible to treat cognitive deficits with implantable devices, but this requires much more advanced technology than the currently available stimulators. This project will lay the foundation for interfacing technology with neural cognitive systems. Being able to steer or control a system of any kind requires a model or mathematical description of the system. The project will establish a framework for deriving such a model directly from neural activity. It will further develop strategies for moving the cognitive system into a desired target state through the application of electrical microstimulation, with the intent to allow for correction of disease-related maladaptive states.This interdisciplinary project, which involves investigators from both neuroscience and engineering, has the following components: 1) System identification: Using a large number of implanted electrodes, the recorded neural activity will be used to identify a dynamical system that is able to capture (and predict) the temporal pattern of neural activity. The focus will be on piecewise linear models for approximating nonlinear dynamics. 2) State decoding: With the help of the identified model and based on the current and recent activity pattern, the current internal state of the neural system can be identified. 3) System control: In the case of the current state of the system deviating from a desired target state, the identified model in combination with control-theoretic strategies can be used to determine a stimulation pattern that needs to be applied to drive the system towards the desired state. The project will use simple cognitive tasks, like working memory and perceptual decision-making tasks, to provide a proof of principle that the state of a cognitive neural system can be adjusted in real time to improve, for example, overall performance on the task. This is an ambitious goal, which, in this form, has not been achieved before, but a successful outcome has the potential to revolutionize how mental disorders might be treated in the future. This work is supported by the Integrative Strategies for Neural and Cognitive Systems (NCS) Program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.