Exploring thalamocortical neural state space for adaptive closed-loop deep brain stimulation of epileptic networks.

尽管癫痫的患病率很高，影响了近4%的人口， 大约三分之一的患者对抗惊厥药物反应不完全， 例神经外科切除术或新方法，如脑深部电刺激（DBS）。最近我 帮助开发了一种基于电生理学的在线DBS协议，用于啮齿动物的癫痫发作检测和中断 失神癫痫模型，一种涉及异常丘脑皮质活动的癫痫形式。然而，像其他 在线DBS程序检测癫痫发作，因为他们发生，几乎没有能力预测早期 癫痫发作在很大程度上是由于有限的时空分辨率的信号，我们使用的皮质脑电图 （ECoG）。ECoG和相关脑电图（EEG）是大规模局部场电位方法， 不能提供获得预测信息可能需要的神经动力学的单细胞分辨率。 在过去的十年中，脑机接口（BMI）领域在神经科学方面取得了突破， 通过开发用于大规模神经尖峰活动的多电极阵列记录的方法进行工程化， 将所得的高维神经活动有效地减少到更少的维数， 有效控制神经假体我们预测，这种方法对于理解 癫痫发作和刺激期间的神经动力学，以及开发预测算法和自适应DBS 协议. 因此，本项目的目标是精确地描述丘脑皮质神经活动， 自发失神发作和丘脑刺激后，通过结合大规模丘脑和 皮质神经记录、光遗传学和BMI数学技术。此外，实验 该提案中提出的将使用记录的神经活动来开发自适应算法， 即将到来的癫痫发作活动，并基于神经状态和对 实时刺激。这项建议旨在增加我们对神经动力学的理解， 癫痫网络和提高在线DBS的有效性和安全性。

Despite the high prevalence of epilepsy, which affects nearly 4% of the population over their lifetime, roughly one third of afflicted patients are incompletely responsive to anticonvulsant drugs, requiring in severe cases neurosurgical resections or novel approaches such as deep-brain stimulation (DBS). Recently, I helped develop an electrophysiology-based online DBS protocol for seizure detection and interruption in rodent models of absence epilepsy, a form of epilepsy involving aberrant thalamocortical activity. However, like other online DBS procedures which detect seizures as they occur, there was little ability to predict incipient seizures due in large part to limited spatiotemporal resolution of the signal we used, the electrocorticogram (ECoG). ECoG and related electroencephalogram (EEG), being large scale local field potential approaches, do not provide single-cell resolution of neural dynamics that are likely required to obtain predictive information. Over the last decade, the field of brain-machine interface (BMI) has made breakthroughs in neuroscience and engineering by developing methods for multi-electrode array recording of large scale neural spiking activity and efficient reduction of the resultant high-dimensional neural activity to a smaller number of dimensions to effectively control neural prostheses. We predict that this approach will be invaluable for understanding neural dynamics during seizures and stimulation, and for developing predictive algorithms and adaptive DBS protocols. Therefore, the goal of this project is to precisely characterize thalamocortical neural activity during spontaneous absence seizures and following thalamic stimulation by combining large-scale thalamic and cortical neural recordings, optogenetics, and BMI mathematical techniques. Additionally, the experiments presented in this proposal will use the recorded neural activity to develop adaptive algorithms that both predict oncoming seizure activity and modify stimulation parameters based on the neural state and response to stimulation in real-time. This proposal is aimed towards increasing our understanding of the neural dynamics of epileptic networks and improving the efficacy and safety of online DBS.