Deep brain stimulation for data-driven circuit-specific neuromodulation

我的研究项目专注于数据科学和工程方法的应用，以推动深部脑刺激(DBS)技术的创新和进步。DBS是一种神经外科疗法，适用于帕金森氏症和许多其他大脑疾病。它包括在大脑深处外科植入永久性电极，这些电极连接到胸部的神经刺激器(很像心脏起搏器)。目前，DBS是“开环”的，这意味着它每天24小时提供电刺激，尽管(I)临床特征可能会随时间而波动，(Ii)DBS可能会引起不想要的副作用。相反，“闭环系统”将监测大脑活动，并仅在必要时提供刺激，从而减少副作用和电力消耗。一个成功的闭环系统需要识别合适的神经反馈信号(即告诉DBS设备何时刺激的大脑“生物标记物”)，并全面了解DBS如何改变大脑活动。我的实验室有一个庞大的数据库，里面有多伦多西部医院神经外科患者的脑部记录。这包括来自数百名患者的数以万计的单个脑细胞(神经元)的记录。我的第一个目标是利用这些数据开发软件，使用人工智能自动分解不同的神经元群体，以帮助实时外科手术识别DBS干预的理想植入位置。此外，我们将应用统计方法来寻找表明症状存在的大脑活动模式(由单个脑细胞的行为定义)。这将导致发现可用于闭环式DBS的生物标记物。我的第二个目标是通过将先进的大脑信号分析技术应用于DBS期间来自大脑皮层(大脑最外层)和其他大脑深层区域的预期颅内数据，来增强我们对DBS工作原理的了解。这种数据收集和分析是我的实验室的专长，将使我们能够深入了解如何应用DBS在调节运动和记忆功能的大脑回路的背景下恢复健康的大脑功能。了解DBS如何影响这些回路，将推动DBS治疗帕金森氏症和阿尔茨海默氏症等疾病的新方法的发展。结合前两个目标的见解，我的第三个目标是开发和测试新的神经反馈驱动的闭环式DBS策略。这将涉及开发一种系统，该系统持续监测单个脑细胞的活动，自动检测异常，然后触发星展银行取消这种不健康的大脑活动。这种闭环式DBS方法的独特之处在于，它使用单个神经元的活动来控制刺激传递，并可能引发未来一代DBS设备的操作发生根本性变化。

My research program is focused on the application of data science and engineering methods to drive innovation and advancement of deep brain stimulation (DBS) technologies. DBS is a neurosurgical therapy that has applications in Parkinson's disease and many other brain disorders. It involves the surgical implantation of permanent electrodes deep within the brain that are connected to a neurostimulator (much like a cardiac pacemaker) in the chest. Currently, DBS is "open-loop," meaning that it delivers electrical stimulation 24hours/day, even though (i) clinical features can fluctuate on a moment-to-moment basis and (ii) DBS may elicit unwanted side-effects. A "closed-loop" system would instead monitor brain activity and deliver stimulation only when necessary, thereby reducing side-effects and power consumption. A successful closed-loop system requires the identification of suitable neurofeedback signals (i.e. brain "biomarkers" that tell the DBS device when to stimulate) and a comprehensive understanding of how DBS changes brain activity. My lab has access to a vast database of intracranial brain recordings from neurosurgical patients of the Toronto Western Hospital. This includes 10s of thousands of recordings of individual brain cells (neurons) from hundreds of patients. My first objective is to use this data to develop software which uses artificial intelligence to automatically disaggregate distinct neuronal populations to aid in real-time surgical identification of the ideal implantation location for DBS interventions. Moreover, we will apply statistical methods to seek out patterns of brain activity (defined by the behaviour of individual brain cells) that are indicative of symptom presence. This will lead to the discovery of biomarkers that can be used for closed-loop DBS. My second objective is to enhance our knowledge of how DBS works by applying advanced brain signal analysis techniques to prospective intracranial data from the cortex (outermost layer of the brain) and other deep brain regions during DBS. This data collection and analysis is a specialty of my lab and will enable us to gain insights into how we can apply DBS to restore healthy brain function in the context of brain circuits that regulate movements and memory function. Understanding how DBS can influence these circuits will promote the development of new DBS methods in disorders like Parkinson's and Alzheimer's. Combining insights from the first two objectives, my third objective is to develop and test novel neurofeedback-driven closed-loop DBS strategies. This will involve the development of a system which continuously monitors the activity of individual brain cells, automatically detects abnormalities, then triggers DBS to abolish this unhealthy brain activity. This method of closed-loop DBS is unique in that it uses the activity of individual neurons to control stimulation delivery and may incite a fundamental change in the operation of future generation DBS devices.