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

探索丘脑皮质神经状态空间以实现癫痫网络的自适应闭环深部脑刺激。

• 批准号: 9257233
• 负责人: Jordan Sorokin
• 金额: $ 4.4万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-22 至 2019-12-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9257233
• 关键词: Absence Epilepsy; Address; Affect; Algorithms; Anticonvulsants; Appearance; Awareness; Basic Science; Brain; Cells; Deep Brain Stimulation; Detection; Development; Dimensions; Electrocorticogram; Electrodes; Electroencephalogram; Electrophysiology (science); Engineering; Epilepsy; Excision; Exhibits; Generalized seizures; Goals; High Prevalence; Implant; Individual; Interruption; Intervention; Machine Learning; Mathematics; Measures; Methods; Monitor; Motor Cortex; Movement; Neurons; Neurosciences; Output; Patients; Pattern; Pharmacological Treatment; Population; Preparation; Primates; Principal Component Analysis; Procedures; Protocols documentation; Rattus; Refractory; Research; Research Personnel; Resistance; Resolution; Resources; Rodent; Rodent Model; Safety; Seizures; Signal Transduction; Sorting - Cell Movement; Surveys; Techniques; Testing; Thalamic structure; Time; base; brain machine interface; design; experimental study; high dimensionality; improved; insight; markov model; multi-electrode arrays; neural circuit; neural prosthesis; neuroregulation; novel strategies; optogenetics; prediction algorithm; prevent; relating to nervous system; response; spatiotemporal; tool

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.

尽管癫痫病的患病率很高，这在一生中影响了近4％的人口，但 大约三分之一的患者对抗惊厥药的反应不完全，需要严重 病例神经外科切除术或新方法，例如深脑刺激（DBS）。最近，我 帮助开发了基于电生理的在线DBS协议，用于啮齿动物的癫痫发作和中断 缺有癫痫的模型，一种涉及异常丘脑皮质活性的癫痫的形式。但是，像其他人一样 在线DBS程序检测出癫痫发作的情况，几乎没有能力预测初期 癫痫发作很大程度上是由于我们使用的信号的时空分辨率有限，电皮质图 （ECOG）。 ECOG及相关脑电图（EEG）是大规模的本地现场潜在方法， 不提供可能需要获得预测信息所需的神经动力学的单细胞分辨率。 在过去的十年中，脑机界面（BMI）领域在神经科学和 通过开发大规模神经尖峰活动的多电极阵列记录的方法和 有效地减少所得的高维神经活动到较小数量的维度 有效控制神经假体。我们预测这种方法对于理解将是无价的 癫痫发作和刺激期间的神经动力学，以及用于开发预测算法和自适应DBS 协议。 因此，该项目的目的是精确表征丘脑皮质神经活动。 通过大规模丘脑和 皮质神经记录，光遗传学和BMI数学技术。另外，实验 在本提案中提出的将使用记录的神经活动来开发自适应算法，这两者都可以预测 基于神经状态和对的反应 实时刺激。该建议旨在提高我们对神经动态的理解 癫痫网络并提高在线DBS的功效和安全性。